# NETWORK PHARMACOLOGY AND TRADITIONAL MEDICINE

EDITED BY : Shao Li, Yuanjia Hu and Shi-Bing Su PUBLISHED IN : Frontiers in Pharmacology

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ISSN 1664-8714 ISBN 978-2-88966-040-7 DOI 10.3389/978-2-88966-040-7

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# NETWORK PHARMACOLOGY AND TRADITIONAL MEDICINE

Topic Editors: Shao Li, Tsinghua University, China Yuanjia Hu, University of Macau, China Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China

Citation: Li, S., Hu, Y., Su, S.-B., eds. (2020). Network Pharmacology and Traditional Medicine. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88966-040-7

# Table of Contents


Tianduanyi Wang, Zengrui Wu, Lixia Sun, Weihua Li, Guixia Liu and Yun Tang

*26 Anti-endometriosis Mechanism of* Jiawei Foshou *San Based on Network Pharmacology*

Yi Chen, Jiahui Wei, Ying Zhang, Wenwei Sun, Zhuoheng Li, Qin Wang, Xiaoyu Xu, Cong Li and Panhong Li

*40 Krukovine Suppresses KRAS-Mutated Lung Cancer Cell Growth and Proliferation by Inhibiting the RAF-ERK Pathway and Inactivating AKT Pathway*

Huanling Lai, Yuwei Wang, Fugang Duan, Ying Li, Zebo Jiang, Lianxiang Luo, Liang Liu, Elaine L. H. Leung and Xiaojun Yao

*49* Cyclocarya paliurus *Leaves Tea Improves Dyslipidemia in Diabetic Mice: A Lipidomics-Based Network Pharmacology Study* Lixiang Zhai, Zi-wan Ning, Tao Huang, Bo Wen, Cheng-hui Liao,

Cheng-yuan Lin, Ling Zhao, Hai-tao Xiao and Zhao-xiang Bian

*61 Network Pharmacology-Based Validation of Caveolin-1 as a Key Mediator of Ai Du Qing Inhibition of Drug Resistance in Breast Cancer*

Neng Wang, Bowen Yang, Xiaotong Zhang, Shengqi Wang, Yifeng Zheng, Xiong Li, Shan Liu, Hao Pan, Yingwei Li, Zhujuan Huang, Fengxue Zhang and Zhiyu Wang

*80 The Yin-Yang Property of Chinese Medicinal Herbs Relates to Chemical Composition but Not Anti-Oxidative Activity: An Illustration Using Spleen-Meridian Herbs*

Yun Huang, Ping Yao, Ka Wing Leung, Huaiyou Wang, Xiang Peng Kong, Long Wang, Tina Ting Xia Dong, Yicun Chen and Karl Wah Keung Tsim

*97 Anti-Inflammatory Effect of a TCM Formula Li-Ru-Kang in Rats With Hyperplasia of Mammary Gland and the Underlying Biological Mechanisms*

Yingying Wang, Shizhang Wei, Tian Gao, Yuxue Yang, Xiaohua Lu, Xuelin Zhou, Haotian Li, Tao Wang, Liqi Qian, Yanling Zhao and Wenjun Zou


Jian Zuo, Xin Wang, Yang Liu, Jing Ye, Qingfei Liu, Yan Li and Shao Li

*142 Unveiling Active Constituents and Potential Targets Related to the Hematinic Effect of Steamed* Panax notoginseng *Using Network Pharmacology Coupled With Multivariate Data Analyses*

Yin Xiong, Yupiao Hu, Lijuan Chen, Zejun Zhang, Yiming Zhang, Ming Niu and Xiuming Cui

*156 Systems Pharmacology Dissection of Multi-Scale Mechanisms of Action of*  Huo-Xiang-Zheng-Qi *Formula for the Treatment of Gastrointestinal Diseases*

Miaoqing Zhao, Yangyang Chen, Chao Wang, Wei Xiao, Shusheng Chen, Shuwei Zhang, Ling Yang and Yan Li


Yu Dong, Ping Qiu, Rui Zhu, Lisha Zhao, Pinghu Zhang, Yiqi Wang, Changyu Li, Kequn Chai, Dan Shou and Huajun Zhao

*224 Network Pharmacology Based Research on the Combination Mechanism Between Escin and Low Dose Glucocorticoids in Anti-rheumatoid Arthritis*

Leiming Zhang, Yanan Huang, Chuanhong Wu, Yuan Du, Peng Li, Meiling Wang, Xinlin Wang, Yanfang Wang, Yanfei Hao, Tian Wang, Baofeng Fan, Zhuye Gao and Fenghua Fu


Jie Ying Zhang, Chun Lan Hong, Hong Shu Chen, Xiao Jie Zhou, Yu Jia Zhang, Thomas Efferth, Yuan Xiao Yang and Chang Yu Li


Yan-Fang Yang, Song-Tao Wu, Bo Liu, Zhou-Tao Xie, Wei-Chen Xiong, Peng-Fei Hao, Wen-Ping Xiao, Yuan Sun, Zhong-Zhu Ai, Peng-Tao You and He-Zhen Wu

*296 Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery*

Wenjuan Zhang, Ying Huai, Zhiping Miao, Airong Qian and Yonghua Wang


Yunyao Jiang, Nan Liu, Shirong Zhu, Xiaomei Hu, Dennis Chang and Jianxun Liu

*350 Closing the Gap Between Therapeutic Use and Mode of Action in Remedial Herbs*

Joaquim Olivés and Jordi Mestres


Pengqian Wang, Li Dai, Weiwei Zhou, Jing Meng, Miao Zhang, Yin Wu, Hairu Huo, Xingjiang Xiong and Feng Sui

*386 Comparative Network Pharmacology Analysis of Classical TCM Prescriptions for Chronic Liver Disease*

Zikun Chen, Xiaoning Wang, Yuanyuan Li, Yahang Wang, Kailin Tang, Dingfeng Wu, Wenyan Zhao, Yueming Ma, Ping Liu and Zhiwei Cao

*395 Transcriptomic Validation of the Protective Effects of Aqueous Bark Extract of* Terminalia arjuna *(Roxb.) on Isoproterenol-Induced Cardiac Hypertrophy in Rats*

Gaurav Kumar, Nikhat Saleem, Santosh Kumar, Subir K. Maulik, Sayeed Ahmad, Manish Sharma and Shyamal K. Goswami


Naijun Yuan, Lian Gong, Kairui Tang, Liangliang He, Wenzhi Hao, Xiaojuan Li, Qingyu Ma and Jiaxu Chen

# Editorial: Network Pharmacology and Traditional Medicine

Xinxing Lai <sup>1</sup> , Xin Wang<sup>1</sup> , Yuanjia Hu<sup>2</sup> , Shibing Su<sup>3</sup> , Wenqing Li <sup>4</sup> and Shao Li 1\*

<sup>1</sup> MOE Key Laboratory of Bioinformatics, TCM-X Centre/Bioinformatics Division, BNRIST/Department of Automation, Tsinghua University, Beijing, China, <sup>2</sup> State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, Macau, <sup>3</sup> Research Center for Traditional Chinese Medicine Complexity System, Shanghai University of Traditional Chinese Medicine, Shanghai, China, <sup>4</sup> Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Cancer Epidemiology, Peking University Cancer Hospital and Institute, Beijing, China

Keywords: network pharmacology, traditional medicine, network target, complex disease, herbal formulae

#### Editorial on the Research Topic

#### Network Pharmacology and Traditional Medicine

With the gradual rise of interdisciplinary subjects such as computational biology, bioinformatics, artificial intelligence, and big data science, researchers have shifted research on traditional medicine from a single and isolated mode to a multi-faceted and systematic research mode. One of the significant changes is understanding the mechanisms of drug action from the perspective of the biomolecular network. Using the "network" to regain the "whole" has brought significant changes and new challenges to medical research. Traditional medicine (TM), characterized by holistic, personalized, and multicomponent therapy, holds great potential to address a number of challenges in modern health care. By generating an unprecedented opportunity for the systematic research of TM, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. From a systematic perspective, it lays emphasis on revealing the systematic pharmacological mechanisms of drugs and further guiding the drug discovery and development, as well as clinical treatment. Network pharmacology integrates computational, experimental, and clinical investigation and creates favorable conditions for exploring the characteristics of TM and further linking to the frontiers of modern science and technology.

Network pharmacology stems from several pioneering works. The holistic theory and practice of TM play a key role in the origin and rapid development of network pharmacology. The original hypothesis referring to the biological associations between traditional Chinese medicine (TCM) syndromes, herbal formula, and molecular networks was proposed in 1999 and 2002 (Li, 1999; Li et al., 2002). On March 2006, the biomolecular networks of cold/hot syndromes were first established, and the network regulation mechanisms of hot/cold herbal formulae were illustrated experimentally (Li et al., 2007), and then a milestone article proposed a new network-based TCM research paradigm in 2007 (Li, 2007). Afterwards, the new term "network pharmacology" was introduced in Nature Biotechnology (Hopkins, 2007). Based on these pioneering works, network pharmacology was then further optimized in terms of theory, methods, database, and applications. Li proposed and continuously improved the concept and theory of "network target" (Li et al., 2011). The network target refers to a novel concept that treats the biological network underlying diseases as a therapeutic target in order to decipher systematic mechanisms of action for multi-target drugs,

#### Edited and reviewed by:

Michael Heinrich, UCL School of Pharmacy, United Kingdom

> \*Correspondence: Shao Li shaoli@tsinghua.edu.cn

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 01 July 2020 Accepted: 22 July 2020 Published: 04 August 2020

#### Citation:

Lai X, Wang X, Hu Y, Su S, Li W and Li S (2020) Editorial: Network Pharmacology and Traditional Medicine. Front. Pharmacol. 11:1194. doi: 10.3389/fphar.2020.01194 particularly for traditional medicine. The theory of "network target" has become the core theory of network pharmacology, exerting considerable influence in traditional medicine.

With the emerging advances in network pharmacology and traditional medicine, Frontiers in Pharmacology organized a Research Topic entitled "Network Pharmacology and Traditional Medicine" to present recent advances pertaining to network pharmacology and traditional medicine. This topic, consisted of 28 original research and two reviews, has so far attracted wide attention with over 75,000 views and more than 17,000 article downloads.

#### MECHANISM OF ACTION OF HERBAL FORMULAE

A fundamental feature of TM is the use of herbal formulae as the typical treatment. Herbal formula contains hundreds of chemical compounds, which makes it complicated and challenging to understand the mechanisms of action and bioactive ingredients. The emerging network pharmacology provides a new strategy and powerful tool to uncover the biological basis underlying herbal formula. After a rigorous selection and peer review, we highlight a group of 12 original articles focus on the mechanisms of action and potential bioactive compounds of herbal formulae on a variety of complex conditions.

The first group of four papers focused on neurological or mental disorders. Among them, a paper introduced an intermodule analysis to identify an overarching view of the target profile and action mode of Huang-Lian-Jie-Du Decoction on ischemic stroke. According to the inter-module "coupling sore" analysis, a module-to-module bridge was constituted to demonstrate the "shotgun-like" pharmacological mechanism (Wang P. et al.). In addition, computational pharmacology approaches and experiments were utilized for determining the neuroprotective and anti-neuroinflammatory mechanisms underlying Tian-Ma-Gou-Teng-Yin for Alzheimer's disease (Wang T. et al.), the holistic anti-insomnia mechanism of Suan-Zao-Ren prescription by targeting multi-neurotransmitter receptors at synapse interface (Gao et al.), and key active compounds and antidepressant mechanism of Xiao-Yao-San (Yuan et al.).

Another group of four papers investigated the mechanisms underlying herbal formulae on hemopathy or infection. A paper integrated transcriptomics-based network pharmacology and metabolomics technologies to elucidate the protective effects of Qi-Jing-Sheng-Bai granule for leucopenia through accelerating cell proliferation and differentiation, regulating metabolism response, and modulating immunologic function at a system level (Tian et al.). Moreover, a computational framework of comparative network pharmacology was proposed by Chen et al. to determine the common and different mechanisms of three classic formulae for chronic liver disease, including Yinchenhao decoction, Huangqi decoction, and Yiguanjian. In addition, based on network pharmacology analysis, mechanisms and molecular targets were elucidated in terms of Yiqi Shexue formula for primary immune thrombocytopenia (Jiang et al.), and Xuebijing Injection for fungal infection-related sepsis (Shang et al.).

The last group of articles focused on gynecology, hypertensive nephropathy and aging-associated diseases. Three papers combined network analysis and in vivo or in vitro experiment to determine the mechanism of Jiawei Foshou San on inhibition of invasion and metastasis of endometriosis (Chen et al.), the protective mechanism of Li-Ru-Kang for hyperplasia of mammary gland via reducing damage of oxidative stress and inflammation (Wang Y. et al.), and the anti-osteoporosis effects of Erxian decoction by reducing production of TNF-a and attenuating osteoblast apoptosis (Wang N. et al.). Furthermore, based on drug perturbation of the disease biological network robustness assessment, a paper showed that Quan-Du-Zhong capsule may specifically target glomerular lesion of hypertensive nephropathy (Guo et al.).

#### HERBAL ACTIVE INGREDIENTS

The development of network pharmacology has provided new perspectives in identifying and understanding the complex bioactive ingredients from numerous herbs. There are eight papers selected and published in the Research Topic to explore the molecular basis of 12 herbs.

For antitumor effect and active ingredients, two papers combined network pharmacology method and pharmacokinetics analysis, or experimental study to investigate antitumor effects and potential bioactive ingredients of Scutellaria barbata D. Don (Liu et al.), and the cytotoxic mechanism of krukovine for non-small cell lung cancer (Lai et al.). Another paper elucidated the antitumor effective substances and mechanism of Phellinus igniarius for colon cancer (Dong et al.).

Traditional medicinal herbs are commonly used for treating lipid metabolism related disorders including hyperlipidemia, hepatic steatosis, and obesity. To identify active ingredients of Cyclocarya paliurus (CP) leaf, a paper revealed phytochemical 14 compounds of CP by utilizing lipidomics, serum pharmacochemistry, and network pharmacology approaches (Zhai et al.). In another paper, six weight-loss herbs for obesity were investigated to identified active compounds, potential target proteins, and elucidate the pharmacological mechanism of action for obesity through a systems pharmacology framework (Zhou et al.).

Moreover, network pharmacology approaches and high performance liquid chromatography-mass spectrometry characterization were used to demonstrated that justicidin B targets the integrin aIIbb<sup>3</sup> protein, which provided a novel perspective to understanding the mechanism on platelet aggregation (Yang et al.). Aqueous extract of the bark of Terminalia arjuna (TA) is widely used in the Indian subcontinent for treating cardiovascular diseases. A paper revealed the protective effects of aqueous extract of the bark TA on isoproterenol-induced cardiac hypertrophy by transcriptomic Validation (Kumar et al.).

#### EFFECT-ENHANCING AND TOXICITY-REDUCING

Traditional herbal medicine is widely used as an adjunctive treatment for a variety of conditions, partially due to the advantages of effect-enhancing and toxicity-reducing. For toxicityreducing, there are two papers focused on rheumatoid arthritis (RA) treatment related side effects. First, methotrexate is commonly used for RA, however, accompanied with remarkable side effects. A paper aimed to elucidate the anti-rheumatic mechanisms of Qing-Luo-Yin (QLY) and its possible interactions with methotrexate. Based on an integrating strategy coupled with network pharmacology and metabolomics techniques, it was shown that QLY notably reduced methotrexate induced side effects by eliciting antifolate resistance (Zuo et al.). Second, another paper investigated the anti-RA effect of Escin combined with glucocorticoids (GCs), which demonstrated that Escin could reduce the adverse effects of GCs (Zhang L. et al.). For effect-enhancing, Wang N. et al. revealed that Ai Du Qing significantly inhibited drug resistance in breast cancer by caveolin-1 as a key mediator, using network pharmacology and experimental validation (Wang N. et al.).

#### TRADITIONAL PROPERTIES OF HERBS AND FORMULAE

One of the major challenges in terms of the modernization of herbal medicine is to uncover the scientific basis of herbal traditional properties. Four papers in this Research Topic focused on the traditional properties of Chinese medicinal herbs and formula. Among them, two papers investigated the molecular association between herbal medicine and TCM syndrome (ZHENG). First, Shen Qi Wan (SQW) is one of the most common TCM formulae in treating Kidney-Yang Deficiency syndrome (KYDS). A paper identified the potential targets of active ingredients in SQW using network pharmacology approaches, and demonstrated that SQW could regulate hypothalamic–pituitary–target gland axis disorder in KYDS rats (Zhang J. Y. et al.). Second, a constituent-targetdisease network and experimental validation were conducted to identify Rk3 and 20(S)-Rg3 as the major constituents of steamed Panax notoginseng for blood-deficiency syndrome (Xiong et al.).

For understanding the herbal property and combinational rules, a paper selected 15 commonly used herbs attributed to spleen-meridian for chemical properties analyses to probe and clarify the theoretical basis of Chinese medicinal herbs. Base on principle component analysis of full spectrum of HPLC, NMR, and LC-MS, the findings showed that LC-MS profile was strongly correlated to the "Yin-Yang" classification criterion (Huang et al.). Furthermore, Jun-Chen-Zuo-Shi is one of the remarkable features of TCM formulae. By incorporating the pharmacokinetic and pharmacodynamics evaluation and network pharmacological analyses, a paper showed that the cooperative roles of herbs in Huo-xiang-zheng-qi prescription for functional dyspepsia conforms to the ancient compatibility rule of "Jun-Chen-Zuo-Shi" (Zhao et al.).

### DATABASE AND METHODOLOGY OF NETWORK PHARMACOLOGY

Two reviews and one article focused on network pharmacology databases and methodology for TM. First, databases and computational tools currently used for TCM research were summarized in a comprehensive review (Zhang R. et al.). The representative applications of network pharmacology for TCM research, including studies on TCM compatibility, target prediction, as well as network toxicology, were also presented in this review. Furthermore, the authors evaluated and compared the search results of several current TCM databases based on 10 famous herbs. Given the significant involvement of the gut microbiota in a broad range of diseases and the potential effect of TCM to prevent gut dysbiosis, a powerful and comprehensive database about TCM and gut microbiota is needed. Second, in the another review article contributed by Zhang W. et al., a novel approach of systems pharmacology method was proposed to identify the bioactive compounds, predict their related targets, elucidate the synergistic effects, and illustrate the molecular mechanisms of action underlying TCM (Zhang W. et al.). Third, to integrate all pieces of data and processes that allow for automatically generating mechanistic hypotheses for the known therapeutic uses of the plant, a paper integrated data linking metabolites, plants, diseases, and proteins, which provided useful systems approaches to contribute to finding a scientific rationale for traditional medicines (Olivé s and Mestres).

Finally, a population-based clinical database was used to investigate the effect of Chinese herbal medicine (CHM) prescription on hip fracture, which demonstrated that CHM usage was associated with a lower risk of overall mortality, readmission, and reoperation (Cheng et al.). And the most crucial core formulae and herbs of hip fracture were identified by using association rule mining and network analysis.

#### CONCLUSION

In conclusion, the collection of 30 articles contributed to this Research Topic illustrates the increasing attraction to network pharmacology and TM. It is important to note that the articles published in this topic cover a wide spectrum of applications of network pharmacology on TM, including the comprehensive understanding on mechanism of action of herbal formula, herbal active ingredients, molecular basis of traditional properties of herbs and herbal formulae, and mechanism underlying effectenhancing and toxicity-reducing, as well as reviews on network pharmacology databases and tools. These articles give deep insights into the methodology and applications of network pharmacology, and also serve to present encouraging advances in this novel and promising research field. Finally, a particularly important consideration is the Four Pillars of best practice in ethnopharmacology (Frontier in Pharmacology, 2020). The evidence to evaluate the potential pharmacological effects of traditional medicine must be critically assessed in future network pharmacology studies. Furthermore, the identification of the compounds, the assessment of bioavailability of the compounds, the validation of transcriptomic and proteomic data should be conducted and reported explicitly in accordance with the Four Pillars principles.

#### AUTHOR CONTRIBUTIONS

XL and XW contributed to the concept and drafting of the manuscript. YH, SS, and WL contributed to the revision of the

#### REFERENCES


manuscript. SL contributed to the concept, design, and critical revision of the manuscript.

#### ACKNOWLEDGMENTS

We would like to thank all the authors and reviewers who contributed to the success of this Research Topic with their high-quality research or crucial comments.


Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Lai, Wang, Hu, Su, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer's Disease

#### Tianduanyi Wang, Zengrui Wu, Lixia Sun, Weihua Li, Guixia Liu and Yun Tang\*

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China

#### Edited by:

Yuanjia Hu, University of Macau, Macau

#### Reviewed by:

Junguk Hur, University of North Dakota, United States István Zupkó, University of Szeged, Hungary

> \*Correspondence: Yun Tang ytang234@ecust.edu.cn

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 17 March 2018 Accepted: 04 June 2018 Published: 26 June 2018

#### Citation:

Wang T, Wu Z, Sun L, Li W, Liu G and Tang Y (2018) A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer's Disease. Front. Pharmacol. 9:668. doi: 10.3389/fphar.2018.00668 Traditional Chinese medicine (TCM) is typically prescribed as formula to treat certain symptoms. A TCM formula contains hundreds of chemical components, which makes it complicated to elucidate the molecular mechanisms of TCM. Here, we proposed a computational systems pharmacology approach consisting of network link prediction, statistical analysis, and bioinformatics tools to investigate the molecular mechanisms of TCM formulae. Taking formula Tian-Ma-Gou-Teng-Yin as an example, which shows pharmacological effects on Alzheimer's disease (AD) and its mechanism is unclear, we first identified 494 formula components together with corresponding 178 known targets, and then predicted 364 potential targets for these components with our balanced substructure-drug–target network-based inference method. With Fisher's exact test and statistical analysis we identified 12 compounds to be most significantly related to AD. The target genes of these compounds were further enriched onto pathways involved in AD, such as neuroactive ligand–receptor interaction, serotonergic synapse, inflammatory mediator regulation of transient receptor potential channel and calcium signaling pathway. By regulating key target genes, such as ACHE, HTR2A, NOS2, and TRPA1, the formula could have neuroprotective and anti-neuroinflammatory effects against the progression of AD. Our approach provided a holistic perspective to study the relevance between TCM formulae and diseases, and implied possible pharmacological effects of TCM components.

Keywords: traditional Chinese medicine, compound–protein interactions, network-based inference, computational systems pharmacology, Alzheimer's disease

### INTRODUCTION

With more than 5,000-year history, the traditional Chinese medicine (TCM) still plays key roles in the treatment of many diseases and disorders worldwide. However, TCM is usually prescribed as formulae, typically consisting of many herbs in different quantity, in which the composition theory "Monarch, Minister, Assistant and Guide" is observed (Jiang, 2005; Xiong et al., 2013; Zhang A. et al., 2013). Thus a TCM formula contains

hundreds of chemical components, which makes it complicated and difficult to elucidate the molecular mechanisms of treatment.

In recent years, as the development of systems biology, network pharmacology has emerged as a new subject for us to understand the complex biological systems from an integrated multi-component network view (Hopkins, 2007, 2008). Network pharmacology is especially advantageous in analyzing 'multicompound, multi-target, and multi-effect' scenario to reveal the molecular relationships among compounds and complex diseases from multiple scales (Zhao et al., 2010; Li et al., 2012). Therefore, it is very helpful for illustrating molecular mechanisms of TCM formulae and finding active constituents from herbs (Hao and Xiao, 2014). There are many studies published to date, such as Radix Curcumae formula against cardiovascular diseases (Tao et al., 2013), Qing-Luo-Yin against Rheumatoid arthritis (Zhang B. et al., 2013) and Ge-Gen-Qin-Lian decoction against type 2 diabetes (Li J. et al., 2014).

In our previous study, we developed a network pharmacological method, named network-based inference (NBI), to predict potential drug–target interactions (DTIs) between drugs and targets (Cheng et al., 2012). Because NBI method only could predict potential DTIs within a known drug–target network, we then proposed a new method, entitled substructure-drug–target network-based inference (SDTNBI), to predict potential targets for novel compounds without known targets (Wu et al., 2017). SDTNBI utilizes chemical substructures to bridge the gap between known drug–target network and novel compounds. Recently, we further improved SDTNBI by introducing three parameters (α, β, and γ) into it, namely balanced substructure-drug–target network-based inference (bSDTNBI), to identify potential targets for both old drugs and new chemical entities (Wu et al., 2016). With these methods, we developed computational systems pharmacology/toxicology approaches to investigate the molecular mechanisms of therapeutic effects of known drugs or active compounds, and side effects of known drugs or environmental compounds (Cheng et al., 2013a,b; Li H. et al., 2014; Li et al., 2016; Lu et al., 2015; Wang et al., 2017).

In this study, we proposed a computational systems pharmacology approach (**Figure 1**) combining our bSDTNBI method and statistical analysis to find out the molecular mechanisms of TCM formulae, taking formula Tian-Ma-Gou-Teng-Yin (TMGTY) as an example. TMGTY consists of 11 Chinese herbs, such as Rhizoma Gastrodiae (Tianma), Ramulus Uncariae Cum Uncis (Gouteng), Concha Haliotidis (Shijueming), Fructus Gardeniae Jasminoidis (Zhizi), and Radix Scutellariae Baicalensis (Huangqin). TMGTY was prescribed to alleviate hypertension-related symptoms and also showed therapeutic effects against dementia and Alzheimer's disease (AD) (Liu et al., 2014; May et al., 2016; Zhang et al., 2016; Chen et al., 2017). TMGTY was one of the 10 most commonly used formulae for treating AD in Taiwan, according to a cohort study of one million patients (Lin et al., 2016). TMGTY was also reported to have neuroprotective effects (Chik et al., 2013; Xian et al., 2016) and could enhance the effect of memory acquisition (Ho et al., 2005, 2008). However, its molecular mechanisms remain elusive.

Alzheimer's disease is a complex neurodegenerative disease that deteriorates memory, cognition, behavior and leads to dementia (Reitz et al., 2011). Several hypotheses were proposed to understand the pathogenesis of AD, including amyloid cascade hypothesis, Tau hypothesis, cholinergic hypothesis and neuroinflammation (Ballard et al., 2011; De Strooper and Karran, 2016; Selkoe and Hardy, 2016). Huge efforts were devoted to the discovery of anti-AD therapies based on these hypotheses, but no curable treatment is available yet. Due to the complex pathology of AD, drugs targeting single protein or pathway may not fully exert expected therapeutic effects. TCM formulae, along with network pharmacology approaches, provide a powerful tool to investigate AD pathology, and they are also promising outsets for anti-AD drug development (Dey et al., 2017; Lai et al., 2017).

For that purpose, we first collected chemical components of TMGTY from related databases, and predicted potential targets for the principal components. Those components were subsequently enriched onto AD-related pathways to find the most disease-relevant component groups. Then we applied gene set enrichment analysis on the groups to examine involved pathways and potential mechanisms in treatment of AD. In order to explore shared mechanisms between diseases, we also mapped disease-related proteins to a protein–protein interaction network to find disease modules. Overlapping sub-modules was presumed as shared mechanisms of which compound targets may affect corresponding diseases. Thus our approach provided a holistic perspective to look into combinations of natural compounds and helped to illustrate their molecular mechanisms.

#### MATERIALS AND METHODS

The whole workflow was illustrated in **Figure 1**.

#### Data Collection and Preparation

The formula of TMGTY was ascertained through literature survey. For each herb medicine in the formula, its constituents were mapped from TCM Systems Pharmacology Database (TCMSP) (Ru et al., 2014), TCM Integrated Database (TCMID) (Xue et al., 2013) and TCM Database@Taiwan (Chen, 2011). Then two important pharmacokinetic properties: human intestinal absorption (HIA) and blood brain barrier (BBB) penetration were predicted for every ingredient by our widely used webserver admetSAR<sup>1</sup> . Ingredients with HIA and BBB penetration classified as negatives were considered poorly absorbed by intestine and can hardly penetrate the BBB, and hence excluded from further analysis.

Alzheimer's disease and hypertension related genes were collected from The Comparative Toxicogenomics Database (CTD) (Davis et al., 2017), Human Genome Epidemiology (HuGE) Navigator (Yu et al., 2008), Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000; Kanehisa et al., 2017), Online Mendelian Inheritance in Man (OMIM) (Amberger et al., 2009), and Pharmacogenetics

<sup>1</sup>http://lmmd.ecust.edu.cn/admetsar1/

and Pharmacogenomics Knowledge Base (PharmGKB) (Whirl-Carrillo et al., 2012). In CTD, only genes having a curated association to the disease were retained, i.e., genes marked as 'marker/mechanism' and/or 'therapeutic' in the 'direct evidence' column. In HuGE Navigator, genes with more than 20 publications were selected. In KEGG, 'KEGG Disease' was used to find disease genes of AD (KEGG Entry ID: H00056) and hypertension (KEGG Entry ID: H01633). In OMIM, associated genes of AD (Phenotype MIM number: 104300) and hypertension (Phenotype MIM number: 145500) were collected. AD (Accession ID: PA443319) and hypertension (Accession ID: PA444552) related genes in PharmGKB were downloaded. The collected genes were filtered using NCBI Gene database (Brown et al., 2015), and only protein-coding genes were retained.

To build network prediction models, a collection of TCM ingredients was made by combining molecules from abovementioned TCM databases. Small molecules from DrugBank (Wishart et al., 2017) database were also collected.

For all ingredients, MacroModel 11.1 program (Schrödinger, LLC, New York, NY, United States, 2016) was applied to desalt and neutralize their structures. Then Epik 3.5 program (Schrödinger, LLC, New York, NY, United States, 2016) was used to generate tautomers. Only the most populated neutral tautomers were retained. The processed compounds were further converted to a canonical SMILES string by Open Babel toolkit (version 2.3.1) (O'Boyle et al., 2011). Duplicates were removed according to canonical SMILES string. Compounds without carbon atoms were also removed from the collection.

For each ingredient, the corresponding targets were matched from BindingDB (Gilson et al., 2016), ChEMBL (Gaulton et al., 2017), IUPHAR/BPS Guide to PHARMACOLOGY (Harding et al., 2017) and NIMH Psychoactive Drug Screening Program (PDSP) Ki Database (Roth et al., 2000) under criteria that: (1) target proteins are from Homo sapiens and have unique UniProt accession numbers; (2) K<sup>i</sup> , Kd, IC<sup>50</sup> or EC<sup>50</sup> ≤ 10 µM, or Potency ≤ 10 µM with "Activity Comment" marked as "Active." The Klekota–Roth (KR) fingerprint was used in this study and generated for every ingredient using PaDEL-Descriptor software (version 2.18) (Yap, 2011).

### Construction of Compound–Target Networks

Three compound–target network models were built by our bSDTNBI method for new target prediction. The first was DrugBank network which contains only small molecules from DrugBank. The second was TCM network consisting of sole TCM ingredients from above collections. The last was a Global network merged by above two networks. The three models were evaluated independently to verify whether a combined model would outperform the others.

Ten-fold cross validation was applied to evaluate the performance of three models. In each fold, roughly 10% of DTIs

were split from the network, serving as test set. Resources were redistributed among the remaining 90% of network (i.e., training set) to predict the 10% missing links. This process was repeated for ten times to reduce contingency. For three parameters α, β and γ used to tune the network performance, grid optimization was employed to search for the best set which maximizes the AUC of 10-fold cross validation. Detailed definition and description of these three parameters could be found in our previous publication (Wu et al., 2016).

Several indicators were calculated to assess model performance, such as precision (P), recall (R), precision enhancement (eP), and recall enhancement (eR). Furthermore, receiver operating characteristic (ROC) curves were plotted by true positive rate (TPR) against false positive rate (FPR). In this study, area under ROC curve (AUC) was calculated and used as an indicator to evaluate model performance since AUC is independent of the number of predicted targets. Basically, the higher the AUC value, the better the model performance. Above indicators were described in details and also widely used in previous studies (Cheng et al., 2012; Lü et al., 2012; Wu et al., 2016).

#### Target Prediction for TMGTY Ingredients

For all TMGTY ingredients, bSDTNBI was used to infer new targets. Most of the ingredients were not in the global network model, i.e., do not have known targets. They were represented by molecular fingerprints to link to the global network. The method redistributes known initial resources of drugs between different types of nodes to infer new targets. The resource diffusion number was set to 2 and the number of predicted targets was set to 20. The detailed description and evaluation of the method can be referred to our previous published papers (Wu et al., 2016, 2018).

#### Identification of AD-Related Components

Considering only a very small portion of natural products have known targets, the number of a compound's targets related to AD conforms approximately to hypergeometric distribution and its probability mass function is as following:

$$\mathbb{P}(X=k) = \frac{\binom{K}{k}\binom{N-K}{n-k}}{\binom{N}{n}}\tag{1}$$

Where N is the total number of genes, K is the total number of AD-related genes, n is the number of predicted genes, k is the number of AD-related genes in predicted genes and P(X = k) is the probability of k AD-related genes occurring in predicted genes for a compound.

However, the constructed network could not cover all proteincoding genes, so in a certain network, N was the number of protein-coding genes it covered, K was the number of all ADrelated genes in the network and n was the number of predicted genes.

Fisher's exact test method was implemented to assess the significance of enrichment of AD-related genes in 20 predicted and known genes for each compound. Compounds with or without known targets were calculated separately. For compounds without known targets, n was set to 20 and k was the number of AD-related genes in n. As for compounds having s known targets, n was set to 20+s, and k was s plus the number of AD-related genes in n. P-value was calculated and adjusted by Benjamini–Hochberg method, and used to rank all compounds. Top-ranked compounds were presumed to be critical components of this formula in treating AD.

In order to take into consideration the properties of chemical components, all compounds collected from the three TCM databases were used to create a background component set. Twenty targets were also predicted for each compound. Then every compound had k AD-related genes. The frequency distribution of k in the background set was calculated and approximated roughly to the probability distribution of the background set:

$$\mathbb{P}(X=k) = \frac{NP\_k}{NP} \tag{2}$$

Where NP is the number of all collectable compounds and NP<sup>k</sup> is the number of compounds having k AD-related genes. Then, if a compound has k AD-related genes where P(X ≥ k) < 0.01, the compound is probably enriched onto AD. This was considered as a calibration and corroboration to Fisher's exact test.

#### Disease Module Analysis

Due to the complexity of biological network, common proteins may be shared among different diseases. Thus a drug acts on a single protein may produce effect on multiple diseases. The overlap of AD and hypertension disease modules was thus investigated to find common proteins. The collected AD-related proteins and hypertension related proteins were mapped onto a protein–protein interaction network which consisted of 13,460 proteins and 141,296 physical interactions. A disease module was calculated as the largest connected component (LCC) of the disease-related proteins in the protein–protein interaction network. Then the sizes of LCCs of 100,000 randomized protein sets in the network as large as the disease-related protein set were calculated and the distribution yielded. The statistical significance of the disease module was calculated as a z-score:

$$z-score = \frac{S - S^{\overline{rand}}}{\sigma(S^{\text{rand}})} \tag{3}$$

Where S, S rand, and σ(S rand) denote the size of LCC of the diseaserelated protein set, the average value and standard deviation of the LCC size random distribution, respectively. A z-score greater than 1.96 indicates a significance p-value < 0.05, which suggests the disease module is larger than random observation. The above used protein–protein interaction network and algorithms were retrieved from the work of Menche et al. (2015).

Then the overlapping proteins of these two modules were extracted and gene set enrichment analysis was conducted to find enriched Gene Ontology biological processes with a cut-off adjusted p-value < 0.05.

### Gene Set Enrichment Analysis

fphar-09-00668 June 23, 2018 Time: 16:8 # 5

Predicted genes and overlapping genes of AD and hypertension modules were enriched onto KEGG Pathway and Gene Ontology (GO) biological process to detect key targets and pathways using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 (Sherman et al., 2007; Jiao et al., 2012).

### RESULTS

#### Formula Ingredients and Known Targets

A total of 731 compounds were collected from the abovementioned three TCM databases for the formula TMGTY. In order to evaluate the pharmacokinetic properties of this oral administrated formula, HIA and BBB penetrations were predicted using our in silico system admetSAR. Only compounds that have both HIA and BBB penetrations predicted as positives were retained. After the screening, 494 herbal compounds were left for further study. 178 different known targets were matched for them from above described databases. Only 68 of 494 compounds had known targets. There were 394 known compound–target interaction pairs. All compounds and known targets information can be found in Supplementary Table S1.

### AD and Hypertension Related Genes

From a series of gene databases, a total of 195 genes were collected and identified as AD-related genes, among which 10 are cytochrome P450 (CYPs). Two hundred and ninety-eight genes were collected as hypertension-related genes, within which nine were CYPs. CYPs are major catalysts contributing to the metabolism of a broad spectrum of endogenous compounds, xenobiotics, and nearly 90% marketing drugs (Guengerich et al., 2016). Due to the substrate promiscuity and wide existence, CYPs were thus excluded from this gene set. Then 185 AD-related genes and 289 hypertension-related genes were used for further experiment. The collected disease associated genes were listed in Supplementary Table S2. Gene set enrichment analyses of these gene groups were also shown in Supplementary Tables S3–S6.

#### Compound–Target Networks

Three compound–target interaction networks were constructed, namely DrugBank, TCM, and Global networks. Their details were shown in **Table 1**. The TCM network collected 1,495 compounds from TCM databases with 899 known targets, among which 287 compounds were also found in DrugBank network. The DrugBank network contains 2,672 small molecules and 1,326 protein targets. Only one hundred targets in TCM network were different from those in DrugBank network. It was thus combined with DrugBank network to form the Global network in order to introduce more targets and expand known network. In Global network, 45.1% targets were enzymes, 10.7% were GPCRs and about 6.7% were ion channels, while 23.7% fell into unknown category, according to IUPHAR classification (**Figure 2A**). As for compounds in Global network, their chemical space was described using three physicochemical descriptors, i.e., molecular weight, ALogP and topological polar surface area (TPSA), depicted in Figure. 2C. About 95% of all compounds have a molecular weight less than 600, TPSA value less than 200Å<sup>2</sup> and ALogP value lying in (−3, 3). Only a few TCM compounds have vast values, whose TPSA, for example, could be up to 800 Å<sup>2</sup> . Similarities between all compounds were assessed by calculating Tanimoto coefficient on FCFP4 (**Figure 2D**). Compounds in **Figure 2D** were ordered as two groups: DrugBank small molecules and TCM compounds. Within each group, compounds were randomly distributed. An average Tanimoto coefficient of 0.13 was yielded, indicating a structural diversity among the compounds. The degree distributions of compound nodes and target nodes were calculated, as shown in **Figure 2B**, which succumb to power law distribution. This demonstrated that these networks are scale-free networks.

Our previously developed method bSDTNBI was applied to these three networks. Ten-fold cross validation was then conducted to assess model performance. Parameters α, β, and γ were determined as 0.32, 0.14, and -0.48, respectively. The performance is usually evaluated by several indicators such as AUC, precision and recall. The higher the values of indicators are, the better the performance is. Those indicators were listed in **Table 2** and corresponding ROC curves were plotted in Supplementary Figure S1. All three models performed well. The average recall values of DrugBank, TCM and Global network models were 0.729 ± 0.014, 0.694 ± 0.020, and 0.724 ± 0.012, respectively. A recall value around 70% indicated that, on average, approximately 70% of a drug's missing links were recovered correctly during 10 rounds of 10-fold cross validation. The value of AUC lies in [0, 1]. AUC value equals to 0.5 means the model gives a random prediction; it equals 1 means an ideal prediction. The AUC values of three above network models were 0.966 ± 0.002, 0.948 ± 0.005, and 0.968 ± 0.002, respectively, all larger than 95%, exhibiting high prediction accuracy. The Global network model outstripped the other two with greater indicator values. The Global network model was thus selected to predict targets for the TCM formula.

### Target Prediction for Formula TMGTY

Based on the Global network, a total of 9,880 new compound– target interaction pairs were predicted via bSDTNBI method, which introduced 364 new targets for the 494 components of TMGTY. Together with the 178 known targets, a total of 542 targets formed 10,274 interactions with the 494 compounds. Compound–target pairs were hugely complemented from previous 394 interactions between 68 compounds and 178 targets. Among most predicted targets for novel compounds,



NC, the number of compounds; NT, the number of targets; NCTI, the number of CTIs; Sparsity: NCTI/(N<sup>C</sup> × NT).

many were related to AD, such as ACHE, BCHE, BACE1, and MAOA (represented by official gene symbol). A complete list of known and predicted targets for all ingredients can be found in Supplementary Table S7.

#### Identification of AD-Related Components

In the Global network, only 1426 proteins were included, among which 65 proteins were AD-related. Thus, in the case of Global network, the probability of a compound having k ADrelated targets conformed to an approximated hypergeometric distribution which had K = 65 and N = 1,426.

Then the adjusted p-value was proposed as an indicator: the lower is the adjusted p-value; the higher is the relevance to AD. A stringent p-value (less than 0.01) was used to identify compounds highly related to AD. Based on P(X ≥ k) =0.007 < 0.01, a compound having k = 6 or more predicted targets were considered significantly related to AD. For compounds having known targets, Fisher's exact test was applied separately. Compounds having p-values less than 0.01 were also considered enriched onto AD, regardless the number of AD-related targets they have.

Then a real distribution was investigated. A total of 57,741 compounds were collected and processed from TCMSP, TCMID, and TCM Database@Taiwan to serve as a background set. Twenty

TABLE 2 | Ten-fold cross validation performance of the three network models.


AUC, area under receiver operating characteristic (ROC) curves; eP, precision enhancement; eR, recall enhancement.

novel targets were predicted for each compound and the number of AD-related genes was matched using previously collected ADrelated genes. The number of AD-related targets of a compound ranged from 0 to 11.

In the background set, the probability of a natural product having k AD-related targets was approximated to the real frequency distribution. Only 3.0% natural products of all had 6 or more AD-related targets and 11.4% had 5 or more, in the real distribution (Supplementary Figure S2). Thus, P(X ≥ 6) = 0.030 < 0.05 and it further corroborated that a compound having 6 or more AD-related targets was significantly enriched onto AD.

All compounds in the formula were assessed by above two methods. Twelve compounds met the criteria and thus were considered significantly related to AD (**Table 3**). Detailed information on these 12 compounds was listed in Supplementary Table S8.

#### Pathways Related to TMGTY

All known and predicted genes of these 12 compounds were enriched onto the KEGG pathways. 121 genes were significantly enriched onto 20 pathways with the adjusted p-value < 0.05 (Supplementary Figure S3). Many target genes were enriched onto pathways related to neurotransmitters. For example, there were 20 targets enriched onto serotonergic synapse pathway (adjusted p-value = 3.5 × 10−13) and 21 targets enriched onto neuroactive ligand–receptor interaction pathway (adjusted p-value = 3.7 × 10−<sup>7</sup> ), while 10 targets were enriched onto dopaminergic synapse pathway (adjusted p-value = 2.3 × 10−<sup>3</sup> ). There were also 15 targets enriched onto calcium signaling pathway (adjusted p-value = 3.7 × 10−<sup>7</sup> ), eight onto arachidonic acid metabolism (adjusted p-value = 3.7 × 10−<sup>7</sup> ) and seven onto inflammatory mediator regulation of TRP channels (adjusted p-value = 3.7 × 10−<sup>7</sup> ). These pathways may involve in Ca2<sup>+</sup> regulation and inflammation. Then a compound–target–pathway subnetwork was built according to target prediction and gene enrichment results, as shown in **Figure 3**. In the network, 11 genes had node degrees larger than 5, which indicated that they were potential targets to half or more than half of those 12 representative compounds. These genes were CYP3A4, ACHE, ABCG2, BACE1, CYP2D6, MAPT, MAOA, PTPN1, EHMT2, PTGS1, and CYP19A1.

#### Overlap of AD and Hypertension Disease Modules

A total of 172 AD-related proteins and 264 hypertension-related proteins were mapped onto the protein–protein interaction network. AD disease module and hypertension disease module were identified as the LCCs consisting of 86 and 166 proteins, respectively. The z-scores of AD and hypertension modules were 14.2 and 11.7, suggesting that the calculated modules were significantly larger than random expectations. Nineteen genes were shared by AD and hypertension, namely ABCB1, AHR, APOE, BCL2, CAT, CRP, ESR1, F2, GPX1, GSK3B, IL1B, LEP, MME, MTOR, NOS2, NOS3, PON1, SOD1, and SOD2. The disease relationship network was shown in Supplementary Figure S4. Then 26 key GO biological processes with p-values < 0.05 were identified by conducting gene set enrichment analysis on these 19 genes (Supplementary Figure S5). Biological processes such as response to reactive oxygen species, response to hydrogen peroxide, positive regulation of nitric oxide (NO) biosynthetic process, regulation of blood pressure, removal of superoxide radicals, and NO mediated signal transduction all together indicate that these overlapping genes are related to the progression of hypertension and inflammation.

#### DISCUSSION

### The Computational Systems Pharmacology Approach Is Valuable for TCM Study

In this study, a computational systems pharmacology approach consisting of network link prediction, statistical analysis and bioinformatics tools was proposed to study TCM formula TMGTY, which has demonstrated a great advantage in investigation of molecular mechanisms of TCM formulae.

The network link prediction was performed with our bSDTNBI method, which was specifically developed for target prediction of new compounds outside of the compound– target network. Due to the scarcity of TCM ingredient–target interaction information, TCM ingredient–target network was highly incomplete and only covered a small range of targets, i.e., 899 targets, which limited its adaptability and impaired its prediction ability. Since an overlap of 287 compounds was found between collected TCM ingredients and DrugBank small molecules, a combined Global network was then used in order to cover more targets. The combined Global model also outperformed the other two. Comparing to other network prediction models for natural products (Fang et al., 2017a,b) which had approximately 750 drug targets in their global models, our global model covered a much wider range of targets, i.e., 1,426 drug targets, which empowered our model a greater potential to predict more diverse and credible targets for natural products with higher accuracy (using AUC value as an evaluation indicator).

The statistical analysis was conducted by Fisher's exact test. Comparing to gene set enrichment analysis methods (Huang et al., 2009), hypergeometric distribution was used to enrich ADrelated genes on compounds. Natural products were enriched onto AD through AD-related genes using Fisher's exact test. For each compounds in the formula TMGTY, its known and predicted targets were identified as whether AD related or nonrelated. From the perspective of our bSDTNBI method, this resource diffusion method can in a way be envisaged as a distance-based similarity method. In the network, the more and the shorter the paths between a compound node and ADrelated target nodes, the more resource it would be portioned from AD-related target nodes, i.e., more AD-related targets would be predicted for this compound. This implied certain intrinsic similarities between compounds represented by network topologies. Since the prediction cannot be perfectly accurate,

TABLE 3 | The 12 compounds were identified highly related to AD, using a cut-off p-value < 0.01.


it was reasonable to consider that a compound having more predicted AD-related targets compared to non-related targets was more topologically similar to compounds having known ADrelated targets, and thus more relevant to AD. Then Fisher's exact test was a good statistical tool to assess the relevance.

The cut-off p-value for compound selection and further analysis was set to 0.01. Twelve compounds were selected and a total of 121 targets were predicted for them. If the cut-off p-value were set to be 0.05, then 108 compounds would meet the criterion and 256 targets predicted. Compounds with a p-value between 0.01 and 0.05 were also considered relating to AD. However, 24 out of 121 targets for 12 compounds were AD-related while only 30 out of 256 targets for 108 compounds were AD-related. This suggested that many targets, especially AD-related targets, were shared among compounds with p-value less than 0.05. So the compounds with p-value less than 0.01 and their corresponding

targets were representatives of the molecular mechanisms of formula TMGTY against AD.

The gap between compounds and diseases were bridged by combining above methods. The link between compounds, target genes and diseases was established and analyzed in the context of network science. Complex compound–protein and protein–protein interaction networks were both taken into consideration to facilitate the apprehension of the formula's mechanisms. Since TCM formulae have complicated constituents and are intrinsically multi-targeted and effective to diverse symptoms, our computational systems pharmacology approach provides a more comprehensive perspective to understand their mechanisms on a systematic level, and is easy to apply to various formulae, thus valuable to TCM study.

#### Evaluation of the Systems Network Pharmacology Approach

In order to further validate the performance of our approach, another traditional Chinese herbal formula Kai-Xin-San (KXS) was analyzed using this approach. KXS is a famous formula used for the treatment of neurosis and AD, which consists of four herbs: Panax ginseng (Renshen), Wolfiporia cocos (Fuling), Polygala tenuifolia (Yuanzhi), and Acorus tatarinowii (Shichangpu) (Zhou et al., 2012; Wang et al., 2015; Chu et al., 2016).

Three hundred and eighty-eight compounds were collected and 369 new targets predicted, then 28 representative compounds were identified and their targets were enriched onto KEGG pathway and GO biological process (Supplementary Tables S9– S13 and Supplementary Figure S6).

Among enriched pathways, nitrogen metabolism, neuroprotective ligand–receptor interaction, serotonergic synapse, dopaminergic synapse, arachidonic acid metabolism, linoleic acid metabolism, and tryptophan metabolism may be involved in AD pathology. GO biological process enrichment analysis indicated that KXS may involve in oxidation–reduction process, steroid metabolic process, memory, heterocycle metabolic process, lipoxygenase pathway and synaptic transmission, dopaminergic. These enrichment analyses suggested that KXS may exert neuroprotective effect by regulating metabolism networks, reversing oxidative damage in brain, as well as targeting neurotransmitter pathways.

Chu et al. (2016) discovered that KXS could alleviate cognitive deficits in AD model rats and more nerve cells survived than that in the control group. KXS could also regulate metabolism network, such as linoleic acid metabolism and arachidonic acid metabolism, by affecting certain metabolites to show anti-AD effects (Chu et al., 2016). Qiong et al. (2016) found that in rat models KXS could reduce the level of 3-nitro tyrosine (3-NT), and increase the activity of choline acetyltransferase, indicating antioxidant effects of KXS. Zhu et al. (2016a,b) discovered that KXS could induce synaptic protein expression in hippocampus neuron in rats and neuronal differentiation in PC12 cells.

In 28 representative compounds, Apigenin, Paeonol were reported to be important anti-AD compounds (Su et al., 2014). Eudesmin could up-regulate the expression of GABAA and Bcl-2, and it has significant anticonvulsant and sedative effects (Liu et al., 2015). 2<sup>0</sup> -O-Methylisoliquiritigenin was reported to have antioxidant activity and it was also active against human neuroblastoma cells (Batovska and Todorova, 2010). Marmesin was reported to have AChE inhibitory effects (Tumiatti et al., 2008; Cabral et al., 2012). Bergapten was discovered to have antiinflammatory effects by suppressing the ROS and NO generation (Yang et al., 2018). Myrcene and eugenol was reported to have anti-inflammatory and antioxidant activities (Irie, 2006; Rufino et al., 2015).

The analyses of KXS further validated and proved our approach would be useful in the analyses of TCM formulae and identification of key herbal constituents. Detailed description of KXS can be found in Supplementary Data.

The precision values of three network models were among 0.049–0.065. Different from machine learning methods, bSDTNBI is a network-based method, and its evaluation indicators are from recommender systems (Zhou et al., 2010; Lü et al., 2012). The precision is defined as:

$$P = \frac{1}{c} \cdot \sum\_{i=1}^{c} \frac{TP\_i(L)}{L} \tag{4}$$

Where C is the number of compounds, L is the number of predicted targets and TPi(L) is the number of recovered missing links of compound i from test set in L targets. Hence, the more targets predicted, the smaller the precision.

The precision value of approximately 0.06 is relatively high (L = 20), comparing to previous network-based studies. Fang et al. (2017b) constructed network models with precision values ranged from 0.010 to 0.049. Precision values were among 0.042 to 0.072 in the work of Wu et al. (2016). They tested 56 available compounds predicted to act on estrogen receptor α (ERα), and 27 compounds were identified as active agonists or antagonists (Wu et al., 2016). Wu et al. (2018) also constructed global network models with precision values ranging from 0.045 to 0.055. The comparison also further validated the performance of our approach.

#### Potential Mechanisms of TMGTY in Treating AD

In the compound–target–pathway subnetwork, many enriched pathways were related to AD. For example, among target genes enriched onto serotonergic synapse pathway, 5 hydroxytryptamine receptor 2A (5-HT2A), 5-hydroxytryptamine receptor 2C (5-HT2c), and 5-hydroxytryptamine receptor 6 (5-HT6) were reported to be related to AD (Wilkinson et al., 2014). 5-HT2A and 5-HT2c can modulate processing of amyloid protein precursor (APP) (Nitsch et al., 1996). Antagonists of 5-HT<sup>6</sup> can improve cognitive performance involving stimulation of glutamate, acetylcholine, and catecholamine release in brain (Benhamú et al., 2014). 5-HT<sup>6</sup> antagonists may also stimulate neurite outgrowth and inhibit mTOR pathway (Claeysen et al., 2015). All 12 compounds were predicted to act on targets in the pathway, mostly 5-HT receptors. Oxidative stress also contributes to neurodegeneration in AD (Barnham et al., 2004). The excessive generation of reactive oxygen species

(ROS) leads to free radical-mediated processes harmful to brain cells (Balaban et al., 2005). Monoamine oxidase A (MAO-A) and monoamine oxidase B (MAO-B) are involved in ROS production while catalyzing various amines (Melo et al., 2011). Acetylcholinesterase (AChE) was predicted to potentially interact with 10 compounds, which was also a neurotransmitter receptor. Thus it may also play an important role. Inhibitors of AChE, such as U.S. Food and Drug Administration (FDA) approved drugs donepezil and galantamine can stabilize or slow decline in cognition (Hansen et al., 2008). Efforts are made to develop multi-target drugs to improve therapeutic efficacy. Ladostigil, for example, is a multi-target drug designed to have AChE, butyrylcholinesterase (BChE) and brain selective MAO-A and MAO-B inhibitory activities (Mangialasche et al., 2010). This design strategy suggests an indigenous advantage of TCM formula in treating complex diseases, such as AD in this case: its multi-targeting attribute. Furthermore, compound TMGTY404 (Dauricine), from the main herb Rhizoma Gastrodiae, may potentially act on dopamine receptors, which could also help to stabilize neurodegeneration and cognitive decline in AD (Martorana and Koch, 2014). Twenty-one targets enriched onto neuroactive ligand–receptor interaction pathway were mostly neurotransmitter receptors. All above pathways suggested that this formula may exert neuroprotective effect by targeting various neurotransmitter receptors to treat AD.

Moreover, there were other enriched pathways may be involved in AD pathology, such as calcium signaling pathway, inflammatory mediator regulation of TRP channels and arachidonic acid metabolism. Ca2<sup>+</sup> plays an important role in neuronal development, synaptic transmission and regulation of many neuronal metabolic pathways (Missiaen et al., 2000). It was also reported that the perturbed cellular Ca2<sup>+</sup> homeostasis correlates with amyloid plaques and neurofibrillary tangles in AD (Hölscher, 1998). Several studies further revealed that the neurotoxicity of Aβ was diminished if cells were incubated in Ca2+-free medium (Mattson et al., 1993) and Ca2<sup>+</sup> from endoplasmic reticulum (ER) and mitochondria is involved into the pathogenesis of neuronal degeneration (Pereira et al., 2004; Takuma et al., 2005). Compounds that target neurotransmitters such as cholinergic receptors and 5-HT receptors may regulate the Ca2<sup>+</sup> homeostasis, since Ca2<sup>+</sup> signaling is initiated by neurotransmitters (Putney, 2003). Transient receptor potential (TRP) channels are plasma membrane cation channels consisting of six subfamilies: TRPA (ankyrin), TRPC (canonical), TRPM (melastatin), TRPML (mucolipin), TRPP (polycystin), and TRPV (vanilloid) (Moran, 2017). Aβ increases the production of ROS which further activates TRPC5, TRPM2, TRPM7, and TRPV1 and then triggers Ca2<sup>+</sup> influx and induces NO production, finally leads to neurodegenerative and inflammatory processes (Yamamoto et al., 2007). In this case, TRPA1 was predicted as potential target for compounds TMGTY332, TMGTY293, and TMGTY291. TRPA1 is involved in the TRPA1-Ca2+-PP2B signaling cascade which contributes to Aβ-triggered inflammation and AD pathogenesis. Aβ can trigger TRPA1-dependent Ca2<sup>+</sup> influx and then enhance the activity of protein phosphatase 2B (PP2B), which then activates NF-κB and nuclear factor of activated T cells (NFAT), leading to produce pro-inflammatory cytokines. The inhibition of TRPA1 channel can slow down AD progression (Lee et al., 2016). The predicted target genes of these 12 compounds including TPRA1, PTGS1, PTGS2, HTR2A, CHRM1, NOS2, and ALOX5 are all important protein-coding genes in these pathways related to AD. Hence the speculation can be made that this TCM formula may exert its therapeutic effect against AD by targeting those proteins to regulate Ca2<sup>+</sup> and NO level and mollify neuroinflammation.

In order to further corroborate the prediction, a literature review was conducted to check if any of these 12 compounds is already experimentally validated to have therapeutic effect against AD. TMGTY404 (Dauricine) was reported to have neuroprotective effect, which could reduce energy depletion and oxidative stress, thus attenuate neuronal apoptotic cell death (Li and Gong, 2007). Dauricine was predicted to have interactions with six AD-related targets: MAO-A, AChE, dopamine receptor D1 (D<sup>1</sup> receptor), 5-HT1A, MAO-B and beta-secretase 1 (BACE1). Targeting MAO-A and MAO-B could reduce the generation of ROS and targeting AChE, D<sup>1</sup> receptor and 5-HT1A together could improve cognitive performance. Thus Dauricine may exert neuroprotective effect through predicted AD-related targets. TMGTY115 (Apigenin) was also reported to have antioxidant and anti-inflammatory properties. Similarly, Apigenin could reduce ROS, protect from Aβ-induced toxicity and suppress inflammatory mediators such as NO and prostaglandin in rat and mouse cell experiments (Venigalla et al., 2015).

Above all indicated the formula TMGTY may treat AD through complex mechanisms, showing both neuroprotective and anti-neuroinflammatory effects.

### Overlapping Genes of AD and Hypertension Disease Modules

Nitric oxide plays a key role in the regulation of many physiological processes, such as vasodilation, inflammation, and neurodegeneration (Förstermann and Münzel, 2006; Brown, 2010). NO is generated by three NO synthase (NOS) isoforms: neuronal NOS (nNOS, encoded by NOS1), inducible NOS (iNOS, encoded by NOS2), and endothelial NOS (eNOS, encoded by NOS3). eNOS is constitutively expressed in the vascular endothelium where NO is continuously produced and involved in the regulation of vascular tone and blood pressure (Lundberg et al., 2015). In neurons, nNOS is activated by an influx of calcium to produce NO (Hall and Garthwaite, 2009). iNOS is highly expressed in inflammatory states and can produce high amounts of NO and other reactive nitrogen species such as peroxynitrite (Beckman et al., 1990). In diseased brain, iNOS is found mainly in microglia and astrocytes and may contribute to neuronal death and inflammatory neurodegeneration (Bal-Price and Brown, 2001; Brown and Bal-Price, 2003). So the overlapping genes may function in these enriched biological processes and involve in the regulation of blood pressure and NO levels in brain, thus contribute to the pathology of both AD and hypertension. Some other enriched biological processes such as positive regulation of neuron death and negative regulation of neuron apoptotic process suggested that

these genes may also engage in the physiology of neuron cells.

These enriched biological processes further suggested that common disease genes should be investigated from a more systematic view. Among common disease genes, several genes were connected in protein–protein interaction network, which suggested that these genes may together exert certain biological effects. The analyses of these genes may also help to understand the concept 'Syndrome' in TCM. Hence, a sub-module was defined as a group of genes containing more than two connected gene nodes in disease modules, which was involved in specific biological processes. Genes enriched onto those above-mentioned biological processes were APOE, BCL2, CAT, ESR1, GPX1, GSK3B, IL1B, LEP, MTOR, NOS2, NOS3, SOD1, and SOD2. Eleven out of these 13 genes were from two submodules of common genes, i.e., sub-module 1 (AHR, APOE, ESR1, GSK3B, MTOR, NOS2, NOS3) and sub-module 2 (BCL2, CAT, GPX1, SOD1, SOD2). Drugs acting on common genes, especially genes in sub-modules, may show therapeutic effect on both AD and hypertension. Several genes from sub-modules were potential targets to the 12 representative compounds. ESR1 was predicted to interact with TMGTY405, TMGTY404, TMGTY115, and TMGTY387; BCL2 was the possible target of TMGTY408, TMGTY405, and TMGTY387; NOS2, NOS3 may interact with TMGTY067, and GSK3B with TMGTY115. Taking compound TMGTY067 (Angustidine) as an example,

it was predicted to interact with two protein-coding genes NOS2 and NOS3 in the sub-module 1. NOS2 and NOS3 were involved in the regulation of blood pressure, removal of superoxide radicals and NO mediated signal transduction. Thus Angustidine may potentially regulate both the blood pressure and neuro-inflammation and worth further investigation on its pharmacological effects through biological experiments.

#### The Synergistic Effect of TMGTY

Tian-Ma-Gou-Teng-Yin was prescribed for neurodegenerative diseases (Chik et al., 2013) such as AD (Liu et al., 2014; Lin et al., 2016) and Parkinson's disease (PD) (Pan et al., 2015), and also the prevention of hypertension (Zhang et al., 2016). Upon previously detected disease modules, a network illustration of potential interactions between herbs and AD, hypertension related gene targets was constructed to speculate the synergistic effect of herb formula (**Figure 4**). According to node degrees, AD-related genes were usually potential targets of multiple herbs, such as ACHE (of 10 herbs), BACE1 (9), MAPT (9), and PPARG (8). Thus TMGTY may have a synergistic effect on neurotransmitter-involved pathways to alleviate symptoms of AD. Nine common disease genes in the disease relationship network were predicted to be targets of herb components. Six of them were in sub-module 1, i.e., ESR1 (10), NOS2 (5), NOS3 (5), AHR (5), GSK3B (4), and MTOR (3). All herbs had potential effect on both AD and hypertension disease modules. Tianma and Gouteng were reported to have anti-AD effects on in vitro or in vivo models (Su et al., 2014). The water extract of Tianma and Gouteng showed antioxidant and antiapoptotic effects on neuronal differentiated PC12 cells (Xian et al., 2016). Yejiaoteng was a commonly prescribed herb for the treatment of AD and sleeping disorder, and a Yejiaoteng decoction was also reported to have sedative-hypnotic effect in an animal model (Chen et al., 2015). Studies have shown that Huangqin had antioxidant and anti-neuroinflammatory effects in PC12 cells and mice models (Shang et al., 2006; Jeong et al., 2011). Yimucao extract was tested to have cerebral protective effect by reducing neurological impairment, oxidative damage and apoptosis in cerebral occluded rats (Loh et al., 2009). The extract of Zhizi also had antioxidant activity (Debnath et al., 2011). Five herbs, Gouteng, Tianma, Yimucao, Zhizi, and Duzhong, were predicted to interact with genes in sub-module 1. Many of these herbs were involved in the production of ROS, and had antioxidant effects. Genes in the sub-module 1 were directly enriched onto GO biological processes such as positive regulation of neuron death and NO mediated signal transduction. Thus acting on submodule 1 to regulate NO-related oxidative state may be attributed to the formula's common protective effects against both AD and hypertension.

A herb usually contains hundreds of compounds, and thus possesses a multi-target quality which results in multiple therapeutic effects. Subtly designed herb formulae consisting of several herbs may have synergistically enhanced therapeutic effects against certain symptoms. These symptoms may be a manifestation of functional gene groups. Thus a herb formula may possess complicated pharmacological activities.

### CONCLUSION

In this study, we proposed a computational systems pharmacology approach to investigate the molecular mechanisms of TCM formula TMGTY. We first collected the principal components of this formula and predicted targets for them. Then using hypergeometric distribution and Fisher's exact test, those compounds were enriched onto AD through target proteins. The most representative compounds were selected and gene set enrichment analysis was conducted. Our approach revealed that formula TMGTY may have neuroprotective and anti-neuroinflammatory effects in treating AD. We further analyzed disease modules of AD and hypertension and found that some sub-modules of genes were shared between two diseases. Compounds (for example, Angustidine) targeting proteins in sub-modules (such as NOS2 and NOS3) may effect on both AD and hypertension.

Yet there are also limitations of our approach. To filter formula ingredients using predicted HIA and BBB properties may lead to an omission of some compounds. The incompleteness of herb ingredients, AD-related genes and PPI network would bring bias to the prediction and analysis. Furthermore our approach cannot discriminate whether a compound agonize or antagonize a target receptor, as well as the causal relationship between compounds and AD. For example, Isorhynchophylline and Gastrodin were not identified as top compounds since they were not predicted to have significant number of AD-related target genes. They had insufficient known target information and the prediction may be biased by above reasons. Thus the performance of our approach needs to be further improved, for example, by integrating drug-phenotype data. However, our approach is from a holistic perspective and easy to integrate new data to increase performance. At last but not least, our approach did not consider the quantity of each component in the formula, which is difficult currently and should be taken into account in future.

## AUTHOR CONTRIBUTIONS

YT, GL, WL, and TW contributed to conception and design of the study. TW performed the experiments and wrote the manuscript. YT wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.

### FUNDING

This work was supported by the National Key Research and Development Program (Grant 2016YFA0502304), the National Natural Science Foundation of China (Grants 81673356 and U1603122) and the 111 Project (Grant B07023).

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2018. 00668/full#supplementary-material

#### REFERENCES

fphar-09-00668 June 23, 2018 Time: 16:8 # 14



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Wang, Wu, Sun, Li, Liu and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Anti-endometriosis Mechanism of Jiawei Foshou San Based on Network Pharmacology

Yi Chen1,2,3 \* † , Jiahui Wei1,2,3† , Ying Zhang1,2,3, Wenwei Sun1,2,3, Zhuoheng Li1,2,3 , Qin Wang<sup>4</sup> , Xiaoyu Xu1,2,3, Cong Li<sup>5</sup> \* and Panhong Li1,2,3 \*

<sup>1</sup> College of Pharmaceutical Sciences and Chinese Medicine, Southwest University, Chongqing, China, <sup>2</sup> Chongqing Key Laboratory of New Drug Screening from Traditional Chinese Medicine, Chongqing, China, <sup>3</sup> Pharmacology of Chinese Materia Medica – the Key Discipline Constructed by the State Administration of Traditional Chinese Medicine, Chongqing, China, <sup>4</sup> Department of Traditional Chinese Medicine and Pharmacy, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China, <sup>5</sup> Department of Obstetrics and Gynecology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China

#### Edited by:

Yuanjia Hu, University of Macau, Macau

#### Reviewed by:

Ruixin Zhu, Tongji University, China Shuai Ji, Xuzhou Medical University, China

#### \*Correspondence:

Yi Chen rachelcy@swu.edu.cn Cong Li b05104@126.com Panhong Li 1311636767@qq.com

†These authors have contributed equally to this work.

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 27 April 2018 Accepted: 09 July 2018 Published: 26 July 2018

#### Citation:

Chen Y, Wei J, Zhang Y, Sun W, Li Z, Wang Q, Xu X, Li C and Li P (2018) Anti-endometriosis Mechanism of Jiawei Foshou San Based on Network Pharmacology. Front. Pharmacol. 9:811. doi: 10.3389/fphar.2018.00811 Jiawei Foshou San (JFS) is the new formula originated from classic Foshou San formula, composed with ligustrazine, ferulic acid, and tetrahydropalmatine. Previously JFS inhibited the growth of endometriosis (EMS) with unclear mechanism, especially in metastasis, invasion, and epithelial–mesenchymal transition. In this study, network pharmacology was performed to explore potential mechanism of JFS on EMS. Through compound–compound target and compound target–EMS target networks, key targets were analyzed for pathway enrichment. MMP–TIMP were uncovered as one cluster of the core targets. Furthermore, autologous transplantation of EMS rat's model were used to evaluate in vivo effect of JFS on invasion, metastasis and epithelial–mesenchymal transition. JFS significantly suppressed the growth, and reduced the volume of ectopic endometrium, with modification of pathologic structure. In-depth study, invasion and metastasis were restrained after treating with JFS through decreasing MMP-2 and MMP-9, increasing TIMP-1. Meanwhile, JFS promoted E-cadherin, and attenuated N-cadherin, Vimentin, Snail, Slug, ZEB1, ZEB2, Twist. In brief, anti-EMS effect of JFS might be related to the regulation of epithelial–mesenchymal transformation, thereby inhibition of invasion and metastasis. These findings reveal the potential mechanism of JFS on EMS and the benefit for further evaluation.

Keywords: Jiawei Foshou San, endometriosis, network pharmacology, invasion and metastasis, epithelial– mesenchymal transition

### INTRODUCTION

Endometriosis is known as the growth of the active endometrial tissue outside the uterus. Even though EMS is considered as a benign gynecological disease, there are malignant performance of invasiveness, angiogenesis, recurrence, and malignant transformation (Ma et al., 2016; Mihailovici et al., 2017). However, the mechanism of EMS is unclear, the reflux theory of menstruation is most

**Abbreviations:** CPPI, core protein–protein interaction; CT–DT, compound target–disease target; EMS, endometriosis; JFS, Jiawei Foshou San; TCM, traditional Chinese medicine.

widely accepted. It is suggested that invasion and metastasis is a very important step in flowing endometrial tissue (Borrelli et al., 2015; Chui et al., 2017).

In TCM, blood stasis and obstruction of uterus are considered as the main cause of EMS (Shan et al., 2017; Wen et al., 2017). So the treatment of EMS is based on activating blood circulation to dissipate blood stasis (Zhao, 2018). Foshou San formula is one of the famous Huoxue Huayu recipes, originally reported in Puji Benshi Fang. JFS is the new formula originated from classic Foshou San formula, composed with ligustrazine, ferulic acid, and tetrahydropalmatine. In previous experiment, good efficiency of JFS has been recovered, including diminishing the growth of EMS, suppression of E2, anti-inflammation and anti-angiogenesis (Tang et al., 2014). However, the effect of JFS on invasion, metastasis, and epithelial–mesenchymal transformation has not been reported in EMS.

Traditional Chinese medicine formula has the complicate characteristic of poly-component with poly-target through polypathway (Wang et al., 2017). Network pharmacology is a new discipline combined systems biology with drug efficacy, generally describing the connection of multi-component with multi-target and multi-pathway (Ning et al., 2017). So application of network pharmacology on TCM will contribute to illustrate the utility of TCM.

In this study, compound–compound target and compound target–EMS target networks were established through network pharmacology data bases. Key targets and pathway enrichment were analyzed. Then autologous transplantation of EMS rat's model were used to evaluate in vivo effect of JFS on EMS. Furthermore, the influence of JFS on invasion, metastasis, and epithelial–mesenchymal transition were investigated (**Figure 1**).

Foshou San; TCMID, Traditional Chinese Medicines Integrated Database; TCMSP, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; SEA, Similarity ensemble approach database; OMIM, Online Mendelian Inheritance in Man; DisGeNET, a database of gene–disease associations; GEO, Gene Expression Omnibus; CPPI, core protein–protein interaction.

### MATERIALS AND METHODS

fphar-09-00811 July 26, 2018 Time: 13:20 # 3

### Collection of Potential Targets for Jiawei Foshou San

In order to collect potential targets of three compounds in JFS as many as possible, the following three databases were used: Traditional Chinese Medicines Integrated Database (TCMID)<sup>1</sup> , Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP)<sup>2</sup> , and the Similarity ensemble approach (SEA) database<sup>3</sup> .

### Collection of Potential Targets in EMS

The potential targets for EMS were obtained from the three resources using "Endometriosis" as the keyword: Online Mendelian Inheritance in Man (OMIM)<sup>4</sup> , a database of gene–disease associations (DisGeNET)<sup>5</sup> , and Gene Expression Omnibus (GEO)<sup>6</sup> .

#### ID Conversion for Searched Targets

All JFS-related and EMS-related targets obtained from databases were aggregated together, and the duplicate ones were removed. Simultaneously, the UniProtKB search function in the protein database UniProt<sup>7</sup> was used to modify the searched targets to their official names. By entering the target name and limiting the species to "Homo sapiens," multifarious ID types of the targets were converted into UniProt IDs.

### Protein–Protein Interaction Data

The STRING database furnishes both predicted protein– protein interaction information and the data which have been experimentally proven (Szklarczyk et al., 2011). The version 10.5 of STRING<sup>8</sup> was employed to search for the protein–protein interaction data, with the species limited to "Homo sapiens" and a confidence score >0.4 (Tang et al., 2016).

### Network Construction and Analysis

The compound–compound target and compound target–EMS target networks were constructed based on their interaction data and visualized by Cytoscape 3.5.0 software. In the generated networks, nodes represented targets and compounds, edges represented the relationship between them. The targets without interaction were excluded from the network. Afterward, the Network Analyzer, a plugin of Cytoscape, was applied to analyze the topological parameters of each node in the network. Among the topological parameters, degree and betweenness centrality were used as crucial factors to describe the most influential nodes in networks (Li et al., 2015; Tang et al., 2015). Thus, the nodes


with higher or equal degrees and betweenness than the average were chosen as the hubs.

### GO Enrichment and Pathway Analysis

The Database for Annotation, Visualization and Integrated Discovery (DAVID)<sup>9</sup> and the Protein Analysis Through Evolutionary Relationships database (PANTHER)<sup>10</sup> were applied for Gene Ontology (GO) enrichment and pathway analysis. The specific operation steps were as following, inputting the protein ID and restricting the species to "Homo sapiens," then utilizing the functional annotation tool to make GO enrichment and pathway analysis.

#### Animals and Chemicals

Female Sprague-Dawley rats aged 6–7 weeks were purchased from Experimental Animals Institute of Chongqing Academy of Chinese Materia (Certification No: SCXK [yu] 2012-0006). The rats were housed at a Ta of 20 ± 2 ◦C and 12 h light/dark cycle, free access to food and water in the Experimental Center, College of Pharmaceutical Sciences, Southwest University. This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of Southwest University (Approval No. 0002183).

Ferulic acid, ligustrazine, and tetrahydropalmatine with the purity of 99.8, 99.3, and 98.1% separately, were provided by

<sup>9</sup>https://david.ncifcrf.gov/, version 6.8 <sup>10</sup>http://pantherdb.org, version 13.1


Nanjing Zelang Medical Technology Co., Ltd. (Nanjing, China). They were mixed by a ratio of 10:5:3, then suspended in CMC-Na to constitute JFS. Gestrinone was purchased from Zizhu Pharmaceutical Co., Ltd. (Beijing, China).

### Rat Endometriosis Model

fphar-09-00811 July 26, 2018 Time: 13:20 # 4

Sixty rats with estrus were surgically induced EMS by autotransplantation of uterine tissue. All operational procedures were conducted as described by Tang et al. (2014) with slight modification. Briefly under sterile condition, uterine horns of anesthetized rats were separated and cut into 5 mm × 5 mm fragments. The uterine segments were suspended in sterile PBS, then sutured onto the inner peritoneum near blood vessels. The incisions were closed and disinfected.

After 28 days of transplantation, the growth of the ectopic endometrium were observed on gross and microscopic examination. The volume of ectopic endometrium were detected by Vernier caliper with volume formula (0.52 × length × width × height) (Pinar et al., 2017). The rats were included with following criteria, viable and well vascularized endometrial explants, and graft volume ≥20 mm<sup>3</sup> in the second laparotomy.

### Experimental Design

After 28 days of auto-transplantation, EMS model were successfully established in 50 from 60 rats. The success rate of the model was 83%. Those rats were randomly divided into EMS group, low, medium, and high JFS groups and gestrinone group. There were no significant difference in endometriotic volume of each group before treatment (**Table 3**). Another 10 normal female rats were treated as control group without transplantation. 0.5% CMC-Na were administered in control and EMS group. Low, medium, and high JFS groups were given with 45, 90, and 180 mg.kg−<sup>1</sup> .d −1 JFS, respectively. A 50 mg.kg−<sup>1</sup> .d −1 gestrinone was given in gestrinone group. All the above groups were administered consecutive 28 days by gavage.

### Hematoxylin and Eosin Staining

Eutopic endometrium in control group and ectopic endometrium in other groups were collected and fixed in paraformaldehyde in the end of administration. Then sections from different groups were stained with hematoxylin and eosin (H&E). Endometrial glands and stroma were identified as the essential criteria for diagnosis. The morphological structure were examined and photographed under a microscope (DFC310 FX, Leica, Germany).

### RNA Isolation and Real-Time PCR

The process for RNA isolation and real-time PCR were performed as described previously (Chen et al., 2012). Briefly, TRIzol reagent (Invitrogen, CA, United States) were used for mRNA extraction from the tissues with acid phenol extraction. RT-PCR was carried out using a PrimeScriptTM RT reagent Kit

(Takara, China) according to the manufacturer's protocols. Realtime PCR was performed with CFX96 Real-Time System (Bio-Rad, United States) with SYBRTM Green Master Mix (Thermo Fisher Scientific, United States). Rat specific primers were synthesized by Dingguo Changsheng Biotechnology (Beijing, China) (**Table 1**). Each PCR was carried out with the following conditions: 95◦C for 2 min, 40 cycles of 95◦C for 15 s and 60◦C for 1 min. Melt curves were analyzed at the end of each assay to confirm the specificity. Fold change was determined using the 2 <sup>−</sup>11CT method normalized with endogenous control GAPDH.

#### Western Blot Analysis

fphar-09-00811 July 26, 2018 Time: 13:20 # 5

The protein were separated and extracted from eutopic endometrium in control group and ectopic endometrium in other groups. The tissue lysates were prepared as described previously (Chen et al., 2015, 2017). Briefly quantified protein lysates were separated by SDS-PAGE, transferred to polyvinylidene difluoride membrane (Millipore, United States) and probed with primary rabbit anti-MMP-2, rabbit anti-MMP-9 (1:100 dilution; Boster Biological Technology, Wuhan, China), rabbit anti-TIMP-1 (1:300 dilution; Proteintech Biotechnology, Wuhan, China), rabbit anti-E-cadherin, rabbit anti-Vimentin, rabbit anti-Snail, rabbit anti-Slug (1:1000 dilution; Cell Signaling Technology, Beverly, MA, United States), rabbit anti-β-actin (1:5000 dilution; Proteintech Biotechnology, Wuhan, China) overnight at 4◦C. Then the membranes were blotted with an appropriate horseradish peroxidase-linked goat secondary antibody (1:2000 dilution; Zhongshan Golden Bridge

Biotechnology, Beijing, China). Electrochemiluminescence was performed according to the manufacturer's instructions with Tanon 5200 imaging system (Tanon, China). β-Actin was used as endogenous control.

#### Statistical Analysis

Data were represented as the arithmetic mean ± SD and compared by one-way ANOVA test using SPSS software (Version 21). P < 0.05 was considered statistically significant.


### RESULTS

#### Compound–Compound Target Network Construction and Analysis

A total of 275 potential targets were obtained for three JFS compounds, 10 for ligustrazine, 86 for ferulic acid, and 179 for tetrahydropalmatine. Detailed information is described in **Supplementary Table S1**. The compound–compound target network consisted of 235 nodes and 1508 edges. In this network, most targets belonged to a single compound, while the targets, such as ADRB2, CA2, F3, LTA4H, PTGS1, PTGS2, SLC6A2, and SLC6A3, belonged to more than one compound (**Figure 2**). It was suggested that these uniform targets might be the foundation of synergistic therapeutic effect of TCM.

#### Compound Target–Disease Target Network Construction and Analysis

By integrating data from disease databases, 401 EMS-related targets were acquired (**Supplementary Table S2**). The CT– DT network consisted of 592 nodes and 6166 edges. Targets of compounds were mapped to the EMS targets to obtain 22 common targets (**Supplementary Table S3**). Then a CPPI network including 22 targets and their first neighbors was extracted from the CT–DT network. The CPPI network comprised 315 nodes and 4703 edges (**Figure 3**). Subsequently, the average values of "Degree" and "Betweenness" for nodes were 29.8603 and 0.0039 in the CPPI network. The 66 nodes with "Degree" ≥ 29.8603 and "Betweenness" ≥ 0.0039 were chosen as the key targets (**Supplementary Table S4**). Interestingly, the 22 common targets were not completely contained in 66 key targets.

#### Pathway Enrichment Analysis for Key Targets

In order to further study the molecular mechanism of JFS on EMS, GO analysis and pathway enrichment of the 66 candidate targets were performed with KEGG and PANTHER database. The results of GO analysis were described by biological process (BP), cell component (CC), and molecular function (MF) terms. In KEGG database, 340 of 417 BPs, 34 of 42 CCs, and 60 of 76 MFs enriched for these targets were recognized as P < 0.05. Twelve BPs, six CCs, and five MFs were enriched from PANTHER database. An overview of the GO analysis was explored with top 5 remarkably enriched terms in the BP, CC, and MF categories (**Figure 4**). According to the results of pathway enrichment, 115 and 76 target-related pathways have been found in KEGG (**Supplementary Table S5**) and PANTHER database (**Supplementary Table S6**). Subsequently, remarkable 12 pathways were presented including MMP/TIMP (**Table 2**).

### Jiawei Foshou San Inhibited the Volume of Ectopic Endometrium

After continuous gavage for 28 days, the volume of ectopic endometrium were evaluated and compared with pretreatment. There were no significantly varieties observed in EMS and JFS 45 mg.kg−<sup>1</sup> .d −1 groups. Using 90 or 180 mg.kg−<sup>1</sup> .d −1 JFS, the transplants became smaller, less adhesion and blood vessels outside, lower height of effusion. The volume of ectopic endometrium were 36.32 ± 11.78 and 17.90 ± 5.17 mm<sup>3</sup> , significantly reduced by 48.68 ± 12.19 and 65.29 ± 9.15 mm<sup>3</sup> , respectively (P < 0.01). Gestrinone diminished the volume of ectopic endometrium tissue from 81.92 ± 19.20 to 16.01 ± 5.53 mm<sup>3</sup> (P < 0.01) (**Table 3** and **Figures 5A–E**). This suggests that JFS restrained the growth of ectopic endometrium in a dose-dependent manner.

#### Improvement of Ectopic Endometrium Morphology by Jiawei Foshou San

In H&E staining, the ectopic endometrium in EMS group had a similar structure with eutopic endometrium in control group.


JFS, Jiawei Foshou San; GTN, gestrinone. Values were mean ± SD. <sup>∗</sup> P < 0.05, ∗∗P < 0.01 vs. pretreatment.

The morphological change of structure were found in eutopic endometrium in control (F), ectopic endometrium in EMS (G), and 180 mg.kg-<sup>1</sup> .d -<sup>1</sup> JFS (H) in H&E staining. ∗∗P < 0.01 to pretreatment. Columns, mean (n = 6). Bars, SD. Magnification, ×100. EMS, endometriosis; JFS, Jiawei Foshou San; GTN, gestrinone.

The ectopic endometrium were constituted with endometrial glandular epithelial cell, endometrial stromal cell, and fibrous connective tissue. After gavage of 90 and 180 mg.kg−<sup>1</sup> .d −1 JFS, the amelioration of ectopic endometrium structure were found, such as thinner ectopic endometrium, looser cell arrangement, less pseudoglandular, decreased blood vessels and inflammatory cells (**Figures 5F–H**).

## Jiawei Foshou San Suppressed Invasion and Metastasis

MMP-2, MMP-9, and TIMP-1 are considered as the important roles in invasion and metastasis (Li et al., 2017). The gene and protein expression of MMP-2, MMP-9, and TIMP-1 were tested in our study. The mRNA levels of MMP-2 and MMP-9 significantly rose in EMS group (P < 0.05), while the mRNA level

of TIMP-1 declined (P < 0.05). On the contrary, JFS obviously inhibited the gene expression of MMP-2 (P < 0.05) and MMP-9 (P < 0.01) compared with EMS group. Meanwhile the mRNA level of TIMP-1 was significantly upregulated in 90 and 180, not 45 mg.kg−<sup>1</sup> .d −1 JFS group, compared with EMS group (P < 0.05) (**Figures 6A–C**). Remarkably higher protein levels of MMP-2 and MMP-9 were founded in EMS group, with lower protein level of TIMP-1 than those in control group (P < 0.05). While treated with JFS, MMP-2, MMP-9 protein were decreased significantly in a dose-dependent manner (P < 0.05). The protein level of TIMP-1 was increased in three JFS groups vs. EMS group (P < 0.05) (**Figures 6D–G**). This suggests that the abatement of invasion and metastasis by JFS were connected with increasing MMP-2, MMP-9, and decreasing TIMP-1.

### Reverse of Epithelial–Mesenchymal Transition by Jiawei Foshou San

Epithelial–mesenchymal transition is related with invasion and metastasis (Zheng et al., 2015). E-cadherin gene expressed significantly lower in EMS group vs. control group (P < 0.01). In contrast, N-cadherin, Vimentin, Snail, Slug, ZEB1, ZEB2, and Twist mRNA were significantly higher in EMS group than those in control group (P < 0.05). Since administration of JFS, the transition from epithelial phenotype to mesenchymal phenotype were reversed, for instance, upregulated mRNA level of E-cadherin, downregulated mRNA levels of N-cadherin, Vimentin, Snail, Slug, ZEB1, ZEB2, Twist compared with EMS group (P < 0.05) (**Figure 7**). Remarkably higher protein levels of Vimentin, Snail, and Slug were founded in EMS group,

with lower protein level of E-cadherin than those in control group (P < 0.05). While using JFS, Vimentin, Snail, and Slug protein were decreased significantly in a dose-dependent manner (P < 0.05). The protein level of E-cadherin was increased in three JFS groups vs. EMS group (P < 0.05) (**Figure 8**).

### DISCUSSION

According to the reflux theory of menstruation, invasion and metastasis of ectopic endometrium is the vital step in EMS, especially following with degradation of extracellular matrix. MMP-2, MMP-9, and TIMP-1 are the important factors in invasion and metastasis, and balance between them regulate degradation of extracellular matrix. In previous study, MMP-2

and MMP-9 increased, meanwhile TIMP-1 decreased in EMS (Huang et al., 2015; Yi et al., 2015; Jana et al., 2016; Szymanowski et al., 2016). The therapy, through suppressing MMP-2 and MMP-9, promoting TIMP-1, have been reported as the effective methods on EMS (Huang et al., 2015; Li et al., 2016; Kim et al., 2017). Consistently, in our experiment, JFS inhibited the growth of ectopic endometrium and recurred the pathological changes. It might be related with the regulation of MMP-2, MMP-9, and TIMP-1 to suppress invasion and metastasis. It is worthwhile to explore role of other MMP or TIMP, for example, MMP-1, MMP-7, TIMP-2 in EMS, and the effect of JFS on them in future.

Using network pharmacological analysis, the kernel targets were collected from the CPPI network for pathway enrichment. Interestingly, we found that there were some pathways related to MMP or TIMP, including estrogen, GnRH and TNF pathways. Estrogen, GnRH, and TNF are the pivotal mediators of endometrial homeostasis (Granese et al., 2015; Leavy, 2015; Simmen and Kelley, 2016; Tosti et al., 2017). Furthermore, MMP and TIMP are regulated by these three pathways (Raga et al., 1999; Voloshenyuk and Gardner, 2010; Yang et al., 2012). In our previous study, JFS suppressed GnRH, estrogen, and TNF (Tang et al., 2014). These might be the reasons of regulating MMP/TIMP balance by JFS.

Epithelial–mesenchymal transition presents the features, loss of polarity, and cellular adhesion in epithelial cells, convert into mesenchymal phenotype (Thiery et al., 2009). During this process, epithelial surface markers (e.g., E-cadherin, keratin) lose, mesenchymal markers (e.g., Vimentin, N-cadherin) express, and more migration and invasion subsequently emerge. Snail, Slug, ZEB1, ZEB2, and Twist, as the transcription factors, induce epithelial–mesenchymal transition through depressing E-cadherin (Cano et al., 2000; Zhang et al., 2006; Chen et al., 2010). Recently, lower E-cadherin, and higher Vimentin, N-cadherin, Snail, Slug, ZEB1, ZEB2, and Twist have been demonstrated in EMS (Furuya et al., 2017; Chatterjee et al., 2018; Wang et al., 2018). Interestingly, epithelial–mesenchymal transition has been inhibited with decline of MMP expression (Wu et al., 2018). We also found that E-cadherin increased, and Vimentin, N-cadherin, Snail, Slug, ZEB1, ZEB2, and Twist decreased. JFS restrained epithelial–mesenchymal transition, at the same time suppressed MMPs and promoted TIMP-1. These consistent data indicated a potential mechanism of increasing invasion and metastasis by JFS through epithelial–mesenchymal transition.

Foshou San formula is composed of Ligusticum chuanxiong Hort and Angelica sinensis. Ligustrazine from L. chuanxiong Hort, ferulic acid from A. sinensis, and tetrahydropalmatine are mixed to JFS with the certain proportion. Firstly, in previous study, ligustrazine has the anti-metastatic effects through decreasing MMP-2, MMP-9, MMP-3, MMP-13, increasing TIMP-1, TIMP-2 (Liang et al., 2014; Jiang et al., 2017; Fang et al., 2018). But up-regulating expression of MMP-2 and MMP-9 is found in bone marrow mesenchymal stem cells by ligustrazine (Wang et al., 2016). These results suggest that ligustrazine might have the different influence on metastasis in different diseases. Tetramethylpyrazine also inhibits epithelial–mesenchymal transition progression (Luan et al., 2016). Secondly, treated with ferulic acid alone or combination with other drugs, its role in suppression of metastatic potential are regulated by the reversal of epithelial– mesenchymal transition (Wei et al., 2015; Zhang et al., 2016). Thirdly, tetrahydropalmatine had a negative effect against invasion in cancer (Yodkeeree et al., 2013). While levotetrahydropalmatine attenuates blood–brain barrier injury and brain edema, but inhibits MMP-2/9 (Mao et al., 2015). In our study, JFS restrained metastasis through accumulating TIMP-1 and attenuating MMP-2, MMP-9. In addition, epithelial–mesenchymal transition were recovered. With all above confused results, the mechanism of JFS on metastasis and epithelial–mesenchymal transition need for further investigation.

### CONCLUSION

In conclusion, these results showed that CPPI network was established through analysis of JFS targets with EMS targets. Then 66 kernel targets were selected for pathway enrichment. In EMS model, JFS was able to inhibit growth and pathological change. Furthermore, the modification of MMP/TIMP balance and down-regulation of epithelial–mesenchymal transition might be the potential mechanisms for JFS on EMS. These findings provide logical support for further evaluation of JFS.

## AUTHOR CONTRIBUTIONS

YC and JW performed the major research in equal contribution. YZ, WS, ZL, QW, and XX provided the technical support. CL contributed to final approval of the version to be published. PL designed the study and revised the manuscript.

## FUNDING

This work was supported by grants from National Natural Science Foundation of China (Nos. 81773984 and 81402441), traditional Chinese medicine research project of Chongqing Health Bureau (No. zy201602125), Southwest University Undergraduate Science and Technology Innovation Fund (No. 20162902003), SWU National Experimental Demonstration Center of Pharmacy (Nos. XY2017-CXZD-04 and YX2017- CXYB-01), and Chongqing Science and Technology Commission of China (No. CSTC2014JCYJA1038).

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2018. 00811/full#supplementary-material

TABLE S1 | Detail and interaction information of targets for JFS compounds.

TABLE S2 | EMS-related targets and interaction information with JFS-related targets.

TABLE S3 | 22 common targets obtained from JFS-related targets and EMS-related targets.

#### REFERENCES


TABLE S4 | 66 key targets extracted from a core protein–protein interaction network.

TABLE S5 | GO analysis and KEGG pathway enrichment of 66 key targets from DAVID database.

TABLE S6 | GO analysis and pathway analysis of 66 key targets from PANTHER database.


of matrix metalloproteinases (TIMP-1) and transforming growth factorbeta2 (TGF-beta2) expression in eutopic endometrium of women with peritoneal endometriosis. Ann. Agric. Environ. Med. 23, 649–653. doi: 10.5604/12321966.1226861


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Chen, Wei, Zhang, Sun, Li, Wang, Xu, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Krukovine Suppresses KRAS-Mutated Lung Cancer Cell Growth and Proliferation by Inhibiting the RAF-ERK Pathway and Inactivating AKT Pathway

Huanling Lai1,2, Yuwei Wang1,2, Fugang Duan1,2, Ying Li1,2, Zebo Jiang1,2, Lianxiang Luo1,2 , Liang Liu1,2 \*, Elaine L. H. Leung1,2 \* and Xiaojun Yao1,2 \*

<sup>1</sup> State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau, <sup>2</sup> Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Taipa, Macau

#### Edited by:

Yuanjia Hu, University of Macau, Macau

#### Reviewed by:

Linlin Lu, Guangzhou University of Chinese Medicine, China SubbaRao V. Madhunapantula, JSS Academy of Higher Education and Research, India

#### \*Correspondence:

Liang Liu lliu@must.edu.mo Elaine L. H. Leung lhleung@must.edu.mo Xiaojun Yao xjyao@must.edu.mo

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 13 April 2018 Accepted: 03 August 2018 Published: 22 August 2018

#### Citation:

Lai H, Wang Y, Duan F, Li Y, Jiang Z, Luo L, Liu L, Leung ELH and Yao X (2018) Krukovine Suppresses KRAS-Mutated Lung Cancer Cell Growth and Proliferation by Inhibiting the RAF-ERK Pathway and Inactivating AKT Pathway. Front. Pharmacol. 9:958. doi: 10.3389/fphar.2018.00958 Oncogenic activation of the KRAS gene via point mutations occurs in 20–30% of patients with non-small cell lung cancer (NSCLC). The RAS-RAF-ERK and RAS-PI3K-AKT pathways are the major hyper-activated downstream pathways in RAS mutation, which promotes the unlimited lifecycle of cancer cells and their metastasis in humans. However, the success of targeted therapy is restricted by many factors. Herein, we show a new pharmacological KRAS signaling inhibitor krukovine, which is a small molecular bisbenzylisoquinoline alkaloid, isolated from the bark of Abuta grandifolia (Mart.) Sandw. (Menispermaceae). This alkaloid targets the KRAS downstream signaling pathways in different NSCLC cell lines, such as H460 and A549, which are established by KRAS mutations. In the present study, we initially investigated the anti-cancer activities of krukovine in KRAS-mutated NSCLC cell lines, as well as KRAS wild type cancer cell line and normal lung cell. Results indicated that krukovine can inhibit the growth and dose-dependently inhibit the colony formation capacity and wound healing ability of H460 and A549. This cytotoxic effect is associated with the induction of cell apoptosis and G1 arrest in those cell lines. Krukovine treatment also suppressed the C-RAF, ERK, AKT, PI3K, p70s6k, and mTOR phosphorylation in H460 and A549. This finding suggests that krukovine represses the growth and proliferation of KRAS-mutated cells by inactivating AKT signaling pathway and downregulating the RAF-ERK signaling pathway. This study provides detailed insights into the novel cytotoxic mechanism of an anti-cancer compound from an herbal plant and promotes the anti-cancer potential of krukovine in NSCLC with KRAS mutation.

Keywords: krukovine, KRAS, non-small cell lung cancer, natural products, inhibitor, RAF, ERK, AKT

### INTRODUCTION

Lung cancer has long been a highly common cancer and the leading cause of cancer-related mortality globally, and its incidence is still increasing (Cancer Genome Atlas Research Network, 2012, 2014). About 85% of lung cancer cases are non-small cell lung cancer (NSCLC), and almost 30% of this proportion were diagnosed at an advanced stage (Abacioglu et al., 2005).

**40**

In most patients with NSCLC, proto-oncogenes, such as KRAS (Kirsten rat sarcoma viral oncogene homolog), and the AKT (also named PKB, protein kinase B) and ERK (extracellular signal-regulated kinase) signaling pathways are constitutionally activated. Aberrant activation of these signaling pathways in cells leads to uncontrolled cell proliferation, apoptotic resistance, and other oncogenic cascades in many cancer types (Brognard and Dennis, 2002; Papadimitrakopoulou and Adjei, 2006; Dutta et al., 2014; Yip, 2015). Therefore, increasing research efforts have targeted these oncogenic signaling pathways to develop novel agents or therapeutics that will effectively treat NSCLC (Harada et al., 2014; Stinchcombe and Johnson, 2014).

Actually, effective inhibitors specific for many key constituents of the RAS-PI3K (phosphatidylinositol 3-kinase)-AKT and RAS-RAF-MEK-ERK pathways have been developed. Many of these inhibitors have been used or evaluated in clinical trials. A study involving 3,620 patients with NSCLC reported that KRAS is a prominent prognostic marker for the survival of patients with lung adenocarcinoma but ineffective for patients with lung small cell carcinoma (Brose et al., 2002). Patients with KRAS mutation show reduced progression-free survival, and the mutation has been adopted for biomarker analyses in NSCLC (De Grève et al., 2012; Yasuda et al., 2012). However, the development of RAS inhibitors, such as farnesyltransferase inhibitors, has been unsuccessful to date (Mazières et al., 2013). Although several AKT inhibitors have been developed and subjected to clinical trials for NSCLC treatment, their adverse side effects, such as severe hyperglycemia and other potential metabolic abnormalities, hinder their applications (Heavey et al., 2014; Yip, 2015). Side effects also limit the clinical use of the ERK inhibitor (Gioeli et al., 2011). In this regard, novel targeted drug therapy that can suppress these oncogenic pathways has attracted much research interest.

Given their low toxicity and high effectiveness, natural products have been studied and used worldwide recently as potential anti-cancer agents. Our current study identified krukovine, a novel anti-NSCLC compound from natural products. Krukovine is a small molecular bisbenzylisoquinoline alkaloid derived from Abuta grandifolia (Mart.) Sandw. (Menispermaceae). Menispermaceae is a well-known family of flowering plants serving as folk herbal medicine for various diseases, including gastrointestinal diseases, such as diarrhea, genitourinary tract diseases, and respiratory tract diseases (e.g., asthma) (Corrêa, 1984). Several compounds, such as bisbenzylisoquinolinic, morphinic, aporphinic, and oxoaporphinic alkaloids, have been isolated from the roots and leaves of this species (Thomas et al., 1997; de Lira et al., 2002; De Sales et al., 2015). Krukovine was first isolated from the bark of A. grandifolia (Mart.) Sandw. and showed potent antiplasmodial activity decades ago (Steele et al., 1999). In the present study, krukovine exhibited a cytotoxic effect and inhibited the growth and proliferation of two KRAS-mutated lung cancer cell lines. Krukovine also inhibited the proliferation of these cancer cells by inducing G1 arrest and apoptosis. Krukovine downregulates the activity of phospho-C-RAF, phospho-AKT, phospho- p70s6k, phospho-mTOR, and phospho-ERK and modulates the PI3K-AKT-mTOR and RAF-ERK signaling pathways. Krukovine may be an alternative candidate for the development of combined targeted therapy against the abnormal expression of RAS oncogenic downstream signaling pathways in NSCLC.

## RESULTS

### Krukovine Shows a Cytotoxic Effect Toward KRAS-Mutated Cells

To evaluate the potential anti-cancer effect of krukovine (**Figure 1A** shows the chemical structure), we subjected the KRAS-mutated cell lines H460 and A549 to cytotoxicity tests. These cell lines were treated with krukovine at 0, 5, 10, and 20 µM for 48 or 72 h. Results showed that krukovine inhibited the growth of H460 and A549 in a timedependent manner, while have less cytotoxicity effect on non-KRAS mutation lung cancer cell line H1299 and normal lung cell CCD19-Lu (**Figure 1B**). IC<sup>50</sup> values revealed the potent cytotoxicity of krukovine to KRAS-mutated cancer cells, as summarized in **Table 1**. The IC<sup>50</sup> values were much lower in the H460 and A549 cell lines treated with krukovine for 72 h (9.80 ± 0.13 and 8.40 ± 0.37 µM, respectively) than in those treated for 48 h (19.89 ± 0.19 and 13.69 ± 0.15 µM, respectively).

### Krukovine Inhibits Cell Colony Formation and Wound Healing Ability in H460 and A549 Cells

Long-term colony formation assays of H460 and A549 cells verified the growth inhibiting effect of krukovine. Krukovine significantly inhibited the colony formation capacities (**Figure 2**) and wound healing ability (**Figure 3**) of the H460 and A549 cells in a dose-dependent manner.

### Krukovine Significantly Induces Apoptosis in H460 and A549 Cells

To explore the anti-cancer properties of krukovine, we measured the level of cell apoptosis by flow cytometry using Annexin V-FITC/propidium iodide (PI) staining. The results are shown in **Figure 4**. Krukovine caused limited apoptosis in H460 and A549 cells in low dosage. With increased treatment dosage, the cells experienced extensive apoptosis. Krukovine inhibits caspase-3 expression while increases cleaved PARP (poly ADP ribose polymerase) expression level. This result indicated that cell apoptosis induction also contributes to the krukovine-mediated inhibition of H460 and A549 cell proliferation.

### Krukovine Induces Cell Cycle Arrest at the G1 Phase in H460 and A549 Cells

To explain the decreased cell viability, we treated H460 and A549 cells with krukovine, and their cell cycles were detected by flow cytometry through PI staining. **Figure 5** show that krukovine induced a moderate accumulation in the G1 phases and a reduction in the sub-G1 phase.

### Krukovine Inhibits the RAF-ERK Pathway and Inactivates AKT

We next evaluated the effect of krukovine on mediating the PI3K-AKT and RAF-MEK-ERK signaling pathways. These two pathways can be activated by KRAS (Turke et al., 2012; Tomasini et al., 2016). The expression levels of phospho-C-RAF, phospho-AKT (Ser473), phospho-ERK (Thr202/Thy204), phospho-PI3K, phospho-p70s6k, phospho-mTOR, total-C-RAF, total-ERK, total-p70s6k, total-mTOR, total-PI3K, and total-AKT were determined by Western blot. Results indicated that krukovine decreased the levels of phospho-C-RAF, phospho-AKT, phospho-p70s6k, phospho-mTOR, phospho-PI3K, and phospho-ERK but did not significantly affect the levels of total-C-RAF, total-ERK, total-p70s6k, total-mTOR, total-PI3K, and

TABLE 1 | IC<sup>50</sup> of KRAS-mutated cell lines after treatment with krukovine.


IC<sup>50</sup> is presented as mean ± SEM. CCD19-Lu is normal cell.

total-AKT (**Figure 6**). These data suggests that krukovine exerts its growth-suppressing and proliferation-inhibiting effects by regulating KRAS downstream signaling pathways.

#### DISCUSSION

The RAS-RAF-ERK and PI3K-AKT-mTOR pathways are two main downstream signaling pathways involved in the KRAS genes (Gioeli et al., 2011; Tomasini et al., 2016; Matikas et al., 2017). About 20–30% KRAS mutation (Prior et al., 2012; Stephen et al., 2014), 50–70% overexpression of phosphorylated AKT (Yip, 2015), and 70% activated ERK expression (Heavey et al., 2014) were found in patients with NSCLC. The relatively limited subset of NSCLC carrying these genetic mutations should be effectively treated by mediated target therapy, such as using RAF, ERK, and AKT inhibitors. Unfortunately, most patients with NSCLC do not harbor these genomic events, and the 5 year survival rate remains unsatisfactory (Nussinov et al., 2018). Moreover, the side effects of the target inhibitors have hindered their clinical use (Gioeli et al., 2011; Heavey et al., 2014; Yip, 2015).

For many years, natural products have been considered as potential resources for novel drug discovery. We identified a new class of small-molecule from herbs that exhibit effects on directly inactivating AKT signaling and downregulating RAF-ERK signaling pathway. In this study, we initially investigated the potential of krukovine to suppress the growth and proliferation of KRAS-mutated NSCLC cell lines. H460 and A549 cells, which contain different codons of KRAS mutation, have served as typical types of KRAS-mutated NSCLC cell lines widely used as in vitro model systems. Krukovine exerts cytotoxic and antiproliferative effects on H460 and A549 cells.

We also found that the activities of pivotal proteins, such as AKT, RAF, and ERK, in the RAS-PI3K-AKT-mTOR and RAS-RAF-MEK-ERK signaling pathways are inhibited by krukovine

in NSCLC cells. The interaction of the activated RAS-RAF-MEK-ERK pathway with multiple effectors can regulate cell growth, cell differentiation, and apoptosis (Cully and Downward, 2008; Montagut and Settleman, 2009). Phosphorylation of the ERK protein is a key component of the RAS-RAF-MEK-ERK downstream signaling pathway. Phosphorylated ERK translocates to the nucleus and then causes gene expression changes and mediates the activities of various transcription factors (Roberts and Der, 2007). The PI3K-AKT-mTOR signaling pathway plays an important role in cell growth, cell proliferation, angiogenesis, and cell survival; these processes determine treatment resistance against systemic chemotherapy and radiation (Pal et al., 2008). AKT is a crucial factor in this pathway. Phosphorylation of AKT downregulates various downstream substrates, such as Bad, and can result in malignant transformation (Chang et al., 2003). During cancer cell proliferation, AKT phosphorylation can accumulate the cyclin D1 protein and also prevent the release of calcium from the mitochondria and hence avert cell apoptosis (Diehl et al., 1998). Meanwhile, inactivating AKT can inhibit the PI3K-AKT-mTOR signaling pathway and achieve a tumorsuppressive effect. In this case, the feedback activation of AKT importantly participates in the unsatisfactory clinical results of several RAS downstream pathway inhibitors in cancer treatment (Sun et al., 2005; Wei et al., 2015). In the clinics, up to 45% of patients with NSCLC show increased AKT expression (Okudela et al., 2007; Spoerke et al., 2012). In our present study, krukovine induced G1 arrest and apoptosis in H460 and A549 cells; such effects can lead to cell growth inhibition. This action can be associated with the inhibitory effect of krukovine by AKT phosphorylation and RAF-ERK pathway downregulation, which can lead to cancer cell death (Cully and Downward, 2008; Pal et al., 2008; Montagut and Settleman, 2009).

The RAF-ERK and PI3K-AKT pathways are the two major hyper-activated downstream pathways in RAS mutation; these pathways promote the uncontrolled growth of abnormal cells and their metastasis in humans. These signaling pathways have been identified as promising targets in cancer therapy in recent years (Asati et al., 2016). However, the success of targeted therapy can be limited by the developed resistance of cancer cells through the mutation of target kinases, redundancy in signaling, feedback activation of pathway components, and compensatory activation of parallel circuits (Shamma et al., 1967). Use multi-targeting synthetic signaling pathway inhibitor to treat NSCLC has been proposed by some studies (Logue and Morrison, 2012; Cheng et al., 2014). In this light, targeting two or more constituents of the same pathway or two different pathways simultaneously, for example, AKT and ERK, has been suggested to improve the success of NSCLC-targeted therapy (Meng et al., 2010; Heavey et al., 2014).

In our study, we identified krukovine as a novel KRAS signaling inhibitor and evaluated its anti-cancer activity in NSCLC cell lines. Krukovine effectively inhibited KRAS downstream signaling and induced G1 arrest and apoptosis to exert a cytotoxic effect on KRAS-mutated lung cancer cells lines.

#### MATERIALS AND METHODS

#### Reagents and Antibodies

Krukovine was purchased from Top Science (Shanghai, China) and dissolved in dimethyl sulfoxide (DMSO). The Annexin V/PI staining kit was produced by BD Biosciences (San Jose, United States). The antibodies for Western blot are as follows. Primary antibodies against phospho-C-RAF, total-C-RAF, phospho-ERK(Thr202-Thy204), total-ERK, phospho-AKT(Ser473), phospho-PI3K, total-PI3K, phosphop70s6k, total-p70s6k, phospho-mTOR, total-mTOR, caspase-3, PARP, and GAPDH were produced by CST (Cell Signaling Technology, United States). The primary antibody against AKT was produced by Santa Cruz Biotechnology (Santa Cruz, United States). Fluorescein-conjugated goat anti-rabbit and -mouse secondary antibodies were produced by Odyssey (Belfast, United States).

#### Cell Lines and Cell Culture

The KRAS mutant NSCLC cell lines used in this study (H460 and A549) were purchased from the ATCC (American Type Culture Collection, United States). All cells were cultured in RPMI-1640 medium containing 10% fetal bovine serum with 100 µg/mL streptomycin and 100 U/mL penicillin. Cells were cultured in an incubator with 5% CO<sup>2</sup> at 37◦C.

### Cell Growth Inhibition Assay

The standard MTT (3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide) assay was carried out to evaluate the cell growth inhibition effect of krukovine. In brief, H460, A549, H1299, and CCD19-Lu cells were each planted at 4 × 10<sup>3</sup> cells or 3 × 10<sup>3</sup> cells per well in a 96-well plate and cultured for 12 h to allow cell adhesion. Different concentrations (0, 5, 10, and 20 µM) of krukovine were applied as treatment for another 48 or 72 h. DMSO treatment served as a vehicle control. Every dosage was repeated three times, and at least three independent experiments were performed. At the end of the treatment, MTT solution (5 mg/mL) was added to each well (10 µL per well), and each plate was placed back to the incubator. After further culture for 4 h, the supernatant was carefully removed, and 100 µL of DMSO, as resolved solution, was added to each well while lightly shaking for 10 min to dissolve the MTT crystals. The absorbance was measured by a Tecan microplate reader at 570 nm and used as a reference at 650 nm. The percentages obtained from the absorbance of the treated cells divided by the absorbance of untreated cells were presented as the cell viabilities. The IC<sup>50</sup> of krukovine was calculated by the GraphPad Prism 5.0 software.

### Cell Apoptosis and Cell Cycle Analysis

H460 and A549 cells were each planted on a six-well plate with a density of 1 × 10<sup>5</sup> cells per well overnight. The cells were cultured for over 12 h to allow cell adhesion and then exposed to various concentrations of krukovine for 48 h. At the end of treatment, cells were harvested using trypsin, washed with PBS, and then collected after centrifugation. For cell apoptosis analysis, cells were treated with 5 µL of PI (1 mg/mL) and 5 µL of Annexin V fluorescein dye and stained for 15 min; this step must be performed away from light and at room temperature. The cells were then resuspended in 300–500 µL of Annexin binding buffer and filtered before analysis by a BD FACSAriaIII flow cytometer (BD Biosciences). The percentage of apoptotic cells was quantitatively determined. For cell cycle assay, the cells were fixed with 70% (v/v) ethanol for at least 30 min at 4◦C. Thereafter, the cells were washed with PBS before treatment with 5 µL of PI (1 mg/mL). The percentages of cells at different cell cycle phases (sub-G, S, G1, and G2) were quantitatively measured by the same equipment in the corresponding process.

#### Western Blot

fphar-09-00958 August 20, 2018 Time: 19:30 # 8

Total-cell protein lysates and Western blot materials were prepared as follows. Then, 48 h after drug treatment, the cells were rinsed with lysed ice-cold PBS and lysed in RIPA buffer (150 mmol/L NaCl, 50 mmol/L Tris–HCl, pH 8.0, 1% deoxycholate, 0.1% SDS, and 1% Triton X-100) containing protease and phosphatase inhibitors (Roche, United Kingdom) for at least 30 min and then centrifuged at 14,000 × g for 10 min at 4◦C. The concentration of total protein for each sample was measured by DCTM protein assay kit (Bio-Rad). Then, equal amounts of total protein of each sample were resuspended in loading buffer and denatured for 5 min in 100◦C. The total protein (30 µg) of each sample was separated by 10% SDS-PAGE and then transferred to PVDF membranes (Millipore, United States). The protein membranes were blocked by 5% nonfat milk in 1× TBST for 1 h at room temperature. The samples were incubated with different primary antibodies [phospho-C-RAF, phospho-AKT (Ser473), phospho-ERK (Thr202/Thy204), total-C-RAF, total-ERK, total-AKT, total-p70s6k, phosphop70s6k, total-mTOR, phospho-mTOR, phospho-PI3K, total-PI3K, caspase-3, PARP, and GAPDH] at 4◦C overnight. The above-mentioned primary antibodies were diluted in 1:1,000. Protein membranes were subsequently incubated with secondary fluorescent antibodies for 2 h and then washed in 1× TBST three times for 5 min each time. All secondary antibodies (anti-rabbit or anti-mouse) were diluted in 1:10,000. All membranes were analyzed by an LI-COR Odyssey scanner (Belfast, United States).

#### Colony Formation Assay

H460 and A549 cells were planted into six-well plates (500 cells/well), respectively. After attachment overnight, the cells were treated with various concentrations (0, 5, 7.5, 10, and 20 µM) of krukovine, and the medium was changed every 3 days. When colony formation was visible, the medium was discharged. The colonies were washed with ice-cold PBS gently, fixed in 4% paraformaldehyde (PFA) for 15 min, and then stained with 0.5% crystal violet (20% methanol, 0.5% crystal violet, and 1% PFA in

#### REFERENCES


ddH2O) for 30 min. After the extra crystal violet was washed away and dried off, the colonies were photographed and analyzed.

#### Scratch Wound Healing Assay

H460 and A549 cells were planted into six-well plates (500 cells/well), respectively. After attachment overnight, they should reach ∼70% confluence as a monolayer, then, the confluent monolayer was scratched with a 200 µL sterile pipette tip. After scratching, gently wash the well twice with medium to remove the detached cells. The cells were treated with various concentrations (0, 5, 7.5, 10, and 20 µM) of krukovine. After growing for additional 24 h, wash the cells twice with 1× PBS, take photos for the monolayer on a microscope in the same configurations.

#### Statistical Analysis

Statistical analysis was carried out by GraphPad Prism 5.0 software. The results were presented as (mean ± SEM) of three individual experiments. ANOVA or Student's t-test followed by Bonferroni's test was used to compare all pairs of columns. p-Values <0.05 were set as statistically significant.

### AUTHOR CONTRIBUTIONS

LLi, EL, and XY conceived the study, participated in the design and coordination of the whole study, and helped in critically revising the draft for important intellectual content. HL carried out the cell culture studies, molecular biology experiments, data collection, statistical analysis, and manuscript drafting. YW participated in the data collection and performed the statistical analysis. ZJ and FD participated in the molecular biology experiments. YL and LLu helped in critically revising the draft for important intellectual content. All authors have checked and approved the final manuscript and agreed to be accountable for all aspects of the work by ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

### FUNDING

This work was supported by Macao Science and Technology Development Fund (Project Nos. 046/2016/A2, 086/2015/A3, 005/2014/AMJ, and FDCT-16-010-SKL).



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Lai, Wang, Duan, Li, Jiang, Luo, Liu, Leung and Yao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

fphar-09-00958 August 20, 2018 Time: 19:30 # 9

# Cyclocarya paliurus Leaves Tea Improves Dyslipidemia in Diabetic Mice: A Lipidomics-Based Network Pharmacology Study

Lixiang Zhai<sup>1</sup> , Zi-wan Ning<sup>1</sup> , Tao Huang<sup>1</sup> , Bo Wen1,3, Cheng-hui Liao<sup>3</sup> , Cheng-yuan Lin<sup>1</sup> , Ling Zhao<sup>1</sup> , Hai-tao Xiao<sup>2</sup> \* and Zhao-xiang Bian1,3 \*

<sup>1</sup> School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong, <sup>2</sup> School of Pharmaceutical Sciences, Health Science Center, Shenzhen University, Shenzhen, China, <sup>3</sup> Shenzhen Research Institute and Continuing Education, Hong Kong Baptist University, Shenzhen, China

#### Edited by:

Yuanjia Hu, University of Macau, Macau

#### Reviewed by:

Xiao Yu Tian, The Chinese University of Hong Kong, Hong Kong Rene Cardenas, Universidad Nacional Autónoma de México, Mexico

\*Correspondence:

Hai-tao Xiao xhaitao@szu.edu.cn Zhao-xiang Bian bzxiang@hkbu.edu.hk

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 11 April 2018 Accepted: 06 August 2018 Published: 28 August 2018

#### Citation:

Zhai L, Ning Z-w, Huang T, Wen B, Liao C-h, Lin C-y, Zhao L, Xiao H-t and Bian Z-x (2018) Cyclocarya paliurus Leaves Tea Improves Dyslipidemia in Diabetic Mice: A Lipidomics-Based Network Pharmacology Study. Front. Pharmacol. 9:973. doi: 10.3389/fphar.2018.00973 Hyperlipidemia and hepatic steatosis afflict over 75% of patients with type 2 diabetes, causing diabetic dyslipidemia. Cyclocarya paliurus (CP) leaf is a herbal tea which has long been consumed by the Chinese population, particularly people suffering from obesity and diabetes. CP appears to exhibit a hypolipidemic effect in lipid loaded mice (Kurihara et al., 2003), although the detailed mechanisms and active ingredients for this hypolipidemic effect have not yet been answered. In this study, we investigated the beneficial effects of CP and predicted the mechanisms by utilizing lipidomics, serumpharmacochemistry and network pharmacology approaches. Our results revealed that serum and hepatic levels of total triglyceride (TG), total cholesterol (T-CHO), low-density lipoproteins (LDL) and high-density lipoproteins (HDL), as well as 30 lipids including cholesterol ester (CE), diglyceride (DG), phosphatidylethanolamine (PE), phosphatidylcholine (PC), and sphingomyelin (SM) in CP-treated mice were improved in comparison with untreated diabetic mice. In parallel, 14 phytochemical compounds of CP were determined in mice serum after CP administration. Mechanistically, the network pharmacology analysis revealed the predicted targets of CP's active ingredients ALOX12, APP, BCL2, CYP2C9, PTPN1 and linked lipidome targets PLD2, PLA2G(s), and PI3K(s) families could be responsible for the CP effects on diabetic dyslipidemia. In conclusion, this study revealed the beneficial effects of CP on diabetic dyslipidemia are achieved by reducing accumulation of hepatic lipid droplets and regulating circulatory lipids in diabetic mice, possibly through PI3K signaling and MAPK signaling pathways.

Keywords: Cyclocarya paliurus, Diabetic dyslipidemia, hyperlipidemia, lipidomic, network pharmacology

**Abbreviations:** ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, urea nitrogen; CE, cholesterol ester; CP, Cyclocarya paliurus; CREA, creatinine; DG, diglyceride; HDL, high-density lipoproteins; LDL, low-density lipoproteins; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PPI, protein–protein interaction; SM, sphingomyelin; STZ, streptozotocin; T-CHO, total cholesterol; TG, total triglyceride.

### INTRODUCTION

Hyperlipidemia and hepatic steatosis are frequently found in the metabolic syndrome and type 2 diabetes (Jung and Choi, 2014; Perry et al., 2014). Hyperlipidemia is characterized as high T-CHO, TG, LDL, and low HDL levels (Richhariya et al., 2015), whilst hepatic steatosis is represented by high TG, T-CHO, AST, and ALT levels (Stern and Castera, 2017). Emerging evidence suggest that dyslipidemia is a significant risk factor for the development of type 2 diabetes (Andriankaja and Joshipura, 2014; Association, 2015), and pharmacological lipid-lowering therapy is effective to alleviate the complications of type 2 diabetes including hyperlipidemia, hepatic steatosis, coronary heart disease, etc. (Zafrir and Jain, 2014; Sattar et al., 2016; Spence et al., 2016). Cyclocarya paliurus (Batal.) Iljinskaja (Juglandaceae) is a native medicinal plant grown in the southwest of China. The leaves of C. paliurus (CP) have been used as a herbal tea in China for its special flavor and the benefits to the obese and diabetic Chinese populations. The bioactive components isolated from CP are flavonoids, triterpenoids, organic acids, and polysaccharides, these components contribute to its versatile biological properties including antihyperglycemia, antihyperlipidemia, and antihypertension (Jiang et al., 2015; Yoshitomi et al., 2017). Previous studies have investigated the anti-diabetic function and mechanism of CP leaves on STZ and high-fat diet-induced type 2 diabetic mice, and its potent hypoglycemic effect has been verified on diabetic mice (Wang Q. et al., 2013; Ma et al., 2015). Moreover, CP has also been reported with hypolipidemic effects in vivo (Yao et al., 2015; Lin et al., 2016; Yang et al., 2016). However, the mechanism of bioactive constituents of CP on diabetic dyslipidemia remain unknown. Therefore, our objective is to study the effects of CP on lipid disorders in diabetes and elucidate its mechanism-of-actions.

It is acknowlegded that the majority of herbal medicines, including TCMs are orally delivered drugs of polypharmacy. Their active components are firstly absorbed into the bloodstream and then selectively and simultaneously interact with multiple targets at the root causes of the disease (Zhao et al., 2015). Serum pharmacochemistry is a rapid and reliable method using the UPLC-MS technique to track the components absorbed into the bloodstream and has been widely used to reveal the efficacy of TCMs (Yan et al., 2017). Moerover, metabolomics is an approach to analyze the metabolites, the intermediate products of metabolic reactions of living systems. This technology contains many subclasses based on the chemical characteristics of metabolites, namely lipidomics, amino metabolomics, and sugar metabolomics, etc. It has been widely used in the evaluation of the therapeutic effects and elucidation of the therapeutic mechanisms of herbal products (Wang X. et al., 2017). Metabolomics profiling can reveal whole metabolic profile changes of living systems in response to endogenous or exogenous stimuli such as drug treatment (Beger et al., 2016). Because any effects of herbal products are mediated by their constituents in a complex biological system, metabolomics can help us to analyze their action network comprehensively. Network pharmacology is a bioinformatics strategy to understand drug action and mechanisms by mapping drug-target-disease networks from the biological level (Li et al., 2014). To date, accumulating evidence suggest that network pharmacology approach is a powerful tool to study the molecular mechanisms of the complex components found in medicinal herbs.

Considering CP as a herbal tea with multiple components and diabetic dyslipidemia as a cluster of lipid abnormalities, we focused on the investigation of the beneficial effect of CP and its mechanisms against diabetic dyslipidemia using lipidomics, serum pharmacochemistry, and network pharmacology approaches.

#### MATERIALS AND METHODS

#### Chemicals and Materials Regents and Standards

UPLC grade organic solvent was purchased from Merck (Darmstadt, Germany). Deionized water was obtained from a milli-Q system (Millipore, Billerica, MA, United States). Formic acid, ammonium formate, and phosphoric acid were obtained from Sigma (St. Louis, MO, United States). Lipids standards including a lipids mixture of TG, CE, DG, PE, PC, and SM, etc, were purchased from Avanti Polar Lipid (Alabaster, AL, United States). Strptomycin and glibenclamide was purchased from Sigma (St. Louis, MO, United States).

#### Plant Material Preparation

fphar-09-00973 August 27, 2018 Time: 10:45 # 3

The leaves of Cyclocarya paliurus (Batal.) Iljinskaja were collected and authenticated by Prof. Hu-Biao Chen from School of Chinese Medicine, Hong Kong Baptist University, and voucher specimens (No. CP20151201) was stored in our Research Laboratory. For the preparation of the extract of C. paliurus leaves (CP extract), the dried leaves of C. paliurus (5 kg) were soaked in boiled water (1:10 m/v) for 2 h twice. The solution was concentrated and dried under vacuum freezer to obtain crude extract. The crude extract was then extracted by 70% EtOH for 2 h (1:10 m/v) in an ultrasound bath. The refined solution was concentrated and dried under vacuum freezer again and the refined extract was stored in 4◦C fridge until use. CP solution was prepared with 0.5% sodium carboxymethyl cellulose (CMC-Na) solution for animal oral administration and MeOH for phytochemical analysis.

## Animal Studies

#### Animal Handling and Diets

Eight-week-old C57/BL6J mice were purchased from Laboratory Animal Services Centre, Chinese University of Hong Kong and raised in the Laboratory Animal Services Center, School of Chinese Medicine, Hong Kong Baptist University. The mice were raised in a 12 h light/dark cycle, temperature controlled (22◦C) standard animal room provided with diet and water ad libitum. All experimental protocols were approved by the Animal Ethics Committees of Hong Kong Baptist University, in accordance with "Institutional Guidelines and Animal Ordinance" (Department of Health, Hong Kong Special Administrative Region) (Registration No. LIUYE/15-16/01-CLNC). Body weight, food consumption, and blood glucose level were monitored each week. Blood glucose level was determined by OMRON glucometer (Beijing, China) using blood samples collected from the tail vein.

#### Animal Groups

The diabetes mice model was induced by high-fat diet (adjusted Calories Diet, 42% from fat) (No. 881372) (Harlan Laboratories, Inc., Indianapolis, IN, United States) for 4 weeks and intraperitoneal (i.p) injection with STZ (25 mg/kg) three times in following 3 days. The mice with fasting glucose level higher than 11 mmol/L were considered as diabetic mice, and the diabetic mice with consecutive 7-day hyperglycemia (11 mmol/L or greater) were used for the experiment. An equal volume of vehicle was injected into the control mice. The diabetic mice were then divided into the diabetic group (model group), the CP treatment group (CP group) and glibenclamide treatment group (positive group). For the CP treatment group, the mice were orally administrated 2 g/kg/day CP until end of the experiment according to previous experimental data (**Supplementary Figure S1**). The normal mice were divided into two groups: vehicle control group (0.5% CMC-Na solution treated, named as blank group) and CP-treated control group (CP treated, named as control group). CP-treated control group mice were orally administered 2 g/kg/day CP extract and blank group mice were orally administered same volume 0.5% CMC-Na solution as CP solution. Glibenclamide was given at 15 mg/kg/day in 0.5% CNC-Na solution to mice in positive group according to the previous study (Xiao et al., 2017).

#### Blood Sample Collection and Preparation

For serum pharmacochemistry study, mice were orally administered CP solution and anesthesia by 3% chloral hydrate through intraperitoneal injection after 10 mins. About 1 mL blood was collected in heparin-tube. Plasma was obtained after 3,000 rpm centrifuge for 30 min at room temperature. A total of 900 µL MeOH was added to 100 µL plasma and centrifuged at 14,000 for 10 min at 4◦C to precipitate protein. A total of 800 µL supernatant was dried under vacuum concentrator within 30 min and dissolved in 200 µL 70% methanol for LC-MS analysis.

For lipidomics study, about 1 mL blood was collected from mice under anesthesia, serum was obtained after centrifuging at 3,000 rpm for 30 min at room temperature. A total of 50 µL serum was used for lipidomics study. Briefly, 250 µL Folch solvent with the internal standard was added in 50 µL serum and vortexed vigorously. Two phases were formed after centrifugation at 5,000 rpm for 15 min. The bottom layer was dried under vacuum concentrator and dissolved in 200 µL of ACN/IPA/H2O (65:30:5 v/v/v) for analysis.

Another 100 µL serum was used for clinical index analysis including ALT, AST, ALP, CREA and BUN using a biochemical analyzer (Hitachi 902 Automatic Analyzer; Hitachi, Japan). TG, TCHO, LDL, and HDL were analyzed using assay kits purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China) according to the manufacturers' instructions.

#### Hepatic Histopathology Analysis and Biochemical Analysis

At the end of the study, the mice were sacrificed, and liver tissue was collected for H&E staining to analyses histology changes. Liver tissue from sacrificed mice was soaked in 10% formalin solution (prepared by 1× phosphate-buffered saline, PBS) and fixed for 12 h. Samples were then made into paraffin sections as specimens and stained with hematoxylin and eosin (H&E) according to manufacturer's protocol (Mayer's Hematoxylin Solution, Sigma-Aldrich). The sections were observed and captured under microscopy. About other 50 mg liver tissues were homogenized in iced 1× PBS solution (1:10, m/v). The homogenized solution was used for TG and TCHO analysis following the protocol of Nanjing Jiancheng Bioengineering Institute (Nanjing, China).

#### Phytochemical and Metabolomics Study Phytochemical Analysis

The chromatographic analysis was performed on an Agilent 1290 UPLC system equipped with an autosampler, binary gradient pump, and PDA detector. The system was operated at 30◦C and a water ACQUITY UPLC HSS T3 column (150 mm × 2.1 mm,

1.7 µm) was used. The injection volume was 5 µL and the mobile phase flow rate was 0.4 mL/min. Solvents that constituted the mobile phase were (A) 0.2% aqueous acetic acid and (B) acetonitrile. The elution conditions were as follows: 0–5 min, linear gradient 2–5% B; 5–10 min, linear gradient 5–10% B; 10– 15 min, linear gradient 10–25% B; 15–25 min, linear gradient 25– 40% B; 25–28 min, linear gradient 40–90% B. Peaks were detected at 254 nm. The compounds of CP extracts were characterized by retention time, MS/MS information and related chemical standards in positive and negative modes.

#### Lipidomics Study

The liquid chromatogram was performed on an Agilent 1290 UPLC system. A waters ACQUITY UPLC HSS C18 column (2.1 mm × 100 mm, 1.8 µm particle size) was used for separation. The column temperature was maintained at 40◦C and autosampler temperature was maintained at 8◦C. The separation was performed with mobile phase A and B within 20 min per sample. Phase A consists of 60:40 water/ACN in 10 mM ammonium formate and 0.1% formic acid, and phase B is made by 90:10 IPA/ACN with 10 mM ammonium formate and 0.1% formic acid. The linear gradient was as follow: 32% B (0–1.5 min); 32–45% B (1.5–4 min); 45–52% B (4–5 min); 52–58% B (5– 8 min); 58–66% B (8–11 min); 66–70% B (11–14 min); 70–75% B (14–18 min); 75–97% B (18–21 min); maintain 97% B (21– 25 min); decrease to 32% B (25–30 min); maintained at 32% B (Gregory et al., 2013). The injection volume is 2 µL for the positive mode and 6 µL for the negative mode.

XCMS package in R was used to convert chromatograms intensity into raw data for multivariate statistical analysis using metaboanalyst (Huan et al., 2017). For lipid identification, the frame m/z values were used to search information on LIPID MAPS, Human Metabolome Database (HMDB) and Metlin database. The matches were confirmed by lipids' exact tandem mass (MS/MS) and retention time based on our internal standards (SPLASHTM Lipidomix <sup>R</sup> Mass Spec Standard, Avanti Polar Lipids, United States).

### Bioinformatics and Statistical Analysis

Potential Targets Prediction and Linking Analysis The detected chemical components of CP in plasma after oral

administration were imported into our in-house target prediction

tool "MOST" (Huang et al., 2017) to construct the target prediction database. Targets from the prediction database were then used to determine the relevance with diabetic dyslipidemia with literature review, and the relevant genes were considered as CP targets for diabetic dyslipidemia. The lipids data were imported into Cytoscape to generate a metabolites-reactionenzyme-gene database. The genes of significantly changed lipids in CP treatment group were considered as lipid targets of CP. The predicted targets and lipids targets of CP were combined together to upload to STRING (Szklarczyk et al., 2014) for PPIs analysis to link the action of predicted targets with lipids targets of CP.

#### Target-Diabetic Dyslipidemia Link Analysis

All determined and predicted targets were searched on PubMed using key words: [targets name] AND [diabetes] AND [lipidemia]. The targets which were studied on lipid disorders in diabetes were also included.

#### Statistical Analysis

GraphPad was used for statistical analysis of the biochemical data. Statistically significant differences (p < 0.05) in mean values were calculated by Student's t-test or one-way ANOVA. The lipidomics data was processed by R package with XCMS. Metaboanalyst (Xia et al., 2015) was used for multivariate statistical analysis. Cytoscape was used trace associated gene-enzyme using KEGG database.

#### RESULTS

#### CP Attenuated Diabetic Dyslipidemia in Diabetic Mice Induced by High Fat Diet and STZ

As shown in **Table 1**, after high fat diet and STZ treatment, blood glucose levels of mice were significantly elevated whereas their body weight was significantly demoted compared with control and blank group, indicating that the diabetic mice model was well established. In contrast, those clinical signs were significantly rescued after CP treatment for 5 weeks. Since both hyperlipidemia and hepatic steatosis are characterized as

#### TABLE 1 | Blood glucose (mM) levels and body weight changes on week 0, 3, and 5.


The effects of CP extract on blood glucose levels and body weight of diabetic mice. All data are presented as means ± SD. Diabetic model group vs. control group: #p < 0.05, ##p < 0.01 and ###p < 0.001. CP or glibenclamide (positive) treatment group vs. diabetic model group: <sup>∗</sup>p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

compared to that of control and blank group, serum TG, TCHO, and LDL levels, as well as hepatic AST, ALT, TG, and TCHO levels of the model group were significantly increased, whereas serum HDL levels were significantly decreased, indicating that

fphar-09-00973 August 27, 2018 Time: 10:45 # 5

the hyperlipidemia and hepatic steatosis were developed in the model group. In CP or glibenclamide-treated groups, those altered lipids-associated indexes were significantly alleviated. In addition, histological examination of hepatic sections also revealed that there were a large number of vesicles of fat accumulating within hepatocytes of diabetic mice, which was significantly reduced after CP or glibenclamide treatment. These results suggested that CP extract has a great potential to attenuate diabetic dyslipidemia and hepatic steatosis induced by high fat diet and STZ in mice.

### CP Improved Circulating Lipid Metabolism in Diabetic Dyslipidemia

To study the CP effect on lipid metabolism in diabetic mice, we performed a lipidomics study on mice serum. Fatty acyls (fatty acids), glycerolipids (DG and TG), glycerophospholipids (PC, PE, and PI), and sphingolipids (SM) were then used for multivariate statistical analysis as shown in **Supplementary Table S1.1**. As the PCA and PLS-DA are two effective multivariate analysis methods for large-scale data matrix, the combining data were analyzed using PCA and PLS-DA approaches. As shown in **Figures 2A,B**, PCA and PLS-DA score showed a clear classification between model group and control group, demonstrating that circulating lipids profiles are significantly different in the model group. As well, the CP-treated group or glibenclamide treated group also had its own distinctive classification, bearing some overlap. Considering the overlap between model group and CP-treated group, we further narrowed down the profiles of lipids in these two groups using OPLS-DA method. Notably, CE, DG, TG, PC, and SM were significantly distinguished between these two groups (**Figures 2C,D**), And then, we applied the hierarchal clustering analysis to segregate the lipid metabolites into three distinct groups (shown in **Figure 2E**). A significant number of lipids were up-regulated in model group in comparison with control or blank groups. After CP or glibenclamide treatment, the levels of these lipids were greatly down-regulated. The results suggest that CP or glibenclamide could significantly regulate lipid metabolism in diabetes. Interestingly, PCA and PLS-DA analysis result showed that the score plot of CP-treated group and glibenclamide-treated group were not separated, and the same result was observed in heat map of hierarchal clustering analysis (**Figures 2B,E**), suggesting CP and glibenclamide may share the similar metabolic pathways to improve the diabetic dyslipidemia.

Subsequently, we analyzed the changes of lipids between control (blank) groups and model groups, and found that 47 lipids were elevated and 53 lipids were reduced in model group. After CP treatment, 30 lipids including CE, DG, TG, PC, and SM species were significantly either up-regulated or down-regulated (p < 0.05) (**Figure 3**). After importing those CP-regulated lipids to Cytoscape, a network pathway was built based on KEGG database and revealed that metabolic pathways including arachidonic acid metabolism, bile acid biosynthesis, de novo fatty acid biosynthesis, glycerophospholipid metabolism, glycosphingolipid metabolism, linoleate metabolism and saturated fatty acids beta-oxidation are involved in the regulating function of CP in lipid metabolism (As shown in **Supplementary Figure S5**).

#### CP Possess Multiple Components-Multiple Targets-Multiple Pathways Properties for Diabetic Dyslipidemia

Since absorption into the bloodstream is one of the prerequisites for drug efficacy, we performed a pharmacochemistry study to detect the CP components in blood stream after oral administration in mice. As shown in **Table 2** and **Supplementary Figure S2**, after oral administration of CP to normal C57/BL6 mice, 13 compounds in total: quinic acid, neochlorogenic acid, chlorogenic acid, 4-hydroxybenzoic acid, gallic acid, quercetin-3-glucuronide, kaempferol, loganin 7-pentoside, astragalin, kaempferol-3-rhamnoside, quercetin, quadranoside IV, and asiatic acid were detected. Subsequently, we used in-house tools "MOST: most-similar ligand-based approach to target prediction" (Huang et al., 2017) to predict the protein targets of major potential active components of CP identified in bloodstream as shown in **Supplementary Table S1.2**. Sixty-nine


targets were matched for flavonoids, 59 targets were matched for organic acids, and 2 targets were matched for saponins. There targets were searched by related key words online to determine the relevance of diabetic dyslipidemia. Five targets were selected after reference searching. Finally, we linked the lipids targets with predicted targets using STRING (Szklarczyk et al., 2014) to build interaction networks of predicted targets and lipids targets via PPI. Among these compounds, the main predicted targets of CP were ALOX12, APP, BCL2, CYP2C9, and PTPN1 while the predicted-targets linked lipids targets via PPI analysis were PLA2G(s) and PI3K(s) families as shown in **Supplementary Figure S3**.

We then conducted reference searching to review the experiment study on these targets as shown in **Supplementary Table S2**. The ALOX, BCL-2, CYP 2C9, PLA2G(s) and PI3K(s) families, PLD2 and PTEN were determined as quercetin and kaempferol targets on diabetic dyslipidemia (Guo et al., 2017). PTPN1, PI3K(s) family and PLD2 were determined as targets of saponins such as quadranoside IV and asiatic acid (Ramachandran and Saravanan, 2015), whilst BCL-2, PI3K(s) family, PLD2 and PTEN were determined as action targets of gallic acid and 4-hydrobenzoic acids (**Figure 4**).

### DISCUSSION

Hyperlipidemia is the most common form of dyslipidemia, refers to the abnormally elevated levels of any/all lipids or lipoproteins in the blood, frequently happened in long-term type II diabetes patients (Dixit et al., 2014). Recent studies have indicated that diabetic dyslipidemia may not only be the consequence but also the cause of disturbed glucose metabolism (Parhofer, 2015). Recently, CP was reported to improve insulin sensitivity (Jiang et al., 2014), attenuates inflammation (Wang Z. et al., 2017), and control hyperglycemic and hyperlipidemic abnormalities (Xu et al., 2017) both in vitro and in vivo, although the hypolipidemic mechanism has not been elucidated yet. In this study, we confirmed CP alleviated lipid dysfunction in diabetes, particularly diabetic dyslipidemia, as revealed by the clinical index, histological analysis, and lipidomics analysis. Mechanistically, we employed a network pharmacology approach to determine that CP's hypolipidemic effect involvement in PI3K and MAPK signaling pathways.

Lipidomics is a powerful tool to investigate lipid profiles changes. Previous studies indicated that circulating lipidomes were correlated with hyperlipidemia and hepatic steatosis

targets based on CP constituents. The PPI network of predicted targets-linked lipid targets. The lipids regulated by CP in diabetic dyslipidemia.

(Wouters et al., 2008), therefore making lipids profiles an adequate indicator of CP effects in this study. Through the lipidomics study, it was shown that STZ and high-fat diet induced type 2 diabetic mice had lipid disorders due to lipid metabolism changes. Among the changed lipids, the majority lipids classes were DG, TG, CE, PC, SM, FA, PI, and PE. We found that CE, DG, TG, SM, PC, PI, and LPE were increased in the model group, while PE was decreased in the model group. CE is a dietary lipid involved in fat digestion and absorption as well as cholesterol metabolism. It directly affects LDL as well as HDL levels, and can be hydrolyzed by pancreatic enzymes to produce cholesterol and free fatty acids (Rosenson et al., 2016). DG and TG represent the main lipid components of fat deposits, which accumulated largely in lipid droplets of hepatocytes when in hepatic steatosis (Szczepaniak et al., 2005). Our results showed that CE, DG, and TG were downregulated after CP treatment which further indiacates the reduxction in fat accumulation in lipid droplets. PC (lecithin) are one class of glycerophospholipids with choline as a head group, which constitute major component of biological membranes, associating with the health benefits such as liver repair and lipolysis (Payne et al., 2014). Lipid absorption and utilization, particularly TG, is closely related to the PC content and its associated enzymes. Besides, PC can protect against steatosis in mice (Niebergall et al., 2011). Our results displayed that CP could reduce the palmitic acids levels and adjust PC content to improve lipid utilization and protect hepatic steatosis. PE is directly associated with diabetes and dyslipidemia and PC/PE ratio is closely correlated with the accumulation of hepatic TG content (Bradley et al., 2015; Ling et al., 2017). Our results showed that the levels of PE were elevated in CP-treated diabetic mice, indicating CP can reduce TG accumulation. SM contains either PC or PE as its head group. As lipid rafts, SM takes part in lipid microdomains and inhibition of SM synthesis would increase the ceramide level, a mediator of non-alcoholic fatty liver diseases (Kasumov et al., 2015). Our results showed that PC could increase SM content by reducing ceramide levels, possibly to alleviate hepatic steatosis. Our results indicate that CP has a great potential in the improvement of circulating lipid metabolism in diabetic dyslipidemia.

The beneficial function of CP on diabetic dyslipidemia can be deduced by its lipid regulating effect on circulatory system through multiple metabolic pathways. Therefore, it is important to identify the CP components and elucidate their biological activities to further define its use as a herbal product. Moreover, identifying the targets of CP's active components can lead to greater understanding of CP's mechanisms of action against diabetic dyslipidemia. By using lipidomics-based network pharmacology approach, we determined several CP targets for diabetic dyslipidemia based on its potential active components. Besides of ACP1, ALDH2, BCL2, and CA2 targets, the ALOX15, CYP2C9, and PTPN1 targets can interact with lipid targets PLD2, PTEN, PLA2G(s), and PI3K(s) family targets. BCL2, PTPN1, and PI3K alters expression of PI3K signaling pathway (Rahmani et al., 2013; Sugiyama et al., 2017). ALDH2, ALOX15, and BCL2 are involved in MAPK signaling pathway (De Chiara et al., 2006; Zhang et al., 2011; Zhao et al., 2011), and activation of MAPK can influence the expression of CYP2C9 (Bachleda et al., 2009). PLD2, and its product phosphatidic acid, can also activate MAPK (Grab et al., 2004). Therefore, the signaling pathway for its pharmacological mechanism may be related with PI3K and MAPK signaling pathway (Ma et al., 2015; Wu et al., 2017; Xiao et al., 2017). The multiple components of CP targeting multiple targets through multiple metabolic pathways to improve diabetic dyslipidemia in type 2 diabetic mice.

To our knowledge, we have provided a novel metabolomicsbased network pharmacology approach to combine and link experimental targets and predicted targets together based on the bioactive components of herbal products. For a network pharmacology study, particularly on herbal medicine or herbal formula, the metabolomics-based network pharmacology strategy can integrate more comprehensive targets for targets prediction and linking their interactions. Integrating the analysis of systemic metabolic profiles based on metabolomics study with computational prediction based on serum pharmacochemistry information can result in more precise pharmacological mechanism associated targets prediction (Huang et al., 2018). It is also the first study to link lipids targets from lipidomics study with predicted targets from computational tools through PPI to improve the network pharmacology analysis. Up to now, there are some questions that still need to be answered: First, some of PPI between predicted targets and lipid targets have not been validated experimentally. Further experimental work can be performed to uncover the relationship between targets-targets. Next, the potential active compounds we identified in this study have been shown to be effective for anti-diabetic studies on animals, the systemic evaluation of combinatorial use on these compounds have not been conducted yet. Additional in vitro and in vivo studies in future will help to uncover the multiple pharmacological mechanisms found in herbal medicines.

### CONCLUSION

In this study, we report CP attenuated diabetic dyslipidemia and hepatic streatosis in high fat diet and STZ-induced diabetic mice. The lipidomics study revealed CP improves circulatory lipids disorder, and the serum pharmacochemistry study revealed quinic acid, neochlorogenic acid, chlorogenic acid, 4-hydroxybenzoic acid, gallic acid, quercetin-3-glucuronide, kaempferol, loganin 7-pentoside, astragalin, kaempferol-3 rhamnoside, quercetin, quadranoside IV, and asiatic acid are potential active components of CP. Combining lipidomics and bioinformatics analysis, ALOX12, APP, BCL2, CYP2C9, and PTPN1 were predicted as direct targets of CP, whilst PLD2, PTEN and PLA2G(s) and PI3K(s) families were predicted as lipids linked targets of CP in diabetic hyperlipidemia. In conclusion, the CP was shown to be a multi-component and multi-targets herbal product with potent lipid regulation properties in dyslipidemia.

### AUTHOR CONTRIBUTIONS

Z-xB and H-tX designed the study and revised the manuscript. Ht-X, BW, C-hL, LxZ, and Z-wN performed the animal

experiment. LxZ performed the clinical index analysis, lipidomics analysis, bioinformatics analysis, and wrote the manuscript. Z-wN performed phytochemicals analysis of CP. TH performed bioinformatics analysis and revised the manuscript. LZ and C-yL provided technical support and advices toward study.

#### FUNDING

This work was kindly funded by grants from Kong Baptist University Research Grant (No. LIUYE/15-16/01-CLNC) and Shenzhen Science and Technology Innovation Committee Grant (No. JCYJ20170413170320959).

#### REFERENCES


### ACKNOWLEDGMENTS

We would like to thank Mr. Chan Chi Leung and Mr. Ho Hing Man for providing technical support for metabolomics study and Ms. Wang Jianying for providing some lipids reference standards. We also thank Dr. Anthony Booker for writing improvement.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.00973/full#supplementary-material


PI3K inhibition-induced apoptosis in human myeloid leukemia cells through a GSK3- and Bim-dependent mechanism. Cancer Res. 73, 1340–1351. doi: 10. 1158/0008-5472.CAN-12-1365


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Zhai, Ning, Huang, Wen, Liao, Lin, Zhao, Xiao and Bian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Network Pharmacology-Based Validation of Caveolin-1 as a Key Mediator of Ai Du Qing Inhibition of Drug Resistance in Breast Cancer

Neng Wang<sup>1</sup> , Bowen Yang1,2, Xiaotong Zhang<sup>1</sup> , Shengqi Wang1,2, Yifeng Zheng1,2 , Xiong Li1,2, Shan Liu<sup>1</sup> , Hao Pan<sup>1</sup> , Yingwei Li1,3, Zhujuan Huang<sup>1</sup> , Fengxue Zhang<sup>1</sup> \* and Zhiyu Wang1,2 \*

<sup>1</sup> The Research Center of Basic Integrative Medicine, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China, <sup>2</sup> Integrative Research Laboratory of Breast Cancer, Discipline of Integrated Chinese and Western Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China, <sup>3</sup> Tropical Medicine Institute, Guangzhou University of Chinese Medicine, Guangzhou, China

#### Edited by:

Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China

#### Reviewed by:

Li-Wha Wu, National Cheng Kung University, Taiwan Juntaro Matsuzaki, National Cancer Center, Japan Chuanbin Yang, Hong Kong Baptist University, Hong Kong

#### \*Correspondence:

Fengxue Zhang zhangfengxue@gzucm.edu.cn Zhiyu Wang wangzhiyu976@126.com

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 16 April 2018 Accepted: 10 September 2018 Published: 02 October 2018

#### Citation:

Wang N, Yang B, Zhang X, Wang S, Zheng Y, Li X, Liu S, Pan H, Li Y, Huang Z, Zhang F and Wang Z (2018) Network Pharmacology-Based Validation of Caveolin-1 as a Key Mediator of Ai Du Qing Inhibition of Drug Resistance in Breast Cancer. Front. Pharmacol. 9:1106. doi: 10.3389/fphar.2018.01106 Chinese formulas have been paid increasing attention in cancer multidisciplinary therapy due to their multi-targets and multi-substances property. Here, we aim to investigate the anti-breast cancer and chemosensitizing function of Ai Du Qing (ADQ) formula made up of Hedyotis diffusa, Curcuma zedoaria (Christm.) Rosc., Astragalus membranaceus (Fisch.) Bunge, and Glycyrrhiza uralensis Fisch. Our findings revealed that ADQ significantly inhibited cell proliferation in both parental and chemo-resistant breast cancer cells, but with little cytotoxcity effects on the normal cells. Besides, ADQ was found to facilitate the G2/M arresting and apoptosis induction effects of paclitaxel. Network pharmacology and bioinformatics analysis further demonstrated that ADQ yielded 132 candidate compounds and 297 potential targets, and shared 22 putative targets associating with breast cancer chemoresponse. Enrichment analysis and experimental validation demonstrated that ADQ might improve breast cancer chemosensitivity via inhibiting caveolin-1, which further triggered expression changes of cell cycle-related proteins p21/cyclinB1 and apoptosis-associated proteins PARP1, BAX and Bcl-2. Besides, ADQ enhanced in vivo paclitaxel chemosensitivity on breast cancer. Our study not only uncovers the novel function and mechanisms of ADQ in chemosensitizing breast cancer at least partly via targeting caveolin-1, but also sheds novel light in utilizing network pharmacology in Chinese Medicine research.

Keywords: breast cancer chemosensitivity, network pharmacology, bioinformatics analysis, Ai Du Qing, caveolin-1

**Abbreviations:** ADQ, Ai Du Qing; CAV1, caveolin-1; DAD, diode array detection; DAPI, 4<sup>0</sup> ,6-diamidino-2-phenylindole; DAVID, Database for Annotation, Visualization and Integrated Discovery; DEGs, differentially expressed genes; DL, drug-likeness; GEO, Gene Expression Omnibus; GO, Gene Ontology; H&E staining, hematoxylin-eosin staining; HPLC, high-performance liquid chromatography; HUMECs, the primary human mammary epithelial cells; HUVECs, the human umbilical vein endothelial cells; IC50, the half maximal inhibitory concentration; IHC, immunohistochemistry; KEGG, Kyoto Encyclopedia of Genes and Genomes; NCBI, National Center for Biotechnology Information; OB, oral bioavailability; PARP1, Poly (ADP-ribose) polymerase 1; PI, propidium iodide; PPI, protein–protein interaction; TCM, traditional Chinese medicine; TCMID, Traditional Chinese Medicine Integrated Database; TCMSP, Traditional Chinese Medicine Systems Pharmacology; TUNEL, terminal dexynucleotidyl transferase(TdT)-mediated dUTP nick end labeling.

## INTRODUCTION

fphar-09-01106 September 28, 2018 Time: 16:12 # 2

Breast cancer is the most common female malignancy and one of the leading causes of cancer-related deaths, with 252,710 new cases of invasive breast cancer and 40,610 breast cancer-related deaths diagnosed in women in the United States in 2017 (DeSantis et al., 2017). Chemotherapy is one of the main therapeutic approaches for treating this disease, but it remains non-selectively toxic to normal tissues (Li et al., 2005). Paclitaxel (taxol) is the first-line treatment for metastatic breast cancer. The activity of paclitaxel is primarily due to its inhibitory effects on microtubule assembly, which leads to arrest of the mitotic phase of the cell cycle and subsequent apoptosis. Nevertheless, paclitaxel application is still limited due to its systemic toxicity and acquired resistance (Janczar et al., 2017; Stage et al., 2018). Thus, there is an urgent need for novel and safe chemosensitizing strategies. Currently, increasing attention has been paid to the synergistic effects of natural phytochemicals in enhancing the chemoresponse and relieving cytotoxic effects (Wang et al., 2014).

Traditional Chinese Medicine (TCM) has attracted worldwide attention from clinicians and researchers due to its multi-target and multi-substance characteristics and good safety profile (Gupta et al., 2013). In contrast to Western medicine, TCM prescriptions are composed of several herbs and are called formulas. Originating from Yellow Emperor's Classic theory (Chinese name Huang Di Nei Jing), the golden principle of formula composition should be based on Jun-Chen-Zuo-Shi (Lu, 1985; Fan et al., 2006). The ADQ formula was created by Prof. Zhiyu Wang based on the Jun-Chen-Zuo-Shi principle and long-term clinical experience. The ADQ formula is mainly composed of four herbs: Jun (Emperor) herb Hedyotis diffusa (Chinese name Bai Hua She Cao, BHSSC), Chen (Minister) herb Curcuma zedoaria (Christm.) Rosc. (Chinese name E Zhu, EZ), Zuo (adjuvant) herb Astragalus membranaceus (Fisch.) Bunge (Chinese name Huang Qi, HQ), and Shi (courier) herb Glycyrrhiza uralensis Fisch (Chinese name Gan Cao, GC). Each herb in the formula can inhibit cancer growth via cell cycle arrest, apoptosis induction, and immune regulation (Fu et al., 2014a; Gao et al., 2014; Wang et al., 2014, 2015b; Feng et al., 2017). In addition, each herb is a drug that is frequently prescribed to cancer patients according to statistical analyses of the medication rules of national TCM masters (Song et al., 2015). However, the multi-target and multi-substance properties of this formula have made it very challenging to explore its underlying mechanisms.

In the past decade, multi-omic technologies including genomics, transcriptomic, proteomics, metabolomics, and serum pharmacokinetics have been developed for the high-throughput screening and identification of targets involved in TCM formulas (Li et al., 2014). However, these traditional approaches are expensive and require multidisciplinary collaboration and complex analytical procedures (Xu et al., 2017). With the development of bioinformatics, systems biology is emerging as a more holistic approach for integrating compound–target interactions from a molecular to system level. One of the most significant applications of systems biology is to use network pharmacology to understand the complex mechanism of actions of TCM formulas. Following ingredient collection and screening, pharmacokinetic evaluation (absorption, distribution, and metabolism), target prediction, and network analysis (Liu et al., 2013), it is becoming faster and easier to present an entire drug-target interaction network and determine the involved core molecule and pathways. In addition, by intersecting with a disease target database, it is more efficient to elucidate how formulas intervene with critical targets that facilitate disease occurrence and progression (Ru et al., 2014).

The current study was designed to determine the preclinical efficacy of ADQ against breast cancer in vitro and in vivo. To this end, we investigated the anti-cancer and chemosensitizing functions of ADQ extracts in this disease. The results showed that ADQ effectively and safely enhanced breast cancer chemosensitivity in vitro and in vivo. To determine the mechanism of action, we constructed a "drug–target–disease" network among ADQ components (drug), ADQ targets (target), and genes in breast cancer chemoresistance (disease). The results of the network analysis and biological experimental findings suggested that ADQ mainly targets CAV1 to induce chemosensitizing effects. The results of this study not only provide scientific evidence to support the application of ADQ formula in the treatment of breast cancer but also highlight the novel role of network pharmacology in the modernization of TCM.

### MATERIALS AND METHODS

#### Preparation and Quality Control of ADQ

For ADQ preparation, BHSSC, EZ, HQ, and GC were mixed at a 1:1:1:1 ratio and then subjected to a grinding machine. The mixture was extracted with 95% alcohol by reflux extraction for 1 h and repeated three times. Then the supernatants were concentrated by rotary evaporation and evaporated to dryness in a water bath to obtain raw ethanol extract powder. The production ratio was calculated as 7.2–9.6%. For quality control analysis, the Agilent 1260 System (Agilent, Palo Alto, CA, United States) with DAD was applied for HPLC analysis. The Agilent C<sup>18</sup> Column (5 µm, 250 mm × 4.6 mm) with the SecurityGuard Cartridge System (Phenomenex, Sacramento, CA, United States) was applied for HPLC analysis. The mobile phases consisted of acetonitrile (A) and 0.05% (v/v) phosphoric acid (B) using a gradient program of 15% A in 0–23 min, 15–38% A in 23–40 min, 38% A in 40–50 min, 38–61% A in 50–60 min, and 61% in 60–75 min. The flow rate was 1.0 mL/min and the column temperature was set to 30◦C The DAD detector was set at 216, 236, 260, 276, and 308 nm. P-coumaric acid, calycosin-7-glucoside, liquiritin, glycyrrhizic acid, and curcumol were prepared and diluted with methanol for the preparation of standard solutions. A volume of 10 µL of these solutions was analyzed with HPLC, and the calibration curves were finally established. For the preparation of sample solutions, 0.1 g ADQ was dissolved in 20 mL methanol. After sonicating for 60 min, the sample solution was filtrated through a 0.2 µm membrane filter for HPLC analysis.

### Cell Culture

fphar-09-01106 September 28, 2018 Time: 16:12 # 3

The human breast cancer cell lines MDA-MB-231 and MCF-7 were purchased from the American Type Culture Collection (Manassas, VA, United States). MDA-MB-231/T and MCF-7/T cells were derived from parental cells by gradually increasing paclitaxel (taxol) treatments for 6 months in the laboratory. MDA-MB-231, MCF-7, MDA-MB-231/T, and MCF-7/T cells were maintained in Dulbecco's Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS) and 1% penicillin and streptomycin (Gibco Life Technologies, Lofer, Austria). Primary human mammary epithelial cells (HUMECs) and its Ready Medium (Catalog No. 12752010) were purchased from Gibco, and human umbilical vein endothelial cells (HUVECs) were purchased from the National Infrastructure of Cell Line Resource<sup>1</sup> . The cells were maintained in DMEM supplemented with 10% FBS, 1% penicillin, 1% streptomycin (Gibco), 40 U/L insulin (Sigma, St. Louis, MO, United States), 40 U/mL heparin (Sigma), and 1% non-essential amino acids (Cyagen Biosciences, Santa Clara, CA, United States).

### Cell Number and Colony Formation Assay

After the indicated drug treatment, cell numbers were counted using trypan blue exclusion on a Cellometer Mini device (Nexcelom, Boston, MA, United States). Experiments were performed in triplicate. For the colony formation assay, cells at a density of 1 × 10<sup>3</sup> cells/well were seeded into 6-well plates. After cell attachment, paclitaxel or ADQ was added to the wells alone or in combination for 4 h. Then the cells were cultured with fresh medium for 2 weeks. The colonies were fixed in 4% paraformaldehyde, stained with Coomassie Blue, photographed, and counted under a microscope.

#### Flow Cytometry Analysis

For the drug efflux assay, MDA-MB-231 or MCF-7 cells were pretreated with ADQ for 24 h, followed by incubation with epirubicin for 60 min at 37◦C. After dug exposure and washing, the cells were released in drug-free medium for 90 min and harvested for flow cytometry analysis. For cell cycle analysis, cells were fixed in ice-cold 70% ethanol at −20◦C overnight. Then cells were washed with phosphate-buffered saline, stained with 50 mg/mL propidium iodide (Sigma), and dissolved in 100 mg/L RNase A (Sigma). For apoptosis analysis, the cells were stained with the Annexin V-FITC Apoptosis Staining/Detection Kit (BD Biosciences, San Jose, CA, United States). All flow cytometry analyses were conducted with FACSAria SORP (BD Biosciences) and analyzed by Modifit LT or FlowJo software.

#### Immunofluorescence Analysis and Hoechst 33258 Staining

For measurements of phosphorylated histone p-H2AX expression, cells were incubated with 4% paraformaldehyde and 0.2% triton X-100 for 10 min. Following blocking in goat serum for 60 min, the samples were co-incubated with primary antibodies against p-H2AX (ABclonal Technology, Boston, MA, United States) at 4◦C overnight and subsequently labeled with fluorescence-conjugated secondary antibodies for 2 h at room temperature. Then DAPI was applied for nuclear staining, and the signals were detected by fluorescence microscopy (TS2R; Nikon, Tokyo, Japan). With regard to Hoechst 33528 detection, cells were seeded at 60–70% confluency in 6-well plates, and then treated with the indicated drug for 48 h. Then Hoechst 33258 staining was conducted according to the manufacturer's instructions.

#### Establishment of the Herb–Ingredient–Target Interaction

The chemical ingredients were collected from TCM databases including the TCMSP Database<sup>2</sup> the TCMID<sup>3</sup> , and the BATMAN-TCM<sup>4</sup> . The ingredients were screened according to drug likeness (DL) and OB values, and the ingredients were retained if DL ≥ 0.18 and OB ≥ 30, a criterion suggested by the TCMSP database. The ingredient–target networks were constructed for these herbs using Cytoscape software (version 3.2.1).

#### Gene Ontology and Pathway Enrichment Analysis

Gene expression data were retrieved from the NCBI GEO database<sup>5</sup> , and then analyzed with the GEO2R online analysis tool<sup>6</sup> . The dataset GSE41112 includes 24 tumors of breast cancer patients with chemotherapy and 37 without chemotherapy, and the GSE87455 dataset includes human breast cancer samples with "no treatment" (n = 122) and "chemo only" (n = 83) groups. The DEGs were screened with P ≤ 0.05 and fold control (FC) ≥ 1.5 criteria, delivered to the Search Tool for the Retrieval of Interacting Genes/Protines (STRING) database to evaluate the PPI information, and also submitted to the DAVID<sup>7</sup> for enrichment analysis. The significant enrichment analysis of DEGs was assessed based on the GO and KEGG<sup>8</sup> .

### Plasmids and Small Interfering RNA Construction and Transfection

The pcDNA 3.1(+)-CAV1 was provided by Vigene Company (Jinan, China) and transfected into cells using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, United States). After 24 h, the transfected cells were passaged and selected for 2 weeks with 10 µg/mL puromycin (Invitrogen). Pooled populations of positive cells were used for subsequent experiments. Negative control cell lines were generated by transfecting cells with scrambled plasmids. The small interfering RNAs (siRNAs) targeting CAV1 or scrambled siRNAs were purchased from

<sup>1</sup>http://www.cellresource.cn

<sup>2</sup>http://lsp.nwu.edu.cn/tcmsp.php

<sup>3</sup>https://academic.oup.com/nar/article/41/D1/D1089/1057998

<sup>4</sup>http://bionet.ncpsb.org/batman-tcm/

<sup>5</sup>http://www.ncbi.nlm.nih.gov/geo

<sup>6</sup>http://www.ncbi.nlm.nih.gov/geo/geo2r/

<sup>7</sup>http://david.abcc.ncifcrf.gov/

<sup>8</sup>http://www.genome.jp/kegg/kegg2.html

Invitrogen (Carlsbad) and transfected using the X-tremeGENE siRNA transfection reagent (Roche Diagnostics, Indianapolis, IN, United States) according to the manufacturer's instructions.

ADQ at the indicated concentrations (0–140 µg/mL) for 24, 48, and 72 h.

#### Western Blotting

To determine the protein concentration, cells were lysed in RIPA buffer (Sigma) containing a protease inhibitor mixture (Roche Diagnostics). The protein concentration was measured with the bicinchoninic acid assay (Thermo Fisher Scientific, Bonn, Germany). Quantified protein lysates (15 µg) were subjected to sodium dodecyl sulfate polyacrylamide gel electrophoresis and resolved on 12% polyacrylamide gels. Then the proteins were transferred onto a PVDF membrane (GE Healthcare, Freiburg, Germany). The membrane was probed with primary antibodies including CAV1, p21, cyclin B1, cleaved poly (ADP-ribose) polymerase (PARP), Bcl2-associated X protein (BAX), B-cell lymphoma 2 (Bcl-2), p53, and phosphorylated p53 (p-p52, ser 15), and β-actin (Cell Signaling Technology, Beverly, MA, United States) at 4◦C overnight. After washing three times with Tris-buffered saline and 0.05% Tween-20, the membrane was incubated with secondary anti-rabbit or anti-mouse antibodies for 2 h at room temperature. The signals were visualized using the ECL Advance Western Blotting Detection Reagent (GE Healthcare) and quantified with FlowJo software.

and MCF-7 cells treated with ADQ (50 µg/mL) and/or paclitaxel (50 ng/mL), as well as (D) in the MDA-MB-231/T and MCF-7/T cells treated with ADQ (50 µg/mL) for 48 h.

### Breast Cancer Mice Models and Drug Treatment

All animal procedures were performed in accordance with institutional guidelines for the care and use of laboratory animals approved by the Animal Care and Use Committee of Guangzhou University of Chinese Medicine and the National Institutes of Health guide for the care and use of laboratory animals. The mouse mammary tumor virus (MMTV)-PyMT mouse model of breast cancer spontaneously develops 100% multiple and luminal-like breast tumors from normal mammary epitheliums by 8–12 weeks, similar to the pathological processes and characteristics in human breast cancer (Lin et al., 2003; Wang et al., 2013). In this study, 9-week-old MMTV-PyMT mice were randomly divided into four groups, and treated with saline (Ctrl group), 10 mg/kg paclitaxel (paclitaxel group), 100 mg/kg ADQ (ADQ group), or 10 mg/kg paclitaxel plus 100 mg/kg ADQ (paclitaxel + ADQ group) for 26 days (n = 6 mice, total of 60 glands), respectively. Paclitaxel was given by intraperitoneal injection at 10 mg/kg every 3 days, and ADQ was given by oral gavage at 100 mg/kg once a day. The body weight and tumor volumes were recorded throughout the whole experimental period.

### Hematoxylin and Eosin Staining, Immunohistochemistry, and Terminal Deoxynucleotidyl Transferase dUTP Nick End Labeling

Hematoxylin and eosin staining and IHC were conducted according to the protocol provided by Wang et al. (2017a). The terminal dexynucleotidyl transferase dUTP nick end labeling (TUNEL) assay was conducted according to the manufacturer's instructions (Catalog No. KGA7051; KeyGen Biotech, Nanjing, China).

#### Statistical Analysis

The data are shown as the mean ± standard deviation. The two-tailed Student's t-test or one-way analysis of variance

was used to determine the significance of the data between groups. P-values less than 0.05 were considered statistical significant.

### RESULTS

#### ADQ Markedly Inhibits Growth in Paclitaxel-Sensitive and Paclitaxel-Resistant Human Breast Cancer Cells

For quality control of ADQ, the chromatographic fingerprinting of ADQ was conducted and quantitative analysis of p-coumaric acid (peak 1), calycosin-7-glucoside (peak 2), liquiritin (peak 3), glycyrrhizic acid (peak 4), and curcumol (peak 5) in ADQ was compared among different batches (**Supplementary Figure S1**). Based on an established HPLC method, good linearity of five compounds was achieved with a correlation coefficient of R <sup>2</sup> ≥ 0.9995 (**Supplementary Table S1**). The retention times of these compounds were determined to be 19.8, 23.9, 25.8, 50.7, and 69.5 min, respectively. The contents of the five compounds in the ADQ samples were also determined (**Supplementary Table S2**). We evaluated the influence of ADQ on the proliferation of breast cancer cell lines including MDA-MB-231 and MCF-7, as well as their derived paclitaxel-resistant cell lines MDA-MB-231/T and MCF-7/T. Significant inhibition of growth was observed in both parental and resistant cells at 24 h (**Figure 1A**),

48 h (**Figure 1B**), and 72 h (**Figure 1C**). IC<sup>50</sup> of ADQ was shown in **Figure 1D** for the indicated cell lines. Specifically, the IC<sup>50</sup> values of ADQ at 48 h for MDA-MB-231, MDA-MB-231/T, MCF-7, and MCF-7/T were 49.809, 57.789, 65.799, and 70.964 µg/mL, respectively, suggesting that ADQ had similar suppressive effects on both sensitive and resistant breast cancer cells. To determine the cytotoxic effects of ADQ on normal cells, we also investigated its effects on HUMECs and HUVECs, and found that ADQ did not have cytotoxic inhibitory effects on both types of normal cells (**Figures 1E,F**). These findings indicated that ADQ exerted selective toxic effects on breast cancer cells.

### ADQ Significantly Enhances the Chemosensitivity of Breast Cancer Cells

To determine the synergistic activities of ADQ with paclitaxel in breast cancer, MDA-MB-231 and MCF-7 cells were treated with ADQ and paclitaxel for 48 h. As presented in **Figure 1** and **Supplementary Figure S2**, the tested concentrations of taxol and ADQ were set according to their IC<sup>50</sup> at 48 h. ADQ significantly enhanced paclitaxel-induced death in MDA-MB-231 and MCF-7 cells. Interestingly, ADQ administration at 50 µg/mL also caused a clear reduction in the number of resistant breast cancer cells (**Figure 2A**). We also evaluated the long-term inhibitory effects of ADQ on the colony formation capabilities of breast cancer cells with or without paclitaxel. The results showed that the combination group exhibited a substantial reduction in the colony numbers of parental MDA-MB-231 and MCF-7 cells (**Figure 2B**). In addition, the growth of paclitaxel-resistant MDA-MB-231/T and MCF-7/T cells was greatly suppressed in the presence of ADQ (**Figure 2C**). The drug efflux assay revealed that ADQ could enhance epirubicin influx into breast cancer cells, as shown by the increased fluorescence intensity of epirubicin in ADQ-treated cells (**Figure 2D**).

These results indicated that ADQ could chemosensitize both paclitaxel-sensitive and paclitaxel-resistant human breast cancer cells.

### ADQ Induced Breast Cancer Cell Cycle Arrest at the G2/M Checkpoint

Uncontrolled cell mitosis represents one of the hallmarks of cancer. Thus, we used PI staining to examine the effects of ADQ on the cell cycle distribution of both paclitaxel-sensitive and paclitaxel-resistant cells. As labeled in **Figure 3A**, the flow cytometry results revealed that paclitaxel or ADQ alone could induce G2/M checkpoint arrest in both breast cancer cell lines. In addition, ADQ and paclitaxel combination increased G2/M arrest by 26% in MDA-MB-231 cells and by 66% in MCF-7 cells (**Figure 3A**). Furthermore, the G2/M population of MDA-MB-231/T and MCF-7/T cells was also arrested by ADQ with a 56 and 63% increase, respectively (**Figure 3B**). Previous studies have shown that perturbation of the G2/M transition was largely due to DNA damage, and p-H2AX could be a marker for monitoring DNA damage (Sancar et al., 2004). The immunofluorescence results showed that p-H2AX intensity was significantly elevated following paclitaxel or ADQ treatment in both breast cancer cell lines. Consistent with the cell cycle findings, ADQ synergistically interacted with paclitaxel to enhance p-H2AX expression in both breast cancer cell lines (**Figure 3C**). Furthermore, the expression of p-H2AX in MDA-MB-231/T and MCF-7/T cells was also significantly enhanced following ADQ administration, indicating that the chemosensitizing effects of ADQ might be attributed to DNA damage-induced G2/M checkpoint arrest (**Figure 3D**).

### ADQ Augmented Paclitaxel-Induced Apoptosis in Breast Cancer Cells

Apoptosis is another important mechanism that causes cell death induced by chemotherapy drugs (Johnstone et al., 2002; Li et al., 2010). To determine if ADQ could synergistically aggravate paclitaxel-induced apoptosis, Annexin V/PI staining was applied to detect the apoptotic events. As shown in **Figure 4A**, the percentage of early and late apoptotic events in MDA-MB-231 and MCF-7 cells reached about 20 and 25%, respectively, after exposure to 50 ng/mL paclitaxel. Interestingly, when ADQ was administrated with paclitaxel concurrently, the percentage of MDA-MB-231 and MCF-7 apoptotic cells was increased to approximately 60% and 51%, respectively. Notably, ADQ was also capable of inducing apoptosis in paclitaxel-resistant breast cancer cells. The percentage of apoptotic cells in MDA-MB-231/T reached 45% following ADQ treatment after 48 h, and MCF-7/T cells reached 30% (**Figures 4B–D**). Based on flow cytometry results, Hoechst 33258 staining was utilized to observe the morphological changes of apoptotic cells by fluorescence imaging (Zhang et al., 2010). In paclitaxel-sensitive breast cancer cells, ADQ

synergistically enhanced Hoechst 33258 staining intensity induced by paclitaxel in both MDA-MB-231 and MCF-7 cells. In paclitaxel-resistant cell models, typical morphological characteristics of apoptosis, such as chromatin condensation and cell pyknosis, were more easily observed following ADQ treatment (**Figure 4E**). Together, these findings confirmed that ADQ promoted paclitaxel-induced apoptosis in breast cancer cells.

#### Network Pharmacology Analysis of ADQ

ADQ consists of four herbs including BHSSC, EZ, HQ, and GC. To establish the ingredient–target network of ADQ, we screened candidate compounds for (OB ≥ 30%) and DL (DL ≥ 0.1) in each herb. There were 12 compounds in BHSSC targeting 225 genes (**Figure 5A**), 14 compounds in EZ targeting 41 genes (**Figure 5B**), 21 compounds in HQ targeting 222 genes (**Figure 5C**), and 94 compounds in GC targeting 241 genes (**Figure 5D**). The "candidate active compounds" are listed in **Supplementary Table S3**. Overall, our results showed that ADQ yielded 132 candidate compounds and 297 potential targets after eliminating all duplicates (**Figure 6**). Specifically, the network included 429 nodes and 2874 ingredient–target interactions, of which 132 candidate compounds had a median of 10 target correlations, suggesting the existence of complex correlations among different compounds and targets.

### Establishment of the Compound–Target–Disease Network of ADQ

To determine the pharmacological mechanisms of ADQ against chemoresistance, the DEGs of breast cancer patients before and after chemotherapy were extracted from GSE41112 and GSE87455 microarray sets. A total of 3286 genes in GSE5764 and 4877 genes in GSE87455 were identified using the GEO2R analysis tool (P ≤ 0.05, FC ≥ 1.5). The Venn diagram analysis showed that ADQ shared 22 putative targets with the two datasets (**Figure 7A**). These hub genes included CAV1, HIF1A, CCNB1, BAX, BCL2, PARP1, ERBB3, MCL1, PRKCA, CDCA7, CDKN1A,

CDKN2A, TOP1, TOP2A, SELE, ATP5B, RUNX2, BIRC5, ATF2, RUNX1T1, MT2A, and EIF5B (**Figure 7B**), among which CAV1 was the key target with the largest node size according to "Degree" in the node size mapping (**Figure 7C**). We further extracted the 22 significant targets to construct the PPI containing 22 nodes and 71 edges based on the STRING database, and the PPI enrichment P-value of these hub genes was 2.22 × 10−<sup>16</sup> , indicating that they were at least partially biologically connected (**Figure 7D**). All of these findings suggested that CAV1 might be one of the the most likely mechanisms affecting ADQ regulation network.

Thus, we continued to identify candidate targets by setting all human genes as background, and using GO and pathway enrichment analysis. GO classified function into categories of cellular components, molecular function, and biological process. The top three enrichments in the cellular components category were organelle lumen, nuclear lumen, and membrane-enclosed lumen (**Figure 8A**); in the molecular function category were enzyme binding, protein binding, and transcription factor binding (**Figure 8B**); and in the biological process category were cell death, cell proliferation, and cell differentiation (**Figure 8C**). In **Figure 8D**, KEGG analysis (P < 0.05) indicated that

multiple cancer-related pathways were significantly involved in the mechanisms of ADQ including focal adhesion, apoptosis, cell cycle, p53, HIF-1, ErbB, phosphoinositide 3-kinase (PI3K)/Akt, Janus kinase/signal transducer and activator of transcription, mammalian target of rapamycin, NF-kappa B, and tumor necrosis factor signaling pathways. Notably, It was various targets in p53 signaling were tightly associated with ADQ pharmacological action (P = 0.00003896097, red rectangle, **Figure 8D**). P53 signaling can be stimulated by a number of stress signals including DNA damage, oxidative stress, and activated oncogenes, consequently leading to cell cycle arrest and apoptosis (Mello and Attardi, 2017). Meanwhile, accumulating evidence has demonstrated that CAV1, the leading hub target of ADQ, is correlated with various stressors including chemotherapy, radiotherapy, fluid shear, oxidative stress, and ultraviolet exposure (Wang et al., 2015c). This novel stress response protein plays significant roles in modulating cell survival, proliferation, and apoptosis (Wang et al., 2017d). In **Figure 9**, we postulated that CAV1 might be activated in response to ADQ treatment, subsequently influencing the cell cycle regulatory proteins p21 and cyclin B1, and apoptotic markers such as BAX.

#### Validation of CAV1 as a Major Chemosensitizing Target of ADQ in Breast Cancer

We continued to validate whether the chemosensitizing activity of ADQ was CAV1-dependent. Among the multiple breast cancer cell lines tested, ZR75–1, SKBR3, and MCF7 had no or low CAV1 expression, whereas MDA-MB-231 and MDA-MB-436 had strong CAV1 expression (Shi et al., 2015). In this study, we detected the expression of CAV1 among a series of breast cancer and normal mammary cell lines. The results showed that CAV1 expression was significantly downregulated in the MDA-MB-231 and MCF-7 breast cancer cells, and was enhanced in their taxol-resistant counterparts (**Supplementary Figure S3**). This phenomenon was consistent with the oncogenic and tumor suppressor roles of CAV1 in breast cancer development (Wang et al., 2015c). To directly examine whether CAV1 was critical for ADQ action, we elevated CAV1 levels by transfecting recombinant CAV1 plasmid in CAV1-deficient MCF-7 cells, and decreased its expression with siCAV1 in CAV1-expressing MDA-MB-231 cells. CAV1 expression in the gene-modified cells was confirmed by western blot analysis (**Supplementary Figure S4A**). Then we determined if CAV1 overexpression abrogated the anti-cancer and chemosensitizing effects induced by ADQ in breast cancer cells. As shown in **Figure 10A**, ADQ synergistically interacted with paclitaxel to suppress MCF-7 cell proliferation, whereas CAV1 overexpression obviously abrogated the synergistic effects of ADQ with paclitaxel in suppressing breast cancer growth (p ≤ 0.01). To determine if CAV1 signaling was involved in ADQ chemosensitizing activity, the indicated proteins were analyzed (**Figure 10B**). Western blot analysis revealed that ADQ alone could inhibit CAV1 expression, accompanied by the increased expression of p21 and decreased expression of cyclin B1. In addition, the expression of cleaved PARP1 and BAX were enhanced by ADQ, whereas Bcl-2 expression was decreased following ADQ treatment. Notably, CAV1 overexpression resulted in decreased p21 and increased cyclin B1 expression. Furthermore, CAV1 overexpression inhibited apoptosis, as shown by the decreased expression of cleaved PARP1 and BAX and increased Bcl-2 expression. Cell cycle analysis further revealed that CAV1

cytometry. The length of each cell cycle phase was calculated; (D) Representative apoptosis analysis indicated CAV1 overexpression reduced the apoptotic events induced by paclitaxel plus with ADQ treatment from 47.890 to 36.780% using flow cytometry (∗P < 0.05 v.s. control, values represented as the mean ± SD, n = 3).

overexpression relieved the G2/M arresting effects of ADQ from 68.08 to 57.08% in MCF-7 cells (**Figure 10C**). CAV1 overexpression also significantly reduced the apoptotic events induced by paclitaxel with ADQ treatment from 47.89 to 36.78% (**Figure 10D**). These data showed that ADQ inhibited CAV1 to improve breast cancer chemosensitivity.

In CAV1high MDA-MB-231 cells, CAV1 knockdown did not significantly aggravate the inhibitory ability of ADQ in the presence of paclitaxel, suggesting that ADQ might target CAV1 to chemosensitize breast cancer (**Figure 11A**). Similarly, siCAV1 did not significantly change the expression levels of p21, cyclin B1, PARP1, BAX, and Bcl-2 when administrated together with paclitaxel and ADQ (**Figure 11B**), nor were significant changes observed in cell cycle distribution and apoptotic cells (**Figures 11C,D**). Furthermore, MDA-MB-231/T and MCF-7/T showed strong CAV1 expression compared with their parental cell lines (**Supplementary Figure S4B**). In paclitaxelresistant breast cancer cell models, both CAV1 silencing and ADQ treatment resulted in the significant suppression of cell proliferation, but CAV1 silencing did not increase the effects of ADQ, namely inhibition of proliferation, cell cycle arrest, or apoptosis induction in MDA-MB-231/T and MCF-7/T cells (**Figures 12A–D**). These findings demonstrated that ADQ could target CAV1 to induce cell cycle arrest and apoptosis, thereby chemosensitizing paclitaxel-resistant breast cancer cells.

### ADQ Enhanced in vivo Paclitaxel Chemosensitivity on Breast Cancer

We finally evaluated the in vivo efficacy of ADQ on breast cancer with the MMTV-PyMT transgenic mouse model, which spontaneously develops 100% multiple and luminallike breast tumors from normal mammary epitheliums by 8– 12 weeks, similar to the pathological processes and characteristics in human breast cancer (Lin et al., 2003; Wang et al., 2013). Therefore, it is an appropriate preclinical model for investigating the chemosensitivity effects of ADQ with paclitaxel (**Figure 13A**). The results of the in vivo experiments revealed that ADQ alone could significantly inhibit breast tumor growth (∗P = 0.0444, **Figure 13C**) and reduce tumor volume (∗P = 0.0104, **Figure 13D**), while with little influence

on mouse body weight (**Figure 13B**). Meanwhile, ADQ alone caused an increase in the apoptotic ratio and a decrease in ki67 expression, further demonstrating its tumor suppressive functions (**Figure 13E**). The results also revealed that the combination of paclitaxel and ADQ exerted stronger inhibitory effects on overall tumor growth than paclitaxel alone (**Figure 13C**). Notably, ADQ did not lead to significant weight loss throughout the experiment, whereas a slight reduction of body weight was found in the paclitaxel-treated mice (**Figure 13B**). Furthermore, both H&E staining and the TUNEL assay revealed that the synergistic use of paclitaxel and ADQ resulted in significant increases of apoptosis in tumor tissues, accompanied by the decreased expression of Ki67 and CAV1 (**Figure 13E**). These results indicate that ADQ is a potential adjuvant drug for breast cancer treatment with good safety.

#### DISCUSSION

With increasing attention being paid to multi-target strategies for cancer treatment, TCM has become a valuable resource. In this study, we investigated the anti-cancer and chemosensitizing functions of ADQ extracts in breast cancer. Our findings revealed that ADQ significantly inhibited the growth of breast cancer cells by cell cycle arrest and apoptosis induction. In particular, ADQ did not cause cytotoxic effects in normal cells. Similar findings have also been shown for other herbs including Huangqi, Ginseng, Banzhilian, Huachansu injection, TJ-48, Shenqi fuzheng injection, and Kanglaite injection (Qi et al., 2015). Notably, the four-herb formula PHY906 was developed as an adjuvant therapy with chemosensitizing effects. Currently, three phase I and one phase II clinical trial on PHY906 have been completed in the United States. In these clinical studies, PHY906 administration led to the reduction of chemotherapy-associated side effects in patients with metastatic colorectal cancer (PHY906+CPT-11/5FU/LV) and advanced pancreatic cancer (PHY906+capecitabine) (Lam et al., 2015). Omics studies have demonstrated that PHY906 can inhibit colon cancer growth by modulating cell apoptosis by intervening interferon-gamma production and responses to steroid hormone stimulus. Our study also demonstrated that ADQ could increase breast cancer chemosensitivity to paclitaxel by increasing breast cancer cell apoptosis and cell cycle arrest at the G2/M checkpoint. In addition, in vivo findings further confirmed the anti-breast

cancer and chemosensitizing effects of ADQ with good safety. Therefore, it is of great value to explore the effects and mechanisms of ADQ in breast cancer.

The development of network pharmacology has shifted our traditional view from the "one drug, one target" model to the "drug–target network" (Hopkins, 2008). Bioinformatics analysis has further optimized the high-throughput screening strategy to validate candidate targets associated with diseases (Eichler, 2012). A target fishing approach has been applied by a number of TCM studies. For example, Wang et al. (2017b) identified tumor-associated macrophages/C-X-C motif chemokine ligand 1 as key modulators of XIAOPI formula in the prevention of breast cancer metastasis based on network pharmacology analysis and cytokine array screening. Chemoinformatics, bioinformatics, and network biology were applied together to predict the active compounds of Tianfoshen oral liquid and to validate its therapeutic targets against colorectal cancer (Wang et al., 2017c). In this study, network analysis revealed a total of 132 active compounds and 297 genes in the ADQ formula, and bioinformatics analysis further identified 22 genes closely correlated with the chemosensitizing activities of ADQ through Venn diagram analysis with breast cancer chemoresponsive genes extracted from GSE41112 and GSE87455. These results suggested that ADQ efficacy was through the synergistic effect of multi-compounds, multi-targets, and multi-pathways. Among these hub molecules, CAV1 was the key target node with the largest "Degree," indicating that it may be one of the most likely mechanisms affecting the ADQ regulation network. Meanwhile, pathway enrichment analysis demonstrated that CAV1 acted as a key upstream node influencing cell cycle, apoptosis, and p53 signaling. Interestingly, CAV1 has also been considered an important stress-responsive molecule by recent studies and our previous report (Lavie et al., 1998; Yang et al., 1998; Glait et al., 2006; Wang et al., 2015c). Therefore, it was selected for subsequent validation. Interestingly, a lot of work has demonstrated that CAV1 is necessary for p53 activation. For example, Volonte et al. (2009) validated that CAV1 expression is required for the activation of ATM-p53-p21 pathway, and Bartholomew et al. (2009) indicated that CAV1 is a novel binding protein for mouse double minute 2 homolog, thereby preventing p53 proteasome degradation and stabilizing its cellular expression. Our study demonstrated that p53 expression in both MDA-MB-231 and MCF-7 cells was downregulated compared with primary mammary epithelial cells, accompanied by CAV1 reduction (**Supplementary Figure S5**). However, although ADQ administration led to CAV1 inhibition, the expression of p53 and p-p53 (ser15) did not significantly change, indicating that ADQ-induced p21 and apoptosis activation might not be related to p53 (**Supplementary Figure S6**). With regard to the genetic status of p53, MDA-MB-231 was mutated and MCF-7

FIGURE 13 | ADQ enhanced in vivo paclitaxel chemosensitivity on breast cancer. 9-week-old MMTV-PyMT mice were randomly divided into four groups, and were treated with vehicle (Ctrl group), 10 mg/kg paclitaxel (Paclitaxel group), 100 mg/kg ADQ (ADQ group), or 10mg/kg paclitaxel plus 100 mg/kg ADQ (Paclitaxel + ADQ group) according to designated treatment schedule. (A) Representative images of tumors dissected, (B) Body weight, and (C,D) Tumor volumes, and (E) H&E staining, TUNEL detection, and IHC detection of Ki67 and CAV1 expressions from the indicated groups (n = 6 mice, total of 60 glands, <sup>∗</sup>P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.0001 v.s. control).

was wild type. Because the degradation speed of mutated p53 was significantly reduced, p53 expression was highly elevated in MDA-MB-231 cells compared with MCF-7 cells (**Supplementary Figure S5**); however, ADQ had little effect on p53 expression in both breast cancer cells (**Supplementary Figure S6**), indicating that the susceptibility of MDA-MB-231 or MCF-7 cells to ADQ was not related to p53 genetic status. Overall, network pharmacology has provided us with a highly efficient strategy to identify key targets and their complex correlation in the development of TCM formulas.

CAV1 plays dual roles in the progression of breast, lung, cervical, gastric, glioma liver, and prostate cancers (Wang et al., 2017d). During tumor initiation, its loss not only triggers tumor-survival signals including PI3K/Akt and MAPK, but also leads to the inactivation of tumor suppressor genes such as BRCA1 and PTEN (Glait et al., 2006). By contrast, accumulating evidence has suggested that CAV1 overexpression correlates with cancer drug resistance, metastasis, the survival of cancer stem cells, and advanced carcinoma (Wang et al., 2015c). Increased CAV1 expression has also been observed in a series of drug-resistant cancer cells compared with their parental cells such as paclitaxel-resistant A549 cells, vinblastine-resistant SKVLB1 cells, colchicine-resistant HT-29 cells, and adraimycin-resistant MCF-7 cells (Lavie et al., 1998; Yang et al., 1998). Moreover, clinical investigations have revealed that CAV1 expression is positively correlated with chemotherapy response in gastric cancer (Yuan et al., 2013) and non-small lung cancer (Brodie et al., 2014). Therefore targeting CAV1 is a promising strategy for overcoming cancer drug resistance. Interestingly, our study demonstrated that ADQ could inhibit CAV1 to improve breast cancer chemosensitivity. In addition, CAV1-overexpressing MDA-MB-231 cells were more susceptible to ADQ than low CAV1 expressing MCF-7 cells. Consistent with our findings, previous studies have revealed that multiple active compounds in ADQ exerted anti-cancer effects partly by mediating CAV1 expression. For example, it was shown that quercetin reversed tamoxifen resistance in breast cancer cells, and its metabolites likely suppressed CAV1 expression (Wang et al., 2015a; Kamada et al., 2016). Calycosin glycoside regulates nitric oxide/CAV1/matrix metalloproteinase signaling (Fu et al., 2014b). The CAV1-mediated anti-cancer effects of ADQ in this study were possibly due to the complex interaction and synergistic/neutralizing effects among the involved compounds. However, ADQ also exhibited a significant inhibitory effects on MCF-7 cell proliferation, indicating that there may be other molecular targets responsible for the effects of ADQ. Because the phytochemicals in Chinese formula are too complex to analyze, it is unlikely that CAV1 is the only target of ADQ. Our results also demonstrated that CAV1 expression was highly elevated in paclitaxel-resistant breast cancer cells, and ADQ chemosensitized breast cancer cells by inhibiting CAV1, consistent with the oncogenic role of CAV1. This phenomenon might be explained by the property of the stressrelated function of CAV1, which plays a key role in protecting cells from hazardous stimuli. During cancer initiation, malignant transformation may be accelerated due to CAV1 loss, which would sensitize normal cells to oncogenic events. In contrast, when cancer progresses and is treated, CAV1 expression may be upregulated to protect cancer cells from escaping death by speeding aerobic glycolysis, increasing stem cell populations, or overexpressing ATP-binding cassette transporters (Wang et al., 2015c).

### CONCLUSION

The results of our study demonstrated that ADQ improved the cancer chemoresponse, and that CAV1 was at least in part responsible for these chemosensitizing effects. Our work has great implications for the discovery of breast cancer therapeutic targets by integrating bioinformatics and network pharmacology followed by experimental validation. This study not only provides experimental evidence and molecular mechanisms that may facilitate the safe and effective therapeutic use of herbal medicines for breast cancer, and may lead to CAV1-based therapeutic strategies for mammary malignancies. However, preclinical studies are needed to confirm its chemosensitizing effects and active ingredients.

## AUTHOR CONTRIBUTIONS

NW and ZW conducted the design of the experiments and wrote the manuscript. FZ contributed to the revised manuscript. NW conducted network pharmacology analysis and bioinformatics analysis. BY, XL, and XZ contributed to the drug preparation and quality control of ADQ. SW, YZ, and SL carried out cell culture and molecular biology experiments. ZH, HP, and YL conducted flow cytometry analysis.

## FUNDING

This work was supported by the National Natural Science Foundation of China (81703764, 81573651, 81873306, and 81703749), Guangdong Science and Technology Department (2016A030306025), Pearl River S&T Nova Program of Guangzhou (201506010098), Combined Scientific Project Funded by Guangdong Provincial Science and Technology Agency and Guangdong Provincial Academy of Traditional Chinese Medicine (2014A020221047), Guangdong High-level University Construction Project (A1-AFD018161Z1510, A1- AFD018171Z11102, and A1-AFD018171Z11101), Guangdong High-level Personnel of Special Support Program (A1-3002- 16-111-003), Guangdong traditional Chinese medicine bureau project (20181132 and 20182044), the Post-doctoral Science Foundation of China (2017M612644, 2017M622669, and 2018T110861), the Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province (2017A030310213) and Guangdong provincial key project (2016kzdxm032), and the Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (YN2018MJ07 and YN2018QJ08).

#### ACKNOWLEDGMENTS

fphar-09-01106 September 28, 2018 Time: 16:12 # 18

We thank LetPub (www.letpub.com) for the linguistic assistance.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.01106/full#supplementary-material

FIGURE S1 | The chromatographic fingerprints of ADQ. HPLC chromatograms on 216 nm detection for (A) standard compounds, (B) ADQ (1: p-Coumaric acid; 2: Calycosin-7-glucoside; 3: Liquiritin; 4: Glycyrrhizic acid; 5: Curcumol).

FIGURE S2 | The IC50s of paclitaxel for MDA-MB-231, MDA-MB-231/T, MCF-7, and MCF-7/T cells. The parental/resistant cells of (A) MDA-MB-231 and (B) MCF-7 were treated with paclitaxel at the indicated concentrations (0–200 ng/mL) for 48 h.

### REFERENCES


FIGURE S3 | The expressions of CAV1 were determined by western blot among MDA-MB-231, MDA-MB-231/T, MCF-7, MCF-7/T, HUMECs, and HUVECs.

FIGURE S4 | The expressions of CAV1 using western blotting analysis. (A) MCF-7 cells were transfected with the recombinant plasmid of CAV1, and MDA-MB-231 cells were transfected with siCAV1 for 48 h. The CAV1 levels were then confirmed by western blot analysis; (B) The expressions of CAV1 on the indicated parental breast cancer cells and the paired paclitaxel-resistant cells (∗∗P < 0.01 v.s. control, values represented as the mean ± SD, n = 3).

FIGURE S5 | The expressions of CAV1, p53 and p-p53 (ser15) were determined by western blot among MDA-MB-231, MCF-7, and HUMECs (∗∗P < 0.01 v.s. control, values represented as the mean ± SD, n = 3).

FIGURE S6 | The expressions of CAV1, p53 and p-p53 (ser15) were determined by western blot with or without ADQ in MDA-MB-231 and MCF-7 (∗∗P < 0.01 v.s. control, values represented as the mean ± SD, n = 3).

TABLE S1 | The establishment of calibration curves for HPLC analysis.

TABLE S2 | The contents of five components in ADQ.

TABLE S3 | Information on candidate active compounds from BHSSC, EZ, HQ and GC herbs of ADQ decoration.



breast cancer stem cells through WIF1 demethylation. Oncotarget 6, 9854–9876. doi: 10.18632/oncotarget.3396


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Wang, Yang, Zhang, Wang, Zheng, Li, Liu, Pan, Li, Huang, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Yin-Yang Property of Chinese Medicinal Herbs Relates to Chemical Composition but Not Anti-Oxidative Activity: An Illustration Using Spleen-Meridian Herbs

Yun Huang1,2, Ping Yao<sup>2</sup> , Ka Wing Leung1,2, Huaiyou Wang1,2, Xiang Peng Kong1,2 , Long Wang<sup>2</sup>† , Tina Ting Xia Dong1,2, Yicun Chen2,3 and Karl Wah Keung Tsim1,2 \*

<sup>1</sup> Shenzhen Key Laboratory of Edible and Medicinal Bioresources, Shenzhen Research Institute, Shenzhen, China, <sup>2</sup> Division of Life Science and Center for Chinese Medicine, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, <sup>3</sup> Department of Pharmacology, Shantou University Medical College, Shantou, China

#### Edited by:

Yuanjia Hu, University of Macau, Macau

#### Reviewed by:

Wei Song, Peking Union Medical College Hospital (CAMS), China Rongbiao Pi, Sun Yat-sen University, China

> \*Correspondence: Karl Wah Keung Tsim botsim@ust.hk

#### †Present address:

Long Wang, Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 03 August 2018 Accepted: 24 October 2018 Published: 15 November 2018

#### Citation:

Huang Y, Yao P, Leung KW, Wang H, Kong XP, Wang L, Dong TTX, Chen Y and Tsim KWK (2018) The Yin-Yang Property of Chinese Medicinal Herbs Relates to Chemical Composition but Not Anti-Oxidative Activity: An Illustration Using Spleen-Meridian Herbs. Front. Pharmacol. 9:1304. doi: 10.3389/fphar.2018.01304 "Yin-Yang" and "Five Elements" theories are the basis theories of Traditional Chinese Medicine (TCM). To probe and clarify the theoretical basis of these ancient Chinese wisdoms, extensive efforts have been taken, however, without a full success. In the classification of TCM herbs, hot, cold and neutral herbs are believed to possess distinct profile of chemical compositions of which the compounds should have different polarity and mass: this view provides a new perspective for further illustration. To understand the chemical properties of TCMs in the classification of "Yin-Yang" and "Five Elements," 15 commonly used herbs attributed to spleen-meridian were selected for analyses. Chemically standardized water extracts, 50% ethanol extracts and 90% ethanol extracts were prepared and subjected to different analytic measurements. Principle component analysis (PCA) of full spectrum of HPLC, NMR and LC-MS of the extracts were established. The results revealed that the LC-MS profile showed a strong correlation with the "Yin-Yang" classification criterion. The Yang-stimulating herbs generally contain more compounds with lower molecular weight and less polar property. Additionally, a comprehensive anti-oxidative profiles of selected herbs were developed, and the results showed that its correlation with cold and hot properties of TCM, however, was rather low. Taken together, the "Yin-Yang" nature of TCM is closely related to the physical properties of the ingredients, such as polarity and molecular mass; while such classification has little correlation with anti-oxidative property. Therefore, the present results provide a new direction in probing the basic principle of TCM classification.

Keywords: Yin-Yang classification criterion, cold and hot properties, principle component analysis, chemical composition, anti-oxidative activity

## INTRODUCTION

In the theoretical basis of TCM clinical application, "Yin-Yang" and "Five Elements" theories could further differentiate into secondary classification principles, e.g., four natures and five flavors, meridian tropism, floating and sinking (Li et al., 2008; Fu et al., 2009, 2015; Mohd et al., 2013; Qiu, 2015). Having a long history of clinical experience, these TCM theories are aiming to merge

**80**

with each other and achieve unity gradually. As the "Yin-Yang" attribution is not only specified to the description of patient symptoms ("Zheng" or syndrome), but also defines the nature of herbal medicines. Thus, the exact relationship between these two identification systems of TCM, i.e., "Yin-Yang" versus chemical composition, remains as a puzzle (Zhou et al., 2009; Ma et al., 2010; Li, 2012). In order to maintain a homeostasis in our body, the herbs with cold nature, also known as Yin-stimulating herbs, were generally used to treat hot diseases like inflammation, while the herbs with nature of warm and hot, belonging to Yang-stimulating herbs, were good for various types of deficiency (Fu et al., 2017a). In recent years, the works on chemical properties of TCMs has made a significant progress, and new ideas have been proposed, e.g., molecular drug hypothesis (Fu et al., 2017b), biodynamics (Yang et al., 2017), genetic hypothesis (Rezapour-Firouzi, 2017), transient receptor potential channel hypothesis (Bishnoi et al., 2018). Indeed, many of these newly proposed methods have been adopted and improved, including micro-calorimetry (Gao et al., 2014), mathematical modeling (Ramirez-Rodrigues et al., 2011), biophoton detection (Han et al., 2011; Zhao and Han, 2013). These methods are mainly derived from the collation of ancient books (Wang et al., 2010), pharmacological effects (Liang et al., 1988; Xu et al., 2011; Xiong et al., 2011), structure of components (Wang and Zhou, 2006) and thermodynamics (Yang et al., 2010). However, these studies are still lacking a co-relationship of the current TCM theory with the chemical composition of different classes of herbs.

Having highly developed analytical techniques and various novel chemometrics methods today, the study on relationship between TCM herb properties with their chemical composition has become possible. The widely employed analytical techniques, including HPLC, NMR and LC-MS, enabled researchers to establish robust methods to qualitative and quantitative determination of almost all kinds of compounds in biological matrices (Kuang et al., 2012). According to clinical application and experimental basis, the major ingredients corresponding to property and taste of TCM herbs are expected to be different. For example, volatile oil and polysaccharide are major bioactive components of pungent and sweet herbs; while alkaloid, glycoside and phenolic acid are generally abundant in bitter and acid herbs (Ung et al., 2007). In addressing these problems being encountered in TCM research, the omics techniques play a key role due to its advantage in solving information-rich puzzles and providing comprehensive and holistic results (Wiklund et al., 2008). Among the most commonly used multivariate statistical and visualization tools, principal component analysis (PCA) is a bilinear decomposition method that reduces original data to a few principal components and reserves the features contributing to variance (Kuang et al., 2012).

According to the concept of "Yin-Yang" nature of herbal medicine, the property of Chinese medicinal herbs was mainly summarized based on the efficacy and cognitive experience gained from patients' response. Some pharmacological studies supported the notion that cold and hot properties of TCM herbs were closely related to excitability of nervous system and endocrine (Li et al., 2008; Fu et al., 2009), mitochondrial ATP generation, and immunomodulatory function (Ko et al., 2004, 2006; Ko and Leung, 2007). On the other hand, the potential relationship between "Yin-Yang" properties and redox system of herbs have been proposed (Ou et al., 2003). Nevertheless, the physical meaning behind the criteria of "Yin-Yang" classification is still missing (Szeto and Benzie, 2006; Wong et al., 2006). Moreover, previous research has mainly focused on chemically based of anti-oxidative activity in TCM herbs; however, the relationship of anti-oxidative property with chemical nature of herbs is still missing. PCA analysis of full spectrum of HPLC, NMR and LC-MS of different classes of herbs were conducted to provide a multi-angle and multi-dimensional perspectives of the connection between the "Yin-Yang" properties and their chemical composition. In addition, the possible relationship between "Yin-Yang" nature and anti-oxidant of various herbs was clarified.

### MATERIALS AND METHODS

#### Chemicals and Preparation of Herbal Extracts

HPLC-grade acetonitrile and ethanol were from Merck (Darmstadt, Germany). Ultra-pure water was obtained from a Milli-Q purification system (Millipore, Molsheim, France). Deuterium oxide (D2O), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetra-zolium bromide (MTT), gallic acid, 1,1-Diphenyl-2-picrylhydrazyl radical 2,2-diphenyl-1-(2,4,6 trinitrophenyl) hydrazyl (DPPH), tert-butyl hydroperoxide (tBHP), vitamin C, 2<sup>0</sup> ,70 -dichlorodihydrofluorescein diacetate (DCFH-DA), β-galactosidase, dimethyl sulfoxide (DMSO) and Hank's Balanced Salt Solution (HBSS) were purchased from Sigma-Aldrich (St. Louis, MO, United States). Lipofectamine 3,000 was bought from Invitrogen (Waltham, MA, United States) and Triton X-100 was from BDH Tech (Marshalltown, IA, United States). The pARE-Luc DNA construct was obtained from Promega (Madison, WI, United States).

Three batches of 15 spleen-meridian herbs (**Table 1**) and a batch of Quinquefolium Radix, Rehmanniae Radix and Rehmanniae Radix Praeparata were collected from their production sites or pharmacy. The collected samples were first authenticated by Dr. Tina Dong and stored in dry and clean container before further analysis. The authentication of these herbs was according to Hong Kong Materia Medica Standards including morphology, microscopy, chemical fingerprint and chemical assay. The voucher specimens were stored in Centre for Chinese Medicine R&D at the university.

A unified extraction method was developed. Water extract, 50% ethanol extract and 90% ethanol extract of each herb were prepared to obtain extracts having different polarities. The specific extraction procedure was as following. First, the herbal samples were milled and passed through a No. 2 sieve and mixed well. Then, 4 g of the powdered sample was extracted in 100 mL distilled water, 50% ethanol and 90% ethanol, respectively, for 2 h by reflux, and the herbs were extracted twice. For the second extraction, the residue from the first extraction was filtered, and the same extraction condition was applied onto the filtered

TABLE 1 | The pharmacological action, origin and extracting yields of the tested 15 spleen-meridian herbs.


(Continued)

fphar-09-01304 November 13, 2018 Time: 14:49 # 4


<sup>a</sup>W, water extract; E50, 50% ethanol extract; E90, 90% ethanol extract. <sup>b</sup>Pharmacological effects were based on the Chinese Pharmacopoeia. The bold words are to emphasize the main pharmacological effects of the TCM.

residue. Then, the extracts were combined, dried under vacuum and stored at 4◦C.

The preparation of wine-treated Angelica Sinensis Radix was according to previous research (Zhan et al., 2011). To be specific, 30 g of the dried roots were sliced and sprayed with 3 mL of 15% ethanol and then processed in an oven at 80◦C for 90 min with 3 flipping. Then 4 g of processed Angelica Sinensis Radix was weighed and prepared as the extraction procedure mentioned above.

### HPLC Fingerprint Analysis of Herbal Extracts

The fingerprint analysis of herbal extracts was performed on an Agilent HPLC 1200 series system (Agilent, Waldbronn, Germany), which was equipped with a degasser, a binary pump, an auto sampler, a thermostated column compartment and a DAD. The samples were separated on a Waters Symmetry C18 column (4.5 mm × 250 mm, 5 µm i.d.) after filtered with a guard column. The mobile phase was composed of acetonitrile (A) and water (B) according to the pre-set gradient program: 0–50 min, linear gradient 0–70% (A); 50–55 min, linear gradient 70–100% (A); 55–60 min, isocratic elution. A pre-balance period of 5 min was used between each run. The injection volume was 10 µL, and the flow rate was set at 1 mL/min. To get the fingerprints of herbal extracts, the wavelength of UV detector was set to 254 nm with full spectral scanning from 190 to 400 nm.

#### NMR Spectrum Profiling

The1H NMR experiments were conducted to the extracts in revealing high abundance chemicals. Fifty mg of the dried extract was dissolved in 400 µL of D2O. All particulate materials were removed by centrifugation at 13,000 × g for 1 min, and the supernatant was transferred to a standard 5-mm NMR tube. NMR spectra were acquired on a Varian 300 MHz NMR spectrometer, operating at 300.13 MHz <sup>1</sup>H NMR frequency at 298 K. Gradient shimming was used to improve the magnetic field homogeneity prior to all acquisition. <sup>1</sup>H NMR spectra of the samples were acquired using a 1D CPMG pulse sequence (RD-90◦ -t1-90◦ -tm- 90◦ -acquire) to generate a spectrum with a reduced residual solvent peak. The experiment time for each sample was around 10 min.

### LC-MS Spectrum Profiling

fphar-09-01304 November 13, 2018 Time: 14:49 # 5

The LC-MS profiles of extracts were established on the Xevo G2-XS <sup>R</sup> Quadrupole Time-of-Flight (Q-TOF) Mass Spectrometer coupled with an integrated ACQUITY UPLC <sup>R</sup> I-Class System. The analysis column is ACQUITY UPLC <sup>R</sup> BEH C18 column (2.1 mm × 50 mm, 1.7 µm). The mobile phase condition was recorded as follows: acetonitrile (A) and water (B), 0–0.5 min, isocratic gradient 5% (A); 0.5-8 min, linear gradient 5–50% (A); 8–12 min, linear gradient 50–90% (A); 12–14 min, linear gradient 90–100% (A); 14–16 min, isocratic gradient 100% (A). The flow rate was set at 0.4 mL/min with injection volume of 5 µL. The column temperature was 25◦C. The effluent was further analyzed by Xevo G2-XS <sup>R</sup> Quadrupole Time-of-Flight (Q-TOF) Mass Spectrometer with an ESI ion source in positive mode. The acquisition range was 50–1200 Da with 0.1 s scan rate. These analytic settings were aiming to reveal most of the chemicals within the herbal extracts.

### Cell Culture

RAW264.7 cell, a mouse blood macrophage cell line, was obtained from American Type Culture Collection (ATCC, Manassas, VA, United States). Cells were cultured in Dulbecco's modified Eagles medium supplemented with 100 U/mL penicillin, 100 µg/mL of streptomycin and 10% heat in-active fetal bovine serum in a humidified CO<sup>2</sup> (5%) incubator at 37◦C. When the cells reached 80% confluence, they were harvested from plate with a scraper (Corning Incorporated, Corning, NY, United States).

### Cell Viability

Cell viability was measured by MTT assay. Cells were seeded in 96-well plates at a density of 1 × 10<sup>4</sup> cells per well. After 24 h drug treatment, cells in each well were incubated with 10 µL MTT (5 mg/mL) at a final concentration of 0.5 mg/mL for 2 h at 37◦C. After the solution was removed, DMSO was used to re-suspend the purple precipitate inside the cells, and the absorbance was detected at 570 nm. The cell viability was calculated as percentage of absorbance value of control (without drug treatment) while the value of control was 100%.

## Folin-Ciocalteu Assay

Total phenolic content of herbal extracts was measured with Folin-Ciocalteu assay. To be specific, 20 µL of each extract together with 40 µL 10% (v/v) Folin-Ciocalteu reagent was added into each well of 96-well microplate. Then, 160 µL Na2CO<sup>3</sup> (700 mM) was added into each well. The assay plates were incubated at room temperature in dark for 2 h and then the absorbance at 765 nm were recorded. Here, gallic acid (Sigma-Aldrich, > 98%) was used as the reference compound, and the total phenolic contents of each extract were expressed as the value compared with gallic acid.

## DPPH Radical Scavenging Assay

Free radical scavenging activity of herbal extracts was measured with DPPH radical scavenging assay. Briefly, 50 µL of each extract with different concentrations (0–8 mg/mL) was mixed with 150 µL DPPH solution in each well of 96-well microplate. After standing for 10 min, the absorbance at 517 nm was recorded. The DPPH free radical scavenging activity was calculated as an inhibition percentage based on the following equation: Inhibition (%) = [(A<sup>0</sup> - A1)/A0] × 100, where A<sup>0</sup> is the absorbance of the control, and A<sup>1</sup> is the absorbance of the RA sample aliquot. Here, gallic acid (0–100 µM) was used as a positive control.

### tBHP-Induced Oxidative Stress Assay

The dose of tBHP (150 µM; Sigma-Aldrich) and positive control (vitamin C, 1 mM) were optimized with MTT assay as previously reported (Huang et al., 2018a,b). Similar to the cell viability assay, the cells were cultured in 96-well plate first. After drug treatments for 24 h, tBHP (150 µM) were added into the wells for 3 h before MTT at a final concentration of 0.5 mg/mL was added. After the solution was removed, the purple precipitate inside the cells was re-suspended in DMSO and then measured at 570 nm absorbance.

### ROS Formation Assay

The measurement of ROS content in cell cultures was performed by using DCFH-DA, an oxidation-sensitive dye. Cultured RAW264.7 cells (1 × 10<sup>4</sup> cells/well) in a 96-well plate were pre-treated with herbal extracts or standard compounds for 24 h, and the cells were labeled with 100 µM DCFH-DA (Sigma-Aldrich) in HBSS (Sigma-Aldrich) for 1 h at 37◦C. After washing three times with HBSS, the cells were then treated with 150 µM tBHP for 1 h at 37◦C. Then the amount of intracellular tBHP-induced ROS formation was detected by fluorometric measurement with excitation at 485 nm and emission at 530 nm.

### Assay for Anti-oxidant Response Element

To reveal the transcriptional activation of anti-oxidant response element (ARE), the pARE-Luc DNA construct, containing four copies of ARE (5<sup>0</sup> -TGACnnnGCA-3<sup>0</sup> ) that drives transcription of the luciferase reporter gene luc2P (Photinus pyralis), was transfected into cultured RAW264.7 cells by Lipofectamine 3000 (Invitrogen) according to the manufacturer's instructions. Transfected RAW264.7 cells were treated with various concentrations of ginseng extracts for 1 day. Then, the medium was aspirated, and the cultures were lysed by a buffer containing 100 mM potassium phosphate buffer (pH 7.8), 0.2% Triton X-100 and 1 mM DTT at 4◦C. After centrifugation at 16,100 × g for 5 min, the supernatant was collected and used to perform luciferase assay. The activity was normalized as absorbance (up to 560 nm) per mg of protein.

### Statistical Analysis

HPLC profile processing were conducted using Agilent MassHunter workstation software version B.01.00. The <sup>1</sup>H

Huang et al. Factors Related to Herbal Property

NMR spectra obtained from each sample were phased, baseline-corrected, calibrated and integrated using MestReNova 6.1.1 software. LC-MS profile were analyzed with Waters MassLynx V4.1 SCN923. The data were then formatted in XML for importing into PCA software SIMCA-P+ version 12.0 (Umetrics, Sweden). All data were expressed as the mean ± SEM for n = 3–5, unless otherwise specified. Statistical tests were performed by one-way ANOVA with multiple comparisons using Dunnett's test. Differences were considered significant at p < 0.05.

#### RESULTS

### Chemical Composition of Herbal Extracts

By reflux, the water extracts and ethanol extracts of individual herbs were prepared with the consideration of their common usage and historical preparation of a herbal decoction in TCM. The origin of herbs and its extracting yields are shown in **Table 1**. The extracting yield of Codonopsis Radix, Astragali Radix, Jujubae Fructus, Ginseng Radix, Nelumbinis Semen and Coicis Semen suggested that the water-soluble ingredients in these herbs were relatively abundant; while Angelicae Sinensis Radix, Amomi Fructus, Aucklandiae Radix and Areca Catechu contained less water-soluble ingredients (**Figure 1**). Among the tested herbs, Codonopsis Radix, Angelicae Sinensis Radix and Astragali Radix possessed the highest yield of extraction up to ∼25% per dried weight. In contrast, the yields of water and ethanol extracts of Amomi Fructus, Poria Cocos and Coicis Semen were less than 3%.

The HPLC fingerprints of water and ethanol extracts showed that there were plenty of trace compounds (**Figure 2**). By comparing the HPLC profiles, the 90% ethanol extracts in general contained higher amounts of trace compounds, recognized by the number of peaks, as compared to that of water and 50% ethanol extracts. The distribution of peaks in HPLC profiles showed apparent migration in accord to decreasing polarity of extracting solvent, i.e., more peaks at the end of run in ethanol extracts (**Figure 2**). The <sup>1</sup>H NMR experiment was conducted to reveal those high abundance compounds in aforementioned

extracts to herbal powders. All values are in Mean ± SD, n = 3. The detailed yield and origin of each batch were shown in Table 1.

herbal extracts. Similar to HPLC fingerprint, the NMR profile showed close similarity between herbs extracted with the same solvents, and generally the 90% ethanol extracts showed higher content of chemical composition, as compared with that of other extracts (**Figure 3**).

Macrocephalae Rhizoma; c, Angelicae Sinensis Radix; d, Astragali Radix; e, Jujubae Fructus; f, Ginseng Radix; g, Amomi Fructus; h, Aucklandiae Radix; i, Areca Catechu; j, Dioscoreae Ahizoma; k, Lablab Semen Album; l, Poria Cocos; m, Nelumbinis Semen; n, Coicis Semen; o, Crataegi Fructus.

High resolution mass spectrometry was established in analyzing the herbal extracts. The LC-MS profiles could be displayed in two forms: total ion chromatogram (TIC) and mass spectrum. **Figure 4** shows a typical profile of 90% ethanol extract of Ginseng Radix. The distribution of trace ingredients with different polarity and molecular mass were shown. A list of mass spectrum with intensity was obtained for the herbal extracts. The LC-MS profiles of 15 spleen-meridian

herbs were established, and the data processing was performed on Waters MassLynx V4.1 SCN923. With MassLynx, each chromatographic peak was identified by a mass-to-charge ratio (m/z) and retention time, as illustrated in **Supplementary Figure 1**. As shown in **Table 2**, 65 compounds in 15 herbs were identified with unique relative molecular mass of [M + H]+, [M + Na]<sup>+</sup> or [2M + H]<sup>+</sup> within acceptable experimental error, which therefore could be used for the purpose of chemical authentication.

Cocos; m, Nelumbinis Semen; n, Coicis Semen; o, Crataegi Fructus.

FIGURE 4 | A typical total ion chromatogram (TIC) and mass spectrum. The 90% ethanol extract of Ginseng was served as an example. (A) Total ion chromatogram (TIC) and (B) mass spectrum of the Ginseng extracts are shown. The X-axis of TIC is time (min); while the X-axis of spectrum is mass (Da). Both profiles were obtained with a Xevo G2-XS <sup>R</sup> Quadrupole Time-of-Flight (Q-TOF) Mass Spectrometer coupled with an integrated ACQUITY UPLC <sup>R</sup> I-Class System. The mobile phase was composed of acetonitrile and water, and the effluent was analyzed with an ESI ion source in positive mode. The acquisition range was set from 50 to 1200 Da with 0.1 s scan rate to reveal most of the chemicals within the herbal extracts. The extracts from 3 batches of herbs were subjected to analysis, a representative profile was shown.

### Principal Component Analysis of Chemical Composition Profiles

To determine the similarities and differences among the herbal extracts, PCA analyses of chemical profiles deriving from HPLC, NMR and LC-MS were conducted. PCA is a chemometrics method to determine the possible relationship of different samples. The general procedure of PCA on full spectrum was shown in **Figure 5**. The spectrums were firstly divided into bins according to the resolution of analytic methods, i.e., 9,000 bins for HPLC, 1,200 bins for NMR, 10,800 bins for TIC and 230,000 bins for mass spectrum, with analytic softwares, e.g., Agilent MassHunter workstation software version B.01.00 for HPLC, Waters MassLynx V4.1 SCN923 for LC-MS and MestReNova 6.1.1 for NMR. The intensity of each bin was extracted and arranged. The obtained excel or txt files were further imported into PCA software (SIMCA-P+ version 14.1) for chemometrics analysis.

According to the TCM theory, the selected 15 spleen-meridian herbs could not only be divided into three different groups

#### TABLE 2 | The compounds identified from the herbal extracts.

fphar-09-01304 November 13, 2018 Time: 14:49 # 8


(Continued)

fphar-09-01304 November 13, 2018 Time: 14:49 # 9


<sup>a</sup>Mass-to-charge ratio (m/z) is [M + Na]+.

according to their "Yin-Yang" nature, and which also could be divided into four groups according to their pharmacological activities (**Table 1**). In clinical application, these herbs nourish the blood, calm the heart and promote the digestion. By characterizing the relationship between the distributions of herbal extracts in score scatter plots with their identified pharmacological activities, the correlation between chemical composition basis and known pharmacology could be clarified. Although the score scatter plots of HPLC fingerprints (**Figure 6A**) and NMR profiles (**Figure 6B**) possessed acceptable first principal component (58.9%, 50.2%) and second principal component (14.1%, 13.2%), the distribution of herbal extracts showed no apparent regular pattern among different groups. The results indicated that HPLC and NMR profiles might not able to distinguish the herbal extracts with different pharmacological properties.

The LC-MS profiles, including TIC and mass spectrum obtained from UPLC/Q-TOF, were further analyzed with the same procedures as that of HPLC and NMR profiles. Both score scatter plots possessed acceptable first principal component and second principal component, especially in the scenario of two principal components of mass spectrum accounting for 97.7% of total variance (**Figures 7A,B**). These results therefore could be an outcome of ultra-high resolution of UPLC/Q-TOF. The two PCA scoring plots showed similar distribution according to their identified pharmacological activities, indicating that the polarity of herbal compounds might have positive correlation with their mass, at least to certain degree. Moreover, the herbal extracts having similar pharmacological activities showed a clustering effect. In the clustering analysis, the blood nourishing herbs could be distinguished obviously from other herbs (**Figure 7**). Thus, the plots showed a strong correlation between distribution of herbal extracts with their pharmacological activities, which was in line with the hypothesis that the bioactivities of herbal extracts should be strongly related to their chemical composition.

The relationship between "Yin-Yang" nature and distribution of the herbal extracts in scoring plots were determined. The distribution of Yin, Yang and neutral herbs were significantly identified to be different, in particular the right part of score scatter plots contained only the Yang-stimulating herbs (**Figures 8A,C**). To clarify the chemical basis of distinct distribution, the variables in loading scatter plots could be divided into three clustering groups, according to their retention time and molecular mass. From the distribution of herbal extracts in score scatter plot, the contents of compounds eluted between 6 and 12 min showed greater impacts in the "Yin-Yang" distinction of herbal extracts (**Figure 8B**). Similarly, the lighter molecules, i.e., less than 900 g/mol, were distributed in marginal positions in the loading scatter plots (**Figure 8D**), indicating that these compounds might have obvious influence

to the herbs' properties. Moreover, it could be deduced that the Yang-stimulating herbs were shown to contain more compounds, i.e., the compounds with higher polarity and lower molecular mass. These results not only illustrated the chemical basis in classify "Yin-Yang" nature of TCM, and which also suggested the polarity and molecular mass of ingredients in the herbs could be two major influencing factors.

To determine applicability of present analytic methods, three pair-herbs having typical cold and hot properties, as recorded in TCM practice, were subjected into our developed PCA analysis. Ginseng Radix and Rehmanniae Radix Praeparata are considered as hot and warm herbs aiming to tonify qi and to nourish blood; while Quinquefolium Radix and Rehmanniae Radix are cold herbs used to clear heat aiming to promote fluid production (Yu et al., 2001; Zhang et al., 2008). Angelica Sinensis Radix is a typical herb in invigorating blood in TCM therapy. However, the treatment of Angelica Sinensis Radix with wine reduces the amount of volatile oil, and this treatment could directly minimize the hot property of Angelica Sinensis Radix (Zhan et al., 2011). These three paired-herbs have the same or similar origin, and however, they have opposite functions. From the PCA score scatter plots of TIC and spectrum, the distribution of herbal extracts with hot or warm properties (i.e., Ginseng Radix and Rehmanniae Radix Praeparata) were significantly different from the herbs having cold or cool properties (i.e., Quinquefolium Radix and Rehmanniae Radix) (**Figures 9A,B**). As expected, the polarity and molecular mass of major components in the extracts of pair-herbs were different, accordingly. Although the difference between Angelica Sinensis Radix and wine-treated Angelica Sinensis Radix in pharmacological properties was not as obvious as other paired-herbs, as mentioned above, the distribution of the two herbal extracts could also be distinguished significantly in the score scatter plots (**Figures 9A,B**). The result strongly supported the precision applicability of the present established analytical model.

#### Anti-oxidative Profile of Spleen-Meridian Herbs

To elucidate the connection between the "Yin-Yang" nature and redox system, a series of in vitro assays were conducted to measure the anti-oxidative properties of herbal extracts. Folin-Ciocalteu assay was used to determine total phenolic compounds in herbal extracts. Phenolic compounds, highly abundant in plant, generally have unstable electrons in the phenolic hydroxyl groups, and which protect cells from being oxidized by directly neutralizing free radicals or decomposing peroxides. The content of total phenolic compounds in the selected herbs were rather similar (**Figure 10** and **Table 3**). In exception, the water and 50% ethanol extracts of Lablab Semen Album contained much less of total phenolic content (**Figure 10**). In contrast, Arecae Pericarpium and Crataegi Fructus possessed relative higher contents, as compared with

other herbs. The 50% ethanol extracts of herbs contained higher amounts of phenolic compounds in general, except in the scenario of Aucklandiae Radix and Nelumbinis Semen (**Figure 10**).

The free radical scavenging activity of the polyphenolic compounds in herbal extracts was measured with DPPH radical scavenging assay. DPPH, having stable free radicals, was used to detect radical-scavenging ability of herbal extracts. Here, the EC50 of each herbal extract was determined (**Figure 10**). The DPPH radical scavenging activity of most extracts were similar (**Figure 10** and **Table 3**). Among all the herbs, Lablab Semen Album showed the largest value of EC50, indicating the weakest anti-oxidant. In contrary, Arecae Pericarpium and Crataegi Fructus showed stronger DPPH radical scavenging activity: the outcome was consistent with the result of Folin-Ciocalteu assay (**Figure 10**).

To reveal the protective effect, the herbal extract was applied onto the tBHP-treated RAW264.7 macrophages. RAW264.7 murine cell was chosen because of its high breeding speed and stability. A stress inducer, tBHP, was chosen to damage

the macrophage, which induced cell death in a dose-dependent manner. Vitamin C served as a positive control (Huang et al., 2018a,b). A series of concentrations was employed, and the 15 herbs were divided into four groups, according to their efficiency (**Supplementary Figure 2**). After the optimization of dose of herbal extracts with MTT assay, the protective effects of the extracts to tBHP-induced cell cytotoxicity were determined. As shown in **Supplementary Figure 3**, the extracts protected the cells from oxidative damage in a dose-dependent manner. The protective effects and EC50 of the herbal extracts were summarized in **Figure 10** and **Table 3**. The extracts of Angelicae Sinensis Radix and Ginseng Radix possessed the best protection effects to more than 50%, as compared to the control. In contrast, Aucklandiae Radix, Nelumbinis Semen and Coicis Semen provided significantly lower protective effects at around 20% (**Figure 10**).

The formation of ROS is one of the vital causes in inducing cell death. By determining the inhibition effects of herbal extracts to tBHP-induced ROS formation, the protective effect of herbal

FIGURE 8 | PCA analyses of LC-MS profiles of signals from the 15 spleen-meridian herbs. (A) Scoring plots of TIC. (B) Loading scatter plots of TIC. (C) Scoring plots of spectrum. (D) Loading scatter plots of spectrum. The distribution of each herbal extract in score scatter plots was calculated according to its correlation with the major components. The classification of herbal extracts was according to the "Yin-Yang" nature of herbs, i.e., Yin, neutral and Yang herbs, according to TCM practice. The results showed very obvious discrimination between herbal extracts with different properties. Each dot in the loading scatter plots represents a variable. Dots in (B) represent time intervals for 0.1 s, and dots in (D) represent mass intervals of 0.005 Da.

scatter plots was calculated according to its correlation with the major components.

extracts was further compared and analyzed. As shown in **Table 3**, the inhibition effects to ROS formation were positively related with the protective effects to cell viability. The 50% ethanol extracts of Atractylodis Macrocephalae Rhizoma, Angelicae Sinensis Radix, Jujubae Fructus possessed the best effect having over 50% of inhibition, as compared to the control (**Figure 10**). Moreover, the luciferase-reporter construct (i.e., pARE-Luc) containing four ARE DNA regulatory elements deriving from the promoters of anti-oxidative genes, tagged upstream of a luciferase gene, was transfected into cultured RAW264.7 cells.

<sup>∗</sup>p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

The activation of pARE-Luc, triggered by herbal extracts, showed the same pattern as the result of ROS formation. Again, the 50% ethanol extracts of Atractylodis Macrocephalae Rhizoma, Angelicae Sinensis Radix, Jujubae Fructus exhibited the best inhibition effects of over 50% (**Figure 10** and **Table 3**).

### Correlation Between Yin-Yang Attributes With Anti-oxidative Profile

After establishment of anti-oxidative profiles of spleen-meridian herbs, the correlation of this activity with the "Yin-Yang" classification criterion was determined. By comparing the specific anti-oxidative data of herbal extracts with the mean value of 45 extracts, the statistical tests were performed by one-way ANOVA with multiple comparisons using Dunnett's test (**Figure 10** and **Table 3**). According to the comparison results, the differences between species were relatively higher than that from different herbal extracts. Moreover, the correlation between the anti-oxidative profiles of the spleen-meridian herbal extracts with the "Yin-Yang" classification criterion was rather weak in the tested parameters. Poria Cocos is a plain medicine used to fortify spleen, while Crataegi Fructus is classified as Yin herb due to its acid flavor. These herbs contain abundant phenolic compounds


 | Thecorrelation between anti-oxidant profiles of the spleen-meridian herbs with the "Yin-Yang" classification

criterion.

TABLE

3

ethanol extract; E90, 90% ethanol extract. dValues are expressed as Mean ± SD, where n = 4, each with triplicate samples. Statistical comparison

GAE/g; mean value of DPPH radical scavenging

formation is 30.5%; mean value of

 EC50 is 1.38 mg/mL; mean value of tBHP protection effect is 37.9%; mean value of tBHP protection effect EC50 is 0.65 mg/mL; mean value of inhibition effects to ROS

pARE-luciferase

 activity increase is 29.6%). ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

 was made with mean value (mean value of total phenolics is 12.87 mg

fphar-09-01304 November 13, 2018 Time: 14:49 # 14

and possess appreciable anti-oxidative activity, resulting in better protective effects to oxidative stressed cells than that from Yang herbs, e.g., Amomi Fructus, Aucklandiae Radix (**Figure 10**).

Furthermore, the PCA analysis of anti-oxidative profiles was conducted. As shown in **Figure 11**, the two principal components of the mass spectrum accounted for 86.9% of total variance, which is enough to support the credibility of the results. The distribution of Yang, neutral and Yin herbs could not be distinguished in the score scatter plot, indicating there are no significant difference between anti-oxidative profiles of these herbs (**Figure 11A**). This insignificant distinction could also be predicted from the loading scatter plot (**Figure 11B**). Having the parameters of tBHP-protected effect (%), ROS formation (% of inhibition) and pARE-luciferase activity (% of increase) at similar locations, there should be insufficient variables to distinguish the samples. Thus, the anti-oxidative properties of herbal extract were not equivalent to its "Yin-Yang" properties of herbal medicine.

### DISCUSSION

The concept of four natures and five flavors of TCM herb is accumulated from long-term medical practice, and this theory plays a key role in clinical medication. To clarify the modern physiological meaning of the "Yin-Yang" properties, 15 spleen-meridian herbs were selected for the illustration of such classification. In accordance to TCM clinical practice, we have chosen different types of herbs that are known to have certain pharmacological functions: (i) herbs to fortify spleen and promote digestion, e.g., Aucklandiae Radix and Crataegi Fructus; (ii) herbs to keep digestion system in normal state by resolving dampness and treating diarrhea, e.g., Atractylodis Macrocephalae Rhizoma, Amomi Fructus, Areca Catechu, Dioscoreae Ahizoma, Lablab Semen Album, Nelumbinis Semen and Coicis Semen; (iii) herbs to replenish Qi and nourish blood, e.g., Codonopsis Radix, Angelicae Sinensis Radix, Astragali Radix, Jujubae Fructus and Ginseng Radix; and (iv) herbs to calm the heart and soothe the nerves, e.g., Poria Cocos and Nelumbinis Semen. Apart from the classification according to their pharmacological activity, these herbs could also be further divided into three groups, Yin, Yang and neutral herbs. Thus, the herbs chosen in present study is trying to cover the most commonly used TCM in treating spleen-related unwell.

Identifying chemical composition in herbal extract is the key to probe the properties of TCM herbs. However, the natural products always accompany with a huge amount of chemical variables, which greatly shake the credibility of any outcome (Kuang et al., 2012). Here, we employed standard methods, i.e., HPLC fingerprinting, NMR profiling and LC-MS profiling, as to reduce the variables in herbal extracts. Generally, HPLC fingerprinting is adopted to show all compounds with ultraviolet absorption; <sup>1</sup>H-NMR profiling is able to display all compounds with active hydrogen; while LC-MS profiling is capable to show trace compounds (Zhao et al., 2005; Crockford et al., 2006). To obtain information of the components as much as possible, the detection wavelength of HPLC was set from 190 to 400 nm, and

Yin-stimulating, neutral and Yang-stimulating herbs, according to TCM practice. The results showed low discrimination between herbal extracts with anti-oxidative properties. Each dot in the loading scatter plots (B) represents a variable, i.e., the parameters reflecting the anti-oxidant of extracts.

the gradient elution programs covered 0–100% acetonitrile. The chemical shift range was set from −2 to 12 ppm covering active hydrogen in almost all kinds of organic matters. Furthermore, the identified signals were subjected to full spectrum PCA determination, and which did not request the true nature of identified chemicals and could therefore reduce the possible variation (Savorani et al., 2010; Lu et al., 2014).

According to previous research, the herbal compounds associated with cold nature generally possess more polar structures; in contrast the compounds associated with hot nature have lower molecular weight; and the neutral compounds have

a higher polar surface area (Fu et al., 2017a). The cold and hot properties of TCM are defined according to the impacts to human physiological condition, indicating the importance of chemical composition of herbal extracts. By using various analytic methods, we generated a large group of chemometrics data, and these data were subjected to PCA analysis. The results of LC-MS profile showed that the Yang-stimulating herbs contained more compounds of lower molecular weight and with less polar structures. This result is consistent with the previous proposed notion (Fu et al., 2017a). However, the results of HPLC and NMR profiles were not able to prove above conclusion, which might due to low resolution and similar chemical shift of most of the organic compounds.

Apart from the chemical basis, the differences of biological effects are always the most concern issues in studying the four natures of TCM herbs. Due to the high similarity of the opposition and interdependence relationship in "Yin-Yang" theory and redox system, the study on their connection was continuously. By using an oxygen radical absorbance capacity (ORAC) assay to compare the free radical-scavenging activity of Yin- and Yang-tonic herbs, Ou et al. (2003) was among the first groups to provide physical meaning of "Yin and Yang" in relating to biochemical processes. The result revealed that the amount of total phenolic compounds in Yin-tonic herbs were generally higher, and meanwhile which possessed a higher antioxidant activity than the Yang-tonic herbs. Thus, the authors deduced that Yin-tonic represents anti-oxidation, and Yang-tonic represents oxidation involved in energy metabolism. However, their result was against by Szeto and Benzie (2006): they measured the 1,1-diphenylpicrylhydrazyl radical-scavenging activity of Yin- and Yang-tonic herbs and found that most of the 'Yang-invigorating' herbs possess higher anti-oxidative activity. However, their conclusion was still not finalized due to different selection criterions, and the thoughtful analytic methods were not used. To set up a more credible and convincing

#### REFERENCES


antioxidant profiles, our present approach adopted various common assays at different levels, as to evaluate anti-oxidative properties of unknown herbal samples. Accompanied with PCA method, the current results showed that the correlation between "Yin-Yang" and anti-oxidative properties was relatively low, indicating the connection between these two systems might not be closely related, at least in ours 15 spleen-meridian herbs.

#### AUTHOR CONTRIBUTIONS

KT and TD conceived and designed the experiments. YH, LW, and PY performed the experiments, analyzed the results, and made figures and tables. KL, HW, XK, and YC contributed to scientific discussions. KT and YH wrote the paper.

#### FUNDING

This study was supported by Hong Kong RGC Themebased Research Scheme (T13-607/12R), Innovation Technology Fund (UIM/288, UIM/302, UIM/340, UIT/137, ITS/022/16FP), TUYF15SC01, Shenzhen Science and Technology Committee Research Grant (CKFW2,016,082,916,015,476; JCYJ20,170,413, 173,747,440; JCYJ20,160,229,205,726,699; JCYJ20,160,229,205, 812,004; JCYJ20,160,229,210,027,564; ZDSYS201,707,281,432,317; and 20,170,326).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.01304/full#supplementary-material



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Huang, Yao, Leung, Wang, Kong, Wang, Dong, Chen and Tsim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

,

# Anti-Inflammatory Effect of a TCM Formula Li-Ru-Kang in Rats With Hyperplasia of Mammary Gland and the Underlying Biological Mechanisms

Yingying Wang1,2, Shizhang Wei<sup>2</sup> , Tian Gao<sup>3</sup> , Yuxue Yang1,2, Xiaohua Lu1,2, Xuelin Zhou<sup>2</sup> Haotian Li<sup>2</sup> , Tao Wang1,2, Liqi Qian<sup>4</sup> , Yanling Zhao<sup>2</sup> \* and Wenjun Zou<sup>1</sup> \*

#### Edited by:

Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China

#### Reviewed by:

Yuanjia Hu, University of Macau, China Weijun Kong, Institute of Medicinal Plant Development (CAMS), China Jian-Guo Ren, Harvard Medical School, United States

#### \*Correspondence:

Yanling Zhao zhaoyl2855@126.com Wenjun Zou zouwenjun@163.com

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 13 March 2018 Accepted: 29 October 2018 Published: 20 November 2018

#### Citation:

Wang Y, Wei S, Gao T, Yang Y, Lu X, Zhou X, Li H, Wang T, Qian L, Zhao Y and Zou W (2018) Anti-Inflammatory Effect of a TCM Formula Li-Ru-Kang in Rats With Hyperplasia of Mammary Gland and the Underlying Biological Mechanisms. Front. Pharmacol. 9:1318. doi: 10.3389/fphar.2018.01318 <sup>1</sup> College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China, <sup>2</sup> Department of Pharmacy, 302 Military Hospital of China, Beijing, China, <sup>3</sup> Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China, <sup>4</sup> Department of Traditional Chinese Medicine, First Affiliated Hospital of Chinese People's Liberation Army General Hospital, Beijing, China

Li-Ru-Kang (LRK), a formula of eight traditional Chinese medicines (TCM), has been used to treat hyperplasia of mammary gland (HMG) in TCM clinics. However, how LRK works in HMG patients is unclear. To explore the possible mechanisms of LRK against HMG, the network pharmacology was used to screen the potential targets and possible pathways that involved in LRK treated HMG. Rat HMG model induced by estrogen and progesterone was used to further verify the effects of the key molecules of LRK selected from the enriched pathways on HMG. Nipple heights and diameters were measured and uterus index was calculated. The histopathological changes of mammary gland tissue were detected by hematoxylin-eosin (H&E) staining. Western blot was used to detect the phosphorylation of ERK, JNK, and P38. And immunohistochemistry staining was performed to evaluate the levels of estrogen receptor α (ERα), progesterone receptor (PR), nuclear factor-(NF-)κB (p65), interleukin-1β (IL-1β), tumor necrosis factor α (TNF-α), cyclooxygenases 2 (COX-2), inducible nitric oxide synthase (iNOS), 8-hydroxy-20deoxyguanosine (8-OHdG), and nitrotyrosine (NT). Our results indicate that LRK treatment rescues significantly nipples height and diameter, decreases uterus index and ameliorates HMG. LRK treatment also markedly attenuates the over-expression of IL-1β, TNF-α, COX-2, and iNOS, and suppressed the formation of 8-OHdG and NT. Furthermore, LRK treatment significantly inhibits the phosphorylation of JNK, ERK, and p38 and expression of NF-κB (p65), interestingly, LRK treatment has no effect on the expression of ERα and PR. Our data suggest that the LRK treatment protects the mammary glands from the damage of oxidative stress and inflammation induced by estrogen and progesterone, via suppresses of MAPK/NF-κB signaling pathways without affecting on the expression of ERα and PR.

Keywords: Li-Ru-Kang (LRK), hyperplasia of mammary gland (HMG), inflammatory responses, oxidative stress, MAPK

## INTRODUCTION

fphar-09-01318 November 20, 2018 Time: 14:43 # 2

Hyperplasia of mammary gland (HMG), a common disease, occurs among middle-aged women with high frequency (Jia et al., 2017). It is a kind of pathological hyperplasia of lobules of mammary gland induced by the disorder of estrogen and progesterone (Bennett et al., 1990; Zhang et al., 2016). The morbidity of HMG is increasing nowadays with a risk of breast cancer (Chen et al., 2015). Tamoxifen is an estrogen antagonist with antiproliferative effects. It has been widely used for breast cancer as well as HMG (Cline et al., 1998; Li et al., 2018). However, some women do not tolerate tamoxifen and have a risk of side effects (Kleinberg et al., 2011; Martinez de Dueñas et al., 2014). Therefore, it is important to discovery safer and more effective drugs with minimum side effects for HMG.

Traditional Chinese medicine (TCM) has been frequently used as alternative treatments for various types of diseases. For complicated or multi-factorial diseases, emerging evidence indicates that multiple drugs that have common or different pharmacological targets often display better therapeutic efficacy than a single medication (Roth et al., 2002). The formulae with multiple herbs exert therapeutic efficacies through the synergistic effects of their multiple ingredients via multiple targets (Che et al., 2013; Shao and Zhang, 2013).

Li-Ru-Kang (LRK) consisted of Ostreae Concha (Ostrea gigas Thunberg.), Cervi Cornu [Alpinia aquatica (Retz.) Roscoe.], Polygoni Multiflori Radix [Polygoni Multiflori (Thunb.) Moldenke.], Curcumae Radix (Curcuma aromatica Salisb.), Cremastrae Pseudobulbus Pleiones Pseudobulbus [Cremastra appendiculate (D. Don) Makino.], Bupleuri Radix (Bupleurum chinense DC.), Prunellae Spica (Prunellae vulgaris L.) and Glycyrrhizae Radix et rhizoma (Glycyrrhizae uralensis Fish. ex DC). LRK is a Chinese preparation formulated through TCM principles of tonifying the kidney, smoothing the liver, dissipating nodule and activating blood circulation. LRK has an overall regulatory effect on HMG. The randomized trials of LRK showed that LRK had favorable effect in the treatment of HMG (Qian et al., 2007; Li et al., 2013). Studies showed that LRK could improve the histological lesions in mammary gland, uterus and ovaries (Qian et al., 2005). LRK also affects abnormal secretion of sex hormones in model of HMG induced by estradiol benzoate. The serum levels of estradiol (E2) and prolactin (PRL) were decreased and progesterone (P) was increased remarkably by LRK (Qian et al., 2004). Our previous study explained the modulatory properties of LRK treatment on HMG using metabolomics and network pharmacology analyses, showed the therapeutic effects of LRK on HMG (Wei et al., 2018). However, the biological mechanisms of LRK for HMG are still blur.

Network pharmacology, a technology for system biology study, could clarify the potential mechanisms of complicated ingredients through large data set analysis. In that TCM formula has been thought to be multi-ingredients and multi-targets, network pharmacology is a suitable approach to meeting this challenge and determining the mechanism on LRK for treating HMG (Che et al., 2013; Tao et al., 2013; Mao et al., 2017).

Therefore, in the present study, network pharmacology approach combined with molecular biology was performed to further investigate the active ingredients and the underlying mechanism of LRK for the treatment of HMG.

#### MATERIALS AND METHODS

#### Database Construction

The chemical structures of the compounds in LRK were obtained from TCM Database@Taiwan (TDT)<sup>1</sup> (Chen, 2011) and Traditional Chinese Medicine Systems Pharmacology (TCMSP) database<sup>2</sup> . All compounds were selected according to selection criteria [oral bioavailability (OB) ≥ 30 and drug-likeness (DL) ≥ 0.18], as suggested by TCMSP (Liu et al., 2013). Known compound targets were collected from Herbal Ingredients' Targets Database (HIT)<sup>3</sup> (Ye et al., 2011), and the putative targets from these were screened out from Therapeutic Targets Database (TTD)<sup>4</sup> through structural similarity comparison (Chen et al., 2002). Gene and protein targets associated with HMG therapy were collected from the Online Mendelian Inheritance in Man (OMIM)<sup>5</sup> database. Other interaction proteins of the aforementioned targets were obtained from Database of Interacting Proteins (DIP)<sup>6</sup> , and different ID types of the proteins were converted to UniProt IDs.

#### Network Construction and Analysis

To provide the scientific and reasonable interpretation of the complex relationships between the compounds and targets associated with HMG, network analysis was carried out. The compound-target-disease network was constructed using candidate compounds, potential targets and HMG significant targets. The network was performed using Cytoscape 3.5.1 software (National Institute of General Medical Sciences, United States). The topological features of each node in the network were calculated by "Degree", "Betweenness centrality", and "Closeness centrality" ("Degree" values twofold greater than the median value of all the network nodes, "Betweenness centrality" and "Closeness centrality" value greater than the median value of all the network nodes) (Wang J.B. et al., 2018). Targets with higher value were screened as the candidates for HMG.

#### Plant Material

LRK consisted of Ostreae Concha (Ostrea gigas Thunberg.), Cervi Cornu [Alpinia aquatica (Retz.) Roscoe.], Polygoni Multiflori Radix [Polygoni Multiflori (Thunb.) Moldenke.], Curcumae Radix (Curcuma aromatica Salisb.), Cremastrae

<sup>1</sup>http://tcm.cmu.edu.tw/

<sup>2</sup>http://lsp.nwu.edu.cn/

<sup>3</sup>http://lifecenter.sgst.cn/hit/

<sup>4</sup>http://bidd.nus.edu.sg/group/cjttd/

<sup>5</sup>https://www.omim.org/

<sup>6</sup>http://dip.doe-mbi.ucla.edu/dip/Main.cgi

Pseudobulbus Pleiones Pseudobulbus [Cremastra appendiculate (D. Don) Makino.], Bupleuri Radix (Bupleurum chinense DC.), Prunellae Spica (Prunellae vulgaris L.) and Glycyrrhizae Radix et rhizoma (Glycyrrhizae uralensis Fish. ex DC) which were purchased from Heyanling, Co., Ltd. (Beijing, China). The origin and quality of the 8 herbs were identified according to the Chinese Pharmacopeia (2015 Edition). The eight TCMs of LRK were composited with the weight ratio of 30: 12: 12: 10: 10: 9: 9: 6. At first, Cervi Cornu slice were smashed into powder. Then it was decocted with appropriate amount of water and kept slightly boiling for 1 h. Next, the filtrate of Cervi Cornu was obtained and combined with the rest of TCM proportionally, followed by another 1 h heat extraction with boiled water (1/10, weight/volume) three times. The final yield of powder to raw materials was about 9.2%.

#### Reagents

The antibodies of phosphorylation-MAPK Family Antibody Sampler Kit used for western blot were purchased from Cell Signaling (United States) (#9910). And nuclear factor- (NF-)κB (p65) (XCJ36131), cyclooxygenases 2 (COX2) (06416080202), inducible nitric oxide synthase (iNOS) (16716110102), interleukin-1β (IL-1β) (01016051201), tumor necrosis factor α (TNF-α) (60291-1-Ig), estrogen receptor α (ERα) (AF6058), and progesterone receptor (PR) (37917011102) for immunohistochemistry were purchased from Wu han goodbio technology Co., Ltd (China). Antibodies of 8-hydroxy-2 <sup>0</sup>deoxyguanosine (8-OHdG) (GR3173165-3), nitrotyrosine (NT) (GR174728-22), GAPDH (AC001) were purchased from Abcam (the United States).

#### Animals and Administration

Female SD rats weighting 180–220 g [license number: SCXK-(A) 2012–0004] were obtained from the laboratory animal center of the Military Medical Science Academy of the People's Liberation Army (PLA). They were maintained separately at animal experimental center of 302 Hospital of People's Liberation Army (Beijing) with a specific pathogen free (SPF) environment (24◦C, 65% humidity, 12 h day/night). The rats were randomly divided into six groups. All rats except for control group were intramuscularly injected with estrogen at a dose of 0.5 mg/kg/d for consecutive 25 days. All rats but control group were intramuscularly injected with progesterone at a dose of 5 mg/kg/d in the following 5 days (Wang et al., 2011). LRK (0.056, 0.112, 0.224 g/kg/d for the low, medium and high dose, respectively) and tamoxifen (Yangtze River Pharmaceutical Co., Ltd. 5 mg/kg/d) were dissolved in normal saline and intragastrically administered to rats, respectively, except for control and model group for 30 days. The rats were sacrificed 12 h after the last administration. All animal studies have been approved by the Ethical Committee of 302 Military hospital of China. The blood, mammary gland and uterus were collected. The blood was centrifuged at 3500 rpm for 15 min to separate the serum without hemolysis. Serum and the rest of mammary gland tissue for hematoxylin-eosin staining were stored at -80◦C.

### Measurement of Body Weight, Nipple Height and Diameter, and Uterus Indexes

Body weight, nipple height and diameter of all rats were recorded after administration. Uterus index (Wang et al., 2011) was calculated by the following formulae:

Uterus index = Wuterus (mg)/Wbody (g)

Wuterus and Wbody stand for the average weight of uterus and body weight of rats.

### Histopathological Evaluations and Immunohistochemical Observation

The mammary gland tissues obtained from the experimental rats were fixed in the 10% neutral buffered formalin and then embedded in paraffin. Subsequently, the embedded mammary gland tissues were cut into thin slices and disposed using hematoxylin-eosin (H&E) staining.

Immunohistochemical analysis was performed using deparaffinized mammary sections. The sections were immersed in freshly prepared 2% H2O<sup>2</sup> at room temperature for 25 min and blocked with 5% rabbit serum for 30 min. Then the primary antibody [NF-κB (p65) (1:100), -COX-2 (1:1000), -TNF-α (1:200), -IL-1β (1:400), -iNOS (1:1000), -NT (1:100), -8-OHdG (1:100), -ERα (1:200), or -PR (1:200)] was added and incubated at 4◦C overnight. After being washed with PBS, the sections were treated with the secondary antibody conjugated with horseradish peroxidase at room temperature for 50 min. Then, they were immersed in diaminobenzidine (DAB) for 3 min. The hematoxylin-stained sections were dehydrated by ethanol. Stained areas of the sections were visualized using an optical microscope at ×200. Image analysis software Image-Pro Plus 6.0 was used to select the yellow area of the immunohistochemical reactant on the image and then the mean integrated optical density (IOD) of these areas was measured.

### Western Blot Analysis

The mammary expressions of p-P38 p-JNK and p-ERK were evaluated by western blot analysis. Mammary gland tissue of rats was homogenized and subsequently lysed by tissuelyser (Shanghai Jingxin Industrial Development Co., Ltd, Shanghai, China) with RIPA buffer containing a protease inhibitor mixture. The protein was distilled and then centrifugated at 12,000 rpm and 4◦C for 10 min to separate debris. Protein concentration was determined using the BCA protein assay kit. Protein samples (25 µg) were separated by SDS-polyacrylamidegel electrophoresis and transferred to a PVDF membrane by electrophoretic transfer. Transferred membranes were blocked for 1 h at room temperature with 2% BSA in Tris-buffered saline containing 0.1% Tween 20 (TBST), and then incubated overnight at 4◦C with different primary antibodies [anti-p-P38 (1:1000), p-JNK (1:1000) and p-ERK (1:1000)]. After washes with TBST 4 times, the membranes were incubated with horseradish peroxidase-conjugated secondary antibody (1:3000) in TBST with 2.5% nonfat milk for 1 h at room temperature. Western blots were developed on films using the enhanced chemiluminescence technique. Quantification of bands was determined by densitometric analysis using Bio-Rad Quantity One. The data were normalized using GAPDH (1:3000) as an internal control.

### Statistical Analysis

fphar-09-01318 November 20, 2018 Time: 14:43 # 4

Data were presented as means ± SD. and were analyzed using the IBM SPSS Statistics 21. Data among groups were analyzed with ANOVA. P < 0.05 and P < 0.01 were considered statistically significant.

### RESULTS

### Prediction for the Direct Targets of LRK on HMG

A total of 72 chemical constituents of LRK, 351 drug targets, 213 HMG targets and 358 interacting proteins were obtained from TDT, HIT, TTD, OMIM, and DIP. Then, the ingredients, drug targets, disease targets and interacting proteins were connected for interaction network of "compound-target-disease" construction, which was presented with color-coded nodes (**Figure 1A**). In the network, the yellow squares represented the direct targets which were the common targets of LRK and HMG. The common targets were the key targets for LRK on treating HMG as well as the relatively important targets screened for further research.

Subsequently, the topological parameters values of each target in the network including "Degree", "Betweenness centrality", and "Closeness centrality" were analyzed for the important key protein targets and related signaling pathways screening. The results showed that 13 direct targets were selected according to their Degree twice greater than the median (>14), Betweenness Centrality and Closeness Centrality greater than the median (Betweenness Centrality value >0.004; Closeness Centrality value

>0.28) (**Table 1**). Additionally, COX2 and NF-κB might be the most important targets of LRK on treating HMG due to their highest degree.

### Prediction of Active Ingredients in LRK

Furthermore, active ingredients from LRK were explored be network pharmacology prediction. As shown in **Figure 1B**, 24 potential active ingredients directly related to LRK on HMG. According to the OB ≥ 30 and DL ≥ 0.18, 19 active ingredients including saikosaponin c\_qt, quercetin, kaempferol, chrysazin, luteolin, beta-sitosterol, isorhamnetin, curcolactone, stigmasterol, 2-methoxy-9,10-dihydrophenanthrene-4,5-diol, areapillin, morin, 3,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl) chromone, delphinidin, troxerutin, cubebin, α-spinasterol, linoleyl acetate, and vulgaxanthin-I might be responsible for the effect of LRK on HMG, and they were listed in **Table 2**.

### Pathway Analysis of LRK on HMG

The results above showed that 19 active ingredients related to 13 direct targets played the key role of LRK in treating HMG. However, the key pathways still unknown. Therefore, the pathways of the direct target proteins were enriched. The analysis of KEGG pathways indicated that there were five pathways enriched related to HMG, including MAPK signaling pathway, Toll-like receptor signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway and Chemokine signaling pathway, which were directly involved in inflammation. The proteins like NF-κB (p65), COX2 were involved in major pathways (**Table 3**). According to the count of relative targets and the percentage on all pathways, MAPK signaling pathway might play the key role in LRK treating HMG. These results indicated that the protect effect of LRK on HMG might associate to MAPK pathway, regulate NF-κB, anti-inflammatory, and anti-oxidative stress.

### Therapeutic Effect of LRK on HMG

In order to assess the therapeutic effect of LRK, the height and diameter of nipples (left 2 and right 2) and uterus index of rats were measured. As shown in **Figure 2**, both of the left and right nipples were markedly decreased by LRK treatment compared to the HMG model group. Similarly, the diameter of nipples (left 2 and right 2) of rat were also significantly decreased compared to the model group (P < 0.01) (**Figures 2A,B**). And the result showed that the effect of LRK is comparable to the tamoxifen treatment. Uterus is the main target organ of estrogen, the high level of exogenous estrogen will induce the obviously increased of uterus index (Jia et al., 2017). Thereby, the uterus index was examined. It showed that the uterus index was decreased in a dose-dependent manner by LRK treatment (**Figure 2C**).

Subsequently, hematoxylin and eosin (H&E) staining was performed to visualized the pathological changes of mammary gland sections. As shown in **Figure 2D**, Estrogen and progesterone stimulation induced severe HMGs in rats, reveled by histological abnormalities, including significant

TABLE 1 | Topological parameters of Li-Ru-Kang (LRK) on HMG obtained from network pharmacology analysis.


TABLE 2 | Information of active ingredients of LRK and their topological parameters.


proliferative lesions, mammary ducts ectasia expansion of mammary lumens, significant increase of acinars and lobules. Conversely, the administration of LRK significantly improved the morphological changes of HMGs dose-dependently. Above all, the results indicated the specific therapeutic effect of LRK on HMG.

TABLE 3 | Potential pathways and the target proteins in KEGG analysis.

fphar-09-01318 November 20, 2018 Time: 14:43 # 6


### Effect of LRK on the Expression of Inflammatory Cytokines (COX-2, IL-1β, iNOS, and TNF-α)

Previously studies reported that inflammatory cytokines such as COX-2, IL-1β, iNOS, and TNF-α play important role in the development of HMG (Yang et al., 2013; Chen et al., 2015; Guo et al., 2017). IL-1β and TNF-α, pro-inflammatory cytokines, were significantly increased in model group induced by estrogen and progesterone. While, the LRK treatment could notably decrease the expression of TNF-α and IL-1β compared to model group. COX-2 and iNOS, acted as pro-inflammatory enzymes. LRK treatment significantly inhibited the over-expressions of COX-2 and iNOS in rats induced by estrogen and progesterone in a dose-dependent manner (P < 0.01) (**Figure 3**). In summary, the results showed the significant anti-inflammatory effect of LRK.

## Effect of LRK on Oxidative Stress [8-OHdG and Nitrotyrosine (NT)]

As shown in **Figure 4**, there was an obvious oxidative stress injury in model group, reveled by the high expression of 8-OHdG and NT, markers of oxidative stress, compared to the control group. In the LRK groups, the high expression of 8-OHdG and NT were significantly inhibited in a dose-dependent elevation, which indicated the anti-oxidative stress effect of LRK (**Figure 4**).

### Effect of LRK on Expression of ERα and PR

The expression of estrogen receptor and progesterone receptor in mammary gland plays an important role in the physiological and pathological changes of mammary gland. So, we examined the expression of ERα and PR. As shown in **Figure 5**, LRK could not affect the expression of ERα and PR.

### Effect of LRK on Expression of p-JNK, p-P38, p-ERK, and NF-κB (p65)

The pathway analysis results in this study indicated that LRK may play anti-inflammatory and anti-oxidative stress role through MAPK/ NF-κB signaling pathways. To determine whether LRK could affect MAPK, we examined the expressions of p-JNK, p-P38, p-ERK. The results showed that p-P38, p-ERK, and p-JNK

were slightly expressed in normal mammary cells. In HMG model group, expressions of p-P38, p-ERK, and p-JNK were notably increased. LRK administration significantly suppressed the over-expressions of p-P38, p-ERK, and p-JNK versus the model group (**Figure 6**).

There existed a large degree of cross-talk within the MAPK cascades and other signaling networks. For example, there were interactions between mediators of the MAPK and NF-κB pathway (Chen et al., 2015). The expression of NF-κB (p65) was examined by immunohistochemical analysis. NF-κB (p65) was increased significantly in the HMG model group. LRK treatment significantly and dose-dependently suppressed the mammary over-expressions of NF-κB (p65) when compared to the model ones (**Figure 7**).

### DISCUSSION

HMG, a common disease in middle-aged women has severe cancerous tendencies, and poses a significant public health challenge to women (Marchant, 2002). It is reported that HMG, especially atypical HMG, leads to higher risk of breast cancer. (Wang et al., 2011; Zhang et al., 2012). Studies showed that HMG is related to endocrine disorders, the most of that are caused by the imbalance of estrogen and progestin. (Liu et al., 2012; You et al., 2017) The treatment of HMG has become a heated topic in the world. HMG is mainly treated by surgery and medication (Henry, 2014; Chen et al., 2015). Tamoxifen, as the main therapeutic drug of HMG could improve the overall survival for patients. However, adherence to and persistence with the medications is poor in part because of bothersome side effects that can negatively affect quality of life (Henry, 2014). Thus, it is important and urgency to find more effective and few side effects drug to block development of HMG.

LRK, a TCM formula, has been clinically used for the treatment of HMG for several years. The First Affiliated Hospital of the General Hospital of PLA conducted clinical trials and demonstrated that LRK could inhibit HMG (Li et al., 2013). Previous study showed high effective rate (88.0%) in the treatment of HMG. Meanwhile the patients' symptoms and abnormalities of gonadal hormone was obvious improved (Qian et al., 2007). However, the underlying mechanisms of LRK for HMG are still unclear. Thus, the aim of this study is to explore the underlying mechanism of LRK on HMG.

To clarify the effect of LRK on HMG in rats, we established HMG model in rats induced by estrogen and progesterone. The decreasing of nipple heights and diameters, uterus index and ameliorate of histopathological in LRK groups indicated that LRK could improving HMG in rats. Then, we further explore the active ingredients and underlying mechanism of LRK on HMG.

To reveal the therapeutic mechanisms of LRK on HMG, we firstly predicted the active ingredients and potential targets of LRK against HMG through network pharmacology. We found 19 potential compounds, including saikosaponin

c\_qt, quercetin, kaempferol and chrysazin, Luteolin, beta-sitosterol, isorhamnetin, curcolactone, stigmasterol, 2 methoxy-9,10-dihydrophenanthrene-4,5-diol, areapillin, morin, 3,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl) chromone, delphinidin, troxerutin, cubebin, α-spinasterol, linoleyl acetate and vulgaxanthin-I might play the key role in LRK-treated HMG. Studies showed that most of those potential active ingredients have anti-inflammatory or anti-oxidative effects, such as quercetin, (Song et al., 2018, kaempferol (Huang et al., 2018; Wang J. et al., 2018), Luteolin (Aziz et al., 2018), beta-sitosterol (Liao et al., 2018), isorhamnetin (Qiu et al., 2016), delphinidin (Wang et al., 2017), troxerutin (Najafi et al., 2018), cubebin (Bastos et al., 2001), morin (Athira et al., 2016), linoleyl acetate (Peana et al., 2002) have anti-inflammatory effects. Compounds like quercetin (Sherif, 2018), kaempferol (Kouhestani et al., 2018), Luteolin (Yang et al., 2018), beta-sitosterol (Yin et al., 2018),

delphinidin (Cheol et al., 2016), troxerutin (Farajdokht et al., 2017), morin (Ma et al., 2016), linoleyl acetate (Peng et al., 2014) showed anti-oxidative stress effect. Studies indicated that HMG was related to inflammatory and oxidative stress (Chen et al., 2015). Therefore, those active ingredients predicted by network pharmacology might play important role in LRK treat HMG.

Meanwhile, KEGG analysis indicated that MAPK signaling pathway contributes the most for the therapeutic effect of LRK on HMG, it might be the essential pathway for our research. Additionally, Toll-like receptor signaling pathway and TNF signaling pathway were either directly involved in inflammation or played other important roles.

Simultaneously, there were studies proved that MAPK signaling pathway is essential in regulating many cellular processes including inflammation, cell stress response, cell differentiation, cell proliferation, metabolism, motility and apoptosis (Arthur and Ley, 2013). There were four distinct MAPK cascades including the extracellular signal-regulated kinase (p42/44 ERK)/MAPK, the p38 pathway, the c-jun N-terminal kinase (JNK)/stress-activated protein kinase (SAPK) pathway and the Big MAP kinase-1 (BMK-1) pathway (Burotto et al., 2014). P38, ERK, and JNK pathway was the most important signaling pathways involving the regulation of many physiological functions of cells and playing an important role in the anthogenesis and pathophysiological process of many kinds of diseases, such as regulating the proliferations and differentiations of cells, participation in oxidative stress reaction (Li et al., 2017). Currently, more and more researches proved that TCM have the protective effect against HMG via the possible mechanism of regulating endocrine, inflammatory and oxidative stress (Wang et al., 2011; Zhang et al., 2012). And studies indicated that TCM can significantly inhibit and improve HMG in a large degree via ERK, JNK signaling pathway, as well as regulating NF-κB (Squires et al., 2003; Chen et al., 2013; Wu, 2015). It is well-known that NF-κB can activate the inflammatory cytokines like IL-1, TNF-α, and COX2 (May and Ghosh, 1998). Researches indicated that MAPK and NF-κB signaling pathway play an important role in HMG treated by TCM while NF-κB building a link among oxidative stress and inflammatory responses in HMG (Chen et al., 2015). Therefore, we verified the MAPK and NF-κB signaling pathway for further experiments.

The experimental results showed that the phosphorylation of P38, ERK, and JNK in HMG rats was significantly increased after stimulation of estrogen and progesterone. While the overexpression of p-p38, p-ERK, and p-JNK were decreased after the treatment of LRK. As the downstream of MAPK signaling pathway and an important inflammatory

transcription factor, NF-κB was decreased after LRK treated. And the expression of inflammatory mediators (including IL-1, and TNF-α) and pro-inflammatory cytokines (such as COX2 and iNOS) were down-regulated after LRK treated. Those results indicated LRK could reduce inflammation in HMG.

ER and PR, as the receptor of estrogen and progestin, and studies showed PR and ERα could active the MAPK signaling

pathway (Liu, 2012). The present study showed that LRK treatment has no effect on the expression of ERα and PR. Those results indicate that LRK might plays therapeutic effect on HMG by activating MAPK signaling pathways directly, rather than through ERα and PR to activating MAPK signaling pathways.

8-OHdG is a predominant form of free radical-induced oxidative lesions in nuclear and mitochondrial DNA. Therefore, 8-OHdG is widely used as a biomarker for oxidative stress (Wu et al., 2004; Arulselvan et al., 2016). Nitrotyrosine (NT) is mainly formed by the reaction of superoxide radicals with NO, which is used to detect peroxynitrite (Fitri et al., 2017). NT formation has been detected in a variety of systemic inflammatory diseases and is considered as a marker of NO-derived species. Estrogen can generate considerable reactive oxygen species (ROS) and lead to high level of oxidative stress (Chen et al., 2015; Das Gupta et al., 2015). Activation of NF-κB could upregulated the expression of NO, further promote ROS and oxidative stress indexes (Qi, 2014). In the present study, LRK treatment significantly reduced the expression of 8-OHdG and NT. Hence, LRK may also act as an anti-oxidative agent on HMG treatment.

Our data indicated that LRK treatment protects the mammary glands from the damage of oxidative stress and inflammation via suppresses of MAPK/NF-κB signaling pathways without affecting on the expression of ERα and PR.

In this study, there were some limitations that several pathways were predicted related to the therapeutic effect of LRK on HMG via network pharmacology, however, parts of pathway were demonstrated by our experiment. Although, 19 potential active compounds were predicted by network pharmacology, and literatures reported showed anti-inflammatory and anti-oxidative effect of those ingredients, the effect of those active ingredients on HMG were still unknown. Maker clear the active ingredients is necessary for the formula research. Accordingly, in the next work of our group, we will have a comprehensive study to illuminate the active ingredients and the mechanisms of LRK against HMG.

Previous study verified the effect of MAPK inhibitor (UO126) in mammary epithelial cells (HC11 and EpH4) and breast cancer cells (MC4-L2), indicated that MAPK inhibitor could inhibit the activation of ERK signaling and suppress the proliferation, to inhibiting hyperplasia and growth (Cotrim et al., 2013). Our research group will further validate the effect of MAPK/NF-κB

#### REFERENCES


signaling pathway in LRK treat HMG by using the MAPK inhibitors to mimic the function of LRK, to proving the importance of MAPK/NF-κB signaling pathway for LRK work in HMG rat. Furthermore, we will comprehensively explicit the active ingredients of LRK by screening the active ingredients of LRK in vitro alternative model, and testing the active components to see whether they could achieve the similar effects as LRK treatment subsequently.

### CONCLUSION

Summarily, the present study demonstrated that there was a therapeutic effect of LRK on HMG by reducing pathological lesions in mammary gland tissue attenuating the over-activation of inflammation cytokines such as IL-1β, TNF-α, COX-2, iNOS, and suppressing the expression of 8-OHdG and NT. Furthermore, the combination of network pharmacology and experiment verification illustrated that the protect effect of LRK on HMG in rats might be associated with MAPK/NF-κB signaling pathways (**Figure 8**).

### AUTHOR CONTRIBUTIONS

YW was responsible for primary data generation, analysis and writing the manuscript. SW, XZ, LQ, YZ, and WZ participated in the design of the study. YY, XL, and TW were involved the in vivo experimentation and technical work. SW and YY were responsible for the extensive statistical analyses. HL, TG, WZ, and XZ gave advice on the writing.

#### FUNDING

This study was supported by the Project of Chinese Medicine Education Association (No. 2016SKT-M035).

### ACKNOWLEDGMENTS

We thank YZ and WZ for kindly revising this manuscript.




**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling Editor and reviewer YH declared their involvement as co-editors in the Research Topic, and confirm the absence of any other collaboration.

Copyright © 2018 Wang, Wei, Gao, Yang, Lu, Zhou, Li, Wang, Qian, Zhao and Zou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A Novel Systems Pharmacology Method to Investigate Molecular Mechanisms of Scutellaria barbata D. Don for Non-small Cell Lung Cancer

Jianling Liu<sup>1</sup> , Meng Jiang<sup>1</sup> , Zhihua Li<sup>1</sup> , Xia Zhang<sup>1</sup> , XiaoGang Li<sup>1</sup> , Yuanyuan Hao<sup>1</sup> , Xing Su<sup>2</sup> , Jinglin Zhu<sup>1</sup> , Chunli Zheng<sup>1</sup> , Wei Xiao<sup>3</sup> and Yonghua Wang<sup>1</sup> \*

<sup>1</sup> Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, Xi'an, China, <sup>2</sup> Pharmacology Department, School of Pharmacy, Shihezi University, Shihezi, China, <sup>3</sup> State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Jiangsu Kanion Parmaceutical, Co., Ltd., Lianyungang, China

#### Edited by:

Yuanjia Hu, University of Macau, China

#### Reviewed by:

Shuai Ji, Xuzhou Medical University, China Jiumao Lin, Fujian University of Traditional Chinese Medicine, China

> \*Correspondence: Yonghua Wang yh\_wang@nwsuaf.edu.cn

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 11 September 2018 Accepted: 30 November 2018 Published: 17 December 2018

#### Citation:

Liu J, Jiang M, Li Z, Zhang X, Li X, Hao Y, Su X, Zhu J, Zheng C, Xiao W and Wang Y (2018) A Novel Systems Pharmacology Method to Investigate Molecular Mechanisms of Scutellaria barbata D. Don for Non-small Cell Lung Cancer. Front. Pharmacol. 9:1473. doi: 10.3389/fphar.2018.01473 Non-small cell lung cancer (NSCLC) is the most ordinary type of lung cancer which leads to 1/3 of all cancer deaths. At present, cytotoxic chemotherapy, surgical resection, radiation, and photodynamic therapy are the main strategies for NSCLC treatment. However, NSCLC is relatively resistant to the above therapeutic strategies, resulting in a rather low (20%) 5-year survival rate. Therefore, there is imperative to identify or develop efficient lead compounds for the treatment of NSCLC. Here, we report that the herb Scutellaria barbata D. Don (SBD) can effectively treat NSCLC by antiinflammatory, promoting apoptosis, cell cycle arrest, and angiogenesis. In this work, we analyze the molecular mechanism of SBD for NSCLC treatment by applying the systems pharmacology strategy. This method combines pharmacokinetics analysis with pharmacodynamics evaluation to screen out the active compounds, predict the targets and assess the networks and pathways. Results show that 33 compounds were identified with potential anti-cancer effects. Utilizing these active compounds as probes, we predicted that 145 NSCLC related targets mainly involved four aspects: apoptosis, inflammation, cell cycle, and angiogenesis. And in vitro experiments were managed to evaluate the reliability of some vital active compounds and targets. Overall, a complete overview of the integrated systems pharmacology method provides a precise probe to elucidate the molecular mechanisms of SBD for NSCLC. Moreover, baicalein from SBD effectively inhibited tumor growth in an LLC tumor-bearing mice models, demonstrating the anti-tumor effects of SBD. Our findings further provided experimental evidence for the application in the treatment of NSCLC.

Keywords: non-small cell lung cancer, Scutellaria barbata D. Don, systems pharmacology, the molecular mechanism, baicalein

**Abbreviations:** ADME, absorption, distribution, metabolism, and excretion; LPSs, lipopolysaccharides; NSCLC, non-small cell lung cancer; SBD, Scutellaria barbata D. Don; TCMs, traditional Chinese medicines.

### INTRODUCTION

fphar-09-01473 December 13, 2018 Time: 15:24 # 2

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer death worldwide (Jemal et al., 2003). Faced with palliative care, chemotherapy is one of the main methods, but may cause severe side-effects and often leads to multidrug resistance (Ho et al., 2007). Therefore, the future of NSCLC treatment depends on the exploration and development of more effective drugs. In recent years, a large number of therapies, such as platinum therapies still represent the most common first-line treatment for NSCLC, however, it's still difficult to achieve the most ideal treatment effect.

Traditional Chinese medicines (TCMs) are effective to relieve complicated diseases in a multi-target/multi-component manner, which makes them unique among all traditional medicines (Qiu, 2015), and have been used to treat various human diseases for over 4,000 years (Tang et al., 2009). For instance, Scutellaria barbata D. Don (SBD), is a perennial herb which is natively distributed in northeast Asia. This herb is known as Ban-Zhi Lian in TCMs which has been used to inhibit inflammatory (Dai et al., 2013) and block tumor (Wang, 2012) growth. Although SBD has been proven to be dramatically efficient in curing NSCLC (Wang Q. et al., 2018), the fundamental molecular action mechanisms are still not systematically explored. The bioactive compounds, the potential targets and the related pathways of SBD remain unknown. With the advancement of analytical tools such as systems biology (Kitano, 2002), network biology (Barabási and Oltvai, 2004) and network pharmacology (Hopkins, 2008), the intricate and holistic mechanisms of TCMs may be elucidated in a fast and highly effective way.

Recently, as a novelty discipline, systems pharmacology provides a new manner that integrates pharmacology and systems biology pharmacology, provides a new approach to explore TCMs across multiple scales of complexity ranging from molecular and cellular levels to tissue and organism levels (Berger and Iyengar, 2009). Systems pharmacology contains pharmacokinetics (ADME properties of drugs) evaluation, target prediction as well as network analysis (Huang et al., 2014), which offers a platform for identifying multiple mechanisms of action of medicine. In our previous work, the systems pharmacology method has been successfully applied to uncover the underlying function mechanisms of TCM formulas for cancer, depression, and cardiovascular diseases treatment (Li et al., 2014; Zhang et al., 2014; Zheng et al., 2014).

Here, we introduce the method of systems pharmacology to resolve the underlying action mechanisms of herbal medicines in the treatment of NSCLC. Firstly, we filtered active compounds from the constructed SBD compound database by calculating pharmacokinetic properties and evaluating their oral bioavailability (OB) and drug-likeness (DL). Then, based on the integrated target prediction methods which united the biological and mathematical models, homologous targets of these active compound were predicted. Subsequently, obtained targets were validated by function enrichment analysis and target-disease interactions analysis. Ultimately, the network pharmacology and NSCLC-related signaling pathways evaluation were carried out to systematically disclose the underlying reciprocity between active compounds, active targets and pathways. The results not only significantly improved our understanding of NSCLC treatment mechanism, but also dissected the molecular mechanism of action of SBD, which promoted the exploitation of TCM in the treatment of sophisticated diseases. And in vitro experiments were conducted to evaluate the reliability of some vital active compounds and targets. Additionally, our in vivo results, which we subsequently confirmed using in vitro mechanism based assays, demonstrate that the significant anti-tumor activity of baicalein from SBD is associated with a direct impact of baicalein on improving tumor-inflammatory microenvironment. Our characterization of baicalein mediated changes in enzymes, cytokines, chemokines, and other growth factors associated with a tumor-inflammatory microenvironment offer multiple candidates to serve as potential biomarkers for ongoing clinical trials. In this paper, the detailed flow chart is shown in **Figure 1**.

### MATERIALS AND METHODS

#### Candidate Compound Database

All candidate compounds of SBD were manually collected from a wide scale text mining and our in-house developed database: the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP<sup>1</sup> ) (Ru et al., 2014). We got a total of 80 candidate compounds including flavonoids, terpenoids, and others. Glycosides were easy to hydrolyze into free glycosides absorbed by intestinal mucosa (Németh et al., 2003). Therefore, two aglycone compounds of glycosides in herbs were also added into the compound database for SBD. Eventually, we obtained 82 related compounds of SBD.

#### ADME Screening

To investigate the active compounds of SBD that play a role in anti-NSCLC, we predicted the OB (predicted OB) and DL (predicted drug-likeness) values of them.

#### Oral Bioavailability

Oral bioavailability is considered to be an important indicator of the efficiency of active drug delivery to the systemic circulation, and OB is therefore one of the most important ADME of oral drugs. In this article, OB screening is calculated by a powerful internal system, OBioavail1.1 (Xu et al., 2012), we set the OB value mainly considering the following two factors as the basic principle. First, the information extracted from the studied medicines should be as much as probable with a minimum of molecules. Second, reasonably explaining the obtaining model by the reported pharmacological data (Wang et al., 2013). In this work, we have obtained an OB value of 30%, and the selected active compounds will be analyzed in the next step.

#### Drug-Likeness

Drug-likeness (DL) is used to estimate the similarity of physical properties of compounds with known drugs. In order to pick out the drug-like active molecules from SBD, based on molecular

<sup>1</sup>http://lsp.nwu.edu.cn/index.php

descriptors and Tanimoto similarity (Yamanishi et al., 2010; Liu et al., 2013), we used a self-constructed model DL to calculate the drug-likeness index of these compounds. The Drug-likeness evaluation method is as follows:

$$\mathbf{T(A,B)} = (\mathbf{A^\*B}) / (|\!/\mathbf{A}|^2 + |\!/\mathbf{B}|^2 - \mathbf{A^\*B})$$

Here, A is defined as a molecular descriptor for herbal compounds and B is defined as the average molecular properties of all compounds in Drug Bank database<sup>2</sup> (Wishart et al., 2008). In this work, a compound with DL ≥ 0.18 was selected as the active compound for further study.

In order to acquire the potential active compounds, the screening principle was defined as follows: OB ≥ 30%; DL ≥ 0.18.

#### Target Prediction

To establish a direct link between the potential active compounds of SBD and the target, target selection for active compounds remains an urgent step. Therefore, compounds were further

<sup>2</sup>http://www.drugbank.ca/

analyzed at the gene level. Firstly, targets exploration was fulfilled based on the weighted ensemble similarity (WES) and systematic drug targeting tool (SysDT). SysDT is a powerful computational model combining mathematics and bioinformatics. However, WES is a in silico model to pinpoint the drug direct targets of the actual bioactive ingredients (Liu et al., 2017). Secondly, we have mapped targets for UniProt<sup>3</sup> , unifying their names and organisms. Normalized compound targets are mapped to the CTD database<sup>4</sup> (Davis et al., 2013), Therapeutic Target Database (TTD<sup>5</sup> ) (Zhu et al., 2012), and Pharmacogenomics Knowledgebase (PharmGKB<sup>6</sup> ) (Thorn et al., 2013) to obtain their associated diseases, providing a clearly defined target-disease relationship.

#### GOBP

To probe the involved biological processes of the obtained targets, in this work, gene ontology (GO) enrichment analysis was performed by linking targets to DAVID (The Database for Annotation, Visualization and Integrated Discovery<sup>7</sup> ) (Wang J. et al., 2018). Terms from "Biological Process" (GOBP) were utilized to symbol gene function (Yang et al., 2018). Only GO terms with p-value ≤ 0.05 were chosen. The false discovery rate (FDR) was introduced to reveal a multiple-hypothesis testing faulty measure of p-values by utilizing the web tool DAVID. We employed a 0.05 FDR criteria as an important cut off in our analysis.

#### Network Construction

Currently, we have completed the screening and mapping of the active compounds and active targets. In order to investigate the multiple action mechanism of active compounds against NSCLC, and further clarify the relationship between active targets and active compounds. The Cytoscape 3.6.0 (Liu et al., 2016), a popular bioinformatics software package for biological network visualization and data integration was used. Two types of global networks were constructed: compound-target (C-T) network and target-pathway network (T-P) (Yu et al., 2012). In the magic network, compounds, targets, and pathways were represented by nodes, and the relationship between them was represented by the edges. In addition, degree (a vital topological parameter) was analyzed by the plug in Network Analyzer of Cytoscape (Shannon et al., 2003). The degree of a node is defined as the number of edges connected to that node.

In order to explore the integrative mechanisms of the formula for NSCLC, firstly, the activity target was mapped to the KEGG database<sup>8</sup> (Kanehisa et al., 2017) and we got the basic information of the pathway. Secondly, according to the latest NSCLC pathological information, an integrated "NSCLC-Pathway" was assembled by integrating the key pathways that obtained through the T-P network and C-P network analysis.

#### Experimental Validation Cell and Mice

Human NSCLC H1975, RAW264.7 and Lewis lung carcinoma (LLC) cells were obtained from Chinese Academy of Sciences Shanghai cell bank. H1975 cells were cultured in RPMI1640 (Gibco) media complemented with 10% heat inactivated fetal bovine serum (FBS). RAW264.7 and LLC cells were cultured in DMEM (Gibco, United States) with 10% FBS. Cells were cultured at 37◦C with 5% CO<sup>2</sup> for all experiments. Mice were maintained under specific pathogen-free conditions at the Institute of Laboratory Animals, Jiangsu Kanion Parmaceutical, Co., Ltd. and used under protocols approved by the respective Institute of pharmacology and toxicology institutional review board (IRB), all animal experiments were performed in accordance with national and European guidelines. C57BL/6 mice (6–8 week-old) were purchased from the Comparative Medicine Centre of Yangzhou University. Female C57BL/6 wild type mice (6–8 week-old) were inoculated subcutaneously in the right flank with 5 × 10<sup>5</sup> LLC cells per mouse (day 0). Before treatment, mice were then randomized into two groups: control (n = 6), Baicalein (n = 6). Baicalein (1.5 mg/kg, Yuan ye, Shanghai) was treated every day administration after the tumor inoculation (day 2). For untreated mice, an isotype control for physiological saline was intraperitoneally (i.p.) injected. Tumors were measured on every alternate day, and tumor volumes were calculated using the formula for a typical ellipsoid length × (width<sup>2</sup> ) × 0.5(mm<sup>3</sup> ). For survival decomposition, mice with tumors greater than the length limit of 20 mm were sacrificed and counted as dead. To examine the requirement of the priming and effector phases of tumor mass, the mice were sacrificed and tumors harvested for analysis, following 17 day observations and measurements.

#### Cell Viability Assay

Baicalein was purchased from Shanghai Yuanye Bio-Technology, Co., Ltd. (HPLC ≥ 98%, shanghai, China). Test samples were dissolved in dimethylsulfoxide (DMSO) (Sigma, United States) to get 100 mM, as a stock solution, and then stored at 4◦C, it was not degrade due to high concentration of DMSO. The final dilutions of DMSO added to the culture medium never exceeded 0.1% what insured there was no effect on cell viability.

H1975 cells in the logarithmic phase were seeded at a density of 1 × 10<sup>5</sup> cells/ml in 96-well culture plates. After incubated 48 h, cells were exposed to different concentrations of baicalein (1.675, 3.125, 6.25, 12.5, 25, 50, 100, and 150 µmol/L), RAW264.7 and H1975 cells have the same experimental protocol. After treatment for 48 h, then, 10 µl of CCK-8 assay (Best Bio, Shanghai, China) was added to each well and the cells were incubated for 1–4 h at 37◦C and 5% CO2. A plate reader was used to detect the optical density (OD) absorbance at 450 nm. The cell viability was calculated as: OD of treatment/OD of control × 100%.

<sup>3</sup>http://www.uniprot.org

<sup>4</sup>http://ctdbase.org/

<sup>5</sup>http://database.idrb.cqu.edu.cn/TTD

<sup>6</sup>https://www.pharmgkb.org

<sup>7</sup>http://david.abcc.ncifcrf.gov

<sup>8</sup>http://www.genome.jp/kegg/

#### Western Blotting

fphar-09-01473 December 13, 2018 Time: 15:24 # 5

Cells were scraped, collected by centrifugation and lysis in QproteomeTM Mammalian Protein Prep Kit (Qiagen, Germany). The protein concentration of lysate was measured by a Quick Stari Bradford Protein Assay Kit (Bio-Rad, United States). Equal amount of protein taken from each sample was electrophoresis by 10% SDS-page gel electrophoresis and electroblotted onto nitrocellulose membranes, which were then incubated in a blocking buffer of 5% bovine serum albumin (BSA) in Tris-buffered saline. Primary antibody incubations were done overnight at 4◦C in blocking buffer. After washing, secondary antibody incubations was done at room temperature for 1–1.5 h in blocking buffer. Primary antibodies recognizing the following proteins were obtained from ABcam: COX-2, iNOS, NF-κB, p38, ERK, p-p38, P-ERK, AKT, p-AKT, Bcl-2, CDK2, Bax, GAPDH. The membranes were detected by using the ClarityTM Western ECL substrate (Bio-Rad) and labeling were visualized by Imagelab software (Bio-Rad).

#### Flow Cytometry Staining and Analysis

Tumors were digested with collagenase and hyaluronidase for 1 h at 37◦C. After lysising of red blood cell, the dissociated cells were incubated on ice for 10 min, and then spun down at 300 g, 4 ◦C for 7 min, cells from these tumors were either used for flow cytometry analysis or further processed and used for functional analyses. Tumor cell suspensions, were washed, blocked with Fc Block (anti-mouse CD16/32 mAb; BD Biosciences) at 4 ◦C on ice for 15 min, and stained with fluorescence conjugated antibodies against surface markers CD49b, CD3E, CD8a, and CD25. These antibodies were purchased from BioLegend, eBioscience, or BD Biosciences. Cells were then fixed in Fixation/Permeabilization buffer (eBioscience) and stained with antibodies against intracellular proteins, including FoxP3 (BioLegend), granzyme B and interferon-γ (IFN-γ) (BD Pharmingen). Stained cells and isotype-control-stained cells, were assayed using a BD FACSVerse (BD Biosciences, United States). Data analysis was performed using the FlowJo (Tree Star) software.

#### Statistical Analysis

All Data are rendered as means ± standard error and the statistical results are analyzed by a one-way ANOVA and Student's t-test. p-Values below 0.05 were considered as statistically significant.

### RESULTS

#### Active Compounds Screening

In this work, a total of 82 SBD related candidate compounds were collected from the SBD. To screen out the active compounds, it is significant to evaluate the compounds' ADME properties including oral bioavailability (OB ≥ 30%) and drug-likeness (DL ≥ 0.18). As a result, based on the satisfactory screening conditions: 33 active components have been identified by us. In order to acquire more comprehensive results and make up for the theoretical screening deficiencies, some certain unqualified compounds, which have relatively poor pharmacokinetic properties, but are the most abundant and active compounds of certain herbs, were also selected as the active components for further study. For example, quercetin has poor OB (25%) property, it has been retained for further analysis as it is the main component of SBD and has anticancer and anti-inflammatory effects (Mukherjee and Khuda-Bukhsh, 2015). Also, luteolin with relatively poor OB (26.5%) was retained for further analysis since it exerts remarkable tumor suppressive activity on various types of cancers, including NSCLC (Jiang J et al., 2018). In the end, we obtained all 33 candidate components (**Supplementary Table 1**) (The structures of the compounds were derived from NCBI<sup>9</sup> ) of SBD. Thereinto, flavonoids compounds have been reported to demonstrate significant biologic activity including anti-inflammatory, inhibit tumor angiogenesis, and cell cycle arrest (Zhang L. et al., 2017; Anwar et al., 2018). Such as, apigenin (MOL001 OB = 33.6% DL = 0.25), baicalein (MOL070 0B = 44.6% DL = 0.21). According to reports, diterpene compounds have activity test results, indicating that diterpene alkaloids have good cytotoxicity and can effectively inhibit the growth of a variety of human tumor cells (Lee and Ychoi, 2010), for example, Scutebarbatine F. In addition to the above components, SBD also contains ursolic acid, β-sitosterol, which has significant antitumor activity (Song et al., 2017; Rajavel et al., 2018). These active compounds could be the main elements for curing NSCLC.

#### Target and Function Analysis

To get the targets related to NSCLC we firstly identified 225 targets of these active compounds by means of the WES and SysDT algorithms. The results shown that the candidate compounds act on multiple targets, and one target can also be linked to multiple candidate molecules. For example, target Nitric oxide synthase 2 (NOS2) corresponds to 15 compounds accounting for 45% of the total active compounds. Subsequently, as we know, increasing evidence has identified that improving the inflammatory microenvironment plays a crucial role in the research progress of NSCLC (Lee et al., 2017). Hence, the targets involved in the biological progress of NSCLC will be further preserved. Then, 225 candidate targets were mapping to the CTD, TTD, PharmGKB database to obtain the corresponding target related diseases. After screening, we finally retrieved 145 potential targets (**Supplementary Table 2**).

#### GOBP Analysis

To identify and analyze whether the biological process corresponding to the active target corresponds to NSCLC. GO (p-value ≤ 0.05) enrichment analysis was used to obtain 24 vital biological processes (**Figure 2**) by mapping targets to DAVID and screening. The results shown that the majority of these targets were strongly associated with various biological processes, including negative regulation of apoptosis process, positive regulation of cell proliferation, positive regulation of cell migration, inflammatory response, and angiogenesis. These

<sup>9</sup>https://pubchem.ncbi.nlm.nih.gov

biological processes are related to the research mechanism of NSCLC.

#### Compound-Target Network Evaluation

genes, and x-axis shows the counts of targets.

In order to more directly reflect the relationship between targets and compounds, we have used Cytoscape 3.6.0 to map out the C-T relation network diagram. As shown in **Figure 3**, C-T diagram consist of 187 nodes (33 active compound nodes and 145 active target nodes) and 684 edges. Subsequently, C-T network topology analysis showed that the average degree of targets for each target was 4.7, illustrating the multi-target nature of SBD. Among the 33 active compounds, 25 of them show a high degree (degree > 10), which may play a key role in the network. Meanwhile, each active compound is associated with multiple targets, manifesting the potential synergistic effects among them.

Here, baicalein (MOL070) is the core component of SBD and display the highest number of target interactions (degree = 46). Previously, baicalein has been shown to have anti-cancer activities in several human cancer cell types including breast cancer, ovarian cancer, and colonic cancer (Yu et al., 2014; Wang et al., 2017; Dou et al., 2018). But beyond that, scutellarin (MOL036, degree = 39), a flavonoid used in Chinese herbal medicine, inhibited the proliferation and migration of human NSCLC cells (Sun et al., 2018). Also, wogonin (MOL007, degree = 27) is one of the active components of favonoids that are present in extracts from SBD. Recently, regulating immune function, anticancer and anti-inflammatory effects of wogonin have been discovered (Wang et al., 2010; Shi et al., 2017; Zhao et al., 2018). So, we speculate that the top three compounds might be the crucial elements in the treatment of NSCLC, which might exhibit anti-tumor and anti- inflammatory effects of SBD in the treatment of NSCLC. For instance, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma (PIK3CG, degree = 7) is targeted by seven active compounds from SBD. PIK3CG induces a transcription process that promotes immune suppression tumor growth and inflammation (Kaneda et al., 2017). Then, prostaglandin G/H synthase 2 (PTGS2, degree = 10) is simultaneously targeted by 10 active compounds, and has high expression in various tumors, which can promote tumor growth and regulates inflammatory response (Liu et al., 2018; Zhu et al., 2018). All of these suggest that SBD probably treat NSCLC by anti-inflammatory, inhibiting tumor angiogenesis, cell cycle arrest, and promoting apoptosis.

#### Target-Pathway Network Evaluation

The result displays that 145 targets are further mapped to 108 pathways, which show an average degree of 6.85 per pathway and 2.8 per target pathway. Then, we discover that several target proteins (71/145) are mapped to multiple pathways (≥5),

exhibiting that these targets might intercede the interactions and cross-talk between different pathways. Meanwhile, numerous pathways (70/108), also regulated by multiple target proteins (≥8), might be the main factors for NSCLC. As shown in **Figure 4**, those pathways were tightly interacted with targets. Such as, PI3K-Akt signaling pathway (degree = 21), VEGF signaling pathway (degree = 11). For instance, The PI3K-Akt pathway is an important signaling pathway that may activate downstream of a series of extracellular signals and impact on cellular processes including cell proliferation, apoptosis, and survival (Jiang Z. Q. et al., 2018; Tan et al., 2018), which can be targeted by numerous active compound like wogonin (MOL007), baicalein (MOL070) quercetin (MOL002), apigenin (MOL001), and so forth. Hence, PI3K-Akt signaling pathway with the highest degree may be a significant pathway involved in the proliferation, apoptosis against NSCLC. Also, tumor angiogenesis, apoptosis, and migration related pathways were also enriched, like VEGF signaling pathway mediates the absolute dependence of tumor cells on the continuous supply of blood vessels to nourish their growth and to facilitate metastasis (Li et al., 2012). Hence, tumor vascularization is a vital process for tumor growth, invasion, and metastasis. SBD may be served as an attractive herb in anti-NSCLC therapy. In addition, P53 signaling pathway also stands out in the enriched pathway list, which can mediate cell cycle arrest and cell proliferation (Hsua et al., 2001). Therefore, it is concluded that SBD opportunity regulates the treatment of NSCLC through anti-inflammation, cell cycle arrest, tumor angiogenesis, cell apoptosis, and other pathways.

### NSCLC-Pathway Construction

Considering the complex mechanism of SBD in the treatment of NSCLC, an integrated "NSCLC-pathway" was constructed by integrating the key pathways that obtained through the T-P network analysis. NSCLC-pathway (**Figure 5**) that comprises of four signaling pathways such as hsa04370: VEGF signaling pathway, hsa04151:PI3K-Akt signaling pathway, hsa04115: p53 signaling pathway and hsa04064: NF-kappa B signaling pathway. The target proteins of the integrated "NSCLCpathway" exhibit markedly close functional relationship with the NSCLC related proteins. As shown in **Figure 5**, the NSCLCpathway can be separated into two represent therapeutic modules (Inflammation related module and tumor related module). Inflammation related module consists of hsa04064: NF-kappa B signaling pathway. Then, tumor related module including hsa04370: VEGF signaling pathway, hsa04151:

PI3K-Akt signaling pathway and hsa04115: p53 signaling pathway.

#### Inflammation Related Module

Inflammatory microenvironment plays a very important role in all stages of tumor development. Many important cytokines and chemical factors participate in this process. At the same time, the tumor microenvironment promotes the continuous response of the inflammation. Thus, it is necessary to control the development of the tumor by targeting the key signaling pathway in the tumor-inflammatory microenvironment. In **Figure 5**, baicalein (MOL070) activates transcription factor p65 (NF-κB), which reduces the expression of the downstream proteins prostaglandin G/H synthase 2 (COX-2) and Nitric oxide synthase (iNOS). For example, The NF-κB family of transcription factors is involved in the activation of a wide range of genes associated with inflammation, differentiation, tumorigenesis, embryonic development, and apoptosis (Ozawa et al., 2018; Zakaria et al., 2018). Then, COX-2 is not expressed in most normal tissues at high levels but is strongly induced by LPS and many cytokines, playing a crucial role in the development of various inflammatory responses (Zhang Q. et al., 2017). The results showed that baicalein form SBD could be utilized to treat NSCLC by regulating the anti-inflammatory activities of NF-κB, COX-2, and iNOS.

#### Tumor Related Module

As shown in the **Figure 5**, tumor related targets in the active targets are mapped to three pathways, including P53 signaling pathway, PI3K-AKT signaling pathway and VEGF signaling pathway. These pathways control tumor development by inhibiting cell proliferation and cell cycle. For example, in the P53 signaling pathway, baicalein (MOL070) can extensively act on cdk-Cyclin complexes and inhibit their activity, especially G1 phase cdk2-CyclinE (CDK2). It has been reported that the cell cycle is blocked by baicalein therapy, thus inhibiting cell proliferation (Hsua et al., 2001). These results suggested that baicalein from SBD inhibits NSCLC proliferation by incomplete DNA synthesis and cell division. Additionally, some targets in the PI3K-AKT signaling pathway engage in equaling the levels between the cell cycle and apoptosis. The apoptosis signaling can be initiated either at face through

a death receptor-induced signaling pathway or within the cell via the release of proapoptotic molecules. For example, Apoptosis regulator Bcl-2 (Bcl-2) can be regulated by baicalein (MOL070), scutellarin (MOL036), and wogonin (MOL007). Previous data in vivo indicated that Caspases are linked to Bcl-2 family which is the key regulator of apoptosis in cancer (Timucin et al., 2018). Furthermore, tumor vascularization is an important process of tumor growth, invasion, and metastasis. Anti-angiogenesis has been considered to be an attractive target anti-tumor treatment (Ferrara and Kerbel, 2005; Cooney et al., 2006; Tran et al., 2007). Such as, In the VEGF signaling pathway was modulated by wogonin (MOL007) and baicalein (MOL070). It has been reported in the literature that inhibition of the activation of VEGF downstream protein kinases Mitogen-activated protein kinase 14 (p38) and Mitogen-activated protein kinase 3 (ERK) can inhibit cell proliferation, survival, and migration (Galaria et al., 2004). Thus, all above suggest that SBD may treat NSCLC by regulating the cell cycle, apoptosis, and anti-angiogenesis.

### In vitro Experimental Validation CCK-8 Assay

In our pre-experiment, the RAW 264.7 cell viability as affected by the baicalein from SBD at various doses was determined by CCK-8 assay (**Figure 6A**), and the outcome showed that high cell viability (>70%) was attained for baicalein at <25 µmol/L, respectively. Thus, three doses of baicalein were taken (5, 15, and 25 µmol/L) for subsequent experiments.

H1975 cells (1 × 10<sup>5</sup> cells/ml) are cultured with the concentrations of 1.675 to 150 µmol per liter culture media with no serum of the baicalein for 48 h. Then, We performed CCK-8 assay to evaluate the inhibitory effects of baicalein induced proliferation of H1975 cells. Growth of H1975 cells was significantly inhibited by different concentrations of baicalein, and the 30% concentration of inhibition (IC30) of baicalein was 15 µmol/L respectively at 48 h (**Figure 6A**). Since lower doses baicalein had no inhibitory effect on the viability of H1975 cells, concentrations of baicalein (5, 10, 15, and 20 µmol/L) were chosen for subsequent experiments.

#### Targets Validation

To further assess the obtained results in systems pharmacology analysis, we have chosen baicalein from SBD to examine the compound potential anti-inflammatory effect using RAW264.7 cells treated with LPSs (1 ug/ml). In particular, we conduct western blot analysis for iNOS, NF-κB, and COX-2 protein expression to confirm anti-inflammatory effects of the predicted compounds.

As shown in **Figure 6B**, the levels of iNOS, NF-κB, and COX-2 proteins in the panel of RAW264.7 cell lines tested are reported. We observe that after baicalein treatment, the protein expressions of iNOS, NF-κB, and COX-2 in RAW264.7 cells are significantly declined. **Figure 6B** illustrates that baicalein treatment, as a single agent, causing a decrease of the iNOS, NF-κB, and COX-2 expression. To sum up, in vitro study provides additional information for screening compound with potentially anti-inflammatory effect and demonstrates the reliability of in systems pharmacology screen strategy.

To verify the reliability of anti-tumor related targets screened from systems pharmacology, we observe that baicalein treatment,

tumor-cell suspensions from LLC-bear mice after control or baicalein treatment. Quantification of NK cell present in the tumor samples. Results are shown as mean ± SEM (∗∗p < 0.01; ∗∗∗p < 0.001).

the protein expressions of CDK2 and Bax in H1975 cells were both declined significantly at different dose levels. Our results show that after 24 h, an increase in Bax levels and a decrease in the level of CDK2 were detected in the P53 signaling pathway (**Figure 6D**), indicating that activating Bax and inhibiting CDK2 were vital for anti-NSCLC. Then, in the VEGF signaling pathway the phosphorylation of p38 was significantly down-regulated in baicalein-induced H1975 cells compared to those of the control group. Also, we examined the activation of ERK, baicalein inhibited phosphorylation of ERK in H1975 cells. However, total protein levels were not affected (**Figure 6C**), suggesting baicalein inhibited angiogenesis and cell proliferation by regulating VEGF signaling pathways. Next, we found that the p-AKT expression was down-regulated in H1975 cells treated with baicalein. In addition, the expression of Bcl-2 was decreased by baicalein treatment. The above results indicates that baicalein may suppress the PI3K/Akt pathway by down-regulating p-AKT (**Figure 6C**). Taken together, these data suggest that baicalein from SBD may treat the NSCLC by cell cycle arrest, promote apoptosis, and anti-angiogenesis.

#### In vivo Experiments

fphar-09-01473 December 13, 2018 Time: 15:24 # 12

To elucidate the molecular mechanism of SBD in treating LLC tumor-bearing mice, we sought evidential insight for the driver of anti-tumor by improving the tumor-inflammatory microenvironment after baicalein from SBD administration (**Figure 7A**). LLC tumor-bearing mice were significantly more resistant to the development of baicalein mediate sarcoma than control mice and observed decreased tumor out growth (**Figures 7B–D**) and a significant survival benefit (**Figure 7E**) in baicalein-treated mice. Here, to evaluate the in vivo therapeutic effect of baicalein on NSCLC, tumor samples were collected from LLC tumor-bearing mice and subjected to fluorescence-activated cell sorting analysis. In the tumor-inflammatory microenvironment, we observed a significant increase in the proportion of cytotoxic CD8 + T cells in baicalein-treated mice, a sixfold of the proportion of CD8+/FoxP3+ cells compared to the control group. Also, reduction in the MFI of FoxP3+ Treg cells was observed in baicalein treatment group (**Figure 8B**), suggesting that an inhibition of Treg function associated with the reduction of FoxP3 protein expression (Ho et al., 2018) may be the direct result of the baicalein. Meanwhile, the baicalein therapy induced expansion of an IFNG and GZB-producing activated CD8+ T cell population in the tumor of mice (**Figure 8A**). Then, we examined the density of natural killer (NK) cells in the tumor sample. Significant positive correlations were observed between control and baicalein natural killer cells (**Figure 8C**). These results firmly establish that the baicalein from SBD therapy improves tumor inflammatory microenvironment mediated tumor control, resulting in a striking benefit in these advanced mouse models relevant to clinical.

### DISCUSSION

Non-small cell lung cancer has a high degree of malignancy and it has the characteristics of an early metastasis, and the prognosis of NSCLC patients remains low despite the many advances that have occurred in early diagnosis and comprehensive therapies (Testa et al., 2018; Wang Z. et al., 2018). Therefore, search for antitumor drugs has become an important problem to be solved urgently.

Recently, SBD has been reported to possess vital biological activities, for instance anticancer activities (Zheng et al., 2018). In our work, the complex mechanism of SBD in the treatment of NSCLC was explored based on the system pharmacological work principle. Firstly, with the aid of the evaluation method, 33 active compounds were obtained from SBD and 145 active targets were predicted. These results reveal that the characteristics of SBD are multi-compounds and multi-targets anti-tumor effects. Then, target and C-T network analysis together display that some vital compounds of SBD such as wogonin, baicalein, and scutellarin may play an important role in the treatment of NSCLC, and SBD positively aiming for some targets like Bax, iNOS, and P38 exhibits the therapeutic effects against NSCLC by anti-inflammatory, promote apoptosis and anti-angiogenesis. In addition, The T-P network and the integrated NSCLC-relates pathway indicate that the major compounds of SBD might exert anti-NSCLC effect by modulating plenty different pathways including hsa04370:VEGF signaling pathway, hsa04151:PI3K-Akt signaling pathway, hsa04115:p53 signaling pathway and hsa04064:NF-kappa B signaling pathway. Based on our present study, the in vitro experiments further confirm that the baicalein from SBD combat NSCLC via regulating the critical proteins of our integrated NSCLC-pathway including COX-2, NF-κB, Bax, ERK, CDK1 and so on, attesting that NSCLC can be treated through a complex system with multi- compound-target-disease interactions. So, SBD exhibits anti-NSCLC effects in various aspects, including cell cycle arrest, anti-inflammatory, promoting apoptosis, and anti-angiogenesis in response to active compound. In vivo, additive therapeutic effects of baicalein were investigated for a tumor-bearing mouse model, where baicalein from SBD was demonstrated to possess high efficiency compared with control in the inhibition of tumor growth.

In summary, our study systematically indicated the inhibitory effect of SBD on anti-tumor in vitro and in vivo. These molecular mechanisms, including improve tumor inflammatory microenvironment, cell cycle arrest, promote apoptosis, and anti-angiogenesis are potentially those by which SBD exhibits its effectiveness in cancer treatment.

### AUTHOR CONTRIBUTIONS

JL, MJ, and ZL contributed conception and design of the study. XZ and XL organized the database. JZ, YH, and XS performed the statistical analysis. MJ wrote the first draft of the manuscript. CZ, MJ, and WX wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.

### FUNDING

This work was supported by grants from Northwest University, National Natural Science Foundation of China (Grant Nos. 81803960 and U1603285). It also was supported by the Key Project of Natural Science Fund of Science and Technology Department of Shaanxi Province (Grant No. 2018JZ3007).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.01473/full#supplementary-material

### REFERENCES

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and anti-oxidant activity. Mol. Med. Rep. 15, 1613–1623. doi: 10.3892/mmr. 2017.6166


Testa, U., Castelli, G., and Pelosi, E. (2018). Lung cancers: molecular characterization, clonal heterogeneity and evolution, and cancer stem cells. Cancers 10:248. doi: 10.3390/cancers10080248

fphar-09-01473 December 13, 2018 Time: 15:24 # 14


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Liu, Jiang, Li, Zhang, Li, Hao, Su, Zhu, Zheng, Xiao and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Integrating Network Pharmacology and Metabolomics Study on Anti-rheumatic Mechanisms and Antagonistic Effects Against Methotrexate-Induced Toxicity of Qing-Luo-Yin

#### Edited by:

Yuanjia Hu, University of Macau, China

#### Reviewed by:

Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China Jianxin Chen, Beijing University of Chinese Medicine, China Cheng Lu, Chinese Academy of Medical Sciences, China

#### \*Correspondence:

Shao Li shaoli@tsinghua.edu.cn Yan Li liyan.0301@163.com †These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 13 July 2018 Accepted: 30 November 2018 Published: 18 December 2018

#### Citation:

Zuo J, Wang X, Liu Y, Ye J, Liu Q, Li Y and Li S (2018) Integrating Network Pharmacology and Metabolomics Study on Anti-rheumatic Mechanisms and Antagonistic Effects Against Methotrexate-Induced Toxicity of Qing-Luo-Yin. Front. Pharmacol. 9:1472. doi: 10.3389/fphar.2018.01472 Jian Zuo<sup>1</sup>† , Xin Wang<sup>2</sup>† , Yang Liu<sup>1</sup> , Jing Ye<sup>3</sup> , Qingfei Liu<sup>3</sup> , Yan Li<sup>1</sup> \* and Shao Li<sup>2</sup> \*

<sup>1</sup> Yijishan Hospital of Wannan Medical College, Wuhu, China, <sup>2</sup> MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Center for TCM-X, BNRist, Department of Automation, Tsinghua University, Beijing, China, <sup>3</sup> School of Pharmaceutical Sciences, Tsinghua University, Beijing, China

Qing-Luo-Yin (QLY) is a traditional Chinese medicine (TCM) formula used to treat Hot Syndrome-related rheumatoid arthritis (RA). Previously, we uncovered partial mechanisms involved in the therapeutic actions of QLY on RA. In this study, we further elucidated its anti-rheumatic mechanisms and investigated its possible interactions with methotrexate (MTX) in vivo using an integrating strategy coupled with network pharmacology and metabolomics techniques. Chemical composition of QLY was characterized by HPLC analysis. Collagen induced arthritis (CIA) was developed in male SD rats. The CIA rats were then assigned into different groups, and received QLY, MTX or QLY+MTX treatments according to the pre-arrangement. Therapeutic effects of QLY and its possible interactions with MTX in vivo were evaluated by clinical parameters, digital radiography assessment, histological/immunohistochemical examination, and serological biomarkers. Mechanisms underlying these actions were deciphered with network pharmacology methods, and further validated by metabolomics clues based on UPLC-Q-TOF/MS analysis of urines. Experimental evidences demonstrated that QLY notably alleviated the severity of CIA and protected joints from destruction. Rebalanced levels of hemoglobin and alanine transaminase in serum indicated reduced MTX-induced hepatic injury and myelosuppression under the co-treatment of QLY. Network-based target prediction found dozens of RA related proteins as potential targets of QLY. Upon the further biological function enrichment analysis, we found that a large amount of them were involved in nucleotide metabolism and immune functions. Metabolomics analysis showed that QLY restored amino acids, fatty acids, and energy metabolisms in CIA rats, which solidly supported its therapeutic effects on CIA. Consistently to findings from network pharmacology analysis, metabolomics study also found altered purine, pyrimidine, and pentose phosphate metabolisms in

**125**

CIA rats receiving QLY treatment. All these clues suggested that inhibition on nucleic acid synthesis was essential to the immunosuppressive activity of QLY in vivo, and could contribute great importance to its therapeutic effects on CIA. Additionally, QLY induced significant antifolate resistance in rats, which would prevent folate from depletion during long-term MTX treatment, and should account for reduced side effects in combination regimen with MTX and QLY.

Keywords: Qing-Luo-Yin, rheumatoid arthritis, network pharmacology, metabolomics, nucleotide metabolism

#### INTRODUCTION

fphar-09-01472 December 14, 2018 Time: 14:35 # 2

Rheumatoid arthritis (RA) is a prevalent systematic autoimmune disease, which is characterized by severe joint destruction and chronic local inflammation (Smolen and Aletaha, 2009). Conventional treatment approaches for RA patients are mainly dependent on disease-modifying anti-rheumatic drugs (DMARDs) (Tanaka, 2016). In the past decades, methotrexate (MTX) has become the anchor DMARD, and is extensively adopted as first-line regimen for the superior efficacy and economical merits (Cronstein, 2005). It significantly slows the progress of disease, and minimizes the detrimental effects on joints. However, there are a number of patients with inadequate response to it. Furthermore, despite the fact that low dose of MTX is well tolerated, the treatment usually accompanies with remarkable side effects (Dubey et al., 2016).

Traditional Chinese medicine (TCM) as one of the oldest continuously surviving traditional medicines is derived from clinical practices in ancient China. Much different from Western medicine, TCM emphasizes the integrality of body (Feng et al., 2006), and aims at multiple pathogenesis factors simultaneously upon the exact diagnosis of Zheng (a term in TCM, generally encompasses etiology, pathology and disease location) (Yu et al., 2006; Su et al., 2012). Contrarily, Western medicine usually modulates one single defined target or symptom based on the methodology of reductionism. By the development of system biology, limits of this strategy are gradually disclosed. Western treatments usually fail to achieve effective and sustainable outcomes in systematic and chronic diseases, and accompany with high risk of side effects (Feng et al., 2006; Yu et al., 2006). By comparison, TCM exhibits notable merits of efficacy and safety in many cases, and provides us an efficient alternative for treatments of some complicated diseases, such as RA. Objectively speaking, both the medical systems have their own advantages and shortcomings, and the integrated therapeutic approach would be more effective and safer when well optimized.

Traditional Chinese medicine formulas are usually composed of many herbs, which results in the complex chemical composition. Hence, it is difficult to fully understand their therapeutic mechanisms. High throughput techniques give us helpful tools to screen out potential bioactive ingredients from them. However, the dilemma is still hard to be resolved, for the formula should be taken as a whole but not the sum of some well investigated natural compounds. This situation makes network-oriented approaches more preferable (Li, 2015). To achieve a comprehensive and systematic understanding of their therapeutic mechanisms, new research strategies based on computational simulation and prediction flourish recent years (Guo et al., 2017). As one of these newly developed disciplines, network pharmacology possesses obvious advantages over conventional methods in elucidation of comprehensive mechanisms (Li et al., 2007; Hopkins, 2008). The network targetbased concept could largely reflect complicated interactions between biomacromolecules and chemical ingredients. The research technique constructed under this guideline is regarded as a representative method of emerging network pharmacology (Barabasi et al., 2011), and has been successfully applied in many TCM related research fields, such as therapeutic mechanisms elucidation (Zhang Y. et al., 2015; Zhang et al., 2016), new pharmacological actions prediction (Zhang et al., 2018) and potential toxic ingredients screening (Zhang B. et al., 2015). Meanwhile, metabolomics focuses on systematic analysis of metabolites from drugs treated objects. It gives us an opportunity to obtain the panoramic view of network effects of formulas on bodies and testify results generated by computational modeling analysis.

Xin'an medical family is an important TCM academic school originated in South Anhui district in Song dynasty, and Zhang-Yi-Tie is one of its main branches still flourishing nowadays. Qing-Luo-Yin (QLY) is widely regarded as the representative formula of this sect, which was created by its 14th generation Jiren Li, a famous contemporary TCM master based on Xin'an medical theory and his medical experiences. It is composed of four components: Kushen (radix of Sophora flavescens Ait.), Qingfengteng [caulis of Sinomenium acutum (Thunb.) Rehder and E. H. Wilson], Huangbai (cortex of Phellodendron chinense C. K. Schneid.), and Bixie [rhizome of Dioscorea collettii var. hypoglauca (Palib.) S. J. Pei and C. T. Ting]. As a Cold natured formula, it is mainly used to treat the Hot Syndrome-related RA. Previously, we revealed that targets of QLY against RA-related key biological processes mainly involved in angiogenesis, inflammatory response and immune functions by using a network target-based research technique, but critical upstream factors leading to these changes were still unknown (Zhang et al., 2013). In the present study, we evaluated therapeutic effects of QLY on collagen induced arthritis (CIA) in rats and investigated its possible interactions with MTX in an integrated regimen. A computational workflow based network pharmacology study and a metabolomics analysis were carried out to further elucidate mechanisms underlying these actions.

### MATERIALS AND METHODS

fphar-09-01472 December 14, 2018 Time: 14:35 # 3

#### Ingredients Preparation From QLY and Target Prediction

Firstly, we collected information about chemical composition of each herb in QLY formula from literatures. Those meeting certain ADME properties and drug-likeness standard (wQED > 0.3) were chosen for further target prediction analysis (**Supplementary File S1**) (Bickerton et al., 2012). After filtering redundant information, we obtained 234 ingredients, including 124, 47, 61, and 13 compounds from Kushen, Qingfengteng, Huangbai, and Bixie, respectively. Their structures were retrieved from the PubChem database<sup>1</sup> . All the chemical information was then used as data source for target prediction.

To achieve in silico prediction, potential targets of these ingredients were analyzed by using the drugCIPHER method, a state-of-art network-based algorithm for global prediction of compound targets (Zhao and Li, 2010). In principle, this technique predicts relationships between bioactive ingredients and candidate targets based on network-based integration of multiple pharmacological similarities by using FDA covered agents, corresponding targets and protein–protein interactions as references. In order to obtain high precision results, top 100 ranking targets in the predicted profile of each compound were kept. The reliability of prediction was further tested through the comparison of predicted targets with literature evidences. The recall efficiency was calculated by the following equation: recall = intersection of predicted targets and reported biomolecules/number of reported biomolecules × 100%.

### Enrichment Analysis and Network Construction

To identify potential pathways regulated by QLY, we carried out the enrichment analysis based on predicted targets through the fisher exact test by the aid of Gene Ontology (GO) consortium<sup>2</sup> and Kyoto Encyclopedia of Genes and Genomes (KEGG) database<sup>3</sup> . The pathways closely related to RA were then subjected to statistical analysis, and those with p < 0.05 after Benjamin's correction were deemed as significantly changed and selected for further research (**Supplementary File S2**). Subsequently, the results were evaluated by comparing occurrence frequency of targeted protein from prediction list of QLY to a random control represented by a Poisson binomial statistical model. With the predicted targets from selected enrichment pathways, we constructed a biomolecular network modified by QLY to elucidate its plausible anti-rheumatic mechanisms by taking protein–protein interactions and crosstalk among pathways into consideration.

#### Reagents

Incomplete Freund's adjuvant (IFA) and lyophilized immunization grade bovine type II collagen (CII) were purchased from Sigma-Aldrich (St Louis, MO, United States) and Chondex (Redmond, WA, United States), respectively. MTX tablet was the product of Sine Pharm (Shanghai, China). Methanol and acetonitrile of chromatographic grade were supplied by Merck Chemicals (Shanghai, China). 2-Chloro-L-phenylalanine was brought from Hengbai Biotechnology (Shanghai, China).

#### Animals

Male SD rats (180 ± 10 g, 5–6 weeks old, supplied by Qinglongshan Laboratory Animal Company, Nanjing, Jiangsu, China) were used in this study. The animals were housed in Specific Pathogen Free (SPF) conditions to avoid any possible infections. Every four rats were kept in a separated cage. The environment was strictly controlled. The light was switched in a 12 h light on/off cycle to mimic natural rhythm. The temperature and relative humidity were set at 22 ± 2 ◦C and 50 ± 2%, respectively. All the rats had ad libitum access to a standard pelleted food and boiled tap water. The animals were kept for 7 days to get accommodated to the situation prior to in vivo experiments. The animal experimental protocols were approved by Ethical Committee of Yijishan Hospital and strictly in accordance with the guideline for the care and use of laboratory animals (United States National Research Council, 2011).

#### Induction of CIA in Rats

Induction of CIA in rats was performed according to the protocol of Chondex with minor modifications. Lyophilized CII was dissolved in 0.05 M acetic acid to obtain a solution at the concentration of 2 mg/ml, and stood overnight under 4◦C. Under continuous stirring using a pestle motor (Kimble Chase, Vineland, NJ, United States), equal volume of CII solution was mixed into IFA drop-wise on the ice to obtain the stable and homogeneous emulsion. A multi-points subcutaneous injection (total volume was 0.1 ml) was carried out at day 0 with a Hamilton syringe. Seven days later, a booster injection with 0.1 ml of emulsion was administered at the base of the tail subcutaneously.

### Preparation of QLY Extraction and Chemical Composition Characterization

Prior to animal experiments, QLY decoction was prepared. Radix Sophorae Flavescentis, Caulis Sinomenium Acutum, Cortex Phellodendri Chinensis, and Rhizoma Dioscoreae Hypoglaucae were brought from Bozhou Herbal Medicine Market, and identified by Professor Jian-Wei Chen (College of Pharmacy, Nanjing University of Chinese Medicine, China). Voucher specimens (ID: QLY-2017-001-004) were deposited in the Herbarium Center, Wannan Medical College, China. The herbs were mixed at the ratio of 1.5:1.2:1:1, and extracted with boiling water for three times. Finally, the filtrates were combined and condensed to sticky extract (4 g/ml relative to crude drugs) by using a rotavapor.

A sample of the extract was diluted with methanol, filtered through a 0.45 µm filter, and then subjected to HPLC analysis to characterize the chemical composition. The detection was performed on a LC-20AT HPLC system (Shimadzu Corporation, Kyoto, Japan) coupled with a UV detector using a Thermo

<sup>1</sup>https://pubchem.ncbi.nlm.nih.gov/

<sup>2</sup>http://www.geneontology.org

<sup>3</sup>https://www.kegg.jp

SCIENTIFIC C<sup>18</sup> column (250 mm × 4.6 mm, 5 µm), and other analysis conditions were summarized as below. A gradient elution at flow rate of 1.0 ml/min was adopted. The mobile phase A and B were ammonium acetate (10 mM in water) and methanol-acetonitrile 1:3 (v/v) containing 10 mM ammonium acetate and 0.2% ammonia, respectively, and the elution program is shown in **Supplementary File S3**. The detection wavelength and column temperature were set at 215 nm and 35◦C, respectively. Main signals in chromatogram were identified by the comparison with the reference compounds (magnoflorine, sinomenine, matrine, sophocarpine, and berberine, supplied by Chengdu Herbpurify Co., Ltd., Sichuan, China). Representative chromatograms and results of the quantitative analysis were also included in **Supplementary File S3**.

#### Administration

Thirty-two CIA rats were divided into equal four groups randomly (with eight rats each), three of which were assigned as treatment groups (MTX, QLY, MTX+QLY), and the other served as model control. MTX suspended in 0.5% CMC-Na was administered to rats at the dose of 0.5 mg/kg twice a week intragastrically. QLY group was treated with QLY extract once a day at the dose of 0.3 g/kg (relative to the dry extract). MTX+QLY group received combined treatments of QLY and MTX. Eight normal animals and CIA models were lavaged with CMC-Na synchronously. Since day 1, treatments lasted for 36 days until sacrifice.

### Assessment of Arthritis

Clinical severity of arthritis in rats was assessed by two scholars in a blind manner periodically since the initial immunization. Volume of the right hind paw was quantitatively determined to evaluate the inflammation using the water displacement method. Onset and progress of arthritis were assessed by arthritis scores. Each paw was scored independently in a four grade scale (1–4), and the theoretical maximum sum was 16. The score criterion was defined as below: 1, slight swelling or redness of one toe; 2, moderate erythema and swelling; 3, severe inflammation in the entire paw; 4, swollen ankle joint and joint rigidity. To investigate radiological outcomes, digital radiography (DR) examination of limbs was carried out 3 days ahead of sacrifice on a Digital Diagnsot DR system (Philips Healthcare, Best, Netherlands).

### Sampling and Sacrifice

On day 35, 24 h urine samples of the rats were collected through automatic micturition, which was centrifuged at 12,000 rpm for 10 min. The supernate was kept at −80◦C until analysis. One day later, the rats were anesthetized with chloral hydrate, and maximum amount of blood was collected via abdominal aorta into promoting coagulating and anticoagulation tubes for the serum separation and blood cell subset analysis, respectively. The serum was obtained after a centrifugation, and immediately divided into aliquots and stored at −80◦C until further analyses. Thereafter, all the animals were sacrificed. Organs and hind paw were promptly dissected, and fixed in 10% buffered formalin for histological examinations.

### Hematological Analyses and Histological and Immunohistochemical Examinations

One portion of anticoagulated blood was subjected to an automated hematology system (ADVIA 120, Bayer Diagnostics, Germany) for complete blood count (CBC) analysis. Another portion was used for CD4+CD25<sup>+</sup> T cells distribution analysis by using APC tagged CD4 and PE tagged CD25 antibodies (Multi-Sciences, Hangzhou, Zhejiang, China) on a flow cytometry (FACS Calibur system, Becton and Dickson, San Jose, CA, United States). Aliquots of serum were subjected to an AU680 biochemical analyzer (Beckman, Tokyo, Japan) for the quantitative analyses of alanine transaminase (ALT) and aspartate transaminase (AST) to evaluate possible detriments on liver. Anti cyclic citrullinated peptides antibody (anti-CCP antibody), rheumatoid factor (RF) and C-reactive protein (CRP) in serum were analyzed by using commercial available ELISA kits (Cusabio, Wuhan, Hubei, China) in accordance to the manufacturers' protocols.

The fixed organs and limbs (decalcified in 10% EDTA for 2 weeks prior to the following experiments) were embedded in paraffin, and sectioned at 5 µm followed by staining with hematoxylin/eosin. The stained sections were observed using an Olympus BH-2 light microscope (Tokyo, Japan). Some other sections were deparaffinized and soaked with 0.3% H2O<sup>2</sup> in 60% methanol. The specimens were then treated with citric acid (10 µM) for 1 h by the aid of microwave heating. After that, they were incubated with goat serum, specific primary and appropriate biotinylated secondary antibodies in turns. After the peroxidase staining with diaminobenzidine, signaling of proteins was visualized by the counterstaining with hematoxylin.

## Urine Sample Preparation

One hundred microliter urine was spiked into 900 µl chilled methanol–acetonitrile–water (2:2:1) solution, and another 20 µl L-2-chlorophenylalanines solution (1 mg/ml in H2O, served as the internal standard) was added. The mixture was vortex for 30 s, and then treated by ultrasound in an ice-water bath for 10 min. Seven hundred microliter of the supernatant was collected after a centrifugation at 13,000 rpm for 15 min under 4◦C and dried using a vacuum centrifugal concentrator (Centrivap console, Labconco Company, United States). The residue was dissolved with acetonitrile–water (1:1) by the aid of ultrasonic treatment in an ice-water bath. After a high speed centrifugation, 50 µl supernatant of each sample was collected. The quality control (QC) was prepared by mixing equal amount of every sample from an identical experiment group.

### UPLC-Q-TOF/MS Analysis

UPLC-Q-TOF/MS analysis was achieved on an 1290 infinity UPLC system (Agilent Technologies, Santa Clara, CA, United States) coupled with an Triple Q-TOF 6600 mass spectrometry (AB SCIEX, Concord, ON, Canada). Analytes extracted from urine samples were separated on an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.7 µm, Waters,

Milford, MA, United States) with a gradient elution program at the flow rate of 0.5 ml/min. The mobile phase was comprised of 25 mM ammonium acetate in water (phase A) and acetonitrile (phase B). The elution gradient of phase B was depicted as follows: 95% (0–0.5 min), 95% down to 40% (0.5–9 min), 40% up to 95% (9–12 min), maintained at 95% for 2 min. Optimized parameters for the mass spectrometer were as below: ion spray voltage, 5 or −4 kV (for positive and negative mode, respectively); declustering potential, 60 V; curtain gas, 25 psi; nebulizer gas of 40 psi; interface heater temperature, 650◦C scan range (m/z), 50–1,200. The stability of the analysis was continuously monitored by analyzing QC samples at intervals of every four samples.

### Data Analysis

Total ion chromatograms were pre-processed with an R package, XCMS to filter redundant signals, match peaks, and accurate mass of LC-MS produced signals (Smith et al., 2006). The molecular features within deviations of 0.5 min retention time and 15 ppm mass tolerance were accepted. Those with presence less than 50% and RSD of intensity over 30% in QC samples were filtered out. The spectrometric features were then assigned by mass and retention time, and the relative intensity was normalized by Support Vector Regression method (Shen et al., 2016). Identification of metabolites were achieved by automated comparison of molecular features (retention time, mass and MS/MS spectra) to a commercial available metabolomics library (provided by Biotree Biotech, Shanghai, China, established on the same experimental platform with purified standards). A similarity threshold of 70% was used for the subsequent annotations. Obtained data were then log-transformed, mean-centered, and fed to SIMCA-P V14.1 (Umetrics, Umea, Sweden) for multivariate statistical analyses. Both unsupervised (principal component analysis, PCA) and supervised pattern discrimination (orthogonal projections to latent structures discriminant analysis, OPLS-DA) analyses were adopted to discriminate differences among groups. PCA as an initial exploratory analysis was employed to get an overview of the urine metabolites, and the further class discrimination and systematic differences extraction were achieved by the means of OPLS-DA analysis (Davis et al., 2012). Sevenfold cross validation was used to estimate the robustness and predictabilities of established model, which was further validated by 200 permutation tests. Upon the analysis, a VIP parameter was assigned to each metabolite based on the discriminatory power on models. These with VIP over 1.0 were subsequently subjected to Student's t-test, and it was considered statistically significant when P-value < 0.05. The screened out metabolites were finally visualized as heatmaps, and mapped onto KEGG pathways. The annotated metabolites were highlighted by different colors dependent on the regulatory manners (red for up-regulation and blue for down-regulation).

The required data from pharmacological experiments were processed by using SPSS software (version 14.0, SPSS Inc., Chicago, IL, United States). Results were expressed as mean ± SEM. To evaluate differences among groups, one-way analysis of variance coupled with post hoc test were applied.

## RESULTS

### Target Prediction and Network Analysis of the Anti-rheumatic Mechanisms of QLY

We have partially elucidated therapeutic mechanisms of QLY on RA using the network pharmacology method (Zhang et al., 2013). Considering the rapid accumulation of relevant knowledge, we updated these results in this study. To achieve a better evaluation of the clinical potentials and test the reasonability of prediction, we extracted main therapeutic targets of now available antirheumatic drugs from TTD<sup>4</sup> and CTD<sup>5</sup> database, and compared them with the predicted results. Computational analysis found predicted targets of ingredients in QLY covered most of them (**Supplementary File S2**). Further, potential regulated pathways implicated in pathogenesis of RA under QLY treatment were screened out from candidates based on these targets by GO and pathway enrichment analysis, and the results were ranked in **Figure 1A**. Apart from some well known RA related pathways involved in angiogenesis, inflammatory response, and immune reactions, the main finding was that QLY significantly altered nucleotide metabolism. Among all these targeted pathways, RA pathway is especially meaningful as it is deeply involved in the evolution of RA. Upon further examination, we found through regulation of this pathway QLY could exert effects on many pathological aspects of RA, including inflammation, joint destruction, angiogenesis and immune dysfunction (**Figure 1B**).

The primary statistical validation test proved that results of pathway enrichment analysis were meaningful, as we found significant statistical difference on target occurrences between QLY derived list and random control (p < 0.005) (**Figure 1C**). Further, we validated the reliability of the predicted targets based on recall of four well investigated characteristic ingredients of QLY (matrine, sinomenine, berberine, dioscin). We searched for related molecular mechanisms of the four compounds in PubMed database by the means of literature mining. The predicted targets of each ingredient are connected to the reported biomolecules in the direct or indirect manner (via protein–protein interactions or signaling pathway crosstalk). Obtained results showed that the predicted targets covered 86%, 71%, 72%, and 81% of reported biomolecular mechanisms for matrine, sinomenine, berberine, and diosgenin, respectively (**Figure 1D**). It suggested that predicted results were reliable.

### Effects of Treatments on Clinical Manifestations of CIA in Rats

According to the target prediction and biological function enrichment analysis, QLY treatment could inhibit angiogenesis, inflammatory infiltration, bone resorption, and pannus formation, and subsequently protect joints from destruction and alleviated severity of arthritis (**Figure 2A**). Evidences from in vivo experiment solidly supported these claims.

<sup>4</sup>http://bidd.nus.edu.sg/group/cjttd/ <sup>5</sup>http://ctdbase.org/

targets (p < 0.05). (B) Comprehensive effects of QLY on rheumatoid arthritis pathway. (C) The statistical validation test of rheumatoid arthritis pathway enrichment analysis. (D) The validation of predicted targets of representative ingredients in QLY with literature evidence.

Twelve days after the first induction, significant edema was developed in paws of rats, and synchronously, arthritis score was remarkably increased (**Figure 2B**). Since day 25, local inflammation was gradually eased, but significant deformation of joints and limitation of motion occurred in CIA rats. MTX exhibited efficient therapeutic effects on CIA, indicated by eased inflammation and reduced arthritis score throughout the experimental period. CBC analysis found some additional clues to validate its anti-inflammatory effects. Levels of both white blood cell and lymphocyte increased under CIA conditions, and MTX brought all these parameters down (data not provided). Similar but weaker effects of QLY were found (**Figure 2C**). Such conclusion was also obtained from DR examination. Compared with normal animals, joint structures of CIA rats were extensively damaged. It found vague and narrowed joint spaces, together with obvious cartilage ossification and bone loss. The rats received MTX/QLY treatments exhibited much clearer joints space, and joint cavity fusion and bone resorption were also suppressed (**Figure 2D**). These evidences suggested QLY possessed substantial therapeutic effects on CIA in rats.

#### Effects of Treatments on Pathological Changes in CIA Rats

Histological examination found totally intact joints structures in normal animals, but severe damages on the joints of CIA rats, including fibrous hyperplasia, inflammatory cells infiltration, cartilage loss, and bone erosion. No obvious histological improvements were achieved under MTX treatments, while QLY alleviated these pathological conditions a lot. The joints cavity narrowing, synovial hyperplasia, bone degradation and cartilage damages were all efficiently inhibited (**Figure 2E**). By the comparison, obvious advantages of QLY were revealed.

To further understand therapeutic mechanisms of the treatments on CIA and predict their effects on the prognosis of

disease, we analyzed levels of some RA related biomarkers in serum. Overall, all the investigated parameters were significantly elevated in CIA rats, but restored by the treatments. Upon the comparison, we found QLY performed better than MTX. Unlike RF and CRP, level of anti-CCP antibody was not so sensitive to these treatments (**Figure 2F**).

Collective results suggested QLY could possess an immunomodulation effect in vivo. Further investigations found it had little influence on the histological structures of immune organs (**Supplementary File S4**), but affected the distribution and differentiation of T cells in both spleen and thymus. Generally, both MTX and QLY decreased the population of CD4<sup>+</sup> cells in CIA rats, and slightly reduced the production of IFN-γ (**Supplementary File S5**). As RA is believed as a Th1 cells driven immune disease, these changes are favorable to the improvement of pathological conditions. The expression of FOXP3 was down-regulated under CIA conditions, and both the treatments achieved no effect on it. The flow cytometric analysis found reduced population of CD4+CD25<sup>+</sup> cells in the peripheral blood, which was even aggravated under MTX treatment. Although QLY could not raise this level, the combination treatment exhibited a tendency of recovery (**Supplementary File S6**). These clues hinted that QLY could restore the immune homeostasis in CIA rats mainly via down-regulation of Th1 cells.

#### QLY Alleviate the Toxicity of MTX on Rats

As the network target method is a powerful tool to predict possible interactions in a treatment involves multiple bioactive components for its latent network topology properties (Li et al., 2011), we used this technique to assess theoretical feasibility of the combined regimen with MTX and QLY. As shown in **Figure 3A**, the predicted targets of QLY are adjacent to those of MTX, which hinted that QLY and MTX might produce combined effects. Further, the network analysis found that QLY can partially offset toxicity related signaling changes induced by MTX, which are mainly related to oxidative stress response and energy metabolism (**Figure 3B**). Metabolomics analysis found that QLY induced significant antifolate resistance in vivo. Because folate depletion is main factor leading to toxicity during a long term MTX treatment, the effects of QLY on folate metabolism are meaningful. Contrary to MTX, QLY significantly increased levels of 7,8-dihydrofolate, homocysteine and 5-phosphoribosyl 1-pyrophosphate, and exhibited antagonistic effects against MTX concerning folate metabolism in the combined treatment (**Figure 3C**).

The in vivo experiment provided more direct evidences to support the theory above. After repeated MTX administration, some rats exhibited gastrointestinal reactions, including nausea, diarrhea and dyspepsia. In the late stage of the treatment, most rats exhibited signs of anemia, such as reduced activity, pale complexion and mental fatigue, but no abnormal reactions were found in MTX+QLY group. Although histological examination found no obvious damages of liver (**Supplementary File S4**), level of ALT in serum was significantly elevated under MTX treatment, while no obvious change happened to AST (**Figures 3D,E**). Meanwhile, MTX induced decrease of hemoglobin (HGB) and red blood cell (RBC) in rats, and the combined use of QLY partly restored the abnormal changes (**Figures 3F,G**). Similar antagonistic effects of MTX and QLY on platelet were observed. These clues confirmed the hepatic and hematological toxicities of MTX (Kivity et al., 2014), and hinted QLY possessed protective effects on MTX induced hepatic injury and myelosuppression.

#### Treatments Changed Metabolic Patterns of CIA Rats

To maximate the information load, fingerprints of urine samples were acquired in both negative and positive modes. UPLC-Q-TOF/MS analysis detected a total of 5,949 and 60,930 signals under the negative and positive modes, respectively (**Supplementary Files S7, S8**). Among them, 190 and 633 metabolites were identified by using the commercial available metabolic library (**Supplementary Files S9**, **S10**). Based on global features of the raw data, we carried out PCA and OPLS-DA analyses to differentiate groups and visualize the metabolic differences among groups. PCA analysis exhibited a separation tendency of groups, but the results were not satisfying (**Figure 4A**). As a supervised learning method, OPLS-DA filters out nonessential variations, and notably improves the accuracy of classification. In this study, score plots from OPLS-DA models clearly discriminated all the groups under both negative and positive modes. It was revealed that the endogenous substance metabolisms of CIA rats were obviously disrupted. Upon QLY and MTX treatments, the metabolic profile of CIA rats was altered. Score plots of the two groups situated in the distinct positions in the map, which hinted different mechanisms were involved in their therapeutic actions on CIA (**Figure 4B**). Results from cross validation suggested the model was robust and had good predictabilities (**Figure 4C**).

By comparison between normal and CIA rats, potential biomarkers involved in pathogenesis of CIA were screened out based on OPLS-DA analysis with the predetermined rules (VIP > 1; p < 0.05) (**Figure 5A**). The discriminatory metabolites were then annotated by KEGG database. It was found the perturbed pathways mainly covered amino acid and fatty acid metabolisms (**Figure 5B**). Generally, short and medium chain fatty acids such as butanoic acid, valeric acid, undecanedioic acid and caprylic acid were decreased but the long chain ones including cis-9-palmitoleic acid, palmitic acid and heptadecanoic acid were increased. The regulation on amino acid metabolism was diverged and sophisticated. Levels of arginine, aspartic acid, N-(omega)-hydroxyarginine and glutamate were raised, while concentrations of norvaline, phenylalanine, sarcosine and leucine were reduced. Energy metabolism in CIA rat was boosted, which was suggested by high amount of intermediates from tricarboxylic acid

(TCA) cycle (fumarate, succinate, and isocitrate) in the urine. Besides, we found increased 3-hydroxybutyric acid and creatine, and disordered vitamin B profile (high riboflavin and thiamine but low nicotinamide) under CIA conditions (**Table 1**). These evidences further validated the disruption of energy metabolism in CIA rats. MTX restored most of the abnormal metabolic changes, while its effect on amino acid metabolism was weak. The selective restoration of MTX on the disordered metabolic state of CIA rats changed the position of score plots in OPLS-DA diagram (situated between CIA and normal rats). This phenomenon indicated the substantial recovery of CIA rats under MTX treatment. Different from the selective regulation of MTX, QLY treatment universally compromised all of the endogenous substances metabolisms (**Table 1**), which gave the treated rats a totally different metabolic profile from the others (**Figure 4**).

#### Metabolic Changes Involved in Therapeutic Actions of QLY on CIA

The biological function enrichment analysis and metabolomics study found QLY could intervene in the nucleotide metabolism (**Figure 1A**). To fully interpret its clinical implication and get a better understanding of the anti-rheumatic metabolism of QLY, we dug deeply into relevant data, and found pyrimidine metabolism, purine metabolism and pentose phosphate pathway (PPP) could all be altered by QLY (**Figure 6A**).

The computational result suggested chemical ingredients of QLY can simultaneously target multiple genes/proteins involved in nucleotide metabolism, and suppress some abnormal cellular functions fueled by the high metabolism status.

Subsequently, we then test this hypothesis with metabolomics evidences. OPLS-DA analysis revealed 39 and 413 discriminatory metabolites between CIA and QLY treated rats at negative and positive mode, respectively (**Supplementary Files S11**, **S12**). Besides from previous mentioned amino acid, energy and thiamine metabolisms, we noticed nucleotide biosynthesis relevant metabolic pathways were also affected a lot by QLY (**Figure 6B**). Enriched KEGG pathway analysis demonstrated that PPP and pyrimidine metabolism in CIA rats were significantly down-regulated under QLY treatment, which contributed to the discrimination of CIA and QLY groups. Relative quantitative analysis demonstrated levels of both free pyrimidine bases (cytosine, uracil, dihydrouracil and 5 methylcytosine) and pyrimidine nucleosides (deoxycytidine, thymidine, and pseudouridine) were remarkably reduced by QLY compared with CIA models. Similar inhibitory effects on PPP were also observed. Many intermediates in this metabolic pathway including 6-phospho-D-gluconate, D-erythrose-4-phosphate, 2-dehydro-3-deoxy-D-gluconate, glyceraldehyde 3-phosphate and D-ribose were all downregulated. By further manual comparison, we found purine metabolism was suppressed by QLY too. Some important endogenous substances implicated in purine metabolic pathway including hypoxanthine, xanthine, adenine, xanthosine, and guanosine were all reduced (**Figure 6C**). These evidences showed QLY compromised nucleotides biosynthesis and disrupted nucleic acid metabolism. For the important role of nucleotides in cell division, these effects could have a close relationship with the protective effects on joints in CIA rats via inhibition of synovial hyperplasia, and also could influence the immune functions in vivo.

### DISCUSSION

By accumulation of knowledge into the pathogenesis, rheumatologists designed many novel therapeutic approaches. However, even the most promising biological therapies cannot obtain sustained remission of RA in the long term. While


All these metabolic intermediates were detected by UPLC-Q-TOF/MS method, and the relative levels in urine were evaluated based on their contribution to the sum of signal intensities.

under the guidance of holistic strategy, TCM formulas can usually substantially alleviate severity of RA by controlling systemic symptoms. QLY has been extensively used in clinical practice for several years (Zhang, 2015; Fan and Li, 2016). Previous studies have found that QLY efficiently inhibited angiogenesis in rheumatoid synovium (Li et al., 2003; Liu, 2012). As well accepted, fibroblast-like synoviocytes in RA (RA-FLS) play a key role in degradation of joint structure and provoking chronic inflammation (Bartok and Firestein, 2010). From this point of view, inhibition on synovial angiogenesis will contribute a lot to the protection of QLY on joints by blocking nutrition supply to the synovial hyperplasia. Despite of these findings, mechanism underlying therapeutic actions of QLY on RA is far away from being well elucidated. For example, clinical observations found obvious improvements of immune mediated symptoms, and down-regulation of RA related biomarkers under QLY treatment, while no reasonable theory has been deduced for this so far (Zhang, 2015; Fan and Li, 2016). By using an integrative platform of TCM network pharmacology, we uncovered some potential target network of QLY against RA-related key processes in a previous report (Zhang et al., 2013). It evidenced the overall mechanism of

QLY on RA for the first time, however, as a theory deduced from mathematical models, these discoveries should be further validated by experimental evidences. In the present study, we updated relevant results and paid more attentions on RA related metabolic changes using a integrating strategy coupled with network pharmacology and metabolomics methods.

The in vivo experiments provided sufficient evidences to support the therapeutic effects of QLY on CIA (**Figure 2**). Integrating network pharmacology and metabolomics study did not only further confirm its anti-rheumatic potential but also aid a better understanding of the underlying mechanisms from a global protective. Of note, this study revealed a novel metabolism mediated therapeutic mechanism of QLY. The most discriminating altered metabolites from both RA patients and animal models included amino acids, lipids, ketone bodies and TCA cycle related intermediates (Castro-Santos et al., 2015; Guma et al., 2016). It reflected the accelerated degradation of tissues and increased energy consumption. A cohort analysis found higher basal metabolic rate of RA patients than that of health population, and the extra energy consumption was believed to be in close relationship with the highly energy dependent inflammatory responses (Metsios et al., 2008). From this point of view, inhibition of energy metabolism is beneficial to the amelioration of RA. Network pharmacological analysis suggested QLY could intervene into the energy metabolism (**Figure 1A**) via a possible means of regulation on fatty acid oxidation indicated by its effects on PPARγ (**Figure 3B**). This hypothesis was solidly validated by the metabolomics evidences. We found increased long chain fatty acids in urine, which indicated enhanced lipid mobilization. Meanwhile, levels of small molecular fatty acids were decreased. It can be concluded that, under CIA conditions glycometabolism was insufficient to meet the energy demands, and fatty acids were utilized as an important alternative energy sources. Enhanced catabolism of fatty acids produced more ketone bodies, and the presence of 3-hydroxybutyrate in urine reflected respiratory chain deficiency and increased oxidative stress in vivo (Sato et al., 1992). Carnitine is an intermediate associated with β-oxidation of fatty acids. Reduced carnitine coupled with increased 3-hydroxybutyrate indicated altered lipid catabolism and reduced antioxidant capacity (Sato et al., 1992). QLY restored all these abnormal changes (**Supplementary Files S11**, **S12**). It significantly brought the basal metabolic rate down and altered vitamin B family profile in CIA rats (**Table 1**). The compromise of fatty acids and carbohydrate metabolisms could serve as an important indicator for the amelioration of CIA, and raised level of nicotinamide hinted functional restoration of respiratory chain. The increase of energy utilization subsequently resulted in reduced generation of ROS, and improved inflammatory microenvironment eventually. According to TCM theory, QLY is characterized as a Cold natured formula, and used to expel "Pathogenetic Hot" in Hot Syndrome-related RA patients. This characteristic indicates its potent negative effects on energy metabolism, and the clinical implications. The present integrating mechanism study provided direct clues to validate this theory and clarify its nature.

The network pharmacology analysis suggested the therapeutic efficacy of QLY on CIA were the sum of multi-target effects from the bioactive ingredients. Among the targeted pathways and biological processes, many of them are deeply implicated in evolution of RA. By inhibiting inflammatory response, angiogenesis, and VEGF signaling pathway, QLY can alleviate the severity of both experimental arthritis and RA. Meanwhile, it exhibited potent effects on immune functions (**Figure 1A**). It could contribute even more to the alleviation of RA, as the pathological changes in RA are mainly caused by immune dysfunction. These results shed some lights on elucidation of anti-rheumatic mechanisms of QLY, but it also raises a fundamental question: where does its immunoregulatory activity come from? We noticed that the regulated pathway ranked in the first position by QLY was nucleotide metabolism. Further analysis found QLY simultaneously altered pyrimidine, purine, and pentose phosphate metabolisms (**Figure 6A**). It suggested nucleotide biosynthesis related pathways could be closely connected to the anti-rheumatic potential of QLY, and also important to resolve the question above. PPP is a fundamental component of cellular metabolism. It provides precursors for nucleotide, which is essential to support the high proliferation rate of cells under some pathological conditions. Besides, it defeats oxidative stress by generation of NADPH, and balances redox homeostasis in cells (Stincone et al., 2015). Because of these reasons, PPP could be a potential therapeutic target for cancers (Yu et al., 2015). Similarly, via inhibition of PPP, QLY could suppress the hyperproliferation of RA-FLS, and elicit DNA damage response by accumulation of intracellular ROS. Besides, PPP is implicated in inflammatory reactions, and the modification on it could be beneficial to the alleviation of systematic symptoms in CIA rats by controlling critical molecular events involved in immune responses (Nagy and Haschemi, 2015; Ham et al., 2016). Hence, inhibition on PPP by QLY is essential to the alleviation of CIA, and similarly, the regulation on pyrimidine and purine metabolisms would also yield some profound benefits (**Figure 6C**). As rapid expansion of autoreactive lymphocytes initiates pathological changes of RA, inhibition on them is a reasonable therapeutic strategy. Agents targeting pyrimidine metabolism will block their excessive proliferation, and are usually used as immunosuppressant for treatments of immune diseases (Peres et al., 2017). As the best known representative, leflunomide has been successfully applied in the therapy of RA for decades. Treatment with QLY significantly suppressed pyrimidine metabolism, which could contribute to the immunosuppressive effects (suppression on the clone and differentiation of Th1 cells) and down-regulation of RA-related biomarkers in CIA rats. Altered purine metabolism could be involved in the therapeutic actions of QLY on CIA too. By depleting the pool of purine required for DNA synthesis, QLY could exert a cytostatic effect on T lymphocytes (**Figure 1A**), and exhibit an immunosuppressive activity in vivo (Allison and Eugui, 1996). But if suppression on purine metabolism is beneficial to RA patients is still elusive, for adenosine derivatives usually elicit anti-inflammatory immune responses (Cekic and Linden, 2016).

The computational prediction suggested different mechanisms were involved in the anti-rheumatic effects of MTX and QLY (**Figure 3A**).Perhaps due to the poor optimization, we didn't notice significant synergetic efficacy of the two treatments on CIA in this study. But QLY notably reduced MTX induced side effects. The collective clues demonstrated QLY elicited antifolate resistance was the main factor leading to this phenomenon. In the combination treatment, QLY significantly offset the negative effects of MTX on folate metabolism, and greatly restored levels of some important intermediates (**Figure 3C**). This finding preliminarily proved the rationality of the combined regimen, as it would greatly improve MTX tolerance in patients. However, we should also take some negative possibilities into consideration, since inhibition of folate metabolism is one of the fundamental mechanisms of MTX in treatment of RA. Also, QLY induced antifolate resistance would affect the adenosine metabolism, which has been revealed in this study (**Supplementary File S13**).This change could exert pronounced effects in the combination regimen, as regulation on levels of adenosine and its derivatives is believed deeply involved in anti-inflammatory effects of low dose MTX treatment on RA. Therefore, more researches are needed to fully evaluate the feasibility of the combination treatment.

According to prescription principles of TCM, the components in a formula should be defined as four types based on their functions, that is, Monarch, Minister, Assistant and Guide. The main characteristic of QLY is that large amount of Kushen is applied as monarch drug, because this herb is usually used for external purposes. Qingfengteng and Huangbai are deemed as minister drugs, while Bixie just functions as the Assistant/Guide. Hence, although all these herbals contribute to the therapeutic effects of QLY on RA, the importance varies a lot. By examination of **Figure 1B**, we found some evidences to firmly support the composition principle. The hierarchy of Kushen, Qingfengteng, and Huangbai in the formula were highlighted by lots of converges with RArelated signals, but Bixie seems having less influence on the anti-rheumatic potentials. Under the guide of serum pharmacochemistry, a previous report found main bioactive ingredients in QLY could be matrine, berberine, sinomenine, and their derivatives, as they are main chemical ingredients from major components in QLY, and can enter the circulation directly after oral administration (Yang, 2010). We further proved this conclusion. All of the three compounds exhibit potent effects on RA-related processes (**Figure 2A**). Among them, the therapeutic effects of sinomenine on RA have been well validated (Xu et al., 2008). Also, there are plenty of reports concerning the anti-rheumatic potentials of berberine. Available evidences suggested berberine can protect joints by inhibiting RA-FLS, and alleviate systemic symptoms by suppressing hyper-activated immune system (Wang et al., 2011; Wang X. et al., 2017). By contrast, there are few reports about anti-rheumatic effects of matrine, and we think its potentials are greatly underestimated. Wang S. J. et al., 2017) found matrine and its derivatives could efficiently intervene into the energy metabolism, which could be associated with the Cold nature of QLY, and its clinical application. Subsequently, we will further investigate the anti-RA related bioactivities of matrine, and evaluate its contributions to the formula.

#### DATA AVAILABILITY STATEMENT

All relevant data about the findings are included in this manuscript and **Supplementary Files**.

### AUTHOR CONTRIBUTIONS

SL conceived the idea of the study. SL and YLi supervised the study. XW and SL performed the computational analysis. JZ and YLiu performed the in vivo experiments and collected the pharmacological data. JY and QL identified the chemical composition of QLY. JZ performed the metabolomics study. All authors participated in the interpretation of experimental results and drafting the manuscript.

### FUNDING

This work was supported by National Natural Science Foundation of China (81630103 and 81603388), Collaborative Project of Tsinghua University and Yijishan Hospital of Wannan Medical College (20172001449), 2016 Key Project of Natural Science Foundation of Anhui Province for College Scholar (KJ2016A419), and Project of Construction on Key Discipline of State Administration of Traditional Chinese Medicine (2012-32).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.01472/full#supplementary-material

FILE S1 | Ingredient information of QLY.

FILE S2 | Biological function enrichment analysis of known anti-RA targets and predicted targets of QLY.

FILE S3 | HPLC-UV chromatograms and quantitative results of QLY chemical composition.

FILE S4 | Histological examinations of main organs of rats.

FILE S5 | Immunohistochemical examinations of spleen and thymus of rats.

FILE S6 | Distribution of CD4+CD25<sup>+</sup> cells in peripheral blood of rats.

FILE S7 | Raw spectrometric data under negative mode.


FILE S13 | Effects of QLY on adenosine metabolism in CIA rats.

## REFERENCES

fphar-09-01472 December 14, 2018 Time: 14:35 # 16



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling Editor and reviewer S-BS declared their involvement as co-editors in the Research Topic, and confirm the absence of any other collaboration.

Copyright © 2018 Zuo, Wang, Liu, Ye, Liu, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Unveiling Active Constituents and Potential Targets Related to the Hematinic Effect of Steamed Panax notoginseng Using Network Pharmacology Coupled With Multivariate Data Analyses

#### Edited by:

Shao Li, Tsinghua University, China

#### Reviewed by:

Songxiao Xu, Artron BioResearch Inc., Canada Qi Wang, Harbin Medical University, China

#### \*Correspondence:

Yin Xiong yhsiung@163.com Ming Niu nmbright@163.com Xiuming Cui sanqi37@vip.sina.com †These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 27 March 2018 Accepted: 11 December 2018 Published: 08 January 2019

#### Citation:

Xiong Y, Hu Y, Chen L, Zhang Z, Zhang Y, Niu M and Cui X (2019) Unveiling Active Constituents and Potential Targets Related to the Hematinic Effect of Steamed Panax notoginseng Using Network Pharmacology Coupled With Multivariate Data Analyses. Front. Pharmacol. 9:1514. doi: 10.3389/fphar.2018.01514 Yin Xiong1,2,3 \* † , Yupiao Hu<sup>1</sup>† , Lijuan Chen<sup>1</sup>† , Zejun Zhang<sup>1</sup> , Yiming Zhang<sup>1</sup> , Ming Niu<sup>4</sup> \* and Xiuming Cui1,2,3 \*

<sup>1</sup> Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, China, <sup>2</sup> Yunnan Key Laboratory of Panax Notoginseng, Kunming, China, <sup>3</sup> Laboratory of Sustainable Utilization of Panax Notoginseng Resources, State Administration of Traditional Chinese Medicine, Kunming, China, <sup>4</sup> China Military Institute of Chinese Materia Medica, 302 Military Hospital of China, Beijing, China

Steamed Panax notoginseng (SPN) has been used as a tonic to improve the blood deficiency syndrome (BDS) in the theory of traditional Chinese medicine. Here, we aim to unveil active constituents and potential targets related to the hematinic effect of SPN, which has not been answered before. In the study a constituent-target-disease network was constructed by combining the SPN-specific and anemia-specific target proteins with protein-protein interactions. And the network pharmacology was used to screen out the underlying targets and mechanisms of SPN treating anemia. Also, the multivariate data analyses were performed for the double screening. According to the results, 11 targets related to chemical constituents of SPN were found to be closely associated with the hematinic effect of SPN. Among them, the direct target protein of mitochondrial ferrochelatase (FECH) had the major role through the metabolic pathway. Meanwhile, Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> were predicted to be major constituents related to the hematinic effect of SPN by both multivariate data analyses and network pharmacology. And it was been validated by the pharmacologic tests that Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> could significantly increase the levels of blood routine parameters, FECH and its downstream protein of heme in mice with BDS. The study provides evidences for the mechanism understanding and drug development of SPN for the treatment of anemia.

Keywords: steamed Panax notoginseng, hematinic effect, active constituents, mechanism, network pharmacology, multivariate data analyses

**Abbreviations:** APH, acetylphenylhydrazine; BDS, blood deficiency syndrome; CCA, canonical correlation analysis; CTX, cyclophosphamide; FECH, ferrochelatase; FEJ; Fufang E'jiao Jiang; Hb, hemoglobin; OMIM, Online Mendelian Inheritance in Man; PLSR, partial least squares regression; PLT, platelet; PN, Panax notoginseng; PPI, protein–protein interaction; RBC, red blood cells; SPN, steamed Panax notoginseng; TCM, traditional Chinese medicine; TDT, TCM Database@Taiwan; WBC, white blood cells.

### INTRODUCTION

fphar-09-01514 December 26, 2018 Time: 18:9 # 2

Blood deficiency syndrome (BDS) is a common syndrome with high incidence in clinic of traditional Chinese medicine (TCM), which often occurs in patients with anemia. It is characterized by a decreased quantity of red blood cells (RBC) and white blood cells (WBC), usually accompanied by the diminished hemoglobin (Hb) levels or altered RBC morphology (Kassebaum et al., 2014). Patients with BDS are often accompanied with tachycardia, dizziness, shortness of breath, poor ability to exercise, and even loss of consciousness (Janz et al., 2013). The causes giving rise to BDS are similar to anemia, including the reduced erythropoiesis, excessive hemorrhage, and destruction of RBC, of which the last one mainly refers to hemolytic anemia (Tian et al., 2017). Hemolytic anemia can be induced by the administration of acetylphenylhydrazine (APH) and cyclophosphamide (CTX), which is also a classical model for BDS (Zhang et al., 2014). Today, hemolytic anemia is mainly treated with blood transfusion. This treatment is associated with mechanical shearing forces that accelerate red blood cell rupture, and lead to severe clinical complications, including intravascular hemolysis, tissue oxidative stress, and multi-organ dysfunctions (Baek et al., 2012). Therefore, the development of effective therapies or drugs with blood-enriching efficacy is beneficial for the prevention and treatment of the disease.

Panax notoginseng (PN) (Burk.) F. H. Chen, a well known medicinal herb in Asia, has been used to treat blood disorders for thousands of years (Li et al., 2017). In 1944, PN and the relative products were classified as dietary supplements according to the United States Dietary Supplement Health and Education Act (103rd Congress, 1994). Traditionally, PN has been used in both non-steamed and steamed forms. Unlike the non-steamed one treating bleeding and removing blood stasis, the steamed PN (SPN) is used as a tonic to enrich blood and tonify the body, which can improve the BDS by increasing the production of various blood cells in anemic conditions (Ge et al., 2015; Gu et al., 2015). In our previous study (Xiong et al., 2017b), we found that the treatment of SPN could reverse significantly the decrease of levels of WBC, RBC, Hb, and platelet (PLT) of anemic mice induced by APH and CTX, which were inapparent when treated with non-steamed PN. The result was consistent with the report from Zhou et al. (2014). This could be due to the variation in the chemical composition of PN during the steaming process (Wang et al., 2012a). Despite various studies on the processing methods, chemical components and bioeffects of PN (Lau et al., 2009; Sun et al., 2010), much less attention has been paid on its steamed form, let alone the specific active constituents and the underlying mechanisms related to the hematinic effect of SPN, which hinders the development and application of this valuable medicine.

Network pharmacology, as a system biology-based methodology, offers an effective approach to evaluate the pharmacological effects of herbal medicines at the molecular level by predicting the complex interactions of small molecules and proteins in a biological system (Tang et al., 2015; Li et al., 2017). It is considered to be a promising way to unveil properties of herbal medicines and provide valuable insights into current drug discovery and development. Compared with conventional "one target, one drug, one disease" mode, network pharmacology focuses on "multi-targets, multi-constituents treatment to diseases," which coincides with the holistic and systematic concepts of TCM (Li et al., 2015; Quan et al., 2016). It was reported that network pharmacology has been applied to detect new pharmacologic effects of herbal medicines, to uncover the interactions between herbal compounds/formulas and complex syndrome systems, to determine the active constituents and their mechanisms of action (Tao et al., 2013; Sheng et al., 2014). Therefore, to better understand the molecular basis of the enriching-blood effect of SPN, we computationally recognized the active constituents and potential targets of SPN treating anemia by the network pharmacology approach coupled with multivariate data analyses, and experimentally validated the predicted results.

#### MATERIALS AND METHODS

#### Computational Prediction of Hematinic Constituents and Targets of SPN Using Network Pharmacology Analyses Database Construction

Based on the literature research and our previous works on chemical analysis of SPN (Sun et al., 2010; Xiong et al., 2017a,b), 20 compounds were selected, including ginsenosides of F<sup>2</sup> (1), Rb<sup>1</sup> (2), Rb<sup>2</sup> (3), Rb<sup>3</sup> (4), Rc (5), Rd (6), Re (7), Rg<sup>1</sup> (8), Rh<sup>2</sup> (9), Rh<sup>4</sup> (10), Rk<sup>3</sup> (11), 20(R)-Rg<sup>2</sup> (12), 20(S)-Rg<sup>2</sup> (13), 20(R)- Rg<sup>3</sup> (14), 20(S)-Rg<sup>3</sup> (15), 20(R)-Rh<sup>1</sup> (16), and 20(S)-Rh<sup>1</sup> (17); and notoginsenosides of C (18), R<sup>1</sup> (19), and R<sup>2</sup> (20) in SPN. The chemical structures of the composite compounds in SPN were obtained from TCM Database@Taiwan (TDT) or drawn with ChemDraw professional 15.0 (Chen, 2011). The targets of constituents were predicted by the online target prediction software of PharmMapper with a criterion of "fit score" >4 1 (Wang et al., 2017). Gene and protein targets associated with the disease of anemia were collected from the Online Mendelian Inheritance in Man (OMIM) database (Amberger et al., 2015). Database of Interacting Proteins for protein-protein interactions (PPI) was employed to identify the possible interactions of the aforementioned targets. And all protein ID codes were converted to UniProt IDs (Wei et al., 2016).

#### Network Construction and Analysis

To provide the scientific and reasonable interpretation of the complex relationships between the constituents and targets associated with anemia, network analysis was performed. The chemical constituents, SPN putative targets, and anemia targets were all connected to construct a "constituent-target-disease" network with PPI information. Cytoscape 4.3 (Smoot et al., 2011) was applied to visualize and analyze the network, and calculate the topological features of each node in the network. Only the hub nodes (twofolds above the median "degree" value of all nodes)

<sup>1</sup>http://lilab.ecust.edu.cn/pharmmapper/index.php

with higher values of "betweenness centrality" and "closeness centrality" (above the median value of all nodes) were identified as the candidate SPN targets for anemia.

#### Targets and Pathways Analyses

fphar-09-01514 December 26, 2018 Time: 18:9 # 3

To unveil the mechanism of SPN treatment of anemia, DAVID Functional Annotation Bioinformatics Microarray Analysis<sup>2</sup> was performed (Dennis et al., 2003) for the pathway enrichment analysis. The key target in the most significant enriched pathway was verified by performing in vivo experiment in the SPN treatment on anemia.

#### Screening Hematinic Constituents of SPN Based on the Fingerprint-Effect Analyses

#### Chemicals

The reference standards of ginsenosides 20 (S)-Rg<sup>3</sup> and Rk<sup>3</sup> with a purity ≥ 98% were purchased from the National Institutes for the Control of Pharmaceutical and Biological Products (Beijing, China). Methyl alcohol and acetonitrile of HPLC grade were purchased from Sigma-Aldrich, Inc. (St. Louis, MO, United States). Ultrapure water was generated with an UPT-I-20T ultrapure water system (Chengdu Ultrapure Technology, Inc., Chengdu, Sichuan, China). APH was purchased from HuaXia Chemical Reagent Co., Ltd. (Chengdu, China). CTX was purchased from Xiya Chemical Industry Co., Ltd. (Shangdong, China). Mouse ferrochelatase (FECH) enzyme-linked immunoassay kit and heme enzyme-linked immunosorbent assay kit were purchased from Shanghai MLBIO Biotechnology Co., Ltd. (Shanghai, China). All other chemicals used were of analytical grade.

#### Sample Preparation

The preparation of SPN refers to our previous study (Xiong et al., 2017a). "Samples were obtained from a single batch of PN root in Yunnan, China. Steamed PN samples were prepared by steaming the crushed raw PN in an autoclave (Shanghai, China) for 2, 4, 6, 8, and 10 h at 105, 110, and 120◦C, respectively. The steamed powder was then dried in a heating-air drying oven at about 45◦C to constant weight, then powdered and sieved through a 40-mesh sieve."

#### Animals

Animal experimental procedures in the study were strictly conformed to the Guide for the Care and Use of Laboratory Animals and related ethics regulations of Kunming University of Science and Technology. The protocol was approved by the Experimental Animal Welfare and Ethics Committee, Kunming University of Science and Technology. The experimental method refers to our previous study (Xiong et al., 2017a), that "Kunming mice, male and female, weighing 18–22 g, were purchased from TianQin Biotechnology Co., Ltd., Changsha, Hunan [SCXK (Xiang) 2014-0011]. Before the experiments, the mice were given one-week acclimation period in a laboratory at room temperature (20–25◦C) and constant humidity (40–70%), and fed with standard rodent chow and tap water freely."

#### HPLC Analyses

The sample solutions were prepared according to the method in our previous research (Xiong et al., 2017a). "HPLC analyses were done on an Agilent 1260 series system (Agilent Technologies, Santa Clara, CA, United States) consisting of a G1311B Pump, a G4212B diode array detector, and a G1329B autosampler. A Vision HT C<sup>18</sup> column (250 mm × 4.6 mm, 5 µm) was adopted for the analyses. The mobile phase consisted of A (ultra pure water) and B (acetonitrile). The gradient mode was as follows: 0– 20 min, 80% A; 20–45 min, 54% A; 45–55 min, 45% A; 55–60 min, 45% A; 60–65 min, 100% B; 65–70 min, 80% A; 70–90 min, 80% A. The flow rate was set at 1.0 ml/min. The detection wavelength was set at 203 nm. The column temperature was set at 30◦C and sample volume was set at 10 µl."

#### Blood Routine Test

210 km mice, half male and half female, were randomly divided into seven groups, namely the control group, model group, Fufang E'jiao Jiang (FEJ) group, and drug groups including raw PN (S1-S3), SPN at 105◦C (S4-S8), SPN at 110◦C (S9-S13), and SPN at 120◦C (S14-S18), 10 mice in each group. The APH and CTX-induced anemia model was applied to evaluate the "blood enriching" function of PN combined with previous methods (He et al., 2015). The anemia model was established by intraperitoneal injected of CTX of 0.07 g/kg for the first 3 days and hypodermic injection of APH of 0.02 g/kg at the fourth day. Mice in the control group were administered with 0.9% normal saline, whereas other groups were administered with FEJ (8 ml/kg), and SPN samples at different steamed conditions (0.9 g/kg), respectively, by gavage for 12 days. Then the blood was collected for the routing analysis, including levels of WBC, RBC, Hb, and PLT after 30 min of the last administration. And the liver tissues were collected for the determination of FECH and heme levels.

#### Multivariate Data Analyses

#### **Canonical correlation analysis (CCA)**

Canonical correlation analysis is a multivariate analysis used to study the correlation between two sets of variables. It is used for the dimension reduction of PCA and to extract the main principal components, and then describes the whole linear relationship of two sets of variables by the relevance of two principal components (Shi et al., 2016). In our study, CCA was used to analyze the relevance between the peak area values from the HPLC fingerprints and blood parameters data.

#### **Partial least squares regression (PLSR)**

Partial least squares regression is performed to find the inner relationship between the independent variables (X) and dependent variables (Y), which are simultaneously modeled by taking into account X variance, and the covariance between X and Y (Martens and Naes, 1991). In our study, the X matrix is composed of the enhanced fingerprints, and the Y vector is constructed with the reference values of hematinic effect obtained by measuring the levels of WBC, RBC, Hb, and PLT. Then, X and

<sup>2</sup>https://david.ncifcrf.gov/

Y are decomposed in a product of another two matrices of scores and loadings; as described by the following equations:

$$X = \begin{array}{c} \text{TP}^T + \text{E} \end{array} \tag{1}$$

$$Y = \ U Q^T + F \tag{2}$$

Where TP<sup>T</sup> approximates to the chromatographic data and UQ<sup>T</sup> to the true Y values; notice that the relationship between T and U scores is a summary of the relationship between X and Y. The terms E and F from the equations are error matrices. Hence, the PLS algorithm attempts to find latent variables that maximize the amount of variation explained in X that is relevant for predicting Y; i.e., capture variance and achieve correlation (Sundberg, 2008).

#### Experimental Validation for the Predicted Results

#### Validation for the Screened Active Constituents

Peaks in the HPLC profile predicted to be responsible for the hematinic activity of SPN were then identified by reference standards, of which the activities were finally verified by pharmacologic evaluation using the methods described in the section of "Blood routine test." 90 km mice, half male and half female, were randomly divided into seven groups, namely the control group, model group, FEJ group, and drug groups [including low, moderate, and high-dose ginsenoside 20(S)- Rg<sup>3</sup> group; and low, moderate, and high-dose ginsenoside Rk<sup>3</sup> group], 10 mice in each group. Mice in the control group were intraperitoneal injected with 0.9% normal saline, whereas other groups were intraperitoneal injected with FEJ (8 ml/kg), 20(S)- Rg<sup>3</sup> (2.5, 5, and 10 mg/kg, respectively), and Rk<sup>3</sup> (2.5, 5, and 10 mg/kg, respectively), respectively.

#### Validation for the Predicted Targets and Its Downstream Protein

The livers of mice from different groups were removed to detect the levels of FECH and heme. The livers of different groups of mice were homogenized with a homogenizer and centrifuged for 20 min to collect the supernatant. Mouse FECH enzymelinked immunosorbent assay kit and mouse heme enzyme-linked immunosorbent assay kit were used to detect the levels of FECH and heme in the supernatant.

#### Statistical Analyses

All data were expressed as means ± SD. SPSS 21.0 software (Statistical Program for Social Sciences, SPSS Inc., United States) was applied to carry out the two-tailed unpaired t-test. Umetrics SIMCA-P 11.5 software (Sartorius Stedim Biotech, Sweden) was applied for PLSR analysis. CCA was performed using MATLAB 7.0 (Matrix Laboratory, United States). A value of P < 0.05 was considered to be significant difference. A value of P < 0.01 was considered to be highly significant difference. EC<sup>50</sup> value was fitted by probit regression with Origin 7.5 software for windows (OriginLab Corporation, United States).

#### RESULTS

#### Computational Prediction Using Network Pharmacology Analyses Constituents and Targets Prediction

On the basis of database construction, 203 putative targets with "fit score" >4 were predicted by PharmMapper for 14 compounds and 90 candidate protein targets associated with anemia therapy were collected by keyword-based searching over the OMIM database, which included 19 common targets out of SPN constituents and anemia. Therefore, ginsenosides 20 (R)- Rg2, 20 (S)-Rg2, Rb3, Rb1, F2, and Rc were eliminated due to their low binding affinity to all the candidate targets.

#### Network Construction

The "constituent-target-disease" network was constructed and the noteworthy features of the network analyzed could provide some important information for us to understand the "drug– target" interaction mechanism of a certain drug on a specific disease. Our study was focused on the effect of SPN on treating anemia. In **Figure 1**, the network for the constituents and their potential targets was illustrated with color-coded nodes. The intermolecular interactions (constituent-target or target- disease interactions) were indicated as links, i.e., edges between nodes (Tao et al., 2013). The red triangles represented the analyzed constituents of SPN, the blue dots represented the indirect targets of those constituents, the yellow dots represented the targets of anemia, the purple dots represented the interactional proteins of the anemia targets and SPN constituents, and the yellow squares represented the common targets of SPN constituents and anemia. Obviously, the common targets, as the directed targets of SPN on treating anemia, were relatively important for further screening.

Based on the network analysis, three topological parameters of "degree," "betweenness centrality," and "closeness centrality" were chosen to screen the potential anemia targets that SPN might affect. After calculating the values of the three parameters for each significant protein in the PPI network, the median values of "degree," "betweenness centrality," and "closeness centrality" were 1, 0, and 0.2183, respectively. The protein targets of which the "degree" was more than twofolds of the median value, and "betweenness centrality" and "closeness centrality" were higher than the median value, were chosen as the major targets of SPN treating anemia (Wang et al., 2018). As shown in **Table 1**, we finally determined that 11 common protein targets with degree =2, betweenness centrality =0, and closeness centrality =0.2183 for anemia therapy.

#### Pathway Analysis of SPN Treating Anemia

Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major component of KEGG is the pathway database that consists of graphical diagrams of biochemical pathways including most of the known metabolic pathways and some of the known regulatory pathways (Kanehisa and Goto, 2000). As shown in **Figure 2**, 10 KEGG pathways were enriched by the pathway-enrichment analysis. And there was the maximum quantity of targets involved in the metabolic pathways.

Based on the analysis of KEGG, these target proteins were P22830 (FECH, mitochondrial), P16442 (histo-blood group ABO system transferase), P00374 (dihydrofolate, reductase), P06744 (glucose-6-phosphate isomerase), P19367 (hexokinase-1), P35228 (nitric oxide synthase, inducible), and P30613 (pyruvate kinase PKLR). Combined with the results in **Table 1**, FECH and histo-blood group ABO system transferase showed higher values of degree than others. And based on the literature research, FECH was reported to be a mitochondrial membrane-associated protein catalyzing the terminal step of heme biosynthesis (Yoon and Cowan, 2004), of which the abnormal synthesis can lead to anemia (Iolascon et al., 2009). Therefore, the protein of FECH and its downstream protein of heme (**Figure 3**) were chosen from the map of metabolic pathways for further verification. Constituents related to the target of FECH included ginsenosides Rh<sup>4</sup> (10), Rk<sup>3</sup> (11), 20(R)-Rg<sup>3</sup> (14), 20(S)-Rg<sup>3</sup> (15), 20(R)-Rh<sup>1</sup> (16), 20(S)-Rh<sup>1</sup> (17), and notoginsenoside R<sup>2</sup> (20).

### Prediction of Hematinic Constituents of SPN Based on Multivariate Data Analyses

#### HPLC Analyses

HPLC fingerprints for 18 batches of PN samples were shown in **Figure 4** (Xiong et al., 2017a). "Peaks with good segregation, which also occupied large areas from consecutive peaks, were determined as the common peaks of PN samples. Therefore, fifteen peaks were selected by comparing their ultraviolet spectra and HPLC retention time." The areas of 15 peaks in 18 batches of PN samples were listed in **Supplementary Table S1**. "The peak area was defined as 0 for peaks lacked in chromatograms. The coefficients of variance for almost all common peaks were higher than 46.6%. This is due to the diversity in the levels of constituents contained in samples under different process conditions. The areas of 15 common peaks were used for the following analysis" (Xiong et al., 2017a).

#### Blood Routine Test

After the administration for 15 days, the quantities of WBC, RBC, Hb and PLT from the peripheral blood of mice were shown in **Figure 5**. Compared with the control group, the levels of WBC, RBC, Hb, and PLT in the model group were significantly decreased (P < 0.01), indicating the anemia model was successfully established. Compared with the model group, WBC, RBC, Hb, and PLT levels in the FEJ and all of PN groups were increased at different degrees. Besides, there were more significant differences in the levels of the above four parameters

TABLE 1 | The information of common target proteins and their corresponding active constituents predicted by network pharmacology analyses.


between the model group and SPN groups steamed at higher temperature and longer time, suggesting that SPN steamed at higher temperature and longer time could significantly reverse the decrease of the quantities of WBC, RBC, Hb, and PLT. While for mice treated with raw PN, the level of RBC was significantly increased, whereas there was no significant difference in levels of WBC, Hb, and PLT between raw PN and the model group, indicating that the blood-enriching effect of raw PN was generally weaker than SPN. According the results, SPN was observed to enhance the hematopoietic effect on mice with chemotherapyinduced anemia, which was consistent with the traditional use of SPN (Gu et al., 2015).

#### Uncovering Active Constituents by Multivariate Data Analyses

#### **CCA**

Canonical correlation analysis was used to establish the fingerprint-effect relationships between area values of 15 common peaks in the HPLC data and four blood routine parameters (WBC, RBC, Hb, and PLT). The analysis result was shown in **Table 2**. The correlation coefficients showed that the

FIGURE 4 | HPLC fingerprints of 18 batches of PN extracts. HPLC analyses were done on a Vision HT C<sup>18</sup> column (250 mm × 4.6 mm, 5 µm) at 30◦C. The mobile phase consisting of A (ultra pure water) and B (acetonitrile) was used at a flow rate of 1.0 ml/min as the following gradient mode: 0–20 min, 80% A; 20–45 min, 54% A; 45–55 min, 45% A; 55–60 min, 45% A; 60–65 min, 100% B; 65–70 min, 80% A; and 70–90 min, 80% A. The detection wavelength was set at 203 nm and the injection column was set at 10 µl. PN, Panax notoginseng (Xiong et al., 2017a).

four parameters were positively correlated with X4, X5, X9, X10, X11, X12, X13, X14, and X15. Besides, eight peaks: X5, X9, X10, X11, X12, X13, X14, and X<sup>15</sup> were highly correlated (| R| > 0.6) with the blood parameters. This indicates that the decrease of the quantities of WBC, RBC, Hb, and PLT might be reversed by these compounds.

#### **PLSR**

The PLSR models to correlate chromatographic data and hematinic effect of 18 batches of PN samples were constructed. Since the total number of samples (18) was small and the prediction for new samples was not our first concern, no division

was made into a calibration set to build a PLSR model and a test set to validate the predictive properties. Our main concern was to focus on the indication of hematinic effect peaks from the modeling results. PLSR models were built from the normalized data matrix X containing the 18 PN fingerprints and the response matrix Y (including Y1, Y2, Y3, and Y4, which represented WBC, RBC, Hb, and PLT, respectively). For the model, four principle components were achieved, accounting for an explained variance of 86.4% for X variable, 87.1% for Y variable, and a predictive ability (Q 2 ) of 81.5% (**Supplementary Table S2**), indicating that the obtained model was excellent. As shown in the regression coefficients plot (**Figure 6**), peaks 1, 3, 6, and 8–15 were positively correlated with the quantities of WBC, RBC, Hb, and PLT, whereas peaks 2, 4, 5, and 7 were negatively correlated with the quantities of the four parameters.

Besides, the importance of the X-variables for the model could be summarized by variable importance for the projection (VIP) values (usually with a threshold >1.0). Thus, constituents corresponding to peaks 5, 9, 11, 12, 14, and 15, of which the VIP values were >1.0 (**Table 3**) with high absolute values of coefficients were considered to be highly related to the hematinic effect of different PN samples. Furthermore, false discovery rate (FDR, usually with a threshold ≤0.05) can effectively solve the control of false positive error in multiple comparisons of highdimensional data, and can significantly improve the efficiency of hypothesis testing (Benjamini and Hochberg, 1995). Therefore, constituents corresponding to peaks 5, 7, 9, 10, and 12, of which the FDR values were ≤ 0.05, indicated that constituents corresponding to peaks 5, 7, 9, 10, and 12 were positively correlated with the hematinic effect. Among them, only peak 10 and 12 had significant correlation with the hematinic effect by P-value correcting (P < 0.05).

#### **Identification of active constituents corresponding to predicted peaks**

Based on CCA and PLSR results, constituents corresponding to peaks 10 and 12 were predicted to be the major active ones related to the hematinic effect of SPN. By comparing the chromatogram



of SPN sample to that of the mixed standard solution (**Figure 7**), peaks 10 and 12 were identified to be ginsenosides Rk<sup>3</sup> and 20(S)- Rg3, respectively, which had the major role in the hematinic effect of SPN.

According to the results of network pharmacology analyses, the two constituents were also predicted to be the active ones. Therefore, ginsenosides Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> were determined to be the target constituents for the further investigation of their hematinic effect.

#### Experimental Validation for the Predicted Results

#### Validation for the Screened Active Constituents

After intraperitoneal injected ginsenosides Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> for 15 days, the quantities of WBC, RBC, Hb, and PLT from the peripheral blood of mice were shown in **Figures 8**, **9**, respectively. Compared with the control group, the levels of WBC, RBC, Hb, and PLT in the model group were significantly decreased (P < 0.01), indicating the anemia model was successfully established. Compared with the model group, the levels of WBC, RBC, Hb, and PLT after treated three doses of ginsenoside Rk<sup>3</sup> were all increased. Meanwhile, the levels of WBC, RBC, and PLT in the high-dose group were significantly increased (P < 0.05), suggesting that Rk<sup>3</sup> could reverse the decrease of the quantities of WBC, RBC, Hb, and PLT in a dose-dependent way. For the treatment with ginsenoside 20(S)-Rg<sup>3</sup> of three doses, the levels of WBC, RBC, Hb, and PLT were all increased compared with the model group. Besides, the levels of WBC and Hb in the moderate-dose and high-dose groups were significantly increased (P < 0.05); and the levels of RBC and PLT in the high-dose group were significantly increased (P < 0.05), suggesting that the ginsenoside 20(S)-Rg<sup>3</sup> could reverse the decrease of the quantities of WBC, RBC, Hb, and PLT in a dose-dependent way.

#### Validation for the Predicted Target Proteins

As shown in **Figure 10**, the level of FECH in the model group was significantly decreased compared with the control group (P < 0.05), suggesting that the model was well established. Compared with the model group, the levels of FECH and heme were all significantly increased after the administration of different doses of Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> (P < 0.05). In general, the levels of FECH and heme in livers of mice treated with the middle-dose Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> were relatively higher than those treated with low and high doses of drugs. The results indicated that ginsenosides Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> had a positive effect on improving the levels of FECH and heme, which was consistent with the predicted results of network pharmacology analyses.

#### DISCUSSION

Herbal medicines exert their therapeutic effects through the synergistic effects of multiple constituents and targets. PN in raw and steamed forms are historically supposed to be different in the efficacies. Lau et al. (2009) reported that the bleeding time of rats treated with raw PN was shorter than those treated with SPN. Zhou et al. (2014) found that SPN could significantly increase the levels of Hb and WBC, as well as the organ index of mice with BDS induced by CTX, which were unconspicuous when treated with raw PN. Based on our previous studies (Xiong et al., 2017b), there was a significant variation in the chemical composition between the two forms of PN, which leaded to the difference in the pharmacologic effects of raw and steamed PN. As shown in **Figure 3**, the levels of blood routine parameters of mice treated with SPN were significantly increased compared with the model group, which were also obviously higher than those of mice treated with raw PN. The result was consistent with the traditional use of SPN as a tonic to enrich the blood.

Currently, methods for uncovering active constituents of herbal medicines treating diseases mainly rely on retrospective analyses. However, this method depends on large consumption of manpower and material resources, which hinders the development of drugs. To address this issue, we have developed firstly a more comprehensive approach that integrates anemia-SPN networks to effectively discover potential active constituents and targets involved. Technically, the prediction accuracy of the drug targets and the completeness of the databases are important to the method and will affect the creditability of the


final results. Therefore, we tried to reduce the false positive cases, such as threshold filtering with the fit score in the drug target prediction, significance analyzing with hypergeometric distribution approach in disease targets enrichment, and reasonable topologic parameter screening in the analyses of network (Wang et al., 2018). In addition, the combined prediction of multivariate data analyses and verification of pharmacologic tests confirmed the credibility of the model. The

predicted results indicated that 14 constituents of SPN were interacted with 11 targets related to anemia in the network. As shown in **Table 1**, many candidate proteins were targeted by more than one compound. It suggested that these targets might play an important role in the hematopoiesis, the modulation of which could lead to the stimulation of various cytokines in the hematopoietic microenvironment, enhancement of the function of internal free radical scavenging system, facilitation of the absorption and utilization of iron, improvement of the bone marrow hematopoietic microenvironment, etc. (Wang et al., 2012b; Liu M. et al., 2014). The common cross-targets shared by multiple constituents implied that SPN might exert the

synergistic therapeutic effect on anemia, which was probably more effective than single compound. This suggested that the herbal medicine might act on polypharmacological level, rather than on one specific protein in order to combat complex diseases, such as anemia.

"Degree," "betweenness centrality," and "closeness centrality" are three key topological parameters that characterize the most influential nodes in a network. According to Li et al. (2007), if the "degree" of a node was more than twofolds of the median degree of all nodes in a network, such gene or protein was believed to play a critical role in the network structure, and it could be treated as a hub gene or a hub protein. "Betweenness centrality" was one of the significant indicators of network essentiality because proteins with high betweenness were essential for the functioning of the system by serving as a bridge of communication between several other proteins in the network (Melak and Gakkhar, 2015). And "closeness centrality" was another one of the significant indicators of network essentiality which represented the average length of the shortest paths to access all other proteins in the network. The higher the value, the more central the protein (Zhuang et al., 2015). Therefore, we used the above parameters to determine the importance of active constituents and action targets (the nodes in our network), as well as the extent of their influence on the spread of information through the network (Tang et al., 2015). Among the 11 predicted targets involved in the pathogenic process of anemia, FECH was shown relatively higher values of degree and closeness centrality, and was reported to be closely related to the production of heme. According to **Figure 11**, FECH is the terminal heme synthesis enzyme to catalyze the insertion of the imported iron into protoporphyrin IX to produce heme. Gene mutation in FECH may cause pathological changes like erythropoietic protoporphyria, an autosomal dominant disease which can develop into cholelithiasis and varying degrees of liver diseases (Casanova-González et al., 2010). It was reported that FECH forms an oligomeric complex with Mfrn1 and Abcb10 to synergistically integrate mitochondrial iron importation for heme biosynthesis (Chen et al., 2010). Since heme is an important raw material for hemoglobin synthesis and the increased heme level can resulted in a significant enhancement of human hemoglobin production (Liu L. et al., 2014), the variation of FECH and heme could be investigated to verify the hemopoiesis induced by active constituents of SPN.

From **Table 1**, the majority of compounds were linked with more than one target, indicating that these compounds might

play the therapeutic effect by acting on multi-targets. Among the 14 compounds in our network, several of them might be essential. For example, in our previous work (Xiong et al., 2017a), we found the levels of some saponins in PN were increased along with the steaming time and temperature. Among them, ginsenosides Rh4, Rk3, 20(R)-Rg3, and 20(R)-Rh<sup>1</sup> with higher contents or exclusively existed in SPN showed higher contributions to the activities of SPN. The result was consistent with the prediction in this research, that seven constituents of ginsenosides Rh<sup>4</sup> (10), Rk<sup>3</sup> (11), 20(R)-Rg<sup>3</sup> (14), 20(S)-Rg<sup>3</sup> (15), 20(R)-Rh<sup>1</sup> (16), and 20(S)-Rh<sup>1</sup> (17), and notoginsenoside R<sup>2</sup> (20) were predicted to be interacting with the target of FECH for the treatment of anemia. It indicated that the network pharmacology approach had great potential to identify active constituents and alternative targets for the mechanism understanding and drug development of herbal medicines.

To further determine the active constituents, the analysis of fingerprint-effect relationship has been applied to screen characteristic constituents related to the hematinic effect of SPN. Multivariate data analyses such as PLSR and CCA are often used to specify a linear relationship between a set of dependent variables from a large set of independent variables, especially when the sample size is small relative to the dimension of these variables (Garza-Juárez et al., 2011; Wu et al., 2015). According to the results, Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> were predicted to be the major bioactive constituents of SPN treating anemia, which were also included in the prediction of network pharmacology analyses. The two constituents were reported to own various protective effects in previous studies. For example, 20(S)-Rg<sup>3</sup> could prevent the progression of renal damage (Kang et al., 2010), protect against benzo[a]pyrene-induced genotoxicity in human cells (Poon et al., 2012), and protect against lipopolysaccharideinduced oxidative tissue injury in the liver of rats (Kang et al., 2009). And ginsenoside Rk<sup>3</sup> was shown a protective effect against hypoxia-reoxygenation induced H9c2 cardiomyocytes damage, which was often used as a major ingredient of the compound preparation for ischemic heart diseases (Sun et al., 2013). To validate the predicted results, the effects of Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> on levels of blood routine parameters were investigated based on the BDS model. Compared to the model group, the high-dose Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> could significantly increase the levels of WBC, RBC, and PLT in a dose-dependent way. The high-dose 20(S)- Rg<sup>3</sup> also made a significant difference on improving the content of Hb. It indicated that the two constituents had positive effect on improving the BDS of mice, which was consistent with the predicted results of network pharmacology and multivariate data analyses. Besides, the levels of FECH and heme could be increased by the treatment of Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> (**Figure 10**), suggesting that the two constituents exert the hematinic effect by regulating the predicted target and its downstream protein. Meanwhile, we noticed that the high-dose of ginsenosides Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> also inhibited the production of FECH and heme. That might be due to the bidirectional adjustment between FECH and heme that FECH was an integral factor for the biosynthesis of heme, whereas an excessive level of heme could inhibit the production of FECH (Dailey and Fleming, 1983; Wu et al., 2016). These results also indicated that the metabolism of FECH and heme was involved in the development of anemia, of which the stabilization could be regulated by SPN to resist hemolysis. However, further studies are needed to understand the precise nature of these contributing factors.

### CONCLUSION

To unveil the bioactive constituents and investigate the action mechanism of SPN for improving BDS, the network pharmacology approach coupled with multivariate data analyses were performed. In this study, we firstly predicted the active constituents and potential targets of SPN related to the treatment of anemia disease. The results showed that ginsenosides Rk<sup>3</sup> and 20(S)-Rg<sup>3</sup> were active constituents related to the hematinic effect of SPN, which acted on the targets of FECH and heme to improve the BDS. Although there could be various pathogens causing the incident of anemia and only the hemolytic type was investigated in the research, it also indicated potential areas for further research of SPN as a botanical remedy for the treatment of related diseases. The strategy employed does not only provide new insights for a deeper understanding of the chemical basis and pharmacology of SPN, but also demonstrate an efficient method for potential discovery of drugs originating from herbal medicines. Additional study on the therapeutic effect of SPN on other types of anemia and the involved pathways will be further carried out.

### AUTHOR CONTRIBUTIONS

YX wrote this paper and carried out parts of data analyses. YH constructed the network and verified the predicted targets. LC did the multivariate data analyses and parts of pharmacologic tests. ZZ and YZ conducted parts of the pharmacologic tests. MN provided the technical support of network pharmacology. YX and XC supervised the project. All authors read and approved the final manuscript.

## FUNDING

This work was supported by National Natural Science Foundation of China (81660661), Kunming University of Science and Technology (KKSY201526065), and Yunnan Applied Basic Research Project (2016FD040).

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.01514/full#supplementary-material

#### REFERENCES

fphar-09-01514 December 26, 2018 Time: 18:9 # 13



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Xiong, Hu, Chen, Zhang, Zhang, Niu and Cui. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Systems Pharmacology Dissection of Multi-Scale Mechanisms of Action of Huo-Xiang-Zheng-Qi Formula for the Treatment of Gastrointestinal Diseases

#### Edited by:

Shao Li, Tsinghua University, China

#### Reviewed by:

Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China Xuetong Chen, Northwest A&F University, China W Tao, University Medical Center Utrecht, Netherlands

#### \*Correspondence:

Yan Li yanli@dlut.edu.cn; adinalee@163.com

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 01 June 2018 Accepted: 26 November 2018 Published: 11 January 2019

#### Citation:

Zhao M, Chen Y, Wang C, Xiao W, Chen S, Zhang S, Yang L and Li Y (2019) Systems Pharmacology Dissection of Multi-Scale Mechanisms of Action of Huo-Xiang-Zheng-Qi Formula for the Treatment of Gastrointestinal Diseases. Front. Pharmacol. 9:1448. doi: 10.3389/fphar.2018.01448 Miaoqing Zhao1,2, Yangyang Chen<sup>3</sup> , Chao Wang<sup>1</sup> , Wei Xiao<sup>4</sup> , Shusheng Chen<sup>5</sup> , Shuwei Zhang<sup>1</sup> , Ling Yang<sup>6</sup> and Yan Li1,2 \*

<sup>1</sup> Key Laboratory of Industrial Ecology and Environmental Engineering, Faculty of Chemical, Environmental and Biological Science and Technology, Dalian University of Technology, Dalian, China, <sup>2</sup> Key Laboratory of Xinjiang Endemic Phytomedicine Resources, Pharmacy School, Shihezi University, Shihezi, China, <sup>3</sup> Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, China, <sup>4</sup> State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Jiangsu Kanion Pharmaceutical Co., Ltd., Lianyungang, China, <sup>5</sup> Systems Biology Laboratory, Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL, United States, <sup>6</sup> Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China

Multi-components Traditional Chinese Medicine (TCM) treats various complex diseases (multi-etiologies and multi-symptoms) via herbs interactions to exert curative efficacy with less adverse effects. However, the ancient Chinese compatibility theory of herbs formula still remains ambiguous. Presently, this combination principle is dissected through a systems pharmacology study on the mechanism of action of a representative TCM formula, Huo-xiang-zheng-qi (HXZQ) prescription, on the treatment of functional dyspepsia (FD), a chronic or recurrent clinical disorder of digestive system, as typical gastrointestinal (GI) diseases which burden human physical and mental health heavily and widely. In approach, a systems pharmacology platform which incorporates the pharmacokinetic and pharmaco-dynamics evaluation, target fishing and network pharmacological analyses is employed. As a result, 132 chemicals and 48 proteins are identified as active compounds and FD-related targets, and the mechanism of HXZQ formula for the treatment of GI diseases is based on its three function modules of antiinflammation, immune protection and gastrointestinal motility regulation mainly through four, i.e., PIK-AKT, JAK-STAT, Toll-like as well as Calcium signaling pathways. In addition, HXZQ formula conforms to the ancient compatibility rule of "Jun-Chen-Zuo-Shi" due to the different, while cooperative roles that herbs possess, specifically, the direct FD curative effects of GHX (serving as Jun drug), the anti-bacterial efficacy and major accompanying symptoms-reliving bioactivities of ZS and BZ (as Chen), the detoxication and ADME regulation capacities of GC (as Shi), as well as the minor symptoms-treating

**156**

efficacy of the rest 7 herbs (as Zuo). This work not only provides an insight of the therapeutic mechanism of TCMs on treating GI diseases from a multi-scale perspective, but also may offer an efficient way for drug discovery and development from herbal medicine as complementary drugs.

Keywords: TCM, gastrointestinal diseases, functional dyspepsia, systems pharmacology, Huo-xiang-zheng-qi, compatibility theory

### INTRODUCTION

fphar-09-01448 January 9, 2019 Time: 10:19 # 2

It is well known that many complex diseases including CVDs, cancers, HIV, etc., are usually, in character, caused by a combined action of multi-factors (organs, tissues and proteins). Therefore, monotherapies may not always produce ideal efficacy. Whereas, TCM, characterized by "multi-components" and "multi-targets" features and regarded as a precious treasure for Asians, has been applied in treating various complex diseases as principle or auxiliary drugs for more than 2,500 years (Pei et al., 2016). Compared with monotherapies, TCM takes into account most aspects of complex diseases (multi-etiologies and multisymptoms), and thus often exerts potent curative efficacy. Specifically, in two ways, TCM contributes to the process of turning dysfunctional living organisms back to their normal states: (1) containing plentiful active components which usually provide patients with beneficial synergistic actions by acting on diverse biological targets; (2) being mostly natural herbal medicines, and sometimes even edible, and thus may be of less side effects and low toxicities (Li et al., 2014). Nevertheless, for most TCMs, not only their mechanism of action, but also the compatibility theory their herbs follow still remain vague.

Gastrointestinal (GI) diseases, a kind of highly prevalent complex diseases, account for substantial morbidity, mortality and health care utilization of humankind world (Peery et al., 2015). Since FD, one of the most common diseases of digestive system, is a typical GI disease due to the multipathological causes, presently it is used as an example to explore corresponding mechanism involved in the GI diseases therapy. In fact, FD is defined as chronic or recurrent clinical syndrome of upper abdominal with complex pathogenesis, with 7 ∼ 45% current morbidity (Buzas, 2007) worldwide. Its symptoms include epigastric pain or burning, early satiety, belching, nausea, bloating, vomiting, fullness after meal, which are usually attributed to slow gastric emptying, failing of the gastric fundus, visceral hypersensitivity to distention, gastroenteritis, duodenal inflammation, or center nervous system dysfunction (Xiao et al., 2012). HXZQ, a famous TCM recipe described in Prescriptions of Peaceful Benevolent Dispensary, has been used for the treatment of gastrointestinal disorder from ancient Song Dynasty in China. The formula is composed of 11 herbs: Pogostemon cablin (Blanco) Benth (GHX), Atractylodes macrocephala Koidz (BS), Magnolia officinalis Cortex (HP), Arum ternatum Thunb (BX), Perilla frutescens (ZS), A. dahurica (Fisch.) Benth. Et Hook (BZ), Citrus reticulata (CP), Poria cocos (Schw.) Wolf (FL), Licorice (GC), Areca catechu L (DFP), Zingiber officinale Roscoe (SJ). Although its efficacy in FD treatment has been confirmed by numerous clinical appliances, its fundamental molecular action mechanisms as well as the combination principle of the herbs are still elusive. Thus, taking HXZQ formula for FD treatment as probe, the present work aims at interpreting the compatibility theory and the action mechanism of TCMs in the treatment of GI diseases. Specifically, by applying a systems pharmacology platform, we explore the pathogenesis of FD disease as well as the therapeutic mechanism of HXZQ prescription. The obtained results, we hope, may not only improve the comprehension of FD pathogenesis and HXZQ pharmacological basis, but also promote the development of TCM herbs as complementary drugs for curing complex diseases.

#### MATERIALS AND METHODS

#### Database Building

The ingredients of all herbs in HXZQ were data-mined from not only relevant databases including TCM Systems Pharmacology Database (TCMSP<sup>1</sup> ), Chinese Academy of Sciences Chemistry Database<sup>2</sup> , Herbal Ingredients' Targets Database (HIT), TCM database @Taiwan<sup>3</sup> , and TCMID<sup>4</sup> , but also all related literatures. Finally, 1,192 chemicals were obtained with structures collected from NCBI PubChem Database<sup>5</sup> . All structures of these chemicals were drawn and optimized by Sybyl 6.9. HXZQ herbs' name, the number of ingredients they contain and corresponding abbreviations are shown in **Table 1**.

### Workflow of the Systems Pharmacology Approach

The specific workflow is displayed in **Figure 1**. Firstly, the active components of HXZQ herbs were identified via an ADME-screening model which incorporates the OB, DL and half-life (HL) screening modules together. Then, the potential targets of the prescription were predicted through target fishing with corresponding compound-target networks

**Abbreviations:** DL, drug-likeness; FD, functional dyspepsia; GDs, gastrointestinal diseases; GI, gastrointestine; GO, Gene Ontology; H. pylori, Helicobacter Pylori; HXZQ, Huo-xiang-zheng-qi; IL-1β, interleukin-1 beta; IL-6, interleukin-6; NSAIDs, non-steroidal anti-inflammatory drugs; OB, oral bioavailability; PGE2, prostaglandin E2; P-gp, p-glycoprotein; RF, random forest; SEE, standard error of estimate; SVM, supporting vector machine; TCM, Traditional Chinese medicine; TLRs, Toll-like receptors; TNF-α, tumor necrosis factor α.

<sup>1</sup>http://lsp.nwu.edu.cn/articles.php?id=2

<sup>2</sup>http://www.organchem.csdb.cn

<sup>3</sup>http://tcm.cmu.edu.tw/

<sup>4</sup>https://omictools.com/tcmid-tool

<sup>5</sup>http://www.ncbi.nlm.nih.gov/pccompound

#### TABLE 1 | The herbs of HXZQ formula.

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mapped with attempt to explore the essence of the herbal medicine from a systematic point. Subsequently, target-pathway networks were constructed for further network pharmacology analysis.

#### Active Ingredients Screening

#### Oral Bioavailability

As a most vital pharmacokinetic parameter, OB is the rate and percentage of an oral dose for a drug that is absorbed into blood circulation and produces pharmacological effects. Presently, a robust in-house model OBioavail 1.1 was employed to calculate the OB values, which was built based on a dataset composed of 805 diverse drugs or drug-like molecules by consideration of the action of P-glycoprotein and Cytochrome P450s in metabolism and information transport (Li et al., 2015a; Wang et al., 2015). Those molecules with OB ≥ 30% were filtered out as candidate compounds with determination coefficient R <sup>2</sup> of 0.80 and SEE of 0.31.

#### Drug-Likeness

Drug-likeness is the comprehensive reflection of a molecule' s pharmaco dynamics properties in human body, which has been applied in drug discovery to identify those molecules with "druglike" traits so as to modulate corresponding targets (Wang et al.,

2015). Presently, a Tanimoto coefficient was applied to calculate the DL value.

$$DL(A,B) = \frac{A\*B}{|A|^2 + |B|^2 - A\*B}$$

in which A represents the molecular descriptors of herbal components, and B the average molecular properties of all 6,511 molecules in DrugBank database<sup>6</sup> (Wishart, 2006), respectively. In this study, DL ≥ 0.18 is adopted as a filter threshold to screen the active components of herbs.

#### Drug Half-Life

Considering that HL is the essential pharmacokinetic parameter of drugs which represents the time taken for a substance to lose half of its pharmacologic and physiologic activities, we introduce a robust prediction drug half-life model that enables us to forecast long or short half-life of drugs by using the C-partial least square (C-PLS) algorithm (Chung and Keles, 2010), which is supported by 169 drugs with known half-life from DrugBank to acquire potential active ingredients with the screening threshold value defined as HL ≥ 4.

#### Target Prediction and Classification

Currently, a computer model established by random forest (RF) and support vector machine (SVM) algorithms which integrates the chemical, genomic, and pharmacological information was applied (Liu et al., 2013) to predict the potential targets with RF score ≥ 0.8 and SVM ≥ 0.7 as threshold. Thereafter, the targets' information was mined by browsing of the HIT, herapeutic Targets Database (TTD<sup>7</sup> ) and DrugBank combined with literatures. All resulted targets were then sent to TTD and PharmGKB<sup>8</sup> for disease mapping. Finally, the targets were further mapped to UniProt Database<sup>9</sup> for the normalization of targets' writing form.

#### GO Enrichment Analysis

Presently, the GO enrichment analysis was performed to further probe the vital biological process of achieved targets which were mapped to DAVID (the Database for Annotation, Visualization and Integrated Discovery<sup>10</sup>) for analyzing targets' biological meaning. The GO terms of biological process were utilized to symbolize genic function. Finally, those GO terms with p-value ≤ 0.05 and FDR ≤ 0.05 were selected for further research.

#### Network Construction

In this work, two types of biological networks were constructed for HXZQ. Firstly, the compound-target (C-T) networks were generated by Cytoscape v3.2.1 incorporating all the active compounds-targets interactions in HXZQ formula. Then, related pathways obtained by mapping the targets to KEGG database<sup>11</sup> were used to build compound-target-pathway networks for further network pharmacology analysis (Li and Zhang, 2013; Zhang et al., 2013).

#### In silico Validation of the C-T Interactions

For exploring the binding modes and offering more insights into the interactions between the candidate compounds and their protein targets, three targets and twelve C-T interactions were selected for docking validations as illustrations. The molecular docking of these protein-ligand complexes was carried out by using GOLD version 5.1, a genetic algorithm-based docking program to generate an ensemble of docked conformations. The X-ray crystal structures of CHRM3, GSK3B and PTGS2 (with PDB entry codes of 4DAJ, 4ACD and 5F19, respectively) were retrieved from RCSB Protein Data Bank<sup>12</sup>. Taking into account the factors including H-bonding energy, van der Waals energy, metal interaction, and ligand torsion strain in the defaulted scoring function, the GOLD Score fitness function was employed.

#### RESULTS

#### Active Compounds Screening

Since HXZQ formula is composed of 11 herbal medicines with each containing dozens or even hundreds of ingredients, the building of an ingredient database for HXZQ is a necessity. Thus, to our best efforts, by data mining a total of 1,192 molecules were obtained as HXZQ's components presently.

It is well known that among the great number of compounds contained in a TCM, many chemicals fail in reaching the target sites due to the lack of suitable pharmaceutical properties in oral administration process (Li et al., 2015b). Actually, it is the ADME (absorption, distribution, metabolism, and excretion) properties of a drug that determine its success or failure in this process. Thus, for finding out those possible active ingredients, a screening platform containing three models we established, which, respectively, evaluate three essential pharmacokinetic parameters, namely, OB, DL, and HL, that reflect the most crucial ADME/T properties of compounds, was employed to screen the ingredient database. As a result, candidate compounds which satisfy the conditions of OB ≥ 30%, DL ≥ 0.18, and HL ≥ 4 were sorted out into the candidate compound pool. It is worth noting that several compounds have relatively low pharmacokinetic values, but they are either the richest ingredients of the herbs, like magnolol (HP01) and honokiol (HP02), or biologically active, thus are also considered as candidate components presently. In this way, finally 132 chemicals are identified as active compounds of HXZQ, with information all listed in **Supplementary Table S1**. **Table 2** displays part of them as examples.

Interesting, among them, plenty compounds have been reported biological active. For instance, baicalein (BX03), a widely reported flavonoid of BX, restrained lipopolysaccharide (LPS) -induced NO production which reflects the severity of inflammation (Chen et al., 2001). It also affected the inflammation related cyclooxygenase activities to relieve

<sup>6</sup>http://www.drugbank.ca/

<sup>7</sup>http://bidd.nus.edu.sg/group/ttd/

<sup>8</sup>http://www.pharmgkb.org

<sup>9</sup>http://www.uniprot.org/

<sup>10</sup>https://david.ncifcrf.gov/

<sup>11</sup>http://www.genome.jp/kegg/

<sup>12</sup>http://www.rcsb.org/

# ID Name Structure ID Name Structure BS03 Atractylenolide I GHX01 Genkwanin BX02 Cavidine GHX04 Irisolidone BZ04 Coumarin GHX05 Patchouli alcohol CP03 Nobiletin GHX06 Quercetin DFP01 Arecoline DFP02 Arecolidine HP01 Magnolol HP02 Honokiol GC35 Licoisoflavone B FL10 Pachymic acid GC11 Medicarpin BX03 Baicalein GHX07 Rutin GC69 Glycyrrhizic acid GC13 7-Methoxy-2-methoxyisoflavone GC08 Inermine BS0 4 Atractylenolide II SJ02 Curcumin

#### TABLE 2 | Certain candidate compounds of HXZQ formula mentioned in the present work.

enterogastritis (Sekiya and Okkuda, 1982). Moreover, BX03 possesses anti-Helicobacter pylori (H. pylori) activity and thus protected the gastrointestinal digestive tract of FD patients (Bae et al., 1999). Beta-sitosterol is not only the mutual ingredient of five herbs including HP, BX, ZS, FL, and SJ in HXZQ formula, but also usually used in modulation of immune system, as well as the prevention of cancer or heart diseases (Saeidnia et al., 2014). And its pharmacological efficacy for FD treatment such as anti-inflammatory, analgesic and anthelmintic activities have already been experimentally proved (Saeidnia et al., 2014).

In observation of the structures of the candidate compounds, an interesting phenomenon attracts our attention that many of them lie in two types, i.e., flavonoids and terpenoids. Actually, nearly half are flavonoids, and 15.1% are terpenoids as shown in **Figure 2**. In nature, flavonoids are widely accumulated in medicinal plants. They are important for plant development, and are also well known as beneficial for human nutrition, health and prevention of cell aging (Hichri et al., 2011). According to structure, the 61 flavonoid active ingredients in HXZQ belong to two categories: 2-phenylchromans (40) and 3-phenylchromans (21, also called as isoflavoids), with either having been reported with a wide range of proper biological activities, including antibacterial, antithrombotic and antiinflammatory effects (Knekt et al., 2002). For example, quercetin (GHX06), a typical flavonoid in plants, exerts remarkable antioxidative and anti-inflammatory effects and thus is capable of treating gastrointestinal inflammation and relieving FD patient's pain. Factually, GHX06 up-regulated several pro-inflammatory mediators like TNF-α and IL-1β in model rats, and in this way fulfilled its anti-inflammatory functions (Ji et al., 2017). GHX06 has also analgesic effects through inhibiting the nociceptive neurotransmission and ameliorating the pathological pain. Specifically, a continuous daily administration of GHX06 at 100 mg/kg for 14 days attenuated the hyperalgesia of model rats in the long-term pain treatment trails (Ji et al., 2017). In addition, licoisoflavone B (GC35), an isoflavone compound, reduced the damage of the gastrointestinal mucosa caused by H. pylori, by potently inhibiting the growth of H. pylori ATCC 43504, ATCC 43526, ZLM 1007, and GP98 even with a minimum inhibitory concentration of 6.25 µg/mL in vitro (Fukai et al., 2002). Therefore, flavonoids, the main ingredients of HXZQ, should be the major molecular bioactivities basis of this formula.

In addition, terpenoids, another major category of HXZQ's active components, are defined as the derivative of mevalonic acid conforming to the (C5H8)<sup>n</sup> general structure. Terpenoids play essential roles in the basic life of plants, and are also applied in industrial production and medical hygiene. For instance, atractylenolide I (BS03), a sesquiterpene derived from herb BS, possesses neuroprotective, all-allergic, anti-inflammatory and anticancer bioactivities (Fu et al., 2018). For inflammatory model mouse, the treatment of BS03 factually significantly decreased the levels of pro-inflammatory factors TNF-α and IL-6 in a dosedependent manner (Wang et al., 2016). Thus, terpenoids are also important active substance of HXZQ.

Besides, HXZQ also contains some other kinds of chemicals like sterols and alkaloids. Actually, many plant sterols have inhibitory functions for the growth of tumors (Rubis et al., 2008). Similarly, alkaloids are also one kind of effective components of TCM. Arecoline (DFP01), the main active constituent of DFP, promotes intestinal peristalsis and enhances GI motion. Experiments showed that DFP01 enhanced bowels' tension in rabbits through mediating muscarinic acetylcholine receptor (Xie et al., 2004). Other categories of substances like essential oils and organic acids in HXZQ may provide nutrition or a proper microenvironment in vivo.

In summary, due to diversified structural distribution of its substance basis, i.e., the various types of candidate compounds that cover flavonoids, terpenoids, sterols and alkaloids etc., HXZQ is capable of holistically treating the complex etiology of FD from several different aspects.

#### Physicochemical Property Analysis

In order to analyze the drug-like physicochemical properties of active compounds, a comparison of the properties of the herbal active ingredients and DrugBank medicines is carried out by consideration of eight common molecular descriptors, which include MW (molecular weight), nCIC (number of rings), nHDon (number of hydrogen-bond donors), nHAcc (number of hydrogen-bond acceptors), RBN (number of rotatable bonds), Hy (hydrophilic factor), TPSA (topological polar surface area) and MlogP (Moriguchi octanol-water partition coefficient), since that these parameters reflect the basic characteristics of the molecules including especially their pharmacokinetic properties (Li et al., 2017).

Lipinski's rule of five is a rule of thumb to evaluate the DL or determine if a compound with a certain pharmacological or biological activity has chemical and physical properties to make it a likely orally active drug in humans. The specific content of this rule is, in general, an orally active drug has no more than one violation of the following criteria: possessing (1) no more than 5 hydrogen bond donors; (2) no more than 10 hydrogen bond acceptors; (3) a molecular weight less than 500 daltons; (4) an octanol-water partition coefficient logP not greater than 5 (Lipinski et al., 2001). As seen from **Figure 3**, four parameters including nHDon, nHAcc, MW, and MlogP are all related to this principle and meet the rule well, indicating that the active ingredients of HXZQ formula are very likely to become


∗∗p < 0.01.

drugs. In addition, concerning with the molecular weight, herbal chemicals and DrugBank compounds have quite similar tendency (with p > 0.05) based on the factor analysis of variance that they both follow a Gaussian distribution characteristics. Whereas, significant difference exists between the herbal and DrugBank chemicals in nHDon, nHAcc, and MlogP as shown in **Table 3** (with p < 0.01). Specifically, for nHDon and nHAcc, the average values of HXZQ formula are lower than DrugBank, which illustrates that the average number of hydrogen bonds generated by HXZQ molecules may be less than DrugBank. As to MlogP, the average value of HXZQ's active compounds is larger than that of DrugBank, implying that the active ingredients of herbs probably are more lipophilic and more soluble in the lipid solution. Actually, this corresponds well with another index, Hy (in **Table 3**), that Hy's average value of HXZQ formula is lower than the DrugBank one, suggesting that the herbs have fewer hydrophilic molecules and lower overall hydrophilicity.

Among the other three indices that are also displayed in **Table 3**, namely, nCIC, TPSA, and RBN, some difference is observed for the former two parameters that the average value of TPSA is lower than those of DrugBank ones, indicating that the compounds in herbal formula are more likely to permeate the membrane and be absorbed by human body. Whereas, for nCIC, its average value of herbal compounds is larger than those drugs in DrugBank database, indicating that HXZQ formula contains many aromatic compounds. As to RBN, little difference exists between herbal and DrugBank database, implying similar flexibility of herbal compounds to DrugBank ones. In a word, the herbal chemicals are characterized by large number of aromatic components, relative high hydrophobicity and moderate molecular weight, and therefore may be easily absorbed by human body.

### The Combining Rule of "Jun-Chen-Zuo-Shi"

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It is well known that the formation of any TCM formula composed of multiple or dozens of herbs is not random. Instead, almost all of them are built based on certain combinational rules, among which the most famous one is the ancient Chinese theory of "Jun-Chen-Zuo-Shi". Its basic assumption holds that if a TCM prescription functions like a government, then its herbs should fulfill different duties including Jun (monarch), Chen (minister), Zuo (assistant) and Shi (guide) either individually or in a combinational way to ensure the smooth run of the formula. Presently, to vividly depict the theory, a salutary meat burger is drawn (as shown in **Figure 4**), where the meat, bread, vegetables and cream correspond to Jun, Chen, Zuo, and Shi, respectively, according to the different roles they play individually if the burger is considered as a TCM formula.

As is known to us, all FD pathological factors belong to three classes, i.e., the gastrointestinal damage caused by bacterial (including especially H. pylori) infection, the inflammation of gastrointestinal tract, and the inadequate gastrointestinal motility in essence. Therefore, presently, we first analyzed the therapeutic functions of individual herbs of the formula on FD treatment from these three classes of factors. Then, based on the analysis of the structural distribution of active compounds in the herbs and the contribution of their specific activities to FD treatment, as well as the investigation and understanding of traditional combination theory about the roles of the main and auxiliary drugs of TCM, we conclude that HXZQ formula conforms to the compatibility rule of "Jun-Chen-Zuo-Shi," which theory can be scientifically interpreted as herbs playing different roles based on their specific contributions to the integrated therapeutic function of the TCM formula. In other words, Jun (monarch) drug, as the

most essential herb/herbs in a TCM prescription, plays lead pharmacological activities in prevention and treatment of disease as the enlightened monarch in a powerful nation. Chen drug, like a minister, either promotes the curative effects of Jun drug or is responsible for treating some accompanying symptoms (Wu et al., 2014). Zuo (assistant) drug enhances the minister drug's treatment efficacy, or aims at minor symptoms. As for Shi drug, it is generally used in low dose, with aim to induce the herbs' impact to the disease location and modulate the interaction among the herbs, as well as to remove toxic substance from the human body, which is also called as a "messenger" drug. This principle signifies the fact that each herb of the recipe has a specific function within the composition, and is organized and arranged integrally under the principle to generate specific effects.

Presently, during the revision process, based on this assumption and the regular dosage of the herbs of HXZQ formula, the influence of the quantity of all medicines in the formula was also investigated. Specifically, in general the dosage of GHX and all other herbs follows a fixed ratio (as shown in **Table 4**), where the content of SJ is usually ignored due to the fact that SJ is only used as soaking solution of HP during the preparation process of the herbs before all processed herbs come together as a formula. Thus, based on this dose ratio, the relative blood concentrations of all herbs in HXZQ formula are calculated, which ends up with results as shown in **Table 4**.

GHX, belonging to Lamiaceae family, is the dried aerial part of a famous herb named "Guanghuoxiang." Actually, it is a frequently used folk medicine in the treatment of common cold, nausea, diarrhea, rhinitis, headaches and fever, and it bilaterally regulates the gastrointestinal smooth muscle, accelerates the secretion of digestive juice, and protects the intestinal barrier function. Clinically, GHX has been long used as a major composition of HXZQ formula due to its multiple beneficial biological activities such as the anti-inflammatory, anti-fungal and gastrointestinal tract regulation effects (Singh and Ganesha Rao, 2009). Presently, GHX contains 94 ingredients, where 7 compounds are identified as candidate compounds. Although GHX has only limited number of active ingredients, most of them directly contribute to the therapy of GI diseases. For example, irisolidone (GHX04), a major isoflavone in GHX, has a wide spectrum of favorable bioactivities such as antioxidative, antiviral, anti-inflammatory and anti-tumor (Park et al., 2006; Kang et al., 2008). It also inhibits the production of LPSinduced NO, cytokines TNF-α to relieve enterogastritis and the expression of matrix metallo proteinases which control the tumor invasion and angiogenesis (Kim and Yun-Choi, 2008). In addition, patchouli alcohol (GHX05), GHX's principal ingredient and a tricyclic sesquiterpene, also demonstrates high selective antibacterial effects against H. pylori (Liao et al., 2013). As a matter of fact, GHX05 potently inhibits the inflammatory response through decreasing those inflammatory mediators including TNF-α, IL-1β, and NO in LPS-stimulated RAW264.7 macrophages (Li X. et al., 2012; Li Y. et al., 2012). As seen from **Tables 5**, **4**, the relative blood concentration of GHX is far higher than all other herbs, which, combined with the broad antibacterial activities as well as the good anti-inflammatory activity the herb possesses, verifies the Jun role of GHX in HXZQ

TABLE 4 | The number of molecules and relative blood concentration of each herbs.




formula. Besides, GHX is the most abundant herb in HXZQ, which may also account for its prominent roles in the formula.

ZS and BZ are Chen drugs of HXZQ. As mentioned earlier, Chen drugs exert their medicinal properties from two aspects: (1) enhancing curative effect of Jun drug; and/or (2) treating some accompanying symptoms. ZS is a traditional herb with a specific aroma which is in charge of the treatment of bacterial and fungal infections. Interestingly, this plant is not only medicable, but also an edible spice. Studies have shown that those components exerting the main effects of ZS are rosemary acid and perillyl alcohol (Gu et al., 2009). Whereas, perillyl alcohol possesses excellent anti-cancer effects which has been applied clinically currently (Loutrari, 2004). Although its bioactivities for GI system have not been reported, perillyl alcohol is assumed to have potentials for FD treatment and is worthy of further research and exploration. As for BZ, it has strong antipyretic, analgesic, antiasthmatic, antispasmodic and antibiosis effects, which are also part of major accompanying symptoms of FD. In fact, one BZ active ingredient, coumarin (BZ04), not only remedied the pain caused by glacial acetic acid in rats and intestinal smooth muscle spasm caused by BaCl<sup>2</sup> in rabbits, but also exhibited anti-inflammatory effects (Zheng et al., 2010). In short, despite of the relatively low levels of relative blood concentration, ZS and BZ are not only responsible for producing direct curative effects by bactericidal action, but also in charge of treating certain accompanying symptoms (like gastrospasm and pain) of FD, and thus serve as Chen drugs in HXZQ formula.

It is well known that Zuo drugs usually function either by treating the minor accompanying symptoms or improving the efficacy of Chen drugs in a TCM prescription. Thus, 7 herbs including BX, HP, FL, SJ, CP, BS, and DFP, attract our attention due to their similar functions in HXZQ formula. In fact, in FD therapy, these drugs mainly undertake a function of promoting the insufficient gastrointestinal motility and dealing with certain minor symptoms like abdominal distension, belching, nausea and vomiting. For example, HP has been extensively applied in the treatment of abdominal distention, pain and dyspepsia in Asia for long time (Yu et al., 2012). Some reports highlight that HP's extract may protect central nervous systems and exhibit anxiolytic effects (Xu et al., 2008). As seen in **Table 1**, HP contains 7 active compounds, from which five molecules including eucalyptol, beta-sitosterol, neohesperidin, HP01 and HP02 exhibit prominent biological activities. Eucalyptol, a saturated monoterpene, exerts various bioactivities including inhibiting cyclooxygenase pathway, suppressing the arachidonic acid metabolism or cytokine production, as well as proper antiinflammatory effects in rats (Juergens et al., 2003, 1998). Betasitosterol, another potent bioactive molecule, widely distributes in various botanicals with blood cholesterol lowering effects reported (Lee et al., 2007). As for neohesperidin, it produces significant antioxidant activities when relieving gastric lesions, and increases the mucus content. In addition, it has also protective effects by significantly decreasing the volume of gastric secretion to prevent gastric dysfunction (Lee et al., 2009). Two other parallel prime bioactive constituents of HP, HP01 and HP02, are both isomers of hydroxylated biphenolic compounds, and both relieve the spasm of smooth muscle and vomiting. Actually, they have demonstrated anti-diarrhea effects by blocking the calcium channel to inhibit the abnormal intestinal ion transport (Park et al., 2004), as well as improving the gastric emptying and intestinal propulsive actions (Zhang et al., 2005). Therefore, these seven herbs with moderate relative blood concentration as demonstrated, we assume, serve as Zuo drugs of HXZQ formula mostly by treating those minor symptoms of FD disease.

Although exerting certain beneficial bioactivities, some alkaloids, like arecoline and arecolidine, still have side effects. Whereas, GC has certain detoxification function like that its unique ingredient glycyrrhizic acid (GC69) reacts with these alkaloids and hence weakens their adverse effects. Thus GC is widely used in concerted application of botanical drugs as a crucial Shi drug. In fact, out of all current TCM prescriptions, about 60% contain GC, thus this herb is almost the most typical Shi drug of herbal formulae. In addition, GC also regulates CYP450 enzymes which are primary phase I isoenzymes in liver responsible for the metabolism of almost all drugs and toxins and hence influences the metabolism property of other herbs. For instance, its ingredient GC69 interacts with CYP3A4 in enterocytes which results in a significant activation of the functions of CYP3A4 (Hou et al., 2012). Besides, in Chai-huo-shu-gan-san formula, GC significantly increases the release of Bupleurum, the formula's Jun drug, and in this way promotes the efficacy of the formula. Besides, GC also regulates the function of certain transporters like p-glycoprotein which is an important protein of the cell membrane that pumps many foreign substances (drugs) out of cells and therefore systematically impacting the delivery of most drugs to their targets. Additionally, due to the wide spectrum of targets

GC possesses, which are widely distributed in almost all vital organs including the cardiovascular, respiratory, GI and nervous systems, GC pharmacologically influences, basically, the whole human body (Liu et al., 2013). In fact, GC possesses a broad range of activities including antiviral, anti-inflammatory, antitumor, immunostimulant, anti-oxidant, antispasmodic metabolic syndrome prevention activities. Moreover, GC also contains some common ingredients with other herbs like GHX06, CP01, which may produce cross-interactions with other herbs' chemicals, and in this way regulate the relationships among herbs. GC, on one hand, has relatively high relative blood concentration (as shown in **Table 5**). On the other hand, it also exerts multiple biological functions, including (1) the detoxication capacity to reduce side effects that other herbs may produce, (2) large number of structural diverse active compounds, and (3) broad spectrum of bioactivities which are involved in the regulation of the ADME properties of other herbs. By consideration of all these factors, GC is assumed as Shi drug to coordinate other herbs as well as an antidote agent in HXZQ formula.

### Target Identification and Network Pharmacology Analysis

Presently, for HXZQ formula, altogether 48 proteins are identified as its targets, with **Supplementary Table S2** listing all corresponding detailed information. Interestingly, the association of many of them with FD treatment has been validated, like dipeptidyl peptidase 4 (DPP4), nitric oxide synthase, prostaglandin G/H synthase (PTGS2), Glycogen synthase kinase 3 beta (GSK3B) (Gao et al., 2014). Thus, based on these targets and corresponding interacting compounds, compound-target (C-T) networks are established for HXZQ formula presently, with network pharmacology analysis conducted for analyzing the interaction mechanism of HXZQ-FD complex system.

#### C-T Network and Analysis

Firstly, a C-T network is constructed by using all 132 active components of HXZQ and their corresponding 48 targets, which is shown in **Figure 5** where the circles and hexagons represent the candidate compounds and targets, respectively.

In this work, to quantify the influence of the nodes and to identify the most influential ones within a network, two important parameters, i.e., degree, which is the number of edges connected to the node, and betweenness, another centrality index defined by the number of times a node acts as a bridge along the shortest path between two other nodes, were calculated. Actually, betweenness reflects the fraction of the shortest paths in the network that pass through any particular node and a measure of the importance of a node as a hub in a network (Grobelny et al., 2018). This measurement favors the nodes that act as connecting links between dense subnetworks, rather than nodes that lie inside a subnetwork. Therefore, if the degree values of some targets or molecules are insignificant but their BCs have relatively high values, these targets or active compounds are also important for the net.

**Figure 6** displays the degree and betweenness distribution of all targets and top 60 active compounds. As seen from the figure, in general, the higher degree, the higher betweenness. And the distribution of degree and betweenness is strongly correlated with each other and the most highly connected nodes have higher centrality scores. Still, several nodes are also noticed that they possess high betweenness values despite of relatively low degrees, which may be due to that they connect certain high-degreed nodes. Thus, those nodes at the peaks of the betweenness line, whether as targets (like ACHE, NOS3, NR3C1 and PGR) or candidate compounds (like GC08), are also important for the formula.

Among the targets, the top three degree-ranked proteins are androgen receptor (AR,), estrogen receptor α (ESR1) and prostaglandin G/H synthase 2 (PTGS2), connecting with 101, 100, and 88 compounds, respectively (**Figure 6B**), indicating their essential roles for the network. As to the average degree of all targets, it is as big as 30, and, actually, the degree values of 17 out of all 48 targets are larger than this mean value. These facts all prove that HXZQ formula exerts its efficacy through a multi-target cooperative mechanism.

As to candidate compounds, cavidine (BX02), 7-methoxy-2 methyl isoflavone (GC13) and medicarpin (GC11) are the top three ones, interacting with 32, 27, and 26 targets, respectively. Actually, their crucial bioactivities for remedying FD have been experimental validated. For instance, BX02 is an isoquinoline alkaloid that has wide spectrum of biological activities including anti-tumor, anti-bacteria and especially anti-inflammation effects (Niu et al., 2015). Actually, BX02 not only decreases the expression of various inflammatory mediators such as nitric oxide (NO), PGE<sup>2</sup> and cytokines like TNF-α and interleukin (IL-6), but also exhibits relative low cytotoxicity (Niu et al., 2015). Thus, these highly connected chemicals are key to HXZQ for exerting proper efficacy. Besides, the average degree of candidate compounds is also as big as 10.7, proving the multi-ingredient cooperative mechanism of the formula.

Actually, to explore which microscopic biological processes these targets are involved in, presently a GO analysis was further performed. **Figure 7** displays the most significantly enriched GO terms, with their p-value and FDR shown in **Supplementary Table S3**. Actually, a majority of the targets are closely related to several or more biological processes, such as the regulation of second messenger-mediated signaling, neurological system process, the response to hormone stimulus and the regulation of smooth muscle contraction. And most biological processes among the listed terms are related to FD pathogenesis. In addition, interestingly and also similar to our above results, majority of these highly enriched GO terms are found tightly associated with the inflammation, immune and gastrointestinal systems (**Figure 7**), like that the "muscarinic acetylcholine receptor signaling" and "regulation of smooth muscle contraction" are closely related to GI motility, and "response to hormone stimulus" is associated with inflammation and immune response. This corresponds well to the experimental findings that FD is conventionally caused by three primarily physiology reasons, i.e., bacterial infection, gastroenteritis and the disturbance of gastric physiologic factors (Talley and Ford, 2015). Therefore, to deeply explore the interaction mechanism of HXZQ formula for FD treatment, currently

three C-T networks, i.e., Inf-C-T (Inflammation-Compound-Target), Imm-C-T (Immune-Compound-Target) and Gas-C-T (Gastrointestine-Compound-Target) networks were built from these three pathogenic factors.

#### **The anti-inflammation function**

size is proportional to its degree.

It is well known that a typical symptom of FD is the inflammation of GI tract, i.e., gastroenteritis, which is a common disease characterized by diarrhea, vomiting, abdominal pain and fever. Factually, gastroenteritis is also an essential cause for the formation and development of FD. From ancient beginnings, HXZQ has been found exerting significant curative efficacy on gastroenteritis as a standard finished drug. And this efficacy is obtained mostly due to the formula's anti-inflammatory effects which have already been verified in many studies, like by the decreased levels of TNF-α in peripheral blood and enteric tissue homogenates of the lab mice that were treated with HXZQ (He et al., 2006). All of this arouses our interest to investigate the mechanism of HXZQ's anti-inflammatory function. Thus, an Inf-C-T network was constructed presently by using all 25 inflammatory targets and corresponding interacting compounds of HXZQ, where the hexagons and circles represent the targets and active ingredients, respectively (**Figure 8**). Interestingly, the calculated average degree of these inflammatory targets is 48, larger than the average degree of all targets of the formula (30), indicating that anti-inflammatory function may account for the main curative effects of the formula for FD treatment.

Since PTGS1 and PTGS2, two isoforms of cyclooxygenase, are the anti-inflammation targets of most NSAIDs (non-steroidal anti-inflammatory drugs like aspirin and ibuprofen), a kind of currently widely used typical anti-inflammatory medicine, they are firstly investigated in the Inf-C-T network. In fact, their antiinflammatory function is closely related to the production of PGE2, a family member of eicosanoids and also a lipid regulator (Langenbach et al., 1995). PGE<sup>2</sup> is in charge of the maintenance of the normal blood flow of gastric tissue, and the inhibition of H<sup>+</sup> formation to protect gastric mucosa. PGE<sup>2</sup> not only participates in the regulation of different stages of inflammatory response, but also is critical for the maintenance of the health of gastric mucosa (Parente and Perretti, 2003). Whereas, PTGS1 and PTGS2 enhance PGE2's level, and thus their inhibition always leads to the inhibition of PGE2's production, which finally results in anti-inflammation effects.

Though both possessing similar anti-inflammation bioactivities, in expression, PTGS1 and PTGS2 differ a lot. For PTGS1, it is basically constitutively expressed throughout the whole GI tract, and thus detected in almost all types of cells in normal tissue's inner muscular layer or even rare villous epithelial cells in mucosa. Whereas, PTGS2 is not detectable in normal GI cells, but only expressed in inflammatory cells (Chulada et al., 2000). Hence, PTGS1's inhibition influences all cells of GI tract; whereas PTGS2's inhibition only affects inflammatory cells. For example, when inhibiting PTGS1, NSAIDs produce not only anti-inflammatory effect, but also certain side effects like reduced synthesis of prostaglandin, gastric toxicity, ulcer

formation or gastric mucosa damage. Nevertheless, the inhibition of PTGS2 only results in preferable anti-inflammatory effects, with no bad impacts on the GI mucosa detected (Williams et al., 1999). Therefore, if the active ingredients of HXZQ largely inhibit PTGS2 instead of PTGS1, the formula may produce less GI toxicity when treating gastrointestinal inflammation.

Though in this network, in degree PTGS2 is larger than PTGS1 (88 vs. 49) implying more compounds in HXZQ targeting PTGS2 than PTGS1, still there are 49 chemicals interacting with PTGS1 which may produce certain GI toxicity. Yet, for HXZQ up to date only mild toxicity is reported, indicating that this side effect has been somehow eliminated. The reason, we assume, is closely related to the mutual interactions among the chemicals and targets of the formula, which may offset and reduce the potential side effects due to their promiscuous properties. For instance, genkwanin (GHX01), as an active component in HXZQ, targets both PTGS1 and PTGS2. GHX01 has a variety of pharmacological effects including anti-bacterial, radical scavenging and anti-inflammation. GHX01 potently decreases the level of proinflammatory mediators, such as iNOS, TNF-α, IL-1β, and IL-6 (Gao et al., 2014). Through interacting with PTGS1, GHX01 may produce certain side effects in GI mucosa, which yet have not been found. The major reason may be that GHX01 also interacts with other targets that are tightly implicated in inflammatory gastrointestine, i.e., PTGS2, PRSS1, GSK3B, MAPK14, PPARG, NOS2, and ESR2. The interactions among these targets and PTGS1, may relieve gastroenteritis and counteract possible adverse effects (Zhang et al., 2015). Factually, during the long time that HXZQ herbs are used for treating gastroenteric disorders in oriental countries, less or no side effects on GI tract are reported.

Two other connected proteins, i.e., GSK3B and PPARG, also attract our attention that we assume their regulation by HXZQ formula should be helpful for controlling the inflammation of FD due to their pivot position in Inf-C-T network, with connection degree of 76 and 87, respectively. Actually, they both exhibit potent anti-inflammatory effects. For instance, GSK3B plays a crucial role in innate and adaptive immune responses in inflammation-mediated disease treatment. Specifically, its inactivation augments the production of antiinflammatory cytokine production and synchronously suppresses the expression of pro-inflammatory cytokines in immune cells (Wang et al., 2011). As for PPARG, it is a regulator in charge of the lipid metabolism, glucose homeostasis and cellular differentiation predominantly expressed in intestine. Since its activators have anti-inflammatory activities in monocyte/macrophages, endothelial, epithelial and smooth muscle cells, PPARG has been proven beneficial for the treatment of inflammatory GI diseases (Chinetti et al., 2000). And its modulatory function in control of inflammatory progress with therapeutic applications in inflammation-related gastrointestinal upset was also validated (Chinetti et al., 2000).

FIGURE 7 | Gene Ontology (GO) analysis of the target genes, where y-axis is the significantly enriched 'Biological Process' categories in GO relative to the target genes, and x-axis is the enrichment scores of these terms (p-value ≤ 0.05 and FDR ≤ 0.05).

In addition, other three highly degreed proteins AR (Degree = 101), ESR1 (100) and ESR2 (75) also arouse our attention due to that they are all key steroid receptors in vivo. Their crucial roles in regulation of the central nervous system, cardiovascular system and digestive system, as well as the reproductive system have long been well known. From the large number of active ingredients they interact with (**Figure 8**), it is speculated that they may also be of significance for anti-inflammatory function of the formula. Actually, in GI inflammatory diseases, ER receptors bring favorable anti-inflammation activities by inhibiting the production of inflammatory cytokine, such as NO, IL-1β, TNF-α (Harnish et al., 2004). In addition, AR, a steroid receptor superfamily member, is an important protein for human genital system. Androgenbound AR functioning as a transcription factor is involved in an array of physiological processes including especially the inflammatory response. Actually, AR exerts anti-inflammation effects may by decrease of the production of pro-inflammatory factors (like IL-β). In a word, steroid receptors like ESRs and AR are key anti-inflammation targets in the treatment of GI disorders.

In short, the anti-inflammatory function of HXZQ accounts for most of its curative effects on FD treatment, which may mainly attribute to the crucial roles of pivot hub proteins including especially the NSAIDs-targeting cyclooxygenases (PTGS1 and PTGS2), GSK3B and PPARG, as well as steroid receptors (AR and ESRs).

#### **The immune protection function**

One important cause of FD is the invasion of viruses and bacteria, such as Salmonella, Escherichia coli O157, Campylobacter jejuni, Giardia lamblia, Norovirus, and H. pylori, which always lead to, firstly, mild immune disorders and then gradually FD. Autophagy is induced against all harmful pathogens, among which H. pylori, a bacterium capable of adapting to stomach environment and thus living in gastric mucosa, is a typical pathogenic factor of stomach and intestine. Actually, it adheres to mucosal epithelial cells and stimulates the gastric mucosa to produce inflammatory factors and thus leads to a variety of upper GI disorders, such as chronic gastritis, peptic ulcer disease and even gastric cancer (Kusters et al., 2006). Hence, it is worthwhile to explore the impact of HXZQ formula on this germ. Actually, on one hand HXZQ enhances the immunity of patients and inhibits certain bacteria which may induce FD (He et al., 2006). On the other hand, HXZQ also up-regulates CD4<sup>+</sup> to the normal content range, indicating its roles of repairing the damaged immune

system (He et al., 2006). Based on these, presently an Imm-C-T network was built by using all 17 immune-related targets (represented as green hexagons) and related bioactive ingredients (as orange circles) of the herbs (**Figure 9**), with attempt to explore the influence of HXZQ on immune system.

A major pathological cause of FD is the immune disorder caused by the invasion of harmful bacterium, therefore antibacterium is one effective means against GI disorders. In the clearance of invading pathogen, autophagy is an important process in immune response, which is in essence an intracellular degradation system that delivers cytoplasmic constituents to the lysosome (Mizushima, 2007). The autophagy activation is a catabolic process which degrades excrescent and impaired organelles, cytosolic proteins, and invasive microbes. Due to its unique immunological functions, autophagy is involved in many essential processes in the innate and adaptive immune responses (Nys et al., 2013). During the developing process of gastrointestinal disorders, the activation of autophagy in the zoic gastric epithelial is beneficial to gastric epithelial cells (Deretic et al., 2013). Therefore, autophagy is crucial for the inhibition of the growth of various pathogens and the enhancement of human immunity in the treatment of GI diseases.

From Imm-C-T network, it is observed that many active chemicals and targets are involved in autophagy (specifically anti-H. pylori) processes, therefore it is presumed that one pharmacological function of HXZQ is the regulation of immune response for FD treatment. Actually, candidate compounds like SJ02, GHX05, GHX01, GHX06, GHX07, HP01, and HP02, all have been proven with inducing autophagy or anti-bacterium activities. For instance, curcumin (SJ02), a representative component of herb SJ, is a yellow pigment commonly used in food. It possesses a variety of favorable pharmacological effects like anti-oxidant, anti-inflammatory, hepatoprotective and antitumor abilities, which have been proven partly attributing to its autophagy-inducing function (Nys et al., 2013), like SJ02's protection of the endothelial cells against Crohn's disease, a severe GI disorder. Besides, GHX05, another active ingredient, protects the gastric epithelial cells from the urease injury induced by H. pylori, and thus exhibited potent anti-bacterial activities in rats (Yu et al., 2015). It also has a wide spectrum of other biological activities, including anti-inflammation, oxidative balance regulation and the enhancement of gastric mucosa defense, etc. (Yu et al., 2015). In addition, GHX01, GHX06, GHX07, HP01, and HP02 as mentioned in previous section, all possess proper anti-bacterium activities. Thus, we speculate that these molecules that are associated with the activation of autophagy constitute the substance basis of HXZQ for its antibacterial function.

In addition, a few targets of HXZQ in Imm-C-T network are also observed getting involved in autophagy or anti-H. pylori processes. Actually, four of them, including GSK3B, NOS2, NOS3, have been proven capable of inducing the initiation of autophagy through inhibiting PI3K/Akt pathway (Heras-Sandoval et al., 2014). Videlicet, an active component of HXZQ may activate the autophagy process by targeting these proteins, and thus relieve the upset of GI system.

For instance, GSK3 is a highly conserved, constitutively active serine/threonine protein kinase. GSK3B, as a central regulator of inflammatory response, play roles in immune system against viral, fungal and parasitic infections (Wang et al., 2014).

The pharmacological inhibition of GSK3B in TLR4-stimulated macrophages, may increase IFN-β production which has an important role in cell growth and differentiation. During the invasion process of H. pylori's invading, the germ exploits GSK3B, seeking to avoid the immune system (Nakayama et al., 2009). Therefore, the regulation of GSK3B may protect host cells from H. pylori's infection, making GSK3B a therapeutic target for the prevention of H. pylori-driven gastric disorder (Wang et al., 2014). Factually, GSK3B's inactivation suppressed the H. pyloriinduced pernicious biological activities. In Imm-C-T network, GSK3B interacts with 64 active ingredients of HXZQ, indicating its essential roles in eradication of H. pylori.

As is known to all, NO is closely related to human immune system that it induces or suppresses apoptosis as a toxic or immune regulatory media. Accompanied by NO's production, the immune system is activated to fight against the invading bacteria. NO's transmitting depends on its concentration or chemical reactivity rather than receptors. To initiate immune system, NO needs to be generated in great numbers to maintain high levels for sustained period of time. Therefore, its concentration and sustained time matter for normal immune response. NOS2 and NOS3, two forms of nitric oxide synthases, are in charge of NO productions, where NOS2 produces high concentrations of NO, which synthesis sustains for hours or days or even longer, whereas NOS3 only intermittently generates NO (Coleman, 2001). In the present Imm-C-T network, 84 and 25 molecules act on NOS2 and NOS3, respectively, indicating that HXZQ tends to produce sustainable and large concentration of NO to exert therapeutic effects on FD. Thus, regulating the production of NO to modulate the immune system through NOS2 and NOS3 targets should also account for the immune regulation function of HXZQ.

Actually, HXZQ formula is a very famous and classical prescription for the treatment of heat wet cold. Heat wet cold is a kind of exogenous cold disease caused by sudden wind, cold or dampness in summer, with main clinical symptoms of fever, dizziness, encephalalgia, tiredness, thirsty, chest tightness, nausea etc. Being a relatively mild disorder with a benign prognosis, heat wet cold belongs to categories of upper respiratory tract infection and influenza. The upper respiratory tract infection is induced by viral and bacterial infections that certain viruses, like rhinovirus, adenovirus (ADV), influenza virus, coxsackie virus (CVB3) and coronavirus, often lead to viral upper respiratory tract infection. In fact, the extract of Jun herb GHX, patchouli oil, has been proved possessing proper antiviral effects both in vivo and in vitro. For instance, it inhibits H1N1, CVB3 and ADV with concentrations of 0.088, 0.080, and 0.084 mg/ml, respectively (Wei et al., 2012). And the main active ingredient of GHX, patchouli alcohol (GHX05), not only has anti-Coxsackie virus, adenovirus, and influenza A virus capacity, but also shows higher potency than certain finished drugs. For example, ribavirin, a marketed medicine in prevention of virosis, inhibits H1N1, CVB3 and ADV with concentrations of 0.078, 0.067, and 0.063 mg/ml, respectively. Whereas, patchouli alcohol inhibits H1N1, CVB3, and ADV with even lower concentrations as 0.031, 0.063, and 0.063 mg/ml, respectively. And it has also been validated that patchouli alcohol exerts anti-adenovirus activity through interacting with Hexon, a target responsible for translating the

capsid protein of adenovirus (Li et al., 2011). All these results demonstrate that anti-virus is an important mechanism of HXZQ for the treatment of heat wet cold.

In summary, the modulation function on immune system through the autophagy activation, the anti-bacteria (especially H. pylori) and antiviral processes, is also one mechanism accounting for HXZQ's clinical protection efficacy for GI system.

#### **The gastrointestinal motility regulation function**

Poor digestion, caused by insufficient gastroenteric movements, is a main symptom of FD, and hence it is necessary to promote the GI motility of the patients for treating this disease ultimately. Thus, presently a Gas-C-T network was constructed employing all 19 GI motility-related targets (represented by blue hexagons in **Figure 10**) and corresponding interacting compounds (orange circles) for exploring the function of HXZQ in promoting the GI motility in FD treatment.

In Gas-C-T network, the average degrees of these gastroenteric targets and components are 17 and 3, respectively, demonstrating complex interactions among multi-chemicals and multi-targets of HXZQ. Among them, three targets, ESR1, ESR2, and ACHE receptors, outstand due to their high connection degree indicating their pivotal roles in improving the gastrointestinal vitality, which has been validated by experimental facts actually. For instance, ESR1 and ESR2 are two subtypes of estrogen receptors who have also the highest degree as 100 and 75, respectively, in the net. In distribution, ER subtypes are detected not only in reproductive systems like mammary gland, uterus, ovary and prostate, but also in GI tissues such as fundus, antrum, and duodenum (Campbell-Thompson, 1997), indicating their potential roles in regulation of GI system. In function, they are in charge of mediating the action of various estrogens, which actually have been proven with inhibition effects on intestinal motility. Thus, ESR1 and ESR2 are involved in the mediation of colonic motility (Choijookhuu et al., 2016), where ESR2 is the predominant ER type in intestinal tract which inhibits A-type K <sup>+</sup> currents of intestines smooth muscle cells and regulates the excitability of smooth muscle (Beckett et al., 2006). Similar to ESR2, ESR1 regulates the GI motility. As for ACHE, it is actually also a target of some western medicines for treating

compounds, respectively. The edges represent the mutual relations among the targets and chemicals. Node size is proportional to its degree.

GI-related diseases (Serralheiro et al., 2013). For instance, two ACHE inhibitors, neostigmine and metoclopramide, are capable of reversing the impairment of gastrointestinal motility and treating gastric motility dysfunctions, respectively. Presently, ACHE interacts with 33 active molecules in Gas-C-T net. These all imply that ER receptors and ACHE may also account for the GI vitality regulation functions of HXZQ on FD treatment.

It is worthy of noting that some proteins, such as CHRM1 and DRD1, also exhibit favorable effects on GI disorders despite of their not so large degree. For instance, CHRM (M1 ∼ M5) are five distinct subtypes of muscarinic acetylcholine receptors, all of which have demonstrated as promising therapeutic targets for GI diseases (Matsui et al., 2002). Muscarinic receptors are widely expressed in smooth muscle in GI tract. The principle subtypes on the sarcolemma are CHRM2 and CHRM3. The activation of CHRM2 decreases the opening times of a potassium channel activated by β-adrenoceptor agonists, also attenuating the relaxation induced by the sympathetic systems (Eglen, 2001). In addition, in smooth muscle, CHRM3 receptor mediates the phosphoinositide hydrolysis and Ca2<sup>+</sup> mobilization which contracts smooth muscle directly. Presently, it is discovered that HP01 and HP02, two out of the eleven components of HXZQ who act on CHRM3, are involved in the regulation of GI motility (as discussed previously). Therefore, we speculate that the other 9 molecules may also possess potentials in participating in the GI regulation. As to DRD1, one of the dopamine receptors, it is widespread in enteric nervous system like gastroesophageal junction, stomach, pylorus, small intestine and colon (Feng et al., 2013). DRD1' substrate, namely, dopamine, reduces human gastric pressure and motility. DA antagonists like domperidone exhibit proper regulation functions on GI motility. Actually, it is through two actions, i.e., the contraction of the circular smooth muscle layer and/or the relaxation of the longitudinal smooth muscle layer (Vaughan et al., 2000), that DA receptors fulfill their direct modulations on the gastric smooth muscle cells responses. In this work, three chemicals (BX02, GC11, and GC13) act on DRD1 receptor. Although no experimental results have been reported, the connections among these molecules and DRD1 indicate the potential of these three compounds for treating GI motility disorders. And thus, the regulation of GI vitality by targets like CHRM (1 ∼ 5) and DRD1 may also be part of the mechanism of action of HXZQ for FD treatment.

It is well known that GI motility is regulated by three factors, i.e., the intact immune system, the enteric nerves, as well as the smooth muscle cells (Locke et al., 2006). Since the influence of immune system has been discussed in the analysis of Imm-C-T network, the impacts of two other factors, enteric nerves and smooth muscle cells, on GI motility are analyzed here.

For enteric nerves, some low-degreed targets produce positive impacts on FD treatment through acting on vagal afferents, which regulate the digestive system. Actually, vagal afferents are a kind of nerves that are extensively distributed in digestive tract from esophagus to colon. Since their function is to signal the initiation of several GI bio-processes including distension, contraction or relaxation of gastric smooth muscle, vagal afferents are often implicated in the flex control of the secretion and motility function of GI tract, and thus reflex FD (Andrews and Sanger, 2002). From Gas-C-T net, it is observed that vagal afferents affect several receptors by either enhancing (e.g., 5-HT3 receptor) or reducing (like κ-opioid and GABAB receptors) their activities, and in this way modulate the function of these proteins on GI motility regulation. These targets of vagal afferents, like 5-HT3 receptor, exert considerable influence on the regulation of GI motility. Over the past decade, 5-HT3 receptor antagonists (e.g., granisetron and ondansetron) have been used in the treatment of acute phase of emesis, post-operative nausea and vomiting. Presently, in Gas-C-T net, molecule maackiain (GC08) acts on HTR3A, indicating its potential capability of controlling GI motility through activating the vagus.

As to another type of target of vagal afferents, i.e., the κ-opioid or GABAB receptors, recorded researches have validated the importance of their activation for vagal afferent. GABAB is expressed on gastric vagal afferent neurones and reversibly inhibits gastric vagal mechanoreceptor responses to distension (Smid et al., 2001). There is a dense distribution of GABAB receptor along central vagal pathways in the nucleus tractus solitarii and dorsal vagal nucleus. GABAB receptor agonists, like baclofen, reduce the triggering of transient lower esophageal sphincter relaxations and thereby inhibit the gastroesophageal reflux in human body (Partosoedarso et al., 2001). Presently, three molecules including atractylenolide II (BS03), atractylenolide I (BS04) and arecolidine (DFP02) act on this target, indicating their potentials on the regulation of GI motility. In summary, the modulation of GI activity through regulating enteric nerves is an important factor for HXZQ contributing to the treatment of GI diseases.

Another factor that influences the GI motility is the smooth muscle cells, thus those proteins that regulate the function of these cells may also be potential targets for FD treatments. In Gas-C-T net, two ion channels that exist in smooth muscle cells, i.e., KCNH2 (encoded HERG potassium channel) and SCN5A (encoded Nav1.5 sodium channel) attract our attention. Actually, the importance of the first channel, namely, K<sup>+</sup> channels, in regulating muscle tone and contractility of stomach has long been highlighted by recent studies that the activation or inhibition of K<sup>+</sup> channels generates profound relaxations or inhibition of gastric smooth muscle (Locke et al., 2006). Specifically, in GI diseases, K<sup>+</sup> channel activators facilitate the muscle relaxant activity. Thus, these channels ameliorate the accommodative function of proximal stomach. In irritable bowel syndrome with diarrhea, K<sup>+</sup> channel activation is capable of reducing propulsive motor activity by relaxing both the circular muscle and the taenia coli (Currò, 2014). In this net, 11 active compounds interact with KCNH2. Among them, nobiletin (CP03) exerts suppressive effects on colon inflammation through down-regulation of cytokines as well as inflammation mediators, and decreases the intestinal epithelial permeability and restoration of barrier function (Xiong et al., 2015). Besides, CP03 bi-direction regulates jejuna contractility through the modulation of enteric nervous system, that is, it reflexes the high-contracting GI smooth muscle while exciting the low-contracting one (Yao et al., 2008). Although the biological efficacy of other 10 molecules have not been experimentally validated, we assume they may also possess potential bioactivities about regulating GI motility.

As to another ion channel, SCN5A, it is closely related to severe, frequent and chronic disorders like abdominal pain (Locke et al., 2006). SCN5A is expressed in circular smooth muscle cells and interstitial cells of Cajal. Factually, SCN5A impacts smooth muscle cells in both direct and indirect ways. The direct way is related with SCN5A's inhibition, which hyperpolarizes human intestinal circular smooth muscle cells. Whereas, the indirect way is through an electrical slow wave which is generated by interstitial cells and tightly associated with the motility of GI tract. When SCN5A is inhibited, this slow wave's rate of rise is slowed and its frequency is decreased, which finally ends up with the contraction of smooth muscles. Consequently, whether KCNH2 or SCN5A ion channels, they both are essential targets responsible for regulation of human intestinal motility (Ou et al., 2003). Presently, 36 active compounds act on SCN5A, similar to KCNH2, indicating that their potential therapeutic effects for FD treatment that they may modulate GI motility through acting on ion channels.

In summary, we find that it is through three mechanisms of action that HXZQ exerts therapeutic effects for FD treatment, i.e., the anti-inflammation, the immune protection (through autophagy activation or anti-bacteria actions like H. pylori) and the GI motility regulation (which is primarily dependent upon the regulation of enteric nerves and smooth muscle cells) based on network pharmacology analysis of the three C-T networks.

#### Target-Pathway Network

By mapping the targets to related pathways, we find that FD treatment is mostly related to four pathways in mechanism, i.e., PI3K-Akt, TLRs, JAK-STAT and Calcium signaling pathways. Therefore, pathway analysis is also conducted with purpose to deeply comprehend the mechanisms of HXZQ for FD treatment.

PI3K, the key component of PI3K-Akt signaling pathway, is a lipid kinase abundant in leucocytes and regulates a wide variety of cellular processes including cellular growth, migration and proliferation. PI3K-Akt signaling pathway negatively modulates LPS-induced acute inflammatory responses. Factually, its inhibition enhances the activation of NF-κB, AP-1, and Egr-1 transcription factors as well as expression of TNF-α, IL-6 and tissue factor in human monocytic cells (Huang et al., 2011), which results in impaired immune responses and reduced susceptibility to autoimmune and inflammation (Andeol et al., 2018). This pathway is also involved in the regulation of autophagy in immune response, so as to remove bacteria from gastrointestinal infections. Thus, its regulation is crucial to the treatment of GI disorders. Presently, 9 targets are involved in PI3K-Akt pathway as important regulator of immune system, including CHRM1, CHRM2, KDR, PIK3CG, HSP90AA1, GSK3B, NOS3, RXRA, and CDK2 (**Figure 11**). Among them, the crucial roles of all proteins (except GSK3B) as anti-inflammation and immune regulation targets have already been discussed previously. While for GSK3B, a downstream target of Akt, it generates inflammatory cytokines and is involved in the immune system against invading pathogens, and in this way controls the cell survival and cell cycle progression (Laprise et al., 2004). Thus, all this indicates that PI3K-Akt pathway is a key channel for regulation of the anti-inflammation and immune protection functions of HXZQ formula.

As to JAK-STAT signaling pathway, it is partly the center of transduction of numerous signals for homeostasis and immune function, and is significant for a wide array of cytokines and growth factors (Coskun et al., 2013). JAK activation induces cell proliferation, differentiation, migration and apoptosis, and these cellular events are critical to immune development. Except for its roles in the regulation of key cellular activities, the JAK-STAT signaling pathway has also been implicated in the mechanism of inflammatory bowel diseases (Alegot et al., 2018). For each T cell subset, certain particular STAT is assigned. STAT protein can be activated by those cytokines presented in immune response which leads to a regulation on the balance of T cells and eventually contributes to the immuneprotection and inflammation-alleviating of GI tracts. Presently, PIK3CG and PIMI participate in the regulation of JAK-STAT signaling pathway (Rawlings, 2004). PIM1 is a serine/threonineprotein kinase, which is in charge of the controls cell survival, proliferation, differentiation and death (Shin et al., 2012). PIM1 kinase, expressed in human eosinophils, also contributes to the survival of T cells in immune cells, and therefore participates in the regulation of immune system to treat various diseases. Thus, we speculate that PIM1 has potential GI protective effects against bacterial damage by regulating the JAK-STAT signaling pathway.

The third pathway involves TLRs signaling pathway, which are also important in modulation of both inflammatory and immune responses (Takeda, 2004). TLRs, expressed in immune cells, astrocytes and microglia, are crucial in early host defense against invading pathogens. TLRs recognize those microbial structures that have been saved in memory including the bacteric lipopolysaccharide and viral RNA, and hence, participate in the microbial recognition to the activation of specialized antigen-presenting cells in T lymphocyte (Akira and Takeda, 2004). Pathogen activates TLR signaling which then results in corresponding immune responses against the microbial infections. In enteroendocrine cells, TLR expression promotes the elimination of pathogens (Abreu, 2010) and thus TLR signaling pathway is important for inducing the immune response against the GI bacteria challenge, such as H. pylori and coxsackie virus. TLR signaling has also been implicated in epithelial cell proliferation, tight junctions' maintenance and antimicrobial peptide expression, and TLR receptors are crucial for maintaining arobust enteric epithelial barrier. Presently, three targets, i.e., PIK3CG, MAPK14, and GSK3B, are involved in the regulation of TLRs signaling. And in HXZQ formula, GHX06 as an active ingredient acts on these three targets, which corresponds well to the experimental findings that GHX06 exerts its anti-oxidative and anti-inflammatory properties by inhibiting TLRs signaling (Ji et al., 2017).

This all indicates that the regulation of TLRs signaling pathway incorporating targets GSK3B, MAPK14, and PIK3CG to produce anti-inflammatory effects as well as to enhance the immunity of human body is also a reason for the curative effects of HXZQ on FD treatment.

Since neural factor is also crucial to FD treatment (Talley and Ford, 2015), calcium signaling pathway, the fourth signaling

path, is also key for its close association with the treatment of GI motility insufficiency. This pathway regulates the synaptic transmission, and takes part in the neurosecretion and neuronal excitability. Besides, as a critical second messenger, Ca2<sup>+</sup> stimulates the cell migration and proliferation and thus regulates a wide variety of functions of GI epithelial cells (Rao et al., 2001). In fact, for the damaged GI surface barrier caused by inflammatory bowel disease and injured/erosive mucosa induced by H. pylori infection, increased Ca2<sup>+</sup> concentration is beneficial for epithelial cells' healing (Rao et al., 2012). Presently, 10 targets (like 5-HT, CHRM family and NOS) with certain interacting active ingredients all function through regulating the Calcium signaling pathway for the treatment of GI disorders. For example, polyamines stimulates both the gastric mucosal restitution and duodenal mucosal erosions through Ca2<sup>+</sup> signaling, and are essential for stimulation of cell migration. The active molecule BS03 (atractylenolide I) also stimulates intestinal epithelial cell migration and proliferation via polyamine-mediated Ca2<sup>+</sup> signaling pathway (Song et al., 2017). In a word, Calcium signaling pathway is critical FD-related pathway in the regulation of GI motility.

Biological cross-talk refers to instances where one or more components of one signal transduction pathway affects another pathway (Kunkel and Brooks, 2002). Due to the existence of overlapping hubs, cross-talk often exists which links various pathways into an adaptable complex network. In **Figure 11**, multiple interactions are observed among these pathways through regulating one hinge protein, i.e., PIK3CG. As previously mentioned, PIK3CG serves as an important anti-inflammation target. Despite of its relatively low connection degree, it is still essential for FD treatment due to its pivotal position of cross-talk in the pathway network. Actually, these pathways are bonded together to regulate PIK3CG activities by mediating the intracellular signaling cascades. PIK3CG (PIKγ) is mainly expressed in leukocytes, and also presents at low concentration in smooth muscle cells. In function, PI3Kγ is necessary for chemokine-dependent migration of neutrophils, macrophages and mast cells to eliminate the infection (Marone et al., 2008). In HXZQ formula, PIK3CG is regulated by 14 active molecules such as GHX07 (Rutin), which is a gastro-protective natural flavonoid with anti-inflammatory activity and also used in the prevention of gastric mucosal ulceration.

In conclusion, PI3K-Akt signaling pathway participates in the process of inflammation and immune responses, whose regulation is helpful for the elimination of gastroenteritis. TLRs and JAK-STAT signaling pathways are mainly involved in the modulation of immune system, and their regulation may exert anti-inflammation activities and enhance immune ability to resist the bacteria infection. As to Calcium signaling pathway, it is mainly involved in learning or memory, as well as the contraction or relaxation of GI smooth muscle. In a word, it is just due to the complex pathway network composed by these pathways and their cross-talks that make HXZQ possess various and complementary functions for FD treatment.

### Computational Validation of Selected C-T Interactions

Since generally speaking, the inhibitory efficiency of a ligand is closely associated with the number and strength of its binding forces with its receptor (Wang et al., 2017), presently we explored the binding modes of several active ingredients with a GI regulating target CHRM3, an immunological target GSK3B and an antiinflammatory target PTGS2 through molecular docking analysis as probes for validation of the C-T interactions of the formula.

Due to that HP02 has a unique array of pharmacological actions including the inhibition of multiple autonomic responses, firstly the molecular docking of HP02 with CHRM3 was performed, with results displayed in **Figure 12A**. From this figure, clearly two weak H-bonds are observed forming between the two –OH groups of HP02 and Tyr406 (3.56 Å) and Asn380 (3.73 Å), respectively. In addition, hydrophobic effects exist between the propyl side chain of HP02 and hydrophobic amino acid. All these interactions ensure the steady state of HP02 in the binding cavity, which well corresponds with the experimental result that HP02 potently inhibits M3 muscarinic receptors (CHRM3) (with EC<sup>50</sup> = 5 µmol/l) and in this way regulates the GI motility (Wang et al., 2013). As for **Figure 12B**, GC08 (Maackiain) is fixed in the cavity through three H-bonds, including Asp84. . .O, Tyr85. . .O, and Ala172. . .O. Actually, GC08 is reported to exhibit great anti-GI bacteria action even at 10 µg/ml (Chan, 2002). The binding pattern explains why GC08 has such great biological activities.

As for BX02 (Cavidine), as the highest-degreed active compound, it participates in the regulation of human immune response as mentioned previously. As shown in **Figure 12C**, an H-bond between -OCH<sup>3</sup> group and Asp160 (3.59 Å) and a π-cation interaction between the N atom of BX02 and the benzene of Phe32, are formed, respectively, in complex GSK3B-BX02. These interactions ensure the tight binding of BX02 and GSK3B which also accounts for BX02's proper

biological activities in immunomodulation. In **Figure 12D**, the H-bonding interactions between GHX06 and GSK3B, including Phe161. . .O, Glu62. . .O, and Val95. . .O, make GHX06-GSK3B complex remain a stable conformation. Actually, experiments have validated the interaction of GHX06 and GSK3B, whose strong binding forces further verify the proper antibacterial and antiviral effects as described in previous section (Bincy et al., 2016).

As shown in **Figure 12E**, CP03 is fixed the binding cavity of inflammatory target PTGS2 through three H-bonds with residues Typ356 (3.33 Å), His357 (3.19 Å) and Asn351 (3.12 Å). Actually, the binding of CP03 and PTGS2 explains CP03's suppressing inflammation effects on GI system. As seen from **Figure 12F**, the complex PTGS2-GHX06 is stabilized by three H-bonds including the –OH groups with Gln423 (3.80 Å), Asn351 (2.46 Å), and Thr175 (2.69 Å), which further verifies GHX06's anti-inflammation effects.

Based on this, the strong interactions between these active components and targets (CHRM3, GSK3B, and PTGS2) are the basis of these molecules' biological activities.

#### DISCUSSION

Traditional Chinese Medicine is a precious heritage which has been practiced for three thousand years in Asia. Herbal medicine has been widely used for complicated disease treatment and is gradually becoming acceptable as alternative medicine worldwide. HXZQ is one of acclaimed TCM preparations in China which has been used in GI diseases treatment for thousands of years. The advantages of HXZQ for treating GI diseases mainly concentrate on the following three aspects. Firstly, corresponding to the present mechanism analysis results, HXZQ formula have multiple pharmacological functions including anti-inflammation, immune-protection and gastrointestinal motility regulation effects. Thus, HXZQ has been clinically applied in treating a few (instead of single) GI diseases which include, mostly, three ones, i.e., the gastroenteric cold, acute gastroenteritis and FD. Secondly, the active ingredients of HXZQ formula are diverse in both species and structures, and are large in number, which greatly reduce the possibility of drug resistance. Lastly, GC has antidote effects, which may account for the less side effects and low toxicity of HXZQ formula. And it is just due to the mutual promotion of the herbs ranking according to the compatibility principle of "Jun-Chen-Zuo-Shi," HXZQ formula exert good polypharmacology activities. Presently, a systems pharmacology approach which contains ADME screening, targets prediction, network analysis and pathway screening was utilized, with purpose to probe the "multi-compound, multi-target, and multi-pathway" properties of HXZQ formula and the pathogenesis of FD from molecular to pathway levels. The main results are:

(1) 132 chemicals out of 1,192 components in 11 herbs, and 48 proteins/enzymes are identified as candidate compounds and FD-related targets of HXZQ prescription, respectively. And the systematic use of these active ingredients may supply useful clues on the combination therapies for FD treatment.

(2) Based on the traditional theory about the roles of the main and auxiliary herbs of TCM and the consideration about the distribution of active components in ingredient herbs and the contribution of their specific activities to the treatment of FD of HXZQ, this work interprets the combination rule of "Jun-Chen-Zuo-Shi" which HXZQ formula conforms to. That is, GHX possesses several beneficial bioactivities which direct impact the curative effects for FD treatment, and thus serves as Jun drug. ZS and BZ exert anti-bacterial effects and are also responsible for reliving major accompanying symptoms like stomach pain and gastro spasm of FD, hence are used as Chen drug. The treatment of minor symptoms, like abdominal distension, belching, nausea and vomiting, is undertaken by BX, HP, FL, SJ, CP, BS, and DFP, as Zuo drugs. Whereas, GC is employed as Shi drug because of not only its detoxication capacity, but also its ability in influencing the ADME properties of other herbs, as well as the relationship among other herbs.

(3) The mechanism of action of HXZQ formula for treating FD is fulfilled through its three function modules, i.e., the suppression of inflammation, the intensification of immunological response and the regulation of GI motility. And the implementation of these functions rely on smooth run of the complex bio-pathway network which includes especially four pathways, namely, PIK-AKT signaling pathway, JAK-STAT signaling pathway, Toll-like signaling pathway and Calcium signaling pathway.

In summary, GI disorders are multi-factors caused complex diseases. For TCMs, such as HXZQ, due to their multicomponents and multi-targets characteristics, they have advantages especially in the multimodality treatment of GI diseases from molecule-tissue-organ-body multi-levels as a holistic medicine. This study will not only facilitate the development of phytomedicines in modern medicine, but also provide a modern interpretation of traditional TCM theory of "Jun-Chen-Zuo-Shi".

Most Chinese herbal medicines come from plants, which often possess dozens or even hundreds of various chemical components with diversified structures. And this makes, factually, the determination of TCM content an indispensable while quite challenging work in TCM research. At present, although series of analysis methods, such as high performance liquid chromatography (HPLC), gas chromatography, thinlayer chromatography, molecular absorption spectrophotometry (ultraviolet), titration, etc., have been developed and applied on determination of TCM ingredients' contents, they usually aim at merely one or several TCM components. Even so, these methods themselves also exist certain limits which makes the determination of all TCM components almost impossible (Zhong et al., 2015). As a matter of fact, the development of testing methods for TCM content is itself a hot topic in Chinese medicine research. Thus, due to the lacking of the specific content data of the full components of herbs, this work did not carry out a deep mathematical analysis on the composition content. Whereas, the influence of the contents has also been considered for that those abundant ingredients are also assumed as candidate compounds even if with relatively low oral availability or other ADME properties. Thus, in the near future, with more integrated and full data of TCM's ingredient content becoming available, the investigation on influence of TCM content on its mechanism of action should also be a significant job.

### AUTHOR CONTRIBUTIONS

fphar-09-01448 January 9, 2019 Time: 10:19 # 22

MZ conducted the statistical analysis and wrote the paper. YC implemented the methods and conducted the analysis. SC, SZ, and YL contributed to the paper and provided guidance. WX provided new ideas in statistical analysis section. All authors read and approved the final manuscript.

#### REFERENCES


#### FUNDING

We are grateful for the Key Program of National Natural Science Foundation of China (Grant Nos. 81530100 and 8153000367).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.01448/full#supplementary-material



against influenza viral infection in mice. Int. Immunopharmacol. 12, 294–301. doi: 10.1016/j.intimp.2011.12.007



Integr. Comp. Physiol. 279, R599–R609. doi: 10.1152/ajpregu.2000.279.2. R599



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling Editor and reviewer, S-BS, declared their involvement as co-editors in the Research Topic, and confirm the absence of any other collaboration. In addition, two reviewers, S-BS and XC, respectively declared shared affiliations, but no collaboration, with two of the authors, LY and YC, to the handling Editor.

Copyright © 2019 Zhao, Chen, Wang, Xiao, Chen, Zhang, Yang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Prediction of Drug Positioning for Quan-Du-Zhong Capsules Against Hypertensive Nephropathy Based on the Robustness of Disease Network

Feifei Guo<sup>1</sup> , Wen Zhang1,2, Jin Su<sup>1</sup> , Haiyu Xu<sup>1</sup> \* and Hongjun Yang<sup>1</sup> \*

1 Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China, <sup>2</sup> College of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China

Hypertensive nephropathy (HN) is a medical condition in which chronic high blood pressure causes different kidney damage, including vascular, glomerular and tubulointerstitial lesions. For HN patients, glomerular and tubulointerstitial lesions occur in different renal structure with distinct mechanisms in the progression of renal damage. As an extraction of Eucommia ulmoides, Quan-du-zhong capsule (QDZJN) has the potential to treat HN due to antihypertensive and renal protective activities. Complicated mechanism of HN underlying various renal lesions and the "multi-component and multitarget" characteristics of QDZJN make identifying drug positioning for various renal lesions of HN complex. Here, we proposed an approach based on drug perturbation of disease network robustness, that is used to assess QDZJN positioning for various HN lesions. Topological characteristics of drug-attacked nodes in disease network were used to evaluated nodes importance to network. To evaluate drug attack on the whole disease network of various HN lesions, the robustness of disease networks before/after drug attack were assessed and compared with null models generated from random networks. We found that potential targets of QDZJN were specifically expressed in the kidneys and tended to participate in the "inflammatory response," "regulation of blood pressure," and "response to LPS and hypoxia," and they were also key factors of HN. Based on network robustness assessment, QDZJN may specifically target glomeruli account to the stronger influence on glomerular network after removal of its potential targets. This prediction strategy of drug positioning is suitable for multicomponent drugs based on drug perturbation of disease network robustness for two renal compartments, glomeruli and tubules. A stronger influence on the disease network of glomeruli than of tubules indicated that QDZJN may specifically target glomerular lesion of HN patients and will provide more evidence for precise clinical application of QDZJN against HN. Drug positioning approach we proposed also provides a new strategy for predicting precise clinical use of multi-target drugs.

#### Keywords: hypertensive nephropathy, Eucommia ulmoides, drug positioning, network robustness, network pharmacology

#### Edited by:

Yuanjia Hu, University of Macau, China

#### Reviewed by:

Shuzhen Guo, Beijing University of Chinese Medicine, China Cheng Lu, Chinese Academy of Medical Sciences, China

#### \*Correspondence:

Haiyu Xu hy\_xu627@163.com Hongjun Yang hjyang@icmm.ac.cn

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 21 August 2018 Accepted: 16 January 2019 Published: 12 February 2019

#### Citation:

Guo F, Zhang W, Su J, Xu H and Yang H (2019) Prediction of Drug Positioning for Quan-Du-Zhong Capsules Against Hypertensive Nephropathy Based on the Robustness of Disease Network. Front. Pharmacol. 10:49. doi: 10.3389/fphar.2019.00049

**Abbreviations:** APL, average length of shortest path; CCnet, clustering coefficient; CCnode, closeness centrality; CFDA, China's Food and Drug Administration; DEGs, differentially expressed genes; EU, Eucommia ulmoides; HN, hypertensive nephropathy; PUFAs, polyunsaturated fatty acids; QDZJN, Quan-du-zhong capsule; RAAS, renin-angiotensin-aldosterone system; ROS, reactive oxygen species.

### INTRODUCTION

fphar-10-00049 February 9, 2019 Time: 17:6 # 2

Hypertension is a disease that leads to organ damage. Renal damage is a common lesion induced by hypertension due to the interaction of multiple factors, including blood pressure, endothelial dysfunction, the RAAS, ROS, and inflammation (Imig et al., 2018). Hypertensive nephropathy (HN) is a medical condition in which chronic high blood pressure causes kidney damage, including damage to two renal compartments, the glomerular and tubulointerstitial compartments. Exploration of interactions between glomerular and tubulointerstitial compartments contributing to HN creates opportunities to find better therapeutic strategies for renal protection against organ damage in hypertension. For HN patients, glomerulosclerosis and tubulointerstitial fibrosis occur in different renal structure with distinct mechanisms in the progression of renal damage. At the level of the tubulointerstitial compartment, it constitutes 95% of the total kidney mass (Berthier et al., 2012). Early tubular injury is caused by renovascular hypertension, leading to tubular cell proliferation and deposition of matrix proteins primarily within the interstitium which extends far beyond the glomeruli (Mai et al., 1993). In some hypertension patients, circulating antibodies and immunoglobin G deposit along the tubular basement membranes (Mai et al., 1993). Thus, tubulointerstitial change is regarded as a determinative factor in the development of renal damage (Nath, 1992) and may be the important initial site of injury (Mai et al., 1993). At the level of the glomeruli, increased blood pressure leads to increased capillary pressure which results in capillary stretching, endothelial damage, and breakdown of the capillary barrier (Folkow et al., 1977' Bidani and Griffin, 2004). This leads to increased glomerular protein filtration that causes segmental necrosis and glomerulosclerosis (Shankland, 2006). Glomerular sclerosis and preglomerular vascular structural alterations can cause a further reduction in renal blood flow and enhance the progression of hypertensive renal injury (Folkow et al., 1977; Campese et al., 1991; Shankland, 2006; Hemmelgarn et al., 2010).

Numerous drugs are used to control blood pressure, including β-blockers, vasodilators, renin-angiotensin system inhibitors and diuretics. The clinical strategy for treatment of HN is to achieve and maintain blood pressure using minimal drug combinations with minimal side effects (Halbach, 2018). However, in later periods, hypertension patients become less responsive to drugs, and drug combinations are associated with significant side effects (Yusuf et al., 2008; Parving et al., 2012; Fried et al., 2013). It is a challenge to find the best treatment for HN patients that can balance the positive and negative effects of drugs. In China, EU has been widely used to treat hypertension and has also been used in Chinese traditional medicine as a folk drink and functional food for several thousand years (Hussain et al., 2016). EU is a plant containing various chemical constituents such as lignans, iridoids, phenolics, steroids, flavonoids, and other compounds; these components of EU possess various medicinal properties, such as antioxidant, anti-inflammatory, and anti-allergic properties, as well as blood pressure control (Kulomaa et al., 1997; Yen and Hsieh, 1998; Hsieh and Yen, 2000; Park et al., 2006; Bonghyun et al., 2009). Eucommia bark extract is a vasorelaxant used for antihypertensive formulations (Kwan et al., 2003; Luo et al., 2010; Greenway et al., 2011). Extract of EU has also been reported to reduce the concentration of hydrogen peroxide in the kidneys and to protect against renal injury (Park et al., 2006; Liu et al., 2012). Thus, EU has the potential to treat HN because of its antihypertensive and renal protective activities with fewer side effects.

However, multicomponents and various medicinal properties make it complex to illustrate the effect of EU on renal protection and blood pressure control. More importantly, the architecture of the kidney increases the complexity. It is difficult to elucidate the mechanisms of EU in treating HN because this involves connecting a multi-component drug with various properties to a complex disease with various risk factors. Based on the "multi-component and multi-target" principle, network robustness methods can be learned from network sciences to identify drug positioning for various HN lesions. Complex systems of disease can be described by networks, in which gene interactions in specific disease conditions are represented by vertices and edges between vertices. Drug disturbance can be presented as an attack on the disease network. Drug effects on system robustness can be addressed by analyzing how the network architecture changes as drugattacked vertices are removed. In the field of network science, network robustness against perturbations provides a standard of measurement for assessing multi-drug attacks on disease network. Health systems are generally robust against various perturbations but can be fragile when faced with perturbations for which the system has not been optimized (e.g., disease conditions) (Kitano, 2007). Drug attacks with stronger effects on the reduction of the robustness of the disease network suggest that this drug maybe more effective for this disease or pathological process.

Based on the various lesions of HN and the genes expressed in different compartments of kidneys of HN patients, we proposed an approach based on the drug perturbation of disease network robustness of two renal compartments, which is suitable for the assessment of multicomponent drug positioning. The drug perturbation of two different disease networks, renal glomeruli and tubules, will be helpful in describing clinical features and applications of drugs. As an example of a multicomponent drug, an extraction of EU called the QDZJN, which is approved by CFDA, was used in our study. We found that potential targets of QDZJN were specifically expressed in the kidneys and tended to participate in the inflammatory response, regulation of blood pressure, and response to LPS and hypoxia, which were also key factors of HN. Based on network robustness assessment, QDZJN may specifically target glomeruli, suggested by its stronger influence on the glomerular network after removal of its potential targets. QDZJN may be used for HN patients with glomerular injury for better pharmaceutical effects. These finding will provide more evidence for the precise clinical application of QDZJN against HN. This approach we proposed maybe helpful for the precision clinical use of multi-target drugs against HN and other complex diseases.

#### MATERIALS AND METHODS

fphar-10-00049 February 9, 2019 Time: 17:6 # 3

#### Literature Mining of Component Compounds of QDZJN

QDZJN is a CFDA-approved drug that is the extraction of EU. The 84 identified component compounds of QDZJN were retrieved by literature mining of research papers about the bioactive constituents of EU. "Eucommia ulmoides," "du zhong," "compounds," "chemical" and related synonyms were used to retrieve relevant literatures. Information on the component compounds collected from the literature was tidied and reorganized. The molecular structure files of all the component compounds of QDZJN were downloaded from the ChemSpider database<sup>1</sup> and saved in InChI format. Detailed information on the constituent components of QDZJN is provided in **Supplementary Table S1**.

#### Target Prediction of QDZJN's Component Compounds

Target prediction of the component compounds was executed by BATMAN-TCM (Liu et al., 2016), which is a web service for discovering the therapeutic mechanisms of multicomponent drug (Liu et al., 2016). In BATMAN-TCM, potential drugtarget interactions were sorted based on possibility scores from largest to smallest. Possibilities of drug-target interactions were predicted according to their similarities to known drug-target interactions that were previously published and validated (Liu et al., 2016). Three types of validation methods, including "leave-one-interaction-out" cross-validation, "leave-one-drugout" cross-validation and validation of the independent test set, were applied to measure the performances. Th results showed that BATMAN-TCM had satisfactory performance for target prediction. In this study, potential targets of 84 compounds for QDZJN were predicted with possibility scores larger than 20.

#### Annotation Enrichment Analysis of Gene Tissue Specificity and Biological Function

Tissue specificity of potential targets of QDZJN was analyzed by the DAVID functional annotation of tissue expression ("Uniprot Tissue") and GO functional enrichment analysis of DAVID was used to detect enriched biological processes of genes (Dennis et al., 2003), both based on the hypergeometric cumulative distribution test. Annotation terms with p-values < 0.05 and fold enrichment larger than 1 were considered statistically significant.

#### Disease Genes of Hypertension and HN

1409 disease genes related to hypertension were downloaded from DisGeNET Database via MeSH term D006973 (Piñero et al., 2017). And 57 disease genes related to HN were downloaded from DisGeNET via MeSH terms D006977 and D006978 (downloaded on December 3rd, 2018).

#### Differentially Expressed Genes From Renal Compartments of HN Patients

Transcriptome data were collected from tubulointerstitial and glomerular compartments from kidney biopsies of HN patients (n = 15) and healthy living donors (n = 27) in Berthier's study (Berthier et al., 2012). In this study, 12,025 human genes were expressed above the 27 Poly-A Affymetrix control expression baseline (negative controls) in the glomerular and tubulointerstitial compartments and were used for further analyses. Microarray data from HN patients will be available on the GEO web site under accession numbers GSE37455 (tubulointerstitial) and GSE37460 (glomeruli). For the microarray data, unpaired statistical analyses for each comparison between the relevant study groups were performed using the significance analysis of microarrays method. DEGs of glomeruli and tubules compared to those of healthy samples were defined by a p-value < 0.05 and with a fold change ≥ 1.1 for the upregulated genes and ≤0.91 for the downregulated genes, which were considered significant and used for further transcriptional and pathway analyses.

#### Construction of Disease Networks for Two Renal Compartments

Disease networks consisted of DEGs and interactions between DEGs. Protein interactions from STRING (Szklarczyk et al., 2017) (version 10) whose confidence scores were larger than 0.4 were used to separately construct protein– protein interaction networks of DEGs of glomerular and tubulointerstitial compartments of HN patients (downloaded on March 19, 2018). Cytoscape (version 3.4.0) was utilized to visualize the networks and calculate the topological characteristics of nodes, including degree, APL, and CCnode (Su et al., 2014). Node size is correlated with node degree in networks. Nodes with degrees twofold larger than the median degree of all nodes were defined as hub nodes. Some nodes were also potential targets of QDZJN, these were marked by red edges and ere regarded as nodes attacked by the drug.

#### Network Robustness Represented by Topological Characteristics of Nodes

First, two characteristics of nodes were used to evaluate the importance of drug-attacked nodes in networks, including APL and CCnode of node. Average length of shortest path (APLnode) is a characteristic of network nodes that provides a more sophisticated view than the node degree of local connections by also considering the degree of a node's neighbors. This approach accounts for the fact that all edges are not equal: connections to a highly connected node may render a node more significant or influential (Baldassano and Bassett, 2016). The shorter the path length is, the more important the node is.

The average shortest path length of source node is

$$API\_{\text{node(s)}} = \sum\_{t \in V} \frac{d\,(s, t)}{n},$$

<sup>1</sup>http://www.chemspider.com/

where V is the set of nodes in G, d (s, t) is the shortest path from s to t, and s is the source node. n is the number of nodes in G.

Closeness centrality of a node s is the reciprocal of the sum of the shortest path distances from v to all n−1 other connected nodes. Since the sum of distances depends on the number of nodes connected with source node s, closeness is normalized by the sum of minimum possible distances n−1,

$$\text{CC}\_{\text{node(s)}} = \frac{n - 1}{\sum\_{\mathbf{v} = 1}^{n - 1} d\left(s, \mathbf{v}\right)},$$

where d (s, v) is the shortest-path distance between s and v, and n is the number of nodes connected with s in the graph. The larger the CCnode is, the more important the node is.

Here, a comparison of drug-attacked nodes and other nodes in disease network provides a more comparative view of drugattacked node topological networks, and a unpaired t-test was used to assess differences between two kinds of nodes; a p-value < 0.05 was considered significant.

#### Network Robustness Represented by Topological Characteristics of Networks

Drug attack on networks were evaluated by changes of network's topological characteristics after removal of the drug target. The Average path length (APLnet) is one of the most robust measures of network topology, along with its CCnet.

The average shortest path length of a whole network is

$$APL\_{\text{net}} = \sum\_{s,t \in V} \frac{d\left(s, t\right)}{n\left(n - 1\right)},$$

where V is the set of nodes in G, d(s, t) is the shortest path from s to t, and n is the number of nodes in G.

The clustering of a node v is the fraction of possible triangles through that node that exist,

$$C\_{\mathbb{V}} = \frac{2T\left(\nu\right)}{\deg\left(\nu\right)\left(\deg\left(\nu\right) - 1\right)},$$

where T(v) is the number of triangles through node v and deg(v) is the degree of v. The CCnet for the graph G is the average,

$$CC\_{\text{net}} = \frac{1}{n} \sum\_{\mathbf{v} \in \mathcal{G}} C\_{\mathbf{v}},$$

where n is the number of nodes in G.

In our study, these two indicators were used to evaluated network robustness, including the APL and the CCnet of the whole network. First, the average path length distinguishes a stable network from one, with a shorter average path length being more robust. Therefore, the quotient of the APL before/after attack was used to evaluate the influence of drug attack on the disease network. The larger the APL was, the less stable the network and, the larger the influence made by the drug. Similarly, the quotient of the CCnet before/after attack was also used to evaluate network robustness after a drug attack. A smaller network CCnet indicated a less stable network and a larger drug influence.

### Null Model of Random Network and Permutation Test of Real and Random Networks

To assess drug attack effects on network robustness, the network robustness indicators (RI) of random networks were generated as a null model. Drug attack to a real disease network was compared with null models with these two indicators (APLnet and CCnet) to investigate whether there were significant differences of network robustness between real network and random network after same drug attack. Null models were generated for the disease networks through randomization of network with preserved node and edge numbers of real network. 1,000 random networks were generated as a benchmark, and 1,000 indicators were generated after the same drug attack on random networks as a null distribution for the permutation test (Baldassano and Bassett, 2016). The indicator of a real network out of a 95% confidence interval of null distribution was regarded as having a significant difference (p-value < 0.05 of permutation test). Comparing to the null distribution generated from 1,000 random networks, drug attack that make a significant disturbance in the real disease network was significative. The p-value of drug attack on a real network can be used as a network robustness index for comparison. Smaller p-values indicate larger network disturbance drug attacks.

### Construction of Hierarchical Network for QDZJN Compounds, DEGs of Glomeruli and HN Related Process

A hierarchical network of chemical compounds, potential targets and pathological processes was constructed, including component compounds and potential targets of QDZJN, HN related biological processes, drug-target interactions predicted by BATMAN-TCM, relationships between targets and pathological processes of HN retrieved from literatures. Potential targets of QDZJN were also DEGs of glomeruli, namely, drug-attacked nodes in the glomerular network.

#### RESULTS

#### QDZJN's Potential Targets Were Upregulated in the Kidney and May Have Anti-hypertension Activity Based on Functional Enrichment Analysis

QDZJN is a CFDA approved drug for hypertension. As multicomponent drug extracted from EU, 84 component compounds of QDZJN were collected via literature mining, including 26 lignans, 17 iridoids, 22 phenylpropanoids, 13 flavonoid, and 8 other kinds of compounds. The structures and other detailed information of the component compounds of QDZJN are shown in the **Supplementary Table S1**. The 427 potential targets of 84 compounds were predicted by BATMAN-TCM (Liu et al., 2016).

Tissue specific expression of potential targets was investigated to explore target tissue attacked by QDZJN. Enriched tissue terms within the UP\_Tissue ("Uniprot Tissue") were detected

using DAVID functional annotation of tissue expression (Dennis et al., 2003). Interestingly, potential targets of QDZJN were enriched in UP\_TISSUE terms including liver and kidney, among which 60 potential targets were expressed in a high level in normal kidney tissue (**Figure 1A**). Besides, 1409 disease genes related to hypertension and 57 disease genes related to HN were downloaded from DisGeNet Database. A chi-square test was used to compare the distribution of drug target in disease gene of hypertension and HN. A small P-value indicates that potential drug targets of QDZJN are enriched in disease genes in HN than hypertension (p-value = 6.80 × 10−<sup>6</sup> , **Figure 1B**). Even though more drug targets are also hypertensive genes, there are 19.3% disease genes of HN targeted by QDZJN which significantly larger than this proportion in hypertension. This result suggests that kidney may be one of the target organs of QDZJN and HN may be a proper indication of QDZJN than hypertension which was in agreement with the renal protection activity of EU. Functional enrichment analysis of GO terms showed that QDZJN potential targets were enriched in biological processes including "inflammatory response," "response to hypoxia," "response

to LPS," "regulation of vasoconstriction and blood pressure" (**Figure 1C**). Consistent with this finding, EU has been reported to exhibit anti-oxidant, anti-hypertensive, and anti-inflammatory activity.18,30, <sup>31</sup> According to the above result, QDZJN has the potential to prevent kidney damage induced by hypertension, as well as hypertension. Of note, a randomized controlled study of QDZJN showed that this drug was effective for the treatment of renal hypertension (Zhen and Liang, 2012; Jing et al., 2015).

### Differentially Expressed Genes in Glomeruli and Tubules of HN Patients

Glomerular and tubulointerstitial lesion are two common types of kidney damage induced by hypertension. To investigate the relationship between QDZJN and two kidney compartments, gene expression data were collected from kidney tubules and glomerular samples of HN patients and healthy living donors in Berthier's study (Berthier et al., 2012). DEGs were identified from comparing the kidneys of human with HN with healthy kidneys, as the kidney maybe QDZJN's target organ. Kidneys from HN patients demonstrated 325 glomerular and 147 tubulointerstitial genes with significantly changed mRNA expression compared with healthy kidneys (**Figure 2A**). A total of 131 upregulated and 194 downregulated genes were identified in glomeruli, and 90 upregulated and 57 downregulated genes were identified in tubule (**Figure 2A**). There were 53 genes differentially expressed in both glomerular and tubulointerstitial compartments (**Figure 2B**). Result of enrichment analysis showed that glomeruli and tubules shared some pathways, e.g., "inflammatory and immune response," "apoptotic process," "angiogenesis," and "response to oxidative stress" (**Figure 2C**) which are key factors in renal damage induced by hypertension (Imig et al., 2018). DEGs in glomeruli specifically participate in the regulation of blood pressure and cholesterol homeostasis which is related to hypothalamic-pituitary-adrenal (HPA) axis. Similar to DEGs, biological processes related to "inflammatory response,""response to hypoxia," and "regulation of blood pressure" were also enriched in QDZJN's potential targets. This finding implied that QDZJN intervenes in renal damage by controlling responses to inflammation, oxidative stress and blood pressure.

### Construction of Disease Networks of Kidney Glomeruli and Tubules

Disease networks based on DEGs in glomeruli and tubules were constructed to exhibit the interaction between DEGs in two kidney compartments and to assess the drug perturbation of disease networks. The glomerular disease network consisted of DEGs in the glomerulus and interactions between DEGs from STRING database, containing 234 nodes with 836 edges (**Figure 3A**). The tubule disease network contained 109 nodes with 280 edges (**Figure 3B**). Node size is relevant to the degree of node in the disease network, which is defined as the number of edges connected to that node (see "Materials and Methods" section). Hubs are important in the disease network. After the hub is attacked by drug, it broadcasts the effect to the nodes to which it is linked. Nodes with degree two-fold larger than median of degrees of all nodes were defined as hub nodes which play important roles. There were 15 and 44 hub nodes in tubular and glomerular networks, respectively. ALB, FOS and EGR1 were both hub nodes in two networks, these genes play critical roles in the progression of HN. Based on previous studies, urinary albumin (ALB) had the potential to be a marker for hypertension (Takase et al., 2015). EGR1 deficiency protects against renal function by attenuating NF-κB and TGFβ-mediated renal inflammation/fibrosis (Ho et al., 2016, 1). However, most hub nodes were specifically expressed in single renal compartment.

To investigate the importance of drug-attacked nodes in the disease network, potential targets were marked with red edges in **Figure 3**. In the glomerular and tubulointerstitial network, 6 and 22 nodes, respectively, were potential targets of QDZJN and were regarded as drug-attacked nodes that were attacked by QDZJN's compounds (**Figure 3**). In the glomerular network, hub node PPARG was targeted by QDZJN, it has been reported to be a new target for the treatment of hypertension as well as pivotal in vascular muscle as a regulator of vascular structure, vascular function, and blood pressure (Leibovitz and Schiffrin, 2007). In both tubulointerstitial and glomerular networks, LTF was a key node that has been verified to be an antihypertensive peptide (Ruiz-Giménez et al., 2012). QDZJN attacked 8 hub nodes in the glomerular network, but 0 node in the tubular network. This result suggested that potential targets of QDZJN may play more important roles in glomerular network.

#### Drug Attack of QDZJN on Disease Networks Based on Topological Characteristics of Nodes

QDZJN affected eight hub nodes of the glomerular network which indicated that drug-attacked nodes may play important roles in the glomerular network. To comprehensively evaluate the importance of drug-attacked nodes in glomerular and tubulointerstitial disease network, two more topological features of drug-attacked nodes were compared with other nodes in the disease network, including average path length and CCnode of node. The average path length of node (APLnode) is defined as the average number of steps along the shortest paths for all nodes connected to that node. It is a measure of the efficiency of information or mass transport on a network. CCnode of a node is a measure of centrality in a network, calculated as the sum of the length of the shortest paths between the node and all other nodes in the graph. The more central a node is, the closer it is to all other nodes. In glomerular network, drug-attacked nodes have relative shorter APLnode and larger CCnode than other nodes in the glomerular network with statistically significant difference (papl = 1.42 × 10−<sup>2</sup> ; pcc = 2.75 × 10−<sup>2</sup> ) indicating that drugattacked nodes tend to connect closely to nodes in the network (**Figures 4A,B**). However, APLnode and CCnode of drug-attacked nodes in tubulointerstitial network had no significant differences with other nodes (**Figures 4A,B**). Comparing drug-attacked node in these two different disease networks, drug-attacked nodes in the glomerular network had more important position and attacks to these nodes will more greatly influence linked nodes and whole

network robustness, but there was no sizeable difference in the tubulointerstitial network.

### QDZJN Tent to Disturb Glomerular Network of HN Patients Based on Network Robustness

Next, the robustness of whole networks against drug attack was assessed to evaluate QDZJN attack on the disease network of glomeruli and tubules (**Figure 4C**). Here, two of the most robust measures of network topology, average length of shortest path (APLnet) and CCnet, were used. The results showed that overall network structure in the tubulointerstitial network was more robust than that of the random network after drug attack with minimal change in APLnet and CCnet (**Figures 4D,E**). However, the network structure of glomerular network was less robust than that of the random network under drug attack with bigger change in APLnet and CCnet (**Figures 4D,E**). This finding indicated that drug attack on the disease networks of two renal regions result in a different reaction of genes connection in glomeruli

FIGURE 3 | Interaction network of DEGs in glomeruli (A) and tubules (B). Node size changes with the node degree in network. The hub nodes were marked with big name labels and potential drug targets of QDZJN were marked with red edges. Nodes with red star label were disease genes related to HN from DisGeNET database.

and tubules: the glomerular network was more sensitive, but the tubulointerstitial network remained stable in structure due to the removal of drug targets.

Interestingly, APLnet was decreased in the tubulointerstitial network (p-value < 0.001 by permutation test) but increased in the glomerular network (p-value < 0.001 by permutation test) after drug attack. The different change in APLnet reflected the underlying difference in network organization for the two renal regions; drug attack on the glomerulus system was characterized by greater rich-club organization, and increasing dependence on hub nodes, resulting in greater fragility under drug attack. In contrast, the tubulointerstitial network was less affected due to its redundant structure. Moreover, CCnet was only decreased in the glomerular network (p-value < 0.001 by permutation test), but not in the tubulointerstitial network (p-value > 0.05 by permutation test). The different change in CCnet implies that the glomerular system is more sensitive to QDZJN attack. Overall structure changes of glomerular and tubulointerstitial networks after drug attack show that QDZJN specifically targeted glomeruli, as shown by the increased APLnet and decreased CCnet after removal of drug targets.

#### Mechanism of Drug Protection Against Glomerular Damage Induced by Hypertension

To illustrate the mechanism of QDZJN against glomerular damage induced by hypertension, a hierarchical network of chemical compounds common targets of QDZJN and DEGs from glomeruli, and pathological processes related to HN was constructed, including drug-target interactions and relationships between targets and HN pathological processes (**Figure 5**). This network shows that 13 drug-attacked nodes in the glomerular network were related to hypertensive pathological processes and 21 chemical compounds of QDZJN interacted with these drug-attacked nodes. These 13 drugattacked nodes belonged to four key pathways of HN, e.g., RAAS, lipid metabolism, immune response, and inflammatory response. PAH interacts with DCC to regulate dopamine synthesis from tyrosine. Recent studies show that the intrarenal dopaminergic system indirectly inhibits renal renin expression (Zhang et al., 2005). Specifically, CYP3A4, CYP27B1, and SNAI2 play important roles in vitamin D metabolism, and evidence has been collected to indicate that vitamin D may interact with renin to represses the RAAS system and reduce the loss of glomerular filtration rate (Santoro et al., 2015). Some drug-attacked nodes maintain lipid metabolic homeostasis (specifically, metabolism of cholesterol and PUFAs, adipogenesis). As a synthesis enzyme of cholesterol, CYP3A4 closely connects to high blood pressure. The SNP genotypes of APOA2, ALOX5, and CYP4F2 were associated with the content of PUFAs (Tagetti et al., 2015; Ballester et al., 2016) which is associated with significant improvement in vascular function and lower blood pressure (Colussi et al., 2016). Epidemiological studies show that circulating PUFAs contribute to preserving renal function (Syren et al., 2017). PPARG influences adipogenesis in glomeruli and knock down of PPARG can lead to glomerular hypertrophy (Toffoli et al., 2017). GATA3, and ITGB2 participated in different immune responses, glomerular immunoglobin A deposition (Yamanaka et al., 2016) and leukocyte recruitment to the inflamed glomerulus (Kuligowski et al., 2006), respectively. In glomerular injury, upregulation of ALOX5 promotes inflammation (Hao and Breyer, 2007), but LTF (Drago-Serrano et al., 2017) and PTGER2 suppress it. As the prostaglandin E2 receptor, activation of PTGER2 accentuates chronic inflammation and protects

against angiotensin II-induced hypertension via inhibition of oxidative stress (Jia et al., 2008; Jiang and Dingledine, 2013). Vitamin D also preserves kidney function via attenuating the inflammatory response during lipopolysaccharide-induced acute kidney injury (Xu et al., 2015). Among these drugattacked nodes, LTF, PPARG, and APOA2 have been proven to be targets for hypertension treatment (Ruiz-Giménez et al., 2012; Ballester et al., 2016; Toffoli et al., 2017). Eight out of 21 possible active compounds have been already found to have anti-hypertension or glomerular protection activity, including ursolic acid (Somova et al., 2003; Zhou et al., 2010), β-sitosterol (Olaiya et al., 2014), ascorbic acid (Duffy et al., 1999), gallic acid (Jin et al., 2017), protocatechuic acid (Safaeian et al., 2016), pyrogallol (Lai and Spector, 1978), epicatechin (Galleano et al., 2013), and catechin (Rhee et al., 2002; Bhardwaj and Khanna, 2013).

### DISCUSSION

As the leading cause of cardiovascular mortality, hypertension also leads to damage of various organs, especially renal damage. As a multicomponent drug, QDZJN was widely used to control blood pressure and protect renal function. In our study,

Frontiers in Pharmacology | www.frontiersin.org

of HN patients.

QDZJN was found to specifically target glomerular damage

including oxidative stress, angiogenesis, inflammatory response and immune response.

Because of the anti-oxidant, anti-hypertensive, and antiinflammatory activities of QDZJN, it has the potential to treat HN. Moreover, potential targets of QDZJN were specifically expressed in renal tissue, which provides more evidence to support QDZJN positioning to HN. If QDZJN has the potential to prevent renal damage, question include how and on which part QDZJN will intervene. To discovery the precise target suborgan position of QDZJN, the robustness of disease networks from glomeruli and tubules was evaluated under QDZJN attack (**Figure 6**). Drug-attacked nodes in the glomerular network had more important positions in disease network with larger CCnode and smaller shortest path length, which implies that attack to these nodes will more strongly influence network robustness.

10 February 2019 | Volume 10 | Article 49

FIGURE 5 | Hierarchy network of chemical compound, drug-attacked nodes in the glomerular network and HN related pathological processes. Compounds with red edge represent drug with anti-hypertensive activity.

fphar-10-00049 February 9, 2019 Time: 17:6 # 10

Overall structural change in the glomerular and tubulointerstitial networks after QDZJN attack show that QDZJN specifically target glomeruli, as indicated by the increased shortest path length and decreased CCnet of the whole network after removal of drug targets.

A hierarchical network of 21 chemical compounds of QDZJN, 13 drug-attacked nodes in the glomerular network, and pathological processes related to HN was constructed to clarify the relationship between drug and disease. And all these 21 possible active compounds may target on 13 genes specifically expressed in glomeruli which were related to RAAS, lipid metabolism, immune response, and inflammatory response. These biological processes can be divided into two categories, primary hypertension factors and kidney protection factors (**Figure 6**). Specifically, lipid metabolism is a critical pathway in primary hypertension and plays important roles in renal damage. CYP3A4, APOA2, PPARG, ALOX, and CYP4F2, proteins related to lipid homeostasis affect the occurrence of hypertension, and PTGER2, ALOX5, LTF, ITGB, and GATA3 relate to the inflammatory and immune response in glomeruli. For kidney protection, GATA3 and PPARG both have protective effects on glomeruli. GATA3 was specifically expressed in glomeruli and is related to renal aplasia (Moriguchi et al., 2016, 3). PPARG influences adipogenesis in glomeruli, and knock down of PPARG can lead to glomerular hypertrophy (Toffoli et al., 2017). SNAI2, CYP27B1, DDC, and PAH inhibits the RAAS system to reserve glomerular function by regulating concertation of vitamin D and dopamine. In summary, QDZJN can preserve renal function and reduce hypertensive risk factor by anti-inflammation, antioxidation and regulation of metabolic homeostasis.

Similar to QDZJN, most multicomponent drugs have various medicinal properties, making it a challenge to maximize the efficacy of drugs and to find optimum indications. Based on the "multi-component and multi-target" principle, multiple attacks on network can simulate multicomponent drug effects to disease network. Evaluation of network robustness can assess the strength of drug attacks on network. In previous studies, network robustness has been used in drug discovery, and cancer biology. In this study, we innovatively proposed a strategy to precisely position the clinical application of drug based on network

#### REFERENCES


robustness which can be applied to the precise clinical positioning of other multi-target drugs. This strategy suggests a new approach for multicomponent drug discovery. For multi-target drugs, including monomers with many targets, and multicomponent drugs, approaches based on network robustness can be applied to many research topics, such as, the best indication for a drug and, upon which part of a complex disease does the drug work. In our study, QDZJN was repositioned to HN, especially glomeruli, which is also a new finding about QDZJN and needs further experimental verification. Prediction of precise position for drug is very helpful for drug clinical applications, QDZJN may be used for patients with glomerular injury for better pharmaceutical effects.

#### AUTHOR CONTRIBUTIONS

FG, HX, and HY conceived the study and wrote the manuscript. FG and WZ performed the data analysis. JS collected the component of QDZJN. All authors reviewed and approved the final manuscript.

#### FUNDING

The authors acknowledge support from the National Major Scientific and Technological Special Project for "Significant New Drugs Development" (2018ZX09201009), the National Natural Science Foundation of China (81703951), the Fundamental Research Funds for the Central Public Welfare Research Institutes (ZXKT17058), and Jiangxi Prozin Pharmaceutical Co., Ltd.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2019.00049/full#supplementary-material

TABLE S1 | Component compounds of QDZJN by literature mining.



index technique. J. Chromatogr. A 781, 523–532. doi: 10.1016/S0021-9673(97) 00357-9


in experimental hypertension. Phytomedicine 10, 115–121. doi: 10.1078/ 094471103321659807


deposition of IgA. Immunobiology 221, 577–585. doi: 10.1016/j.imbio.2015. 12.001


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Guo, Zhang, Su, Xu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment

Runzhi Zhang, Xue Zhu, Hong Bai\* and Kang Ning\*

School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China

The research field of systems biology has greatly advanced and, as a result, the concept of network pharmacology has been developed. This advancement, in turn, has shifted the paradigm from a "one-target, one-drug" mode to a "network-target, multiple-component-therapeutics" mode. Network pharmacology is more effective for establishing a "compound-protein/gene-disease" network and revealing the regulation principles of small molecules in a high-throughput manner. This approach makes it very powerful for the analysis of drug combinations, especially Traditional Chinese Medicine (TCM) preparations. In this work, we first summarized the databases and tools currently used for TCM research. Second, we focused on several representative applications of network pharmacology for TCM research, including studies on TCM compatibility, TCM target prediction, and TCM network toxicology research. Third, we compared the general statistics of several current TCM databases and evaluated and compared the search results of these databases based on 10 famous herbs. In summary, network pharmacology is a rational approach for TCM studies, and with the development of TCM research, powerful and comprehensive TCM databases have emerged but need further improvements. Additionally, given that several diseases could be treated by TCMs, with the mediation of gut microbiota, future studies should focus on both the microbiome and TCMs to better understand and treat microbiome-related diseases.

Keywords: network pharmacology, Traditional Chinese Medicine, database, assessment and comparison, completeness

### OPPORTUNITIES AND CHALLENGES FOR THE MODERNIZATION OF TRADITIONAL CHINESE MEDICINE

#### Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) is one of the greatest treasures of Chinese culture, has a long history of use in East and Southeast Asia, and has been widely used since ancient civilization. Through continuous development and innovation, physicians have selected the essence and discarded the dross of TCM. As a result, TCM has become one of the main forms of alternative medicine in East Asia, North America, and Europe. TCMs use therapeutic herbs to treat diseases according to the combinatorial principle of "King, Vassal, Assistant, and Delivery servant" based on a patient's syndrome and to restore balance of life and body functions (Yi and Chang, 2004; Qiu, 2007). Each prescribed combination of these herbs is referred to as a TCM preparation, or TCM prescription, for example the LiuWeiDiHuangWan (LWDHW) pill. Traditionally, the

Edited by:

Shao Li, Tsinghua University, China

#### Reviewed by:

Wei Li, Toho University, Japan Pinarosa Avato, Università degli Studi di Bari, Italy

#### \*Correspondence:

Hong Bai baihong@hust.edu.cn Kang Ning ningkang@hust.edu.cn

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 12 October 2018 Accepted: 31 January 2019 Published: 21 February 2019

#### Citation:

Zhang R, Zhu X, Bai H and Ning K (2019) Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment. Front. Pharmacol. 10:123. doi: 10.3389/fphar.2019.00123

**194**

discovery of most new drugs is focused on identifying or designing a pharmacologically effective agent that specifically interacts with a single target. During the past 30 years, such an approach has generated highly successful drugs. However, drugs acting on individual molecular targets usually exert unsatisfying therapeutic effects or have toxicity when used to treat certain diseases, such as diabetes, inflammation, and cancer (Kola and Landis, 2004). Given the major bottleneck in drug discovery, drug research, and development have gradually shifted from a "one-target, one-drug" mode to a "network-target, multiplecomponent-therapeutics" mode (Li, 2011; Li et al., 2011, 2014b). To treat disease, the holistic philosophy of TCM shares key concepts with the new mode of drug discovery. Therefore, making full use of TCM is of great importance for drug research. In recent years, TCM has gradually garnered interest thanks to its low toxicity and therapeutic effects (Chan, 1995; Corson and Crews, 2007; Qiu, 2007). For example, Ganoderma lucidum, termed "LingZhi" in China, has been used as a healthpreserving and therapeutic agent. Previous studies have shown that it possesses medicinal properties, including anti-cancer, anti-diabetic, anti-hepatotoxic, and immunomodulatory effects (Paterson, 2006; Boh et al., 2007, Boh, 2013; Ma et al., 2015). Panax ginseng is another TCM that has been used therapeutically for more than 2,000 years to treat such diseases as cardiovascular disease, Alzheimer's disease, and diabetes (Lee et al., 2008; Kim, 2012; Yuan et al., 2012; Chan et al., 2013). Unlike modern drugs discovered by targeting a specific protein, however, the understanding of the molecular basis of TCM remains limited, and research into modern TCM theory has lagged, which has slowed down the development of novel TCMs (Corson and Crews, 2007). TCM studies must keep pace with the rapid development of modern science to remain relevant. Therefore, a scientific and effective assessment system needs to be established, which will be key for studying and making full use of TCMs.

### Network Pharmacology: An Appropriate Approach for Modern TCM Research

Given the rapid progress in bioinformatics, systems biology, and polypharmacology, network-based drug discovery is considered to be a promising approach for cost-effective drug development. Systems biology examines biological systems by systematically perturbing them; monitoring the gene, protein, and informational pathway responses; integrating these data; and, ultimately, formulating mathematical models to describe the structure of the system and its response to individual perturbations (Ideker et al., 2001). Based on a systems biology approach, the concept of network pharmacology was first proposed by Li et al. (2014b). Because network pharmacology can provide a full or partial understanding of the principles of network theory and systems biology, it has been considered the next paradigm in drug discovery (Hopkins, 2008). Furthermore, the network pharmacology approach has been used to study "compound-proteins/genes-disease" pathways, which are capable of describing complexities among biological systems, drugs, and diseases from a network perspective, sharing a similar holistic philosophy as TCM. Applications of systems biology methods to determine the pharmacological action, mechanism of action, and safety of TCMs are invaluable for modern research and development of TCM. Thus, a new interdisciplinary method termed TCM network pharmacology has been proposed (Li and Zhang, 2013; Liang et al., 2014a; Li et al., 2014b), which has initiated a new research paradigm for transforming TCM from an experience-based to evidence-based medicine. In this work, we first summarized the currently widely used databases and tools for TCM network pharmacology research. Second, we concentrated on the different applications of network pharmacology to TCM research, including TCM recipes, target prediction, and network toxicology. Third, we compared different TCM databases based on their basic properties and search results (**Figure 1**).

### TOOLS AND DATABASES FOR TCM NETWORK PHARMACOLOGY RESEARCH

With the rapid development of research, network pharmacology has become a new approach for drug mechanism research and drug development. In recent years, a variety of related databases and tools have provided crucial support for TCM network pharmacology research. Commonly used databases for TCM network pharmacology research include TCM databases [TCM database@Taiwan (Chen, 2011), HIT (Ye et al., 2011), TCMSP (Ru et al., 2014), and TCMID (Xue et al., 2013)), compound and drug information databases (Drugbank (Wishart et al., 2006), STITCH (Kuhn et al., 2014), ChEMBL (Gaulton et al., 2012), and PubChem (Wang et al., 2009)], target interaction databases [STRING (Szklarczyk et al., 2015), HPRD (Peri et al., 2003), MINT (Zanzoni et al., 2002), IntAct (Kerrien et al., 2012), Reactome (D'Eustachio, 2009), and HAPPI (Chen et al., 2009)], and gene-disease association databases [OMIM (Hamosh et al., 2002) and GAD (Becker et al., 2004; **Table 1**)]. Recently, we also proposed TCM-Mesh (Zhang et al., 2017), a more comprehensive TCM database that embodies the core idea of TCM network pharmacology. Based on these biological databases and clinical trial results, researchers can analyze the "herbscompounds-proteins/genes-diseases" interaction network from the perspective of systems biology, which will provide an understanding of the effects of herbs on diseases. Furthermore, network pharmacology algorithms and tools are particularly vital in mining these databases for knowledge. For example, the Random Walk (Chen et al., 2012) algorithm is a commonly used network clustering algorithm, which starts from a random node (drug, target, or disease) and calculates the similarity of this node and its adjacent node to construct a "drugtarget-disease" network. Moreover, the PRINCE (Vanunu et al., 2010) algorithm is used to prioritize disease genes and infer protein complex associations, based on formulating constraints on the prioritization function which is related with its smoothness over the network and usage of prior information. In addition to data acquisition and analysis, visualization is a key element of network pharmacology that makes the network more intuitive. Cytoscape (Shannon et al., 2003) is

statistics and search results.

an open-source platform suitable for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles, and other state data. Pajek (Dohleman, 2006) is another powerful network analysis tool that is used to examine various complex non-linear networks.

### NETWORK PHARMACOLOGY RESEARCH AND TCM

In recent years, an increasing number of studies have focused on the area of TCM network pharmacology. According to statistics in PubMed and the China National Knowledge Infrastructure (CNKI) databases, the number of published papers (**Figure 2**) has increased over time. These statistics demonstrate the increasing interest in the application of network pharmacology to TCM.

To date, network pharmacology has been applied to studies of many traditional Chinese herbs and herbal prescriptions. As listed in **Table 2**, researchers have conducted many networkbased computational and experimental studies to detect effective substances and determine the mechanisms of herbal formulas against many diseases.

### Compatibility of TCM Ingredients

Traditional Chinese Medicine preparation (in other words, TCM prescription) is the main form of TCM, and it is produced under the guidance of TCM syndrome differentiation and treatment, which is highly scientific. The traditional "King, Vassal, Assistant, and Delivery servant" combination rule of TCM herbal formulas contains the rich and profound scientific connotation of TCM theory. Thus, the ability to explain the compatibility of TCM is one of the most challenging tasks for TCM research. Li et al. (2010) proposed a method named the

TABLE 1 | Public databases, algorithms, and software related to TCM network pharmacology.


These public databases are categorized into four types: TCM-related, drug-related, target-related, and disease-related. Publicly available tools and visualization software use data from these databases. Websites and references are provided for all of these resources.

FIGURE 2 | Published papers identified from PubMed and CNKI databases. Papers in PubMed were searched by Title/Abstract containing the following keywords: "network pharmacology," "Chinese," and "medicine." Papers in CNKI were searched with the following key words: "network pharmacology" and "Chinese herb," excluding all conference papers. The analysis time was from 2010 and before, to the end of 2018.



<sup>a</sup>The biological ingredients of herbal prescription were listed in Supplementary Table S1.

Distance-Based Mutual Information Model (DMIM) to identify useful relationships that exist among herbs in numerous herbal formulas. DMIM combines mutual information entropy and "between-herb-distance" to score herb interactions and construct an herb network. It achieves a good balance among the herb's frequency, independence, and distance in herbal formulas when

used to retrieve herb pairs. They used 3,865 collateral-related herbal formulas to construct an herb network and illustrated the traditional herbal pairing and compatibility. LWDHW was used as an example to identify possible compatibility mechanisms, and it was found that LWDHW-treated disease shows high phenotype similarity and that certain "co-modules" are enriched in cancer pathways and neuro-endocrine-immune pathways, which may be the mechanism of action by which the same LWDHW formula treats different diseases. Zhang et al. (2008, 2017) focused on the five flavors of Chinese medicinal properties and constructed Bayesian network models of bitterness, pungent flavor, and sugariness. The five flavors of some TCMs were predicted by these established models, providing crucial support for research on TCM compatibility.

### Target Prediction of TCM

Understanding the molecular basis of TCM is crucial for the modernization of TCM, which not only would provide full theoretical support for TCM research but also would increase acceptance of TCM worldwide. In recent years, researchers have done a lot of work to study the molecular mechanisms of TCMs. We proposed the TCM-Mesh system (Zhang et al., 2017), which was designed as an integration of a database and a data-mining system for network pharmacology analysis of TCM preparations. We used TCM-Mesh to identify candidate targets for ginseng and LWDHW based on existing clues (e.g., molecular structures, bioactivity) from curated databases along with a pharmacology approach. The results showed that dammarane-type tetracyclic triterpenoids from ginseng (i.e., ginsenoside Rg1, ginsenoside Re, and ginsenoside Rb1) might be used to treat Alzheimer's disease, hypertension, and atherosclerosis by targeting tumor necrosis factor (TNF), nitric oxide synthase 3 (NOS3), and AKT serine/threonine kinase 1 (AKT1). Chemical constituents from LWDHW (i.e., loganin, paeonol) can be used to treat diabetes, diabetic nephropathy, and diabetic retinopathy by targeting intercellular adhesion molecule 1 and connective tissue growth factor. Li et al. (Liang et al., 2014b) established a novel pharmacology approach based on a network pharmacology approach to analyze the traditional herbal formulas for LWDHW. The authors found that the compounds of LWDHW may play an important role in esophagitis and colon cancer by regulating the expression of C-C chemokine receptor 2 (CCR2), estrogen receptor 1 (ESR1), peroxisome proliferatoractivated receptor gamma (PPARG), and retinoic acid receptor alpha (RARA). They also selected a XinAn medical family's anti-rheumatoid arthritis (RA) herbal formula "QingLuoYin" (QLY) as an example (Zhang et al., 2013), which consisted of four herbs: KuShen (Sophora flavescens), QingFengTeng (Sinomenium acutum), HuangBai (Phellodendron chinensis), and BiXie (Dioscorea collettii). Some anti-angiogenic and antiinflammatory active ingredients, such as kurarinone, matrine, sinomenine, berberine, and diosgenin, were identified among the 235 ingredients of QLY. In addition, the synergistic effects of the major ingredients were identified in QLY, such as matrine and sinomenine, which may have been derived from the feedback loop and compensatory mechanisms by targeting TNF- and vascular endothelial growth factor-induced signaling pathways involved in RA. According to the molecular structures of proteins and the corresponding targets of 1,401 drugs approved by the U.S. Food and Drug Administration, Wu et al. (2011) constructed a target prediction model by using the "Random Forest" algorithm. The authors used the compounds of "fuzi" (Aconiti Lateralis Radix Praeparata) to predict the targets and construct a multitarget network. The study found that the 22 compounds of "fuzi" have been used to predict several targets that reflect the characteristics of TCM. In addition, Li et al. (An and Feng, 2015) studied the antioxidant effects of ZhiZiDaHuang Decoction (ZZDHT) based on a network pharmacology approach. The authors found that ZZDHT may exert antioxidant effects to regulate reactive oxygen species, thereby treating alcoholic liver disease by targeting cytochrome P450 2E1 (CYP2E1), xanthine dehydrogenase (XDH), nitric oxide synthase 2 (NOS2), and prostaglandin-endoperoxide synthase 2 (PTGS2).

### Network Toxicology

Network toxicology is based on an understanding of the "toxicity-(side effect)-gene-target-drug" interaction network, which utilizes network analysis to speculate and estimate the toxicity and side effects of drugs. Liu et al. (2012) proposed that technologies that feature rapid preparation, high-throughput screening, toxic components exclusion, and biochip along with the drug-target network are of great importance to TCM research on active ingredient screening, toxic components exclusion, and molecular mechanism, which could increase the safety of TCMs. Fan et al. (2011) proposed the concept and framework of network toxicology of TCM. The related tools and technologies were briefly introduced, and the prospects for network toxicology of TCM were forecasted. In their work, they used network pharmacology methods to reconstruct the network of "compound-protein/gene-toxicity" to identify toxic substances and predict the toxic side effects of known compounds, which provides valuable information for understanding the toxic mechanisms. Currently, the related databases for the study of network toxicology includes CTD (Davis et al., 2011), TOXNET (Wexler, 2001), and the National Toxicology Program. Furthermore, additional foresting toxicity software, such as TOPKAT, HazardExpert, and DEREK (Wolfgang and Johnson, 2002), are available for TCM network toxicology studies.

### PROS AND CONS OF CURRENT TCM DATABASES: BENCHMARKING

### Comparisons Among TCM Databases

As discussed, various databases are used for TCM analysis. However, network analysis of TCM is limited by several aspects of the currently available databases (**Figure 3**, current statistics on various databases can be obtained from the homepage of each database).

For example, HIT is a comprehensive TCM database that supports many different input formats. It is limited, however, in that the total information from HIT for TCM analysis is not sufficient, as it only contains about 586 compounds and 1,301 targets. Thus, the data that can be collected from the


FIGURE 3 | Comparisons among the four TCM databases. The comparison contains the paper information, different input types, general statistics, and limitations of several TCM databases.

HIT are limited. TCMSP is a TCM database that captures the relationships among drugs, targets, and diseases. It allows users to use many input formats; however, TCMSP should be more concise as the database has a large amount of redundancies when searching for herbs, which must be trimmed to be improved. TCMID is another comprehensive database that provides information and bridges the gap between TCM and modern life science. It collects TCM-related information from the TCM@Taiwan database and the literature, including prescription of TCM, and also offers a module for network visualization. Although TCMID is available for multiple inputs, including compound name, STITCH database ID, and CAS number, when searching for ingredients, it is not sufficiently comprehensive, and many compounds are searched without a related network in TCMID. The newly launched TCM-Mesh is a more comprehensive database that not only contains the data on network pharmacology, including the "herb-compound-targetdisease" network, but also includes data on TCM side effects and toxicity. As a result, TCM-Mesh offers a more holistic perspective for those conducting TCM research. Problems in TCM-Mesh remain to be resolved, however; for example, the web service is limited and many functions need to be improved, as the CAS number query is not supported. In addition, detailed descriptions about the compounds, genes, and diseases are lacking.

### Detailed Information on Herbs and Prescriptions for Assessment

We used prescriptions for LWDHW and ZZDHT to assess TCM-Mesh and randomly selected 10 well-known herbs to test different TCM databases for systematic assessment (**Table 3**).

### Assessment Using LWDHW as an Example

As mentioned earlier, Li et al. (2010) found that the compounds of LWDHW may play an indispensable role in esophagitis and colon cancer by regulating the expression of CCR2, ESR1, PPARG, and RARA. To validate the effects of LWDHW on esophagitis and colon cancer, we used TCM-Mesh. We found 11 candidate targets (including PPARG) for colon cancer and four potential targets for esophagitis (**Table 4**), all of which were validated by the literature.

### Assessment Using ZZDHT as an Example

As discussed in Section 3.2, "Target prediction of TCM," ZZDHT decoction has been applied to treat alcoholic liver disease by targeting CYP2E1, XDH, NOS2, and PTGS2. We used TCM-Mesh to validate the effects of ZZDHT, leading to the identification of 12 candidate targets (including CYP2E1) for alcoholic liver disease (**Table 5**), all of which were supported by the literature.

### Comparison of Search Results Among the TCM Databases

To further compare the databases, we selected 10 well-known herbs to randomly evaluate the TCM databases: G. lucidum, TABLE 3 | Detailed information on the data sets of herbs and prescriptions.


ginseng, Codonopsis pilosula, Astragalus membranaceus, Chinese yam, pseudo-ginseng, Polygonum multiflorum, Radix Angelicae dahuricae, Coptis chinensis, and Cordyceps sinensis. We used each herb to search the different databases and compared the search results (**Table 6**).

We compared the results from different databases and found that although the compound data recorded in TCM-Mesh were abundant, the data on the ingredients of the herb were limited. For each herb, the number of ingredients obtained from TCM-Mesh was lower than the number obtained from TCMID and TCMSP. We also found that the number of ingredients collected from TCMSP was much higher than that collected from the other two databases, in accordance with the fact that the database had a large amount of redundancies when searching for herbs. Additionally, we discovered higher completeness of TCM-Mesh in the ingredient-target associations and targetdisease associations. For many herbs, the search results produced more hits in ingredient-target associations and target-disease associations in TCM-Mesh, although fewer ingredients were collected. Furthermore, a search for P. multiflorum in TCMSP produced no hits, revealing the limited herb data in TCMSP. The TCM database must keep pace with the development of TCM research to support the modernization of TCM. Therefore, a more comprehensive and complete database with powerful web services is necessary.

#### DISCUSSION AND CONCLUSION

Research on TCM has a long history in China, illustrating the enormous potential and opportunities for new drug innovation. The inception of TCM network pharmacology offers researchers a new chance to gain systematic insights into TCM, as the


research strategy of network pharmacology is in accordance with the holistic understanding of the effects of TCM on disease. This understanding may lead to a new direction for the research of pharmacological mechanisms and safety evaluation of TCM. In addition, related methods and studies must keep pace with the rapid developments in TCM research and must continue to be powerful.

#### Unification and Integration: A More Powerful Database System

In recent years, several databases have been proposed, many of which share similar functions although they have different data sources. For most databases, the patterns of input differ from database to database. For example, the herb "Dried Tangerine Peel" in TCMSP must be searched by "chenpi" or "Citrus reticulata," whereas in TCMID, it must be searched by "chen pi" or "Citri reticulatae pericarpium." In addition, many compounds and proteins have different aliases in different databases (e.g., compounds: chemical name, CID number, STITCH ID, CAS number, PubChem CID, EC number, UNII; proteins: protein name, gene symbol, node ID, target ID, target drugbank ID, uniprot ID). The uniform format of input and output not only would make the current databases more concise, but also would enable researchers to take full advantage of the database resources. Thus, a standard transforming platform is needed. Such a platform should fully understand the data structure and content of various databases. Furthermore, it should support the translation among different formats of herbs, compounds, or proteins to bridge the gap between current databases for TCM research. Currently, the output of previous databases can be directly used as the input for another database. Most databases have associated web services; thus, the transforming platform that could transfer data from one database to another, can be presented to users as web services that are linked to different databases or to a browser plug-in. In addition to the unification of databases, the integration of diverse databases is also of great importance. Currently, more than 10,000 herbs used in more than 100,000 herbal formulas have been recorded in TCM (Qiu, 2007), posing a huge challenge for researchers who want to better understand the efficacy and the safety of the TCM. It is difficult for a single database to resolve all these problems as a result of the incompleteness of data. Each database has its own advantages regarding the specificity of its data and its algorithm. Therefore, the full integration of available resources will facilitate the production of an even more powerful and comprehensive database system, which undoubtedly will more effectively promote the modernization of TCM.

### The Content of Compounds: A Crucial Factor

TCM aims to restore whole-body balance in patients by using a herbal formula (Li et al., 2007), and herbal prescriptions are usually composed of two or more medical herbs in a certain proportion. For example, LWDHW consists of six herbs (Zhao et al., 2007; Xie et al., 2008): Rehmannia glutinosa libosch., Cornus officinalis Sieb., Dioscorea oppositifolia L., Paeonia ostii, Alisma orientale Juz, and Poria cocos (Schw.) Wolf, with a dose proportion of 8:4:4:3:3:3 (Wu et al., 2007; Wang et al., 2010). For current studies on TCM formulas, researchers focus on the chemical composition rather than on the proportion of each




I, Ingredients of herb; I-T, Ingredient-target associations; T-D, Target-disease associations.

compound of TCM. Therefore, the influence of the compound content on the effect of the TCM is often ignored. The weight of the content of each compound should be taken into consideration in future studies to better understand the pharmacological effects of the TCM.

#### TCM-Gut Microbiota Network Pharmacology: A New Frontier

The significant involvement of the gut microbiota in human health and disease suggests that manipulation of commensal microbial composition through combinations of probiotics, antibiotics, and prebiotics could be a novel therapeutic approach. Previous studies have shown that many TCMs can be used as agents to prevent gut dysbiosis (Chang et al., 2015; Zhou et al., 2016). Therefore, a more comprehensive database about TCM and gut microbiota is needed, which should not only include information on the interaction between the TCM and the gut microbiota but also integrate respective advantages of TCM databases and microbiome databases. This integration will be conducive to accelerating the internationalization of TCM and will allow researchers to fully understand the efficacy of TCM from a holistic perspective. Research in the area of TCM network pharmacology is still in its infancy and remains to be developed. However, with data accumulation on TCM and clinical research and interaction of various methods of analysis and experiment, researchers can obtain more substantive and authentic information. This information is conducive to the modernization and internationalization of TCM and may offer critical technological support for drug development, clinical diagnosis, and personalized medicine.

#### AUTHOR CONTRIBUTIONS

KN and HB conceived and proposed the idea. KN, HB, and RZ designed the work. KN, RZ, and XZ contributed to the interpretation of data for the work. KN, HB, and RZ drafted the work. KN, HB, XZ, and RZ revised it critically for important intellectual content. All authors read and approved the final

manuscript to be published, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

#### FUNDING

This work was partially supported by National Science Foundation of China grants 81573702, 81774008, 31871334,

#### REFERENCES


and 31671374, and the Ministry of Science and Technology (High-Tech) grant (No. 2018YFC0910502).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2019.00123/full#supplementary-material

influence the likelihood of colon adenoma recurrence. Cancer Res. 70, 1496– 1504. doi: 10.1158/0008-5472.CAN-09-3264



C282Y and H63D mutations of HFE gene in patients with advanced alcoholic liver disease. Rev. Esp. Enferm. Dig. 93, 156–163.



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Zhang, Zhu, Bai and Ning. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A Combined Phytochemistry and Network Pharmacology Approach to Reveal the Potential Antitumor Effective Substances and Mechanism of Phellinus igniarius

Yu Dong1,2† , Ping Qiu<sup>2</sup>† , Rui Zhu<sup>2</sup> , Lisha Zhao<sup>1</sup> , Pinghu Zhang<sup>3</sup> , Yiqi Wang<sup>2</sup> , Changyu Li<sup>2</sup> , Kequn Chai1,2,4, Dan Shou<sup>1</sup> \* and Huajun Zhao<sup>2</sup> \*

<sup>1</sup> Department of Medicine, Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, China, <sup>2</sup> College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China, <sup>3</sup> Institute of Translational Medicine and Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Medical College, Yangzhou University, Yangzhou, China, <sup>4</sup> Zhejiang Key Laboratory of Tumor Diagnosis and Treatment with Integrated TCM and Western Medicine, Hangzhou, China

Phellinus igniarius (P. igniarius) is a medicinal fungus that is widely used in East Asia for the adjuvant treatment of cancer. To elucidate the antitumor effective substances and mechanism of P. igniarius, we designed an approach incorporating cytotoxicity screening, phytochemical analysis, network pharmacology construction, and cellular and molecular experiments. The dichloromethane extract of P. igniarius (DCMPI) was identified as the active portion in HT-29 cells. Nineteen constituents were identified, and 5 were quantified by UPLC-ESI-Q/TOF-MS. Eight ingredients were obtained in the network pharmacology study. In total, 473 putative targets associated with DCMPI and 350 putative targets related to colon cancer were derived from online databases and target prediction tools. Protein-protein interaction networks of drug and disease putative targets were constructed, and 84 candidate targets were identified based on topological features. Pathway enrichment analysis showed that the candidate targets were mostly related to reactive oxygen species (ROS) metabolic processes and intrinsic apoptotic pathways. Then, a cellular experiment was used to validate the drug-target mechanisms predicted by the system pharmacology analysis. Experimental results showed that DCMPI increased intracellular ROS levels and induced HT-29 cell apoptosis. Molecular biology experiments indicated that DCMPI not only increased Bax and Bad protein expression and promoted PARP and caspase-3/9 cleavage but also down-regulated Bcl-2 and Bcl-xl protein levels to induce apoptosis in HT-29 cells. In conclusion, our study provides knowledge on the chemical composition and antitumor mechanism of P. igniarius, which may be exploited as a promising therapeutic option for colon cancer.

Keywords: Phellinus igniarius, antitumor, phytochemistry, network pharmacology, effective substances, mitochondrial apoptosis pathway

Edited by: Shao Li, Tsinghua University, China

#### Reviewed by:

Wentzel Christoffel Gelderblom, Cape Peninsula University of Technology, South Africa Yan Zhu, Zhejiang University, China Yanqiong Zhang, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China Bo Zhang, Tianjin International Joint Academy of Biotechnology and Medicine, China

#### \*Correspondence:

Dan Shou shoudanok@163.com Huajun Zhao zhj@zcmu.edu.cn †These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 28 June 2018 Accepted: 04 March 2019 Published: 19 March 2019

#### Citation:

Dong Y, Qiu P, Zhu R, Zhao L, Zhang P, Wang Y, Li C, Chai K, Shou D and Zhao H (2019) A Combined Phytochemistry and Network Pharmacology Approach to Reveal the Potential Antitumor Effective Substances and Mechanism of Phellinus igniarius. Front. Pharmacol. 10:266. doi: 10.3389/fphar.2019.00266

## INTRODUCTION

fphar-10-00266 March 19, 2019 Time: 14:8 # 2

Cancer serves as a major public health problem worldwide and is expected to surpass heart disease as the leading cause of death in the next few years (Siegel et al., 2016). In the past decade, numerous significant advances in cancer research have revealed the genetics and pathologies of malignant tumors, which, in turn, facilitates the development of novel anticancer agents (Hare et al., 2017). Interestingly, multiple bioactive phytochemicals in consumed edible and medicinal fungi have recently attracted considerable attention as potential candidates for anticancer agents (Li et al., 2015).

Phellinus igniarius (P. igniarius), a well-known mushroom belonging to the genus Phellinus in the polyporaceae family, is a physiologically functional food and exemplary source of natural medicine that has been widely used in China, Japan, Korea, and other countries (Ma et al., 2016). P. igniarius possesses high antiinflammatory, antioxidant and antitumor biological activities due to its accumulation of various secondary metabolites, including polysaccharides, flavonoids, polyphenols, steroids and organic acids (Dong et al., 2015; Shou et al., 2016). Notably, recent studies have creatively focused on the potential functions of P. igniarius extracts and their constituent compounds in the prevention and treatment of cancer (Zhou et al., 2014; Sangdee et al., 2017). A considerable amount of evidence indicates that P. igniarius extracts resulting from water, alcohol, ethyl acetate and other solvent extractions have a significant inhibitory effect on various tumor cells, such as S180, PC3, SK-HEP-1, and HT-29 cells (Yang et al., 2006; Song et al., 2008; Jeon et al., 2013). To date, no prior system reports on the chemical composition of P. igniarius contribute to its antitumor activity or functional mechanisms.

Network pharmacology is now popularly utilized to discover the basis of pharmacodynamic substances, explore their molecular mechanisms of action, and elucidate their scientific connotations (Kim et al., 2017). Especially, TCM network pharmacology focuses on the wholeness and systemicity of the interactions between components, targets and diseases of TCM (Cao et al., 2018; Huang et al., 2018), and is crucial to select the beneficial therapeutic targets of TCM, typical TCM syndromes and corresponding classic formulas (Zhang et al., 2017). At the same time, TCM network pharmacology substantially reduces the workload of follow-up experimental studies on TCM.

In the present study, we designed an approach incorporating cytotoxicity screening, phytochemical analysis, network pharmacology construction, and cellular and molecular biology validation to clarify the antitumor effective substance and mechanism of P. igniarius. Notably, this is the first integral study using multiple methods in combination to elucidate the antitumor efficacy substances and mechanism of this large medicinal fungus.

#### MATERIALS AND METHODS

#### Chemicals and Materials

Phellinus igniarius sporocarps were purchased from Zhejiang Qingzheng Biotechnology Co. Ltd. (Hangzhou, China). Reference standards (purity > 98%) of protocatechuic aldehyde (671E-QHX2) and osmundacetone (RC5E-FH31) were purchased from the National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China), and naringenin (170124), eriodictycol (170309) and sakuranetin (170124) were purchased from Beijing Century Aoke Biology Research Co., Ltd. (Beijing, China). HPLC grade methanol and acetonitrile were purchased from Merck (Darmstadt, Germany). Distilled water was purchased from Watson's Food & Beverage Co., Ltd. (Guangzhou, China). Leucine enkephalin and formic acid were purchased from Sigma-Aldrich (Darmstadt, Germany).

Human hepatoma carcinoma (HepG2, SMMC7721), human gastric carcinoma (BGC-823, SGC790, AGS), human colon carcinoma (HT-29) and human lung carcinoma (A549) cells were purchased from American Type Culture Collection (Rockefeller, MD, United States). DMEM and RPMI 1640 cell culture mediums were purchased from HyClone Corporation (Logan, UT, United States). Fetal bovine serum was purchased from Gibco Corporation (Grand Island, NE, United States). MTT was purchased from Sigma-Aldrich (Darmstadt, Germany). An AnnexinV-FITC/PI apoptosis detection kit was purchased from BD Biosciences (Franklin lakes, NJ, United States). A ROSs assay kit and JC-1 dye were purchased from Beyotime Biosciences (Nanjing, China). Antibodies against PARP (#9532), Caspase-3 (#9662), Caspase-8 (#6790), Caspase-9 (#9508), Bax (#5023), Bcl-2 (#2870), Bcl-xl (#2764), and β-actin (#4970) were purchased from Cell Signaling Technology (Boston, MA, United States). Antibody against Bad (ab32445) was purchased from Abcam (Cambridge, MA, United States). HRP-conjugated secondary antibody was purchased from Bio-Rad (Hercules, CA, United States).

#### P. igniarius Extract Preparation

The dried P. igniarius sporocarps were crushed into a powder (with an approximately 100 mesh screen) using a plant disintegrator. In total, 600 g of powder was weighed, immersed in 3 L of 95% (v/v) ethanol for 30 min, and extracted in an ultrasonic bath 3 times for 1 h each time. The extracted solution was merged and evaporated by a rotary evaporator, and distilled water was added to 1 L of the suspension (containing 20% ethanol). The suspension was then subjected to liquid-liquid extraction with petroleum ether, dichloromethane, ethyl acetate and n-butanol successively. Each extract fraction was merged, evaporated and then freeze-dried to obtain the petroleum ether extract (0.32 g),

**Abbreviations:** BP, biological process; BPI, base peak intensity; CC, cellular component; DAPI, 4',6-diamidino-2-phenylindole; DEGs, differentially expressed genes; DMSO, dimethyl sulfoxide; ECL, enhanced chemiluminescence; GAD, genetic association database; GEO, gene expression omnibus database; GO, gene ontology; HMDB, Human Metablome Database; MF, molecular function; MMP, mitochondrial membrane potential; MTT, 3-[4,5-dimethylthiazol-2-yl]- 2,5-diphenyltetrazolium bromide; OMIM, online mendelian inheritance in man; PARP, poly ADP-ribose polymerase; PBS, phosphate buffer saline; P. igniarius, Phellinus igniarius; P. igniarius DCM, dichloromethane extract of Phellinus igniarius; PPI, protein–protein interaction; ROS, reactive oxygen species; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis; SEA, similarity ensemble approach; TCM, traditional Chinese medicine; TCMSP, traditional Chinese medicine systems pharmacology; TTD, therapeutic target database; UPLC-ESI-Q/TOF-MS, ultra performance liquid chromatography-electrospray ionization- quadrupole/time-of-flight mass spectrometry.

dichloromethane extract (0.51 g), ethyl acetate extract (2.23 g), and n-butanol extract (1.28 g).

### Cell Culture

fphar-10-00266 March 19, 2019 Time: 14:8 # 3

Human hepatoma carcinoma (HepG2) and human gastric carcinoma (BGC-823) cells were cultured in DMEM. Human gastric carcinoma (SGC790, AGS), human colon carcinoma (HT-29) and human lung carcinoma (A549) cells were maintained in RPMI 1640 medium. Human hepatoma carcinoma cells (SMMC7721) were cultured in DMEM/F12 medium. Then, the three media were supplemented with 10% fetal bovine serum, 100 units/mL penicillin and 100 µg/mL streptomycin. The cultures were maintained in a humidified incubator containing 5% (v/v) CO<sup>2</sup> at 37◦C.

### Cytotoxicity Experiment

The cytotoxic effects of the P. igniarius extracts were detected using the MTT calorimetric method. Briefly, cells (3 × 10<sup>3</sup> cells/well) were seeded in 96-well plates and incubated for 24 h. The supernatant was subsequently aspirated, and the cells were exposed to various concentrations of extracts and 0.05% DMSO (v/v). After 72 h of incubation, MTT solution (0.5 mg/mL) was added to each well and incubated for 4 h. Thereafter, the medium containing MTT was removed and replaced with 120 µL of DMSO to dissolve the formazan crystals. Next, the plates were shaken for 20 min, and the absorbance at 570 nm was recorded using an enzyme-linked immunosorbent assay reader (TECAN, Switzerland).

In addition, HT-29 cells (5 × 10<sup>5</sup> cells/well) were seeded into a six-well plate and incubated for 24 h. Then, the medium was replaced with RPMI-1640 medium containing different concentrations of dichloromethane extract of P. igniarius (DCMPI). After incubation for 72 h, the cytomorphology of the cells was observed and digitally photographed using a phase contrast microscope (Olympus, Japan).

#### Chemical Profile Analysis by UPLC-ESI-Q/TOF-MS

The UPLC-ESI-Q/TOF-MS system consisted of an AcquityTM ultra performance liquid chromatography (UPLC) system (Waters Corporation, Milford, MA, United States) and a Synapt G2 mass spectrometer (MS) (Waters MS-Technologies, Manchester, United Kingdom) equipped with an electrospray ion (ESI) source. The system and data were controlled by MassLynx (V4.1) software. Chromatography was performed on an Acquity UPLC BEH C<sup>18</sup> column (2.1 × 150 mm, 1.7 µm, Waters Corporation, Milford, MA, United States) at a flow rate of 0.3 mL/min and a 40◦C column temperature. The optimal mobile phases consisted of A (HCOOH: CH3CN = 0.1: 100, v/v) and B (HCOOH: H2O = 0.1: 100, v/v): 0–1 min, 1–8% A; 1–2.5 min, 8– 15% A; 2.5–4.5 min, 15–20% A; 4.5–5.5 min, maintained at 20% A; 5.5–7.5 min, 20–30% A; 7.5–13 min, 30–60%; 13–14 min, 60– 99% A; and 14–15 min, maintained at 99% A. The extracts were dissolved in methanol to a concentration of 1 mg/mL and filtered through a 0.22 µm membrane. A 2 µL aliquot of sample solution was injected for analysis.

The full-scan LC-MS data were acquired in both positive and negative ion modes from 50 to 1500 Da with a 0.3 s scan time. The optimal Q/TOF-MS conditions were as follows: capillary voltage 3.0 kV, sampling cone voltage 40.0 V and extraction cone voltage 5.0 V for positive ion mode; capillary voltage 2.4 kV, sampling cone voltage 35.0 V and extraction cone voltage 3.5 V for negative ion mode. The source temperature and desolvation gas temperature were set to 150 and 350◦C, respectively, and the cone gas flow and desolvation gas flow were set to 60 L/h and 550 L/h, respectively. The collision energy was set to 4 eV for positive ion mode and 2 eV for negative ion mode. Sodium formate solution was used to calibrate the mass spectrometer prior to the experiment. Leucine-enkephalin was used as an external reference (Lock-SprayTM) to correct the mass during data acquisition via a LockSpray interface, generating reference ions at m/z 556.2771 Da ([M+H]+) and m/z 554.2615 Da ([M-H]−) in the positive and negative ion modes, respectively.

### Constituent Identification

All LC-MS and MS/MS data were processed with MassLynxTM (V4.1) software. Molecular formula speculations of the compounds were determined with Elemental Composition software. Structural identification of the main compounds, which included the chemical structure, accurate molecular mass and potential molecular fragmentation pathways, was determined with Mass Fragment software. Previously published compounds were identified by comprehensively searching databases, such as PubMed<sup>1</sup> , Chemspider<sup>2</sup> , HMDB<sup>3</sup> , and Metlin<sup>4</sup> . Compounds with standard materials were validated using reference standards.

#### Main Ingredient Quantification for DCMPI

DCMPI was dissolved in methanol to a concentration of 1 mg/mL as a sample solution. Mother stock solutions (approximately 1 mg/mL) of protocatechuic aldehyde, osmundacetone, eriodictyol, naringenin and sakuranetin were separately prepared in MeOH. Further, combined spiking stock solutions of the five reference substances were prepared in MeOH from the mother stock solutions by stepwise dilution in the ranges of 4.61–73.15 µg/mL for protocatechuic aldehyde, 3.97–63.44 µg/mL for osmundacetone, 3.91–62.50 µg/mL for eriodictyol, 4.20–67.19 µg/mL for naringenin and 4.04– 64.69 µg/mL for sakuranetin. The sample solution and each combined spiking stock solution were filtered through a 0.22 µm membrane, and a 2 µL volume was injected into the UPLC-ESI-Q/TOF-MS system for analysis. The LC-MS conditions were the same as those previously described.

### Prediction of Drug Targets for DCMPI

Eight major DCMPI components, including five validated components (protocatechuic aldehyde, osmundacetone,

<sup>1</sup>http://www.ncbi.nlm.nih.gov/pubmed

<sup>2</sup>http://www.chemspider.com/

<sup>3</sup>http://www.hmdb.ca/

<sup>4</sup>http://metlin.scripps.edu

eriodictyol, naringenin and sakuranetin) and three identified components (inoscavin A, phelligrin A and phelligrin B) with a peak area greater than 5% were included in the network pharmacology study. By searching for the relevant predictive genes of the 8 main components at the websites PharmMapper Server<sup>5</sup> , SEA Search Server<sup>6</sup> , STITCH<sup>7</sup> and TCMSP<sup>8</sup> , the DCMPI prediction genes were obtained.

#### Collection of Predicted Targets Related to Colon Cancer

Two main methods were used to identify colon cancerrelated targets. To identify the main DEGs between normal human colon samples and colon cancer samples, GDS4382 microarray data were downloaded from the GEO<sup>9</sup> . The data set consisted of 34 human samples; GEO analysis was performed using 17 normal colon samples and 17 colon cancer samples. DEGs were defined by the Bioconductor/R limma package. P < 0.05 and a fold change ≥ 2 were applied. The known targets associated with colon cancer were derived from five databases, GAD, OMIM<sup>10</sup> , TTD<sup>11</sup>, DrugBank<sup>12</sup> and CoolGen<sup>13</sup>, using 'colon cancer' as the key word.

#### Construction of the PPI Network

Protein–protein interaction data from the six currently available PPI databases, the Human Protein Reference Database, the Biomolecular Interaction Network Database, the Biological General Repository for Interaction Datasets, the Database of Interacting Proteins and the Molecular INTeraction Database, were searched using the Cytoscape plugin BisoGenet. An interactive network of DCMPI drug targets and colon cancerrelated targets was constructed based on interaction data, and the network was visualized utilizing Cytoscape software (version 3.2.1).

#### Definition of Network Topology Feature Set

By calculating the 'betweenness centrality,' 'degree centrality,' 'eigenvector centrality,' 'closeness centrality,' 'network centrality' and 'local average connectivity' using CytoNCA, the topological nature of each node in the interactive network was analyzed. The definitions and calculation formulas of these six parameters represent the topological importance of the nodes in the network, with more important nodes yielding higher quantitative values in the network.

#### GO Enrichment and Pathway Analysis

DAVID Bioinformatics Resources 6.8<sup>14</sup> was used to perform GO enrichment analysis of the differentially expressed targets to explore their roles in many BPs. Twenty significantly enriched terms in the CC, BP, and MF categories are shown. ClueGO is a Cytoscape plugin for visualizing the non-redundant features of a large number of gene clusters in a functional grouping network to assess the enrichment of DCMPI candidate targets. The ClueGO network was created using kappa statistics, reflecting the relationship between terms based on the similarity of related genes. The significances of terms and groups are automatically calculated.

#### Nuclear Staining With DAPI

HT-29 colon cancer cells were seeded into 24-well plates (5 × 10<sup>4</sup> cells/well) and treated with 30 and 60 µg/mL DCMPI for 24 h. Then, the cells were rinsed with PBS, stained with DAPI (0.5 µg/mL) and further incubated for 20 min in the dark. The slides were observed under a fluorescence microscope (Nikon, Japan), and the cells with nucleus condensation or fragmentation were considered apoptotic.

#### Apoptosis Assay With Annexin V-FITC/PI Staining

HT-29 colon cancer cells were seeded into 6-well plates (5 × 10<sup>5</sup> cells/well) and cultured overnight prior to exposure to different concentrations of DCMPI (30 µg/mL, 60 µg/mL) for 24 h. Then, the percentage of apoptotic cells was determined by an Annexin V-FITC/PI staining kit following the manufacturer's protocol. Cells were then analyzed with the Guava Easy cytometer (Merck Millipore Co. Ltd., Darmstadt, Germany) within 1 h.

### Mitochondrial Membrane Potential (1ψm) Measurements

Mitochondrial membrane potential (1ψm) was monitored utilizing the JC-1 cationic dye (Molecular Probes) as recommended by the manufacturer. HT-29 cells were seeded into 6-well plates (5 × 10<sup>5</sup> cells/well) and cultured overnight prior to exposure to different concentrations of DCMPI (30 µg/mL, 60 µg/mL) for 24 h and then resuspended in a buffer solution containing 10 µg/mL JC-1 cationic dye. After 20 min of incubation in the dark at 37◦C, the samples were rinsed with PBS to remove unreacted dye. The fluorescence intensity was read with the Guava Easy cytometer within 1 h.

#### Intracellular ROS Measurement

The cell-permeable dye DCFH-DA (Molecular Probes) was used to assay intracellular ROS levels. This dye diffuses into cells and becomes trapped inside by de-esterification. After a reaction with peroxides, the fluorescent product, 5 chloromethyl-2<sup>0</sup> -70 -dichlorofluorescein (DCF), is formed. HT-29 cells were seeded into 6-well plates (5 × 10<sup>5</sup> cells/well) and cultured overnight prior to exposure to different concentrations of DCMPI (30 µg/mL, 60 µg/mL) for 24 h and then

<sup>5</sup>https://omictools.com/pharmmapper-tool

<sup>6</sup>http://sea.bkslab.org/

<sup>7</sup>http://stitch.embl.de/

<sup>8</sup>http://lsp.nwu.edu.cn/tcmspsearch.php

<sup>9</sup>http://www.ncbi.nlm.nih.gov/geo/

<sup>10</sup>http://www.omim.org/

<sup>11</sup>https://db.idrblab.org/ttd/

<sup>12</sup>http://www.drugbank.ca/

<sup>13</sup>http://ci.smu.edu.cn/CooLGeN/Home.php

<sup>14</sup>https://david.ncifcrf.gov/

resuspended in DMEM/F-12 medium containing 10 µM DCFH-DA. Thereafter, the samples were incubated in the dark at 37◦C for 20 min and then rinsed with PBS to remove unreacted dye. The fluorescence intensity was read with the Guava Easy cytometer within 1 h.

#### Apoptosis-Related Protein Assay

Protein extraction was performed as follows: after treatment with DCMPI (30 µg/mL, 60 µg/mL) for 24 h, HT-29 cells were cultured, collected, rinsed twice with ice-cold PBS, lysed, incubated in RIPA buffer containing a 1% protease inhibitor cocktail for 30 min on ice, and then centrifuged at 12,000 × g for 15 min. The supernatants were harvested and prepared by mixing with 5× sample buffer for subsequent Western blot analysis. The protein samples were then separated by 10% SDS-PAGE and transferred onto 0.22 µm polyvinylidene fluoride membranes (Millipore, Bedford, MA, United States). The membranes were blocked with 5% non-fat skim milk for 1 h at room temperature and then incubated with the appropriate primary antibodies overnight at 4◦C. The membranes were incubated with a horseradish peroxidase-conjugated secondary antibody at room temperature for 2 h, followed by rinsing three times with 1× TBST. The protein bands were visualized utilizing ECL detection reagents (Bio-Rad, United States).

#### Statistical Analysis

All data were analyzed with GraphPad Prism 5.0 software (GraphPad Software, Inc., La Jolla, CA, United States) and SPSS 20.0 Software (SPSS, Inc., Chicago, IL, United States). Furthermore, all statistical comparisons were performed using one-way analysis of variance followed by a post hoc Dunnett's test. Values of P < 0.01 were considered statistically significant.

### RESULTS

#### Cell Cytotoxicity Assay of P. igniarius Extracts

The cell viability inhibition induced by P. igniarius extracts, including the petroleum ether fraction, dichloromethane fraction, ethyl acetate fraction, n-butanol fraction and ethanol extract of P. igniarius, was evaluated in vitro with an MTT assay. Moreover, each cancer cell line was incubated with different concentrations of P. igniarius extracts for 72 h to compare their cytotoxicities. Notably, DCMPI as the strongest inhibitor decreased the viabilities of all the tested cell lines in a concentration-dependent manner, and especially significant cytotoxicity was observed in HT-29 cells (**Figure 1A**). Normal HT-29 cells appeared whole, healthy, and polygonal in shape under a microscope; however, after administering DCMPI, the HT-29 cells exhibited a number of morphological alterations, including cell shrinkage, condensed chromatin, membrane blebbing, and an increased density of apoptotic cells (**Figure 1B**).

### Characterization of DCMPI Chemical Constituents

The chemical profiles of DCMPI were analyzed by UPLC-ESI-Q/TOF-MS. All LC-MS data, including retention times, accurate molecular masses, and MS/MS data, are necessary for the structural analysis of compounds. The element compositions were calculated and clearly confirmed by combining with a mass accuracy (ppm) less than 5.0 using MarkerLynx (4.1) software. The majority of the components detected in the positive ion mode were also detected in the negative ion mode; therefore, data analysis was performed in only the negative ion mode. Using the optimal UPLC and Q/TOF-MS conditions described

TABLE 1 | Characterization of chemical constituents of DCMPI by UPLC-ESI-Q/TOF-MS.


above, the BPI chromatogram was obtained in the negative ion mode (**Figure 2A**). A total of 20 peaks were obtained from the BPI chromatogram, and 19 of these peaks were identified or characterized (**Table 1**). Peak 15 was tentatively characterized as phelligrin A based on the mass spectrum and fragmentation pathway illustrated in **Figures 2B,C**.

#### Quantification of Five Main DCMPI Ingredients

The established quantitative method was applied to determine the contents of protocatechuic aldehyde, osmundacetone, eriodictyol, naringenin, and sakuranetin in DCMPI. The linear parameters of the five ingredients are listed in **Table 2**. Typical chromatograms of the five ingredients are shown in **Figure 2D**. DCMPI contained approximately 1.17% protocatechuic aldehyde, 0.62% osmundacetone, 1.10% eriodictyol, 3.59% naringenin, and 3.71% sakuranetin.

#### Putative DCMPI Target Prediction

By screening the eight main components of DCMPI (the structures are shown in **Figure 3A**) in the PharmMapper Server, SEA Search Server, STITCH and TCMSP databases, potential targets of DCMPI were obtained. A total of 1727 potential targets were predicted for the 8 main compounds (196 for protocatechuic aldehyde, 149 for osmundacetone, 284 for eriodictyol, 293 for naringenin, 285 for sakuranetin, 250 for inoscavin A, 270 for phelligrin A, and 264 for phelligrin B), and 473 targets remained after deleting duplicates.

#### Compound-Target Network Construction and Analysis

Traditional Chinese medicine always exhibit versatile biological and pharmacological activities with complex chemical compositions by acting on multiple targets. Studying the complicated interactions between compounds and their targets at the system level may help comprehensively understand the mechanisms underlying TCM effects. We constructed a compound-target network based on the 8 candidate DCMPI compounds and their 473 potential targets. In total, 480 nodes and 1988 compound-target interactions are embodied in this network (**Figure 3B**).

#### GO Enrichment and Pathway Analysis

GO analysis of the putative DCMPI targets was employed based on DAVID software for the visualization, annotation, and integrated discovery described by the BP, CC, and MF terms. In total, 671 BPs, 73 CCs, and 192 MFs that were enriched for this dataset were identified, of which 140 BPs, 55 CCs, and 140 MFs had P-values < 0.05. **Figure 3C** exhibits an overview of the GO analysis, and 20 remarkably enriched terms in the BP, CC, and MF categories are shown.

### Collection of Colon Cancer-Related Targets

We herein collected colon cancer-related targets from two main sources: DEGs obtained from publicly available microarray data and disease-related databases. As shown **Figure 4**, 200 DEGs were identified from the GEO repository microarray data, while other targets were obtained from five databases: 163 from GAD, 54 from OMIM, 25 from TTD, 66 from DrugBank, and 246 from CoolGen. After removing redundant genes, 349 colon cancerrelated targets were collected. Of these, 84 genes were also the targets of DCMPI, which suggests an obvious therapeutic potential for P. igniarius.

#### Identification of Candidate DCMPI Targets for Colon Cancer Treatment

Numerous evidence in network biology has shown that genes and proteins exert their functions via interactions. Thus, we selected proteins as nodes for constructing the network. To elaborate the pharmacological mechanism by which DCMPI ameliorates colon cancer, we established PPI networks that may reflect the behavior and properties of biological molecules. First, a putative target PPI network of DCMPI-related genes was obtained using Cytoscape 3.2.1 software with the plugin BisoGenet (8933 nodes and 195,493 edges) and database retrieval of the PPI network of colon cancer-related targets (4615 nodes and 113,727 edges). We then merged these two networks to obtain a core PPI (CPPI) network that consisted of 4614 nodes and 113,727 edges. Subsequently, colon cancer targets of DCMPI were screened using the topological features of CPPI. Then, utilizing a Cytoscape plugin (CytoNCA), the main hubs of the network were screened by calculating the topological features for each hub, which were identified when their degree exceeded twice the median degree of all nodes in the network. A flow chart depicting the core target screening is presented in **Figures 5A–C**.

### Pathway Enrichment Analysis of DCMPI Targets

A Cytoscape plugin (ClueGO) was applied to further define the pathways involved in the biological networks identified


above. As shown in **Figures 5D,E**, the biological networks consisted of 189 nodes and 1503 edges, and the potential targets were mainly assigned to positive regulation of ROS metabolic processes, intrinsic apoptotic pathways and mammary gland development. Of these, the first two pathways have wellestablished roles in cell apoptosis, and the results suggested that DCMPI is highly likely to exert its antitumor effect via the apoptotic pathway.

#### Nuclear Staining With DAPI

In this study, HT-29 cells were monitored by DAPI staining to determine the pro-apoptotic effect of DCMPI. The administration of DCMPI at doses of 30 and 60 µg/mL for 24 h dramatically induced nuclear morphological changes, such as apoptotic bodies and nuclear fragmentation, compared with the control group (**Figure 6A**).

### Apoptosis Assay With Annexin V-FITC/PI Staining

Next, the percentage of apoptotic cells induced by DCMPI treatment was detected by Annexin V-FITC/PI staining. As shown in **Figure 6B**, the numbers of HT-29 cells in both earlyand late-stage apoptotic populations were remarkably increased in response to DCMPI (30 and 60 µg/mL). Therefore, this finding was consistent with the DAPI staining results and confirmed that DCMPI induced HT-29 cell apoptosis in a concentrationdependent manner.

### Mitochondrial Membrane Potential (MMP, 1ψm) Assessment

The loss of 1ψm, which occurs prior to caspase activation, is regarded as a hallmark of apoptosis. To determine whether DCMPI treatment activated the mitochondria-mediated apoptotic pathway, we utilized the JC-1 fluorescent probe to evaluate alterations in 1ψm by flow cytometry. In apoptotic cells with low 1ψm, JC-1 remains in the monomeric form, and the loss of 1ψm is followed by a red-to-green shift. The percentage of green JC-1 was significantly elevated by treatment with DCMPI (**Figure 6C**), and DCMPI thus markedly increased the mitochondrial membrane permeability and induced 1ψm collapse.

## Intracellular ROS Detection

Reactive oxygen species generation was closely associated with the cancer cell death caused by mitochondrial dysfunction and subsequent mitochondria-induced apoptosis. To identify the roles of ROS in DCMPI-induced colon cancer cell death, we measured intracellular ROS production by DCFH-DA staining with flow cytometry. DCMPI significantly increased the accumulation of fluorescent dye in HT-29 cells in a concentration-dependent manner (**Figure 6D**).

## Apoptosis-Related Protein Assay

DCMPI not only significantly up-regulated the protein expression of Bax and Bad but also simultaneously attenuated the protein levels of Bcl-2 and Bcl-xl. These results revealed that DCMPI alters the Bax/Bcl-2 ratio in HT-29 cells, which subsequently stimulates the mitochondrial-mediated apoptosis pathway. Furthermore, DCMPI (30 and 60 µg/mL) induced both PARP and caspase-3/9 cleavage to aggravate HT-29 cell apoptosis (**Figure 7**).

To clarify whether apoptosis is initiated by caspase cascade activation, we adopted the caspase inhibitor Z-VAD-FMK. Western blot analysis revealed that the cleavage of PARP and caspase 3/9 caused by DCMPI was dramatically inhibited by Z-VAD-FMK (50 µM) pretreatment (**Figure 8**). Together, these results clearly support that DCMPI induces HT-29 cell apoptosis via the mitochondrial apoptosis pathway.

### DISCUSSION

Recently, medicinal fungi were shown to be ubiquitously present in nature, recognized as a promising source of food and medicine with multi-targeted effects and to have low toxicity (Gargano et al., 2017). Intriguingly, numerous preclinical and clinical trials have demonstrated that medicinal fungi contain a variety of structurally unique metabolites, which most likely possess significant activities like anti-inflammatory and antitumor (Piska et al., 2017). For instance, P. igniarius has been proposed as a prevalent type of edible and medicinal mushroom that has multiple highly effective protective properties (Dong et al., 2016). More importantly, P. igniarius attracts overwhelming attention as a potential candidate for anticancer therapeutics (De Silva et al., 2012). Cancer is a complex disease and tumorigenesis is related to inflammation (Guo et al., 2017). P. igniarius has potential anti-inflammatory and antitumor activities, hopeful to be developed a functional food for the prevention and control of inflammationinduced tumorigenesis in the future. Previous studies had established that the ethanol extract of P. igniarius suppresses SK-Hep-1 and RHE cell proliferation in a dose-dependent manner (Song et al., 2008). Furthermore, the hot water extract of P. igniarius directly inhibits S180 tumors and simultaneously exhibits an immune-regulatory effect on S180-bearing mice (Yang et al., 2006). However, to date, these studies largely emphasize the pharmacological effects of P. igniarius extracts, and no studies have characterized the chemical composition P. igniarius extracts or clarified their underlying mechanisms.

Traditional Chinese medicine network pharmacology was a new research strategy for translating TCM from an experience-based medicine to an evidence-based medicine system, which predicted the target profiles and pharmacological actions of herbal compounds, and revealed drug-gene-disease co-module associations to interpret the combinatorial rules and network regulation effects of herbal formulae (Liang et al., 2014; Li and Zhang, 2013). It provided a new paradigm for revealing the pharmacodynamics substance basis and mechanisms of TCM and revealing the problem of the effectiveness of TCM (Wang et al., 2017).

Therefore, we herein utilized a combination method that included a cytotoxicity test, phytochemical analysis, TCM network pharmacology, and cell and molecular biology experiments to elucidate the antitumor substances and

was detected using DCFH-DA probes and flow cytometry. ∗∗P < 0.01. The data are presented as the mean ± SD from at least three independent experiments.

FIGURE 7 | Western blot analysis of apoptosis-related protein expression. (A) Bad, Bax, Bcl-xl, Bcl-2, PARP, caspase-3, caspase-9, and caspase-8 protein expression in HT-29 cells after DCMPI treatment (30 and 60 µg/mL) for 24 h. (B) Densitometric analysis of Bad, Bax, Bcl-xl, Bcl-2, PARP, caspase-3, caspase-9, and caspase-8 expression. ∗∗P < 0.01. The data are presented as the mean ± SD from at least three independent experiments.

potential mechanisms of P. igniarius. First, cytotoxicity experiments were used to show that DCMPI had a certain inhibitory effect on each tumor strain. Among the strains, the inhibitory effects on the BGC-823, SMMC7721 and HT-29 cell lines were higher than those on the others. Second, we elucidated the systemic phytochemical composition of DCMPI via UPLC-ESI-Q/TOF-MS. As previously described, 19 constituents were identified or tentatively characterized, and 5 of them were quantified by standard substances. Numerous ingredients in DCMPI have been reported to possess antitumor activity or cytotoxicity. For example, naringenin, a flavanone compound, has highly effective, diverse bioactivities, including anticancer activity (Chang et al., 2017). Eriodictyol also exerts multiple bioactive effects, and it especially inhibits cell proliferation and transformation (Zhu et al., 2015). Dihydrokaempferol serves as a flavonol extensively in citrus fruits and provides anticancer effects via suppressing cell migration and invasion (Liu X. et al., 2017). The flavonoids phelligrin A, phelligrin B, methylphelligrin A and methylphelligrin B were first discovered in the Phellinus family and shown to possess multiple bioactivities (Wu et al., 2011). Furthermore, several studies revealed that phelligrin A and phelligrin B provide selective cytotoxicity against cancer cells (Lee and Yun, 2011). In summary, the main compounds in DCMPI are attributed to the flavone family, and most have anticancer activity and likely account for the antitumor activity of P. igniarius.

Because of their complex compositions and mechanisms of action, investigating the pharmacology of TCM is a daunting task. Here, to explore the antitumor mechanism of DCMPI, we selected eight major components of DCMPI and utilized TCM network pharmacology approach to clarify their potential

mechanisms. The network pharmacological analysis showed that DCMPI exerted its anticolon cancer function mainly via the positive regulation of ROS metabolic processes and intrinsic apoptotic pathways.

Apoptosis induction has been recognized as an essential mechanism in antitumor therapeutics (Janssen, 2001). Notably, numerous studies have demonstrated that anticancer agents exert their anti-proliferative effects mainly via two different apoptosis pathways involving mitochondria or death receptors (Liu B. et al., 2017). In particular, the mitochondriaassociated pathway is a classic intrinsic pathway that is initiated by ROS overproduction, resulting in the depletion of 1ψm (Kuranaga, 2012). In general, a variety of protein molecules are involved in regulating the mitochondrial apoptotic pathway, including pro-apoptotic members (e.g., Bax and Bad) and anti-apoptotic members (e.g., Bcl-2 and Bcl-xl) (Czabotar et al., 2014). Furthermore, the pathway activates the specific pivotal proteinases as initiator caspase-9 and effector caspase-3, which subsequently leads to DNA fragmentation and nuclear PARP degradation during apoptosis (Kuranaga, 2012).

To verify the network pharmacology prediction results, in vitro cell experiments were carried out. DAPI and Annexin V-FITC/PI staining were performed to determine the apoptotic alterations associated with the cytotoxicity of HT-29 cells. Apoptotic bodies, nuclear fragmentation and early- and late-stage apoptotic populations were increased by DCMPI. Therefore, the cytotoxic effect of DCMPI on HT-29 cells appeared to be mediated by apoptosis. Apoptosis is considered to be a regulated form of cell death, and dysregulated apoptosis leads to a variety of aberrant pathological conditions and subsequent diseases, especially cancer (Sankari et al., 2012; Goldar et al., 2015). Remarkably, two main signaling pathways involving caspase-mediated apoptosis have been identified: the mitochondrial-mediated intrinsic pathway and the death receptor-mediated extrinsic pathway (Khan et al., 2014). Compelling evidence has indicated that increased levels of intracellular ROS result in mitochondrial swelling and MMP loss, which, in turn, activates the intrinsic apoptotic pathway. Because ROS overproduction not only causes severe DNA damage but also suppresses tumor growth, aggravated cell structures break down, and cancer cells die (Renschler, 2004). In this study, DCMPI stimulated the generation of intracellular ROS in a dose-dependent manner, induced MMP collapse and ultimately contributed to mitochondriamediated apoptosis.

Bcl-2 family members, including Bcl-2, Bcl-xl, Bax, and Bad, are major regulators located in the mitochondria (Borkan, 2016). As anti-apoptotic proteins, Bcl-2 and Bcl-xl intervene in various apoptotic signals, whereas Bax and Bad serve as pro-apoptotic proteins and trigger the activation of caspases by mitochondrial dysfunction. In the present study, DCMPI down-regulated anti-apoptotic Bcl-xl and Bcl-2 expression and simultaneously enhanced the translocation of the activated proapoptotic proteins Bax and Bad. More importantly, subsequent activation of the caspase-dependent pathway was recognized as a pivotal step in the apoptotic process, and a series of initiator

caspases was involved in the progression. Briefly, activation of the initiator caspase as caspase-9 activates downstream or executioner caspases undergoing apoptosis, such as caspase-3 (Nuñez et al., 1998; Kim and Hong, 2011). Furthermore, executioner caspase activation was responsible for a series of apoptotic biochemical characteristics, especially the cleavage of PARP, which facilitated cellular disassembly and resulted in DNA fragmentation. Herein, our findings demonstrated that DCMPI induced apoptosis in HT-29 cells by up-regulating cleaved caspase-3/9 and PARP. In addition, pretreatment with the pan-caspase inhibitor Z-VAD-FMK inhibited caspase and PARP cleavage, which confirmed that the pro-apoptotic effect of DCMPI in HT-29 cells was exerted via a caspase-dependent pathway (**Figure 9**).

#### CONCLUSION

fphar-10-00266 March 19, 2019 Time: 14:8 # 15

In the current study, we first demonstrated that the DCMPI dramatically suppressed HT-29 colon cancer cell proliferation. Then, we characterized 19 main constituents of DCMPI by UPLC-ESI-Q/TOF-MS, and quantified 5 of them. Furthermore, network pharmacology analysis focusing on the 8 high content compounds of DCMPI (different from other fractions) was performed, revealing that the potential targets were mainly associated with the positive regulation of ROS metabolic processes and intrinsic apoptotic pathways. Finally, cellular tests verified that DCMPI increased intracellular ROS levels to induce HT-29 cell apoptosis. Taken together, our results provide the antitumor chemical composition of and mechanistic insights into

#### REFERENCES


P. igniarius, which may be exploited as a promising therapeutic option for colon cancer.

#### AUTHOR CONTRIBUTIONS

YD and PQ wrote the manuscript. YD, PQ, RZ, LZ, PZ, and YW conducted the research and analyzed the data. CL, KC, DS, and HZ conceived or designed the studies. All authors read and approved the final manuscript.

#### FUNDING

This research was funded by National Natural Science Foundation of China (Nos. 81603349 and 81673809) and Key Research and Development Plan of Zhejiang Province (No. 2015C03033).

industry perspective. Adv. Drug Deliver. Rev. 108, 25–38. doi: 10.1016/j.addr. 2016.04.025



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer YZhu declared a shared affiliation, with no collaboration, with several of the authors to the handling Editor at the time of review.

Copyright © 2019 Dong, Qiu, Zhu, Zhao, Zhang, Wang, Li, Chai, Shou and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

,

\*

# Network Pharmacology Based Research on the Combination Mechanism Between Escin and Low Dose Glucocorticoids in Anti-rheumatoid Arthritis

Leiming Zhang<sup>1</sup>† , Yanan Huang<sup>1</sup>† , Chuanhong Wu<sup>2</sup>† , Yuan Du<sup>1</sup>† , Peng Li<sup>3</sup> , Meiling Wang<sup>1</sup> Xinlin Wang<sup>1</sup> , Yanfang Wang<sup>1</sup> , Yanfei Hao<sup>1</sup> , Tian Wang<sup>1</sup> , Baofeng Fan<sup>4</sup> \*, Zhuye Gao<sup>5</sup> and Fenghua Fu<sup>1</sup> \*

<sup>1</sup> Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, School of Pharmacy, Yantai University, Yantai, China, <sup>2</sup> The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China, <sup>3</sup> College of Arts and Sciences, Shanxi Agricultural University, Taigu, China, <sup>4</sup> Air Force General Hospital, PLA, Beijing, China, <sup>5</sup> Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China

Rheumatoid arthritis (RA) is characterized by chronic progressive symmetrical synovitis and destruction of multiple joints. Glucocorticoids (GCs) are widely used in the treatment of RA. However, their adverse effects can be serious. Escin, which is isolated from Aesculus hippocastanum L., has been reported to have anti-inflammatory effects. We investigated the anti-RA effect of Escin combined with low dose GCs (dexamethasone, Dex) and the underlying mechanism. Adjuvant-induced RA rats and lipopolysaccharides (LPS)-injured RAW264.7 cells were used to investigate the anti-RA effects of Escin combined with low dose Dex in vivo and in vitro. The results showed that Escin combined with low-dose Dex significantly decreased arthritic index, serum IL-6 and TNF-α levels, reduced paw swelling, and ameliorated the joint pathology and immune organ pathology. Gene chip results revealed that Nr3c1 (GR) expression was significantly altered, and that GR was activated by Escin and low dose Dex in vivo and in vitro. Additionally, Escin combined with low dose Dex also significantly increased GR mRNA expression. However, when GR expression was suppressed by its specific inhibitor, the anti-RA effect of Escin combined with low-dose Dex was abolished. The data in this study demonstrated that Escin combined with Dex reduced the dose of Dex, and exerted significant anti-RA effects, which could also reduce the adverse effects of Dex. This combination might result from GR activation. This study might provide a new combination of drugs for the treatment of RA.

Keywords: rheumatoid arthritis, glucocorticoids, glucocorticoid receptor, Escin, dexamethasone

#### Edited by:

Yuanjia Hu, University of Macau, China

#### Reviewed by:

Run-Yue Huang, Guangzhou University of Chinese Medicine, China Jianxin Chen, Beijing University of Chinese Medicine, China

#### \*Correspondence:

Baofeng Fan baofengfan2018@126.com Zhuye Gao zhuyegao@126.com Fenghua Fu fufenghua@sohu.com †These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 10 December 2018 Accepted: 05 March 2019 Published: 22 March 2019

#### Citation:

Zhang L, Huang Y, Wu C, Du Y, Li P, Wang M, Wang X, Wang Y, Hao Y, Wang T, Fan B, Gao Z and Fu F (2019) Network Pharmacology Based Research on the Combination Mechanism Between Escin and Low Dose Glucocorticoids in Anti-rheumatoid Arthritis. Front. Pharmacol. 10:280. doi: 10.3389/fphar.2019.00280

Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes progressive articular damage, functional loss, and comorbidity. RA affects about 1% of the population, and it can present at any age (McInnes and Schett, 2017). Its physiopathology includes synovial inflammation with proinflammatory cytokine overexpression (Courbon et al., 2018).

Glucocorticoids (GCs) are widely used in the treatment of RA. However, long-term and high dose GC use can lead to serious adverse effects, such as immunosuppression, osteoporosis and metabolic disorders (Gossye et al., 2009). One of the methods to minimize the undesirable adverse effects is through administering them with other pharmaceuticals, aiming at synergistic effects to reduce the dose and duration of cortical therapy.

Escin (polyhydroxyolean-12-ene 3-O-monodesmosides) is a natural mixture of triterpene saponins isolated from Aesculus hippocastanum L. (Xin et al., 2011a). Several studies have reported that it has anti-inflammatory (Wang et al., 2013), anti-edematous (Wang et al., 2011), and anti-cancer properties (Cheong et al., 2018). It was also reported that Escin could synergize with GCs to enhance their anti-inflammatory effect (Xin et al., 2011b).

Recent years, network-based approaches offer the potential to explore, in a systematic way (Hopkins, 2008), the effect of a drug candidate in a global physiological environment (Xiong et al., 2019), and was commonly used to decipher new drug-target relationships in drug discovery (Boezio et al., 2017). And this method was also used in this study to give a constructive result of the Escin effect on RA. Based on the network pharmacology, we investigated whether the combination of Escin and GCs would improve the anti-RA effects and the underlying mechanism.

### MATERIALS AND METHODS

#### Materials

Escin, including sodium aescinate tablets (batch no: 201509321) and sodium aescinate for injection (batch no: 2017110103), were purchased from Shandong Luye Pharmaceutical Co., Ltd. (Yantai, China). Dexamethasone tablets (Dex, batch no: 160601201) were purchased from Chen Xin Pharmaceutical Co., Ltd. (Jining, China). Dexamethasone powder (D4902, purity ≥ 97%), lipopolysaccharides (LPS) and 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) were purchased from Sigma-Aldrich (St. Louis, MO, United States). Complete Freund's adjuvant (CFA) (containing 10 mg/ml of dry, heat-killed Mycobacterium tuberculosis) was purchased from Chondrex Co., Ltd. (Redmond, WA, United States, batch no: 170334). RPMI medium 1640 was purchased from Gibco (Carlsbad, CA, United States, batch no: 1960297). Fetal bovine serum (FBS) was purchase from Zhejiang Tianhang Biological Technology Co., Ltd. (Hangzhou, China, batch no: 11011- 8611). Griess reagent kit (S0023) was purchased from Beyotime Institute of Biotechnology (Haimen, China). Primary antibody for IκBα (K2017), and GR (A2518) were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, United States). Primary antibody for p-IκBα (S32) and p-p65 (S536) were purchased from Cell signaling Technology (Danvers, MA, United States). P65 (ab16502) was purchased from Abcam company (Burlingame, CA, United States). Second antibodies for anti-rabbit IgG HRPlinked antibody and anti-mouse IgG HRP-linked antibody were purchased from Beyotime Institute of Biotechnology (Haimen, China). RU486 (m8046) were purchased from Sigma-Aldrich (United States). qPCR reagents: UltraSYBR Mixture (CW2602M) were purchased from Kang Wei century Biotechnology Co., Ltd. (Beijing, China).

#### Animals

Animal experimental procedures were performed in strict accordance with the National Institutes of Health Regulations on the use and care of animals for scientific purposes. Male Sprague-Dawley rats (weight, 180–220 g) were purchased from Shandong Jinan PengYue Experimental Animal Breeding Co., Ltd. (Jinan, China). All the rats were housed in diurnal lighting conditions (12 h/12 h) and allowed free access to food and water. All experimental procedures in this study were performed in accordance with the Guidelines for the Care and Use of Laboratory Animals of Yantai University, and were approved by the ethics committee.

### Induction of Adjuvant-Induced Rheumatoid Arthritis

Adjuvant-induced RA (AIA) was induced in the rats according to the method described by Pearson (Pearson, 1963). Briefly, the animals were inoculated with a subplantar injection of 0.1 mL CFA into the right hind paw at day 0 and were randomly allocated to 6 groups of eight rats each (n = 10) as follows: the control group; AIA group; AIA + Dex (0.2 mg · kg−<sup>1</sup> ) group; AIA + Dex (0.05 mg · kg−<sup>1</sup> ) group; AIA + Escin (10 mg · kg−<sup>1</sup> ) group; AIA + Escin (10 mg · kg−<sup>1</sup> ) + Dex (0.05 mg · kg−<sup>1</sup> ) group. Dex and Escin were administered orally 15 days after inoculation, and once daily for a period of 2 weeks (from day 15 to day 28). Body weight was determined on Day 0, 3, 6, 9, 12, 15, 18, 21, 24, and 27.

#### Measurement of Paw Volume

Paw volume was measured using a Plethysmometer (YLS-7B, Shandong Academy of Medical Sciences, Jinan, China) on Day 0 before CFA injections and thereafter on Day 3, 7, 11, 15, 18, 22, 25, and 28. The change in paw volume was calculated as the difference between the final (28 days) and initial (0 day) paw volume (Lee et al., 2009).

### Measurement of Arthritic Index

The morphological features of arthritis such as redness, swelling and erythema were determined using a five-point ordinal scale (0–4) scoring system as follows: 0, narrow paw; 1, mild swelling and erythema of the digits; 2, swelling and erythema of the digits; 3, severe swelling and erythema; 4, gross deformity and inability to use the limb (Paval et al., 2009). Thus, the maximum score for both the paws was 8.

#### Histopathological Examination

fphar-10-00280 March 21, 2019 Time: 21:41 # 3

After 2 weeks of Dex/Escin administration, the speen, thymus, right leg joint, and left leg joint tissues with pathologic changes were stained with haematoxylin-eosin (HE) to evaluate the effect of Dex and Escin. The tissues were removed and fixed in 10% buffered formalin (pH = 7.0) for 3 days. Samples were then trimmed, decalcified in 10% EDTA at room temperature for 60 days, embedded in paraffin and cut into slices. Sections (4 µm) were stained with HE. The histopathological changes and severity were observed with a light microscope (Pan et al., 2017).

#### Immune Organ Index

After Dex/Escin administration for 2 weeks, the spleen and thymus weights were determined and the organ-to-body weight ratio was calculated.

### Glucocorticoid Signaling RT2 Profiler OCR Array

The GC signaling gene changes (including 84 genes) in synovial tissue of the joints were detected by Shanghai Bo Hao Biotechnology Co., Ltd. (Shanghai, China) using a RT2 Profiler PCR Array. Briefly, the procedure started with conversion of experimental RNA samples into first-strand cDNA using the RT2 First Strand Kit. Then, the cDNA was mixed with an appropriate RT2 SYBR Green Mastermix. This mixture was aliquoted into the wells of the RT2 Profiler PCR Array. PCR was performed and the relative expression was determined using data from the real-time cycler and the 11CT method.

#### Cell Culture

Murine RAW264.7 macrophages obtained from the American Type Culture Collection (ATCC, VA, United States), were cultured in complete RPMI 1640 containing 10% heatedinactivated FBS, 100 U/ml penicillin, and 100 µg/ml streptomycin. Cells were incubated at 37◦C in a humidified atmosphere of 5% CO<sup>2</sup> in air.

#### MTT Assay

RAW264.7 cells (5 × 10<sup>4</sup> cells/well) were cultured in 96-well plates for 24 h after Dex/Escin treatment with or without LPS (1 µg/mL). MTT solution (10 µL; 5 mg/ml) was added, and the cells were incubated at 37◦C for an additional 4 h. After washing out the supernatant, the insoluble formazan product was dissolved in DMSO. Then, the optical density was measured at 570 nm using a microplate reader.

#### CCK-8 Assay

RAW264.7 cells (5 × 10<sup>3</sup> cells/well) were cultured in 96-well plates for 24 h after Dex/Escin treatment with or without LPS (1 µg/ml). CCK-8 solution (10 µL) was added to each well of the plate, and the cells were then incubated for 2 h in an incubator. The absorbance was measured at 450 nm using a 96-well plate reader.

### Determination of Nitrite

RAW264.7 cells (1 × 10<sup>5</sup> cells/well) were cultured in 96-well plates with Dex/Escin pretreatment for 2 h and incubated with LPS for 24 h. Supernatant (50 µl) was collected and mixed with equal volumes of Griess reagent for 10 min at room temperature. Optical density was measured at 540 nm (Wu et al., 2015b).

#### Determination of IL-6 and TNF-α

RAW264.7 cells (1 × 10<sup>5</sup> cells/well) were cultured in 96-well plates with Dex/Escin pretreatment for 2 h and then incubated with LPS for 6 h. The concentrations of IL-6 and TNF-α in the culture medium were determined using ELISA kits, in accordance with the manufactures' instructions.

After 2 weeks of Dex/Escin administration, rat serum was obtained. The IL-6 and TNF-α serum concentrations were determined using ELISA kits in accordance with the manufactures' instructions.

#### RT-PCR Assay

RAW264.7 cells were seeded in six-well plates treated with Dex/Escin and 1 µg/ml LPS for 30 min. Total RNA was isolated and the RNA concentration was detected using a spectrophotometer. Total RNA (1 µg) was converted to cDNA and real-time PCR (RT-PCR) was performed using a PrimeScriptTM RT reagent kit. The PCR primers were as follows: GR, sense 5<sup>0</sup> -GTTGCGCAGCCTGAATGGCG-3<sup>0</sup> , antisense 5 0 -GCCGTCACTCCAACGCAGCA-3<sup>0</sup> ; GAPDH, sense 5<sup>0</sup> -CGA CTTCAACAGCAACTCCCACTCTTCC-3<sup>0</sup> , antisense 5<sup>0</sup> -TGG GTGGTCCAGGGTTTCTTACTCCTT-3<sup>0</sup> . The amplification sequence protocol was conducted using three steps: 1 step: Cycle 1, 95◦C 10 min; 2 step: Cycle 40, 95◦C 15 s, 60◦C 60 s; 3 step: Cycle 1, 95◦C 15 s, 60◦C 60 s, 95◦C 15 s, 60◦C 15 s.

#### Western Blotting

The expression of p-p65, p65, p-IκBα, IκBα, and GR in rat tissue and RAW264.7 cells was determined by western blot. The rat lysate and RAW264.7 lysates were incubated on ice for 30 min and then centrifuged at 12,000 rpm for 20 min at 4◦C. The supernatant was collected, and the protein concentrations contained in the supernatant were measured using the BCA assay. The protein samples with loading buffer added were boiled for 5 min before loading onto SDS-polyacrylamide gel. After electrophoresis, the gel was electroblotted onto PVDF membranes. Membranes were blocked in Tris-buffered saline (TBS) with 1% Tween-20 (TBST) and 5% non-fat dry milk, and incubated with primary antibody overnight at 4◦C. Then, the membranes were washed several times with TBST before incubation with horseradish peroxidase-conjugated secondary antibody for 60 min at room temperature. After subsequent washes in TBST, the protein bands were visualized using the ECL detection kit. The relative intensities of the bands were quantified by densitometric analysis. The densitometric plots of the results were normalized to the intensity of the actin band (Wu et al., 2015a).

### Statistics

Data were expressed as the mean ± standard deviation (SD) of at least three independent experiments. The protective effects were assessed using the GraphPad Prism 6 software (GraphPad, San Diego, CA, United States). Relative protein semi-quantification was performed using the QUANTITY ONE software (Bio-Rad, Hercules, CA, United States). Differences between groups were assessed by ANOVA. A P-value less 0.05 was considered statistically significant.

## RESULTS

#### Symptom Network of Escin Constructed Using SymMap

Symptom network of Escin was constructed with SymMap<sup>1</sup> (Wu et al., 2019) to predict the disease that Escin might be intervened. As displayed in **Figure 1A**, this network including Herb, Ingredient, Traditional Chinese Medicine symptom (TCM symptom), Modern Medicine symptom (MM symptom). The detailed component of Escin network was displayed in **Figure 1B**. Escin was isolated from Aesculus hippocastanum L. TCM symptom related to Aesculus hippocastanum L. were Teng Tong, Tong, Du Chong Yao Shang, Ji Wei Wu Li, Xiong Fu Zhang Men, Tou Hun and Wei Wan Teng Tong. Meanwhile, MM symptom correspondent to TCM symptom were Chest symptom heaviness (Xiong Fu Zhang Men), Light headedness, Orthostasis and Dizziness (Tou Hun), Muscle weakness (Ji Wei Wu Li), Gastrointestinal pain (Wei Wan Teng Tong), Venomous bite (Du Chong Yao Shang). Among the symptoms, muscle weakness also occurred in RA. In addition, TCM symptom, Tong, Teng Tong had no related MM symptom until now. However, these symptoms also occurred in RA. This network provide a trace for Escin in anti-RA.

#### Effect of Escin Combined With Dex on Arthritic Index and Serum Inflammatory Factor Secretion in AIA Rats

The rats gradually developed multiple-joint RA beginning on day 15 after CFA injection. The arthritic index peaked on day 21 (**Figure 2A**). Dex at daily dose of 0.2 mg/kg and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) significantly reduced the arthritic index compared with the AIA-treated group between days 18 and 27 post-CFA (P < 0.05). As illustrated in the representative day 28 paw graphs in **Figure 1B**, the AIA-treated rats displayed a swelling paw compared with the control rats. After administration of Dex (0.2 mg/kg), Dex (0.05 mg/kg), Escin (10 mg/kg) or Escin (10 mg/kg) combined with Dex (0.05 mg/kg), this swelling was ameliorated (**Figure 2B**). Moreover, the serum TNF-α and IL-6 concentrations in AIA rats were significantly higher compared with the control group (P < 0.05). The serum TNF-α and IL-6 concentrations in the Dex (0.2 mg/kg), Dex (0.05 mg/kg), Escin (10 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) groups were significantly lower compared with that the AIA-treated group (**Figures 2C,D**; P < 0.05).

### Effect of Escin Combined With Dex on Histopathological Changes and Paw Swelling in AIA Rats

Haematoxylin-eosin staining was used to evaluate inflammation and bone lesion induced by AIA. As illustrated in **Figure 3**, no pathological findings of arthritis were observed in normal joints in the control group. However, the AIA-treated group exhibited severe synovitis, with synovial hyperplasia, inflammatory cells infiltration into the joint cavity, and bone and cartilage erosion on the primary and secondary sides. Treatment with Dex (0.2 mg/kg), Escin (10 mg/kg) combined with Dex (0.05 mg/kg), significantly decreased synovial hyperplasia, cartilage surface erosion, and joints degradation (P < 0.05), and substantially reduced the amount of infiltrated inflammatory cells. However, low dose Dex (0.05 mg/kg) and Escin (10 mg/kg) alone did not

<sup>1</sup>http://www.symmap.org

FIGURE 3 | Effect of Escin combined with Dex on histopathological changes and paw swelling in AIA rats. (A,C) Histopathological changes of joints in primary side and secondary side were determined with HE staining. (B,D) Paw swelling in primary side and secondary side were determined by a Plethysmometer. Data were expressed as the mean ± SD, and were analyzed by ANOVA. #P < 0.05 versus the control group; <sup>∗</sup>P < 0.05 versus the AIA-treated group; Dex, dexamethasone.

reverse these effects. After CFA injection, paw swelling on the primary side significantly increased on day 11 and peaked on day 22 (P < 0.05). while paw swelling occurred on the secondary side on day 15 and peaked on day 22. Dex (0.2 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) significantly decreased paw swelling on the primary and secondary sides beginning on day 22 (P < 0.05), and the effect of the combined medication was better than that of low-dose Dex (0.05 mg/kg).

### Effect of Escin Combined With Dex on Immune Organ Histopathological Change and Index in AIA Rats

Red and white pulps were neatly distributed in the spleen of control group. However, in the AIA-treated group, hyperplasia of the white pulp and a larger lymphocyte aggregation area were observed. In the Dex (0.2 mg/kg) group, lymphoid nodules were significantly reduced and atrophied. However, Escin (10 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) treated groups showed no significant pathological changes (**Figure 4A**).

Rat thymus tissue from the control and AIA-treated groups showed clear demarcation of the dermal medulla and abundant lymphocytes. In the Dex (0.2 mg/kg) group, the rat thymus tissue medullary volume was atrophied, but the Escin (10 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) groups showed no significant pathological changes (**Figure 4C**).

The AIA-treated group displayed a remarkable increase in spleen index compared with the control rats. However, Dex (0.2 mg/kg) caused a marked decrease in spleen index compared with the AIA-treated rats and control rats. Escin (10 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) showed no marked decrease, compared with Dex (0.2 mg/kg) (**Figure 4B**). The Dex (0.2 mg/kg) group showed a remarkable decrease in thymus index compared with the AIA-treated rats and control rats (P < 0.05). However, Escin (10 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) showed no marked decrease in thymus index, compared with Dex (0.2 mg/kg) (**Figure 4D**).

### Pathways Regulated by Escin Combined With Dex in AIA Rats

Considering that Dex specifically targeting GR, gene chips were further used to explore the combination mechanism between Escin and low GC (Dex) doses (Zhou et al., 2015). As illustrated in **Figure 5**, heat map and principal component analysis results of the gene chip revealed that the AIA group, AIA + Dex (0.05) group, and AIA + Escin group clustered together, which suggested that the AIA + Dex (0.05) group and AIA + Escin group had little influence on the GR pathway that was altered by AIA. AIA + Dex (0.2) clustered together with the control group, which suggested a significant anti-RA effect of Dex (0.2) in AIA-induced rats. However, AIA + Escin + Dex (0.05) group clustered together with the control group and AIA + Dex (0.2) group, which suggested that Escin combined with Dex (0.05) had a similar influence compared with Dex (0.2). Further analysis of specific genes showed that the control group, AIA + Dex (0.2) group and AIA + Escin + Dex (0.05) group shared the same trend in the Tnf, Il-6, Nr3c1, Sphk1 mRNA expression. Moreover, these three groups improved the expression of Nr3c1 (GR) and the key genes, Sphk1, in the GR pathway, which suggested that Escin combined with Dex (0.05) might treat the disease by increasing GR expression and further affecting its relevant pathways.

## Confirmation of the Gene Chip Results in AIA Rats

As illustrated in **Figure 6**, GR expression was significantly downregulated in the AIA-treated group (P < 0.05), and its expression was significantly up-regulated after Dex (0.2 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) administration (P < 0.05). Moreover, the AIA-treated group significantly increased p-p65, p65 and p-IκBα expression. When administrated with Dex (0.2 mg/kg), Escin (10 mg/kg) combined with Dex (0.05 mg/kg) significantly decreased p-p65, p65 and p-IκBα expression (P < 0.05). Dex (0.05 mg/kg) significantly decreased p-p65 and p-IκBα expression (P < 0.05). Escin (10 m/kg) significantly decreased p-p65 expression (P < 0.05).

FIGURE 6 | Escin combined with Dex activated GR in AIA rats. (A,C) Representative western blots of GR, p-p65, p65, p-IκBα, IκBα, and β-actin. (B,D) The quantified densitometric analysis of GR, p-p65, p65, p-IκBα, IκBα. Data were expressed as the mean ± SD, and were analyzed by ANOVA. #P < 0.05 versus the control group; <sup>∗</sup>P < 0.05 versus the AIA-treated group; Dex, dexamethasone.

#### Zhang et al. Escin Enhances Dex Effect

#### Confirmation of the Gene Chip Results in LPS-Induced RAW 264.7 Cells

To further confirm the gene chip results, the LPS-induced RAW264.7 cell model was constructed. As displayed in **Figure 7**, the TNF-α, IL-6 and NO concentrations were significantly increased in the LPS group (P < 0.05). Dex (50 nM) and Escin (10 µM) combined with Dex (12.5 nM) significantly decreased the TNF-α, IL-6 and NO concentrations (P < 0.05). Moreover, the TNF-α concentration in Escin (10 µM) combined with Dex (12.5 nM) group was significantly lower than the Dex (12.5 nM) group (P < 0.05). The IL-6 concentration in the Escin (10 µM) combined with Dex (12.5 nM) group was significantly lower than that in the Dex (12.5 nM) and Escin (10 µM) (P < 0.05) group.

We then confirmed GR activation in this cell model in vitro. The LPS-treated group significantly decreased GR expression (P < 0.05). Escin (10 µM) combined with Dex (12.5 nM) significantly increased GR expression (P < 0.05). Moreover, the LPS-treated group significantly increased p-p65, p-IκBα expression and decreased IκBα expression (P < 0.05). Administrated with Escin (10 µM) combined with Dex (12.5 nM) significantly decreased p-p65 and p-IκBα expression and increased IκBα expression (**Figure 8**) (P < 0.05).

#### Escin Enhances the Effect of Low Dose Glucocorticoids Through GR Activation

GR mRNA expression was also detected. As shown in **Figure 9**, LPS decreased GR mRNA expression significantly. Escin (10 µM) combined with Dex (12.5 nM) significantly increased the GR mRNA. When compared with Dex (50 nM), Dex (12.5 nM) and Escin (10 µM) groups, the Escin (10 µM) combined with Dex (12.5 nM) group displayed a higher GR mRNA expression (P < 0.05). Cell supernatant IL-6 concentration in the LPS treated group significantly increased (P < 0.05), and the IL-6 concentration in the Dex (50 nM), Dex (12.5 nM), Escin (10 µM), Escin (10 µM) combined with Dex (12.5 nM) groups was significantly decreased compared with LPS (P < 0.05). After pre-treatment with the GR specific inhibitor, RU486, the cell supernatant IL-6 concentration was significantly increased in the group treated with Dex (50 nM) and Escin (10 µM) combined with Dex (12.5 nM) (P < 0.05). In the LPS-treated group, p-p65 expression was increased significantly (P < 0.05). Dex (50 nM), Dex (12.5 nM), Escin (10 µM), and Escin (10 µM) combined with Dex (12.5 nM) significantly decreased p-p65 expression (P < 0.05). After pre-treatment with RU486, p-p65expression in Dex (50 nM), Escin (10 µM) and Escin (10 µM) combined with Dex (12.5 nM) group were increased.

#### DISCUSSION

Rheumatoid arthritis is a chronic systemic disease with clinical manifestations of multi-joints damage, leading to chronic pain, joint deformity and functional disability. There are many types of medications for RA, such as non-steroidal anti-inflammatory drugs (NSAIDs), disease-modifying antirheumatic drugs (DMARDs), biological agents and GCs (Scott et al., 2010). GCs are 21-carbon steroid hormones. They are still used in the treatment of RA although a variety of adverse effects exist. Because GCs show strong anti-inflammatory effects and can reduce signs and symptoms of the disease, exert disease-modifying effects, especially in the active stage of RA (Strehl et al., 2017). Indeed the adverse effects of GCs such as infections, immunosuppression and osteoporosis limited their widely clinic use (Dubois-Camacho et al., 2017). So some new treatment strategies for RA with GCs are under development. For example, previous studies reported that transactivation is responsible for most of the adverse reactions of GCs while transrepression is considered to mediate their anti-inflammatory effects (Barnes, 2017). Novel drugs such as selective GR agonists, also called dissociated agonists, are under development (Sundahl et al., 2015). Escin is a natural mixture of triterpene saponins isolated from Aesculus hippocastanum L. Based on the network pharmacology, the TCM network related to Escin was constructed. MM symptom muscle weakness was related to RA, and the TCM symptom such as Tong, Teng Tong was also closely related to RA. This was consistent with our previous study. Our previous studies found that Escin exerts synergistic anti-inflammatory effects with GCs (Xin et al., 2011b), shows anti-arthritic effects combined with low dose of GCs with reduced adverse effects (Du et al., 2016). The present study elucidated the possible anti-arthritic mechanism of Escin, which may be another strategy to improve the efficacy and diminish any possible adverse effects of GCs.

The rat AIA model is an easy, reliable, and reproducible experimental model of polyarthritis with a short duration. It has been commonly used for preclinical evaluation of anti-arthritic drugs because of its similar pathology of human RA (Wang et al., 2016). In the present study, we demonstrated that low-dose Dex combined with Escin exerts an enhanced protective effect in a rat model of AIA. After injection of adjuvant, clinical evidence of arthritis occurred and was gradually exacerbated in immunized rats. Escin and Dex were given orally. Rat paw swelling and serum inflammatory factors served as indicators of systemic inflammation. Results revealed that Escin (10 mg/kg) combined with Dex (0.05 mg/kg) and Dex (0.2 mg/kg) significantly decreased the arthritic index, reduced the paw swelling, decreased serum IL-6 and TNF-α levels, and ameliorated the joint pathology. The values of the indexes showed similar trends. However, Escin combined with Dex, improved the effect of the low dose Dex.

It is known to all that high dose and long-term use of glucocorticoids can lead to pleiotropic side effects, including immunosuppression. In the present study, we investigated the effect of Escin combined with Dex on immune organ histopathological change and index in AIA rats. The results demonstrate that Escin combined with low dose of Dex shows good anti-arthritic effects but not induces apoptosis of immune cells or cause damage to immune organs compared to high dose of Dex.

To explore the underlying combination mechanism, a GC signaling RT2 Profiler OCR Array was used. The results revealed that Nr3c1, Asph, Got1, Il-6, Tnf, and Sphk1 were altered significantly, especially Nr3c1, which showed high expression

in both Dex and Escin combined with Dex groups. This result suggests that activation of GR is key events in the combination mechanism between Escin and low doses Dex.

We further confirmed the activation of GR in vivo and in vitro. Our results revealed that Dex (0.2 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) significantly up-regulated GR expression in AIA-induced RA rats in vivo. The value of GR expression in Escin (10 mg/kg) combined with Dex (0.05 mg/kg) was higher than that of the Dex (0.2 mg/kg) alone. This result was further confirmed in the LPS-induced RAW 264.7 cells in vitro. Escin (10 µM) combined with Dex (12.5 nM) significantly upregulated GR expression in vitro.

Ample amounts of evidence support the idea that GCs exert their beneficial effects, at least in part, via interference with the NF-κB signaling pathway (De Bosscher et al., 2006; Gossye et al., 2008). NF-κB signaling pathway activation was also confirmed in the present study. Dex (0.2 mg/kg) and Escin (10 mg/kg) combined with Dex (0.05 mg/kg) activated p-p65, p65 and p-IκBα expression in AIA rats in vivo, and they activated p-p65, p-IκBα and IκBα in LPS-induced RAW 264.7 cells in vitro. This suggests that GR activation plays an important role in the effects of the combination of Escin and Dex.

Additionally, we evaluated the GR mRNA expression. The results revealed that Escin (10 µM) combined with Dex (12.5 nM) showed a significant high GR mRNA expression, and the levels observed in the combination treatment group were higher than that any of drug used alone. These results suggested that activation of GR pathway might be the underlying anti-RA mechanism in the Escin (10 µM) combined with Dex (12.5 nM) group. Additionally, GR was suppressed by its specific inhibitor, RU486. After GR suppression, the cell supernatant IL-6 and the p-p65 expression levels were reversed, which further confirmed the important role of GR pathway underlying the effects in the Escin (10 µM) combined with Dex (12.5 nM) group.

#### CONCLUSION

This study provided comprehensive evidence supporting the anti-RA effects of Escin combined with Dex and the underlying mechanisms of this combined medication. Escin combined with Dex reduced the dose of Dex, and exerts a significant anti-RA effects, which may also reduce the adverse effects of Dex.

#### REFERENCES


This combination might be attributed to GR activation. This study might provide a new combination of drugs for the treatment of RA.

#### AUTHOR CONTRIBUTIONS

LZ, YHu, CW, YD, MW, XW, YW, YHa, and TW were involved in data acquisition. FF, ZG, and BF conceived and designed the study. LZ, YHu, and PL have made statistical analyses. CW and LZ wrote the manuscript. All authors contributed to analysis and interpretation of the data and approved the final manuscript.

#### FUNDING

This study was supported by the Taishan Scholar Project. This study was also supported by Natural Science Foundation of Shandong Province (Nos. ZR2017MH068, ZR2017LH043, and ZR2017PH073), the Graduate Science and Technology Innovation Fund Project of Yantai University (No. YDZD1811), and the National Natural Science Foundation of China (No. 81703945).

#### ACKNOWLEDGMENTS

We thank Jodi Smith, Ph.D., from Liwen Bianz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.



**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Zhang, Huang, Wu, Du, Li, Wang, Wang, Wang, Hao, Wang, Fan, Gao and Fu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Systems Biology Analysis of the Effect and Mechanism of Qi-Jing-Sheng-Bai Granule on Leucopenia in Mice

Saisai Tian<sup>1</sup>† , Pengli Huang<sup>2</sup>† , Yu Gu<sup>2</sup>† , Jian Yang<sup>1</sup> , Ran Wu<sup>3</sup> , Jing Zhao<sup>2</sup> \*, Ai-Jun Liu1,4 \* and Weidong Zhang1,2 \*

<sup>1</sup> School of Pharmacy, The Second Military Medical University, Shanghai, China, <sup>2</sup> Institute of Interdisciplinary Complex Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China, <sup>3</sup> Shanghai Tenth People's Hospital, Tongji University, Shanghai, China, <sup>4</sup> Department of Pharmacy, Shanghai Pulmonary Hospital, Shanghai, China

#### Edited by:

Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China

#### Reviewed by:

Shuai Ji, Xuzhou Medical University, China Liren Qian, People's Liberation Army General Hospital, China

#### \*Correspondence:

Jing Zhao zjane\_cn@163.com Ai-Jun Liu mrliuaijun@163.com Weidong Zhang wdzhangy@hotmail.com

†These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 19 October 2018 Accepted: 01 April 2019 Published: 25 April 2019

#### Citation:

Tian S, Huang P, Gu Y, Yang J, Wu R, Zhao J, Liu A-J and Zhang W (2019) Systems Biology Analysis of the Effect and Mechanism of Qi-Jing-Sheng-Bai Granule on Leucopenia in Mice. Front. Pharmacol. 10:408. doi: 10.3389/fphar.2019.00408 Qi-Jing-Sheng-Bai granule (QJSB) is a newly developed traditional Chinese medicine (TCM) formula. Clinically, it has been used for the treatment of leucopenia. However, its pharmacological mechanism needs more investigation. In this study, we firstly tested the effects of QJSB on leucopenia using mice induced by cyclophosphamide. Our results suggested that QJSB significantly raised the number of peripheral white blood cells, platelets and nucleated bone marrow cells. Additionally, it markedly enhanced the cell viability and promoted the colony formation of bone marrow mononuclear cells. Furthermore, it reversed the serum cytokines IL-6 and G-CSF disorders. Then, using transcriptomics datasets and metabonomic datasets, we integrated transcriptomicsbased network pharmacology and metabolomics technologies to investigate the mechanism of action of QJSB. We found that QJSB regulated a series of biological processes such as hematopoietic cell lineage, homeostasis of number of cells, lymphocyte differentiation, metabolic processes (including lipid, amino acid, and nucleotide metabolism), B cell receptor signaling pathway, T cell activation and NOD-like receptor signaling pathway. In a summary, QJSB has protective effects to leucopenia in mice probably through accelerating cell proliferation and differentiation, regulating metabolism response pathways and modulating immunologic function at a system level.

Keywords: Qi-Jing-Sheng-Bai granule, leucopenia, transcriptomics, metabolomics, network pharmacology

### INTRODUCTION

Chemotherapy has been widely used for the treatment of cancers. However, chemotherapy usually induces many adverse effects, including hematologic toxicity and neurotoxicity (Magge and DeAngelis, 2015; Ratti and Tomasello, 2015; Zhou et al., 2018). Among them, hematologic toxicity, like leucopenia, is a very common adverse reaction that can delay the subsequent therapy, induce the risk of cancer metastasis, and even lead to life-threatening events (Xu et al., 2011; Cui et al., 2015). Hence the hematologic indexes, including the white blood cells (WBC), neutrophile granulocytes, blood platelets and monocytes, are important objective indexes for cancer patients after chemotherapy. Studies have shown that granulocyte colony-stimulating factor (G-CSF) and

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related agents have clinical efficacy. They are recommended to prevent leucopenia (Xu et al., 2011). However, G-CSF only reduces the neutropenia duration for 1–2 days while also increases adverse reactions and the cost of treatment notably (Dygai et al., 2012; Aarts et al., 2013). Traditional Chinese medicine (TCM) formulae have been widely used in China for the treatment of leucopenia (Liu et al., 2014). Formulae usually consist of several types of Chinese medicines. Among them, one herb is the principal component, and the others serve as adjuvant ones to assist the function of the principal component. It is believed that multiple components contained in the formulae could hit multiple targets and exert synergistic effects (Jiang, 2005).

Qi-Jing-Sheng-Bai granule (QJSB) is a modern TCM formula. It is made from extracts of nine Chinese medicines, namely, Astragalus membranaceus (Huangqi), Panax quinquefolium (Xiyangshen), Epimedium brevicornum (Yinyanghuo), Angelica sinensis (Danggui), Polygonatum sibiricum (Huangjing), Eclipta pwstmta (Mohanlian), Lycium barbarum (Gouqi), Psoralea corylifolia (Buguzhi), and Spatholobus suberectus (Jixueteng), as well as one raw material component of Cervi Cornus Colla, at a ratio of 6:2:3:2:2:3:2:2:6:1. Both A. membranaceus and Cervi Cornus Colla are principal components. In a clinical study, QJSB has been used for the treatment of leucopenia (Wu et al., 2018). In our previous study, we identified 143 compounds, including 56 flavonoids, 51 saponins, and 36 other compounds, from QJSB (Wu et al., 2018). It has been accepted that the effective ingredients can be detected in serum after medicine administration. After the oral administration of QJSB, 42 compounds, including 24 prototype compounds and 18 metabolites, have been detected in the serum of rats (Wu et al., 2018). Among the 42 compounds, many ingredients have been proven to have bioactivities. For example, ferulic acid improves hematopoietic cell recovery in whole-body gamma irradiated mice and increases levels of granulocyte-colony stimulating factor (G-CSF) (Ma et al., 2011); several flavonoids including formononetin, ononin, calycosin, and calycosin-7-O-β-D-glucoside, induce the expression of erythropoietin in human embryonic kidney fibroblasts via the accumulation of hypoxia-inducible factor-1α (Zheng et al., 2011); quercitrin protects endothelial progenitor cells from oxidative damage via inducing autophagy through extracellular signal-regulated kinase (Zhi et al., 2016); icaritin improves the hematopoietic function in cyclophosphamideinduced myelosuppression mice (Sun et al., 2018). The identification of compounds absorbed into blood revealed the effective substance of QJSB to some extent. However, the therapeutic mechanism of QJSB in leucopenia remains unclear. Thus, the effect on the treatment of leucopenia needs further detailed investigation.

Systems biology studies the pharmacological mechanism of TCM by integrating transcriptomic, proteomic and metabolomic profiles (Su et al., 2011; Meierhofer et al., 2014). Network pharmacology is a new system biology approach, generally describing the association of multiple components with multiple targets and multiple pathways (Ning et al., 2017). Recently, the application of integrated systems biology and network pharmacology is a promising approach for the delineation of effects and mechanisms of TCM formulae.

In this study, we firstly investigated the effect of QJSB on leucopenia using mice induced by cyclophosphamide. We further investigated the therapeutic mechanism of QJSB using the transcriptomics datasets derived from the bone marrow and metabonomic datasets from plasma. Finally, we applied transcriptomics-based network pharmacology and metabolomics technologies to study the mechanism of QJSB for the treatment of leucopenia.

### MATERIALS AND METHODS

### Chemical Reagents

Cyclophosphamide was purchased from Zaiqi Biotechnology Corporation (Shanghai, China). QJSB granules were kindly provided by Zhendong Pharmaceutical (Shanxi, China).

#### Animals and Treatments

All animal studies were performed according to the institutional ethical guidelines of animal care and were approved by the Committee on the Ethics of Animal Experiments of the Second Military Medical University, China. Male ICR mice (18–22 g) were obtained from Laboratory Animal Company (Shanghai, China). The mice were acclimated for 2–3 days under conditions of controlled temperature (24 ± 2 ◦C), relative humidity of 60 ± 5%, 12 h light/dark cycle, and ad libitum access to standard laboratory food and water. All the mice were randomly allocated into four groups: normal group and 3 leucopenia model groups (cyclophosphamide, 80 mg/kg/day). The model groups were treated with vehicle, leucogen (20 mg/kg/day) and QJSB (3 g/kg/day), respectively. The mice in each group were orally administered with respective medicines for 1 week, and an equivalent volume (0.2 ml/10 g) of 0.9% saline solution was used for normal group and model group. Next, the animals in each group were sacrificed by dislocation of the cervical vertebra and were prepared for subsequent experiments. In each group, the whole blood of 10 mice was used for routine blood examination and bone marrow cells in the femurs were used for the bone marrow nuclear cell count, cell viability assay and colony-forming unit assay. The sera of another 10 mice were used to detect the levels of IL-6 and G-CSF. Bone marrow cells in the femurs of three of these mice were collected for RNA isolation, sequencing and real-time quantitative PCR (RT-qPCR). The plasma of the mice was collected for metabonomic analysis. After 2 weeks of treatment, the mice were sacrificed by dislocation of the cervical vertebra and the whole blood was used for routine blood examination (n = 10).

#### Leucopenia Model

The leucopenia model was established as follows: for 1 week of treatment, the mice in the model groups were treated with 80 mg/kg/day of cyclophosphamide intraperitoneally for 3 consecutive days (from day 5 to day 7), and the normal group was treated with an equivalent volume (0.1 ml/10 g) of normal saline. For 2 weeks of treatment, the mice were treated with cyclophosphamide (80 mg/kg/day) intraperitoneally for 6 days (3 consecutive days per week), and the normal group was treated with an equivalent volume (0.1 ml/10 g) of normal saline.

#### Cell Viability Assay

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Cell viability was measured using the Cell Counting Kit-8 (CCK8) reagent (Dojindo, Japan). The mice were sacrificed by dislocation of the cervical vertebra and the femurs were immediately collected. Bone marrow was eluted from the shaft by RPMI 1640 medium, and filtered through a 70 micron filter. Bone marrow nuclear cells were dispensed in 96-well culture plates (100 µL/well) at a density of 5 × 10<sup>5</sup> /mL. Next, the cells were incubated with 10 µl of CCK8 reagent. Finally, the absorbance at 450 nm was measured.

### Colony-Forming Units Assay

The mice were sacrificed by dislocation of the cervical vertebra and the femurs were immediately harvested. Next, bone marrow cells were eluted from the shaft by RPMI 1640 medium, and filtered through a 70 micron filter. Thereafter, bone marrow mononuclear cells were obtained by centrifugation in a Ficoll density gradient. The cells were diluted with M3434 methylcellulose medium (StemCell Technologies, Canada) at a density of 3 × 10<sup>4</sup> /mL, dispensed into 6-well culture plates (1 mL/well), and cultured in an atmosphere of 5% CO<sup>2</sup> at 37◦C for 12 days. Colonies consisting of 50 cells or more were counted.

### The Detection of IL-6 and G-CSF

Whole blood was collected from mice by removing the eyeballs. Blood samples were placed to clot for 2 h at room temperature before centrifuging for 20 min at 2000 × g. Serum was collected and stored in aliquots at −80◦C for later use. Commercially available sandwich enzyme-linked immunosorbent assay (ELISA) kits (eBiosciences, San Diego, CA, United States) were used for the quantitation of IL-6 and G-CSF. The optical density of each sample at 450 nm was measured. Cytokine levels were quantified using standard curves, and the values were expressed in units of pg/ml.

### RNA Isolation, Library Preparation, and Sequencing

The total RNA of bone marrow cells from 12 mice (leucopenia group, n = 3, QJSB group, n = 3, normal group, n = 3, and leucogen group, n = 3) were extracted using TRIzol (Invitrogen, Carlsbad, CA, United States) reagent for RNA sequencing and were purified according to the manufacturer's instructions. Strand-specific libraries were prepared using the VAHTS Total RNAseq Library PrepKit for Illumina (Vazyme, China) following the manufacturer's instructions. Using Ribo-Zero rRNA removal beads, ribosomal RNA was removed from total RNA. Following purification, the mRNA was fragmented into small pieces using divalent cations under 94◦C for 8 min. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers. This is followed by second strand cDNA synthesis using DNA polymerase I and RNase H. These cDNA fragments then went through an end repair process, the addition of a single "A" base, and then ligation of the adapters. The products were then purified and enriched with PCR to create the final cDNA library. Purified libraries were quantified by Qubit <sup>R</sup> 2.0 Fluorometer (Life Technologies, United States) and validated by Agilent 2100 bioanalyzer (Agilent Technologies, United States) to confirm the insert size and calculate the mole concentration. Cluster was generated by cBot with the library diluted to 10 pM, followed by sequencing on the Illumina HiSeq 2500 (Illumina, United States).

### Data Analysis for Gene Expression

Sequencing raw reads were preprocessed by filtering out rRNA reads, sequencing adapters, short-fragment reads and other lowquality reads using Seqtk. Hisat2 (version 2.0.4) was used to map the cleaned reads to the mouse GRCm38.p4 (mm10) reference genome with two mismatches. Gene expression was evaluated in FPKM (fragments per kilobase per million mapped fragments) from RNAseq data. The formula to calculate FPKM was as follows: FPKM = (number of mapping fragments) × 10<sup>3</sup> × 10<sup>6</sup> /[(length of transcript) × (number of total fragments)]. Differential expression analysis of two groups was performed using the "DESeq2" R package at the cutoff of | log2 fold change| > 0.585 and P-value < 0.05 (Love et al., 2013). Then, we constructed a pre-ranked gene list of all differentially expressed genes ordered by the absolute value of log2 fold change and selected the top 300 genes for further analysis.

### Pathway Enrichment Analysis and GO Analysis

We firstly used R package "clusterProfiler" to perform pathway enrichment analysis to identify KEGG (Kanehisa and Goto, 2000) (Kyoto Encyclopedia of Genes and Genomes) pathways enriched with the top 300 differentially expressed genes. Significant pathways with P-value < 0.05 were selected. Next, GO (Gene Ontology) enrichment analysis was also performed to explore the biological processes of the top 300 differentially expressed genes. At a cutoff of P < 0.05.

### Evaluation of Drug's Effects by the Network Scores

The background protein-protein interaction (PPI) network was downloaded from the STRING database v10.5<sup>1</sup> and the organism was chosen as "Mus musculus." All genes were standardized by mapping to the Entrez ID for further analysis. In this paper, the top 300 DEGs under the treatment of a drug were regarded as the drug's potential target genes. Similarly, the top 300 DEGs between the model and normal groups were regarded as disease associated genes. We applied the algorithm random walk with restart (RWR) to measure the seed genes' influence on the background network. Specifically, the drug's potential target genes and disease associated genes were used as seed nodes, respectively. Additionally, genes in the background network were scored by RWR. Next, we calculated the Pearson correlation coefficient between the network scores based on each gene set

<sup>1</sup>https://string-db.org/

to estimate the relevance of the drug's potential target genes and disease associated genes. The relevance was estimated as follows:

$$\text{Relevance} = \text{cor}(\text{Score}\_{drug}, \text{Score}\_{d\bar{s}asse})$$

where cor represents the Pearson correlation coefficient and Scores represents the genes' network scores. To evaluate the significance of the correlation between the drug's target genes and disease genes, a reference distribution was built. Genes with the same number of drug's target genes were randomly selected from the background network and the correlation coefficient was calculated between the disease genes and random set. We performed 100 repetitions to generate the reference perturbation distribution. The mean and standard deviation of the random correlation coefficients were denoted by µRelevance and σRelevance, respectively. The Z-score was finally calculated and the absolute value of the Z-score larger than 3 suggests that the drug's effect on the disease was statistically significant.

$$Z\_{score} = \frac{|Relevance - \mu\_{Relevance}|}{\sigma\_{Relevance}}$$

### Sample Preparation and LC-MS Conditions for Metabonomic Analysis

Because the endogenous metabolites play an essential role in the physiology of hosts, we explored the host metabolic profiling in the plasma of a subset of 27 subjects by liquid chromatographymass spectrometry (LC/MS). The detailed method was as follows. Each plasma sample was thawed at 4◦C and vortexed for 5 s at room temperature. Next, 100 µL of plasma was transferred into another 1.5 mL tube with 300 µL of methanol and was mixed 45 s in a vortex. Thereafter, the sample was centrifuged for 10 min (12000 rpm, 4◦C). Finally, the supernatant was transferred into auto-sampler vials and stored at −80◦C for LC-MS analysis. The QC sample was prepared by mixing 10 µL of aliquot from the six above prepared samples, respectively. The QC samples were injected every six samples.

Chromatographic separation was performed on a ACQUITY UPLC <sup>R</sup> HSS T3 (2.1 × 100 mm, 1.8 µm, Waters, United States) using an ACQUITY Ultra Performance LC system (Waters corp., Milford, MA, United States). The column was maintained at 40◦C. The flow rate was set at 0.4 mL/min, and the sample injection was 1 µL. The optimal mobile phase consisted of a linear gradient system of water mixed with 0.1% formic acid (phase A) and acetonitrile (phase B): 0–6.0 min, 5–100% B, 6.0– 8.0 min, 100% B; 8.0–9.0 min, 100–90% B; 9.0–14.0 min, 90–80% B; 14.0–14.1 min, 80–5% B; 14.1–19.0 min, 5% B. MS detection was acquired on a Micromass Quadrupole (Q) SYNAPT G2- Si high-resolution mass spectrometer (Waters Corp., Milford, MA, United States) equipped with an electrospray ion (ESI) source. Both positive and negative modes were utilized in the current research. The temperature of the ion source was 120◦C. The capillary voltage and cone voltage were 2000 V and 49V, respectively. The desolvation gas temperature and flow were 350◦C and 750 L/h, respectively. The cone gas was set at 50 L/h. Data were collected between 50 and 1200 m/z with a 0.2 s scan time and a 0.02 s interscan delay. All analyses were conducted using the lock spray to ensure the accuracy and precision of the mass information for compound identification. Leucine encephalin [(M + H)<sup>+</sup> = 556.2771, (M−H)<sup>−</sup> = 554.2615] was used as the lock spray at a concentration of 1 µg/mL, and the flow rate was set at 5 µL/min. Additionally, mass spectrometry elevated energy (MS<sup>E</sup> ) collection was applied for compound identification. This technique obtains precursor ion information through low collision energy and full-scan accurate mass fragment information through the ramp of the high collision energy. The collision energy of MS<sup>E</sup> was set from 15 to 35 V.

### Metabonomic Data Processing and Analysis

The raw plasma LC-MS data were pre-processed using Waters Progenesis QI 2.0 software (Non-linear Dynamics, Newcastle, United Kingdom). Progenesis QI includes the steps of importing data, reviewing alignment, experiment design setup, picking peaks, identifying and reviewing compounds, and performing compound statistical analysis. Then, the data were exported into SICMA 14.1 (Umetric, Umeå, Sweden) for multivariable statistical analysis. The multivariate statistical analysis (MVA) included principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least square-discriminant (OPLS-DA) models, which were used to observe the classifications for different groups. Thereafter, based on the OPLS-DA plot, the ions were filtered by VIP > 1 (variable importance in the projection) to identify the metabolites contributing to the classifications. Next, ions with P-values < 0.05 were regarded as the differential metabolite ions. Subsequently, the differential metabolites ions with the two filters were structurally identified and interpreted based on the metabonomic associated databases: METLIN<sup>2</sup> , HMDB<sup>3</sup> , and KEGG<sup>4</sup> . Finally, using the MetaboAnalyst 4.0, we performed pathway analysis for the metabolites contributing to the classifications and identified the most relevant pathways.

#### RNA Extraction and Real-Time Quantitative PCR

Total RNA was extracted in bone marrow cells from three groups (leucopenia, QJSB and normal groups) mice using the Trizol reagent (Thermo Fisher Scientific) according to the manufacturer's instructions. And cDNA was generated using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific). RT-qPCR was performed using the Stratagene Mx3005P RT-PCR System (Applied Biosystems) and the PowerUpTM SYBR <sup>R</sup> Green Master Mix (Thermo Fisher Scientific), according to the protocol. Melt curves were analyzed at the end of each assay to confirm the specificity. Fold change was determined using the 2−11CT method normalized with endogenous control GAPDH. The PCR primers used are listed in **Supplementary Table S1**.

<sup>2</sup>https://metlin.scripps.edu/

<sup>3</sup>www.hmdb.ca

<sup>4</sup>https://www.genome.jp/kegg/

#### Statistical Analysis

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GraphPad was used for statistical analysis of the biochemical data. The animals were randomly assigned by using the random permutations table. The data were expressed as the means ± standard deviation (SD). The data were analyzed using by two-tailed Student's t-test or one-way analysis of variance (ANOVA). P < 0.05 was considered statistically significant. The transcriptomics data were processed by R package with "DESeq2" and "clusterProfiler." SICMA 14.1 (Umetric, Umeå, Sweden) was used for MVA. Cytoscape was used to trace the associated gene–enzyme relationship using KEGG database.

#### RESULTS

#### QJSB Increased Peripheral WBCs and Platelets in Leucopenia Model Mice

After treatment with cyclophosphamide, the peripheral WBCs and platelets were significantly decreased. After 1 week of treatment, QJSB increased peripheral WBCs (P < 0.001) and platelets (P < 0.001) significantly (approximately 2.6-fold and 2.4-fold, respectively) compared with those in model group (**Figures 1A,B**). Administration of QJSB or cyclophosphamide had no significant effect on the other peripheral hemogram parameters such as RBCs and hemoglobin concentration (**Figures 1C,D**). We also got the similar results when the leucopenia model animals were treated by QJSB for 2 weeks (**Figures 1E,F**). For WBC differential counts at 1 week of treatment, cyclophosphamide decreased the percentage of monocytes and increased the percentage of eosinophils. The percentage of eosinophils returned to normal after being administrated with QJSB and leucogen. In addition, an increase was observed in the percentage of basophils in QJSB and leucogen groups (**Figure 2**).

#### QJSB Increased Bone Marrow Nuclear Cells and Enhanced Cell Viability in Leucopenia Model Mice

The cell number and cell viability of bone marrow nuclear cells were significantly decreased in the cyclophosphamide treatment group (P < 0.05). QJSB significantly increased the number of bone marrow nuclear cells, and enhanced the cell viability (P < 0.001) (**Figures 3A–C**).

### QJSB Promoted Bone Marrow Mononuclear Cells Colony Formation

To determine the effect of QJSB on bone marrow hemopoietic stem/progenitor cells, we performed the methylcellulose semisolid colony-forming units assay. Mononuclear cells were extracted from the bone marrow of ICR mice. Colony formation of bone marrow mononuclear cells was significantly decreased by cyclophosphamide. After 1 week of treatment, the colony number and colony size were both significantly increased by QJSB (P < 0.001) (**Figure 3D**).

### QJSB Reversed Cytokines Secretion in Serum of Leucopenia Model Mice

The hematopoiesis-related cytokines are important factors for the regulation of hematopoietic function (Alexander, 1998). Both G-CSF and IL-6 in serum were detected. Compared with the normal control group, after cyclophosphamide treatment, the levels of G-CSF and IL-6 in serum were dramatically increased from 100.6 and 5.5 pg/ml to 1143.0 and 89.0 pg/ml, respectively. QJSB significantly reversed the increases of G-CSF (from 1143 to 345 pg/ml, P < 0.001) (**Figure 3E**) and IL-6 (from 89.0 to 9.4 pg/ml, P < 0.001) (**Figure 3F**) induced by cyclophosphamide.

#### Differential Expression Genes Identification and Functional Analysis

In order to identify potential molecular mechanisms, highthroughput sequencing was used to identify the affected gene by QJSB in bone tissue. The raw data of fastq format of RNAseq are available through the National Center for Biotechnology Information's Gene Expression Omnibus (GEO<sup>5</sup> ), and the GEO series accession number is GSE120707. Then, the top 300 differentially expressed genes between QJSB treatment and model group were identified, which included 172 upregulated and 128 downregulated genes. Comparing the leucopenia groups with the normal groups, the top 300 differentially expressed genes, with 14 upregulated and 286 downregulated genes, were detected. Additionally, comparing the leucogen groups with the leucopenia groups, the top 300 differentially expressed genes, with 177 upregulated and 123 downregulated genes, were also identified. The details of the top 300 differentially expressed genes are listed in **Supplementary Table S2**. To identify potential affected pathways of QJSB, KEGG pathway enrichment analysis was performed using the top 300 differentially expressed genes between QJSB and model group. We found that "hematopoietic cell lineage," "osteoclast differentiation, "B cell receptor signaling pathway, "NOD-like receptor signaling pathway," "arachidonic acid metabolism," and "Ferroptosis" were significantly enriched (P < 0.05) (**Figure 4**, left). To further identify the biological processes, we did GO terms enrichment analysis and found the most significantly enriched terms are "homeostasis of number of cells," "cellular response to TGF-β stimulus," "TGF-β receptor signaling pathway," "response to TGF-β," "lymphocyte differentiation," "regulation of immune effector process," and "T cell activation" (**Figure 4**, right). The detail parameters of pathways and GO terms are shown in **Tables 1**, **2**. These results indicate that QJSB may influence these pathways and biological process, thus increasing peripheral WBCs and platelets in leucopenia model mice.

#### RWR-Based Evaluation of QJSB

As mentioned above, the top 300 differentially expressed genes were identified from leucopenia-normal groups, leucogenleucopenia groups, QJSB-leucopenia groups, respectively. We took the top 300 genes of each set as seeds to apply the RWR

<sup>5</sup>http://www.ncbi.nlm.nih.gov/geo/

algorithm. In total, 153 genes from leucopenia-normal groups were mapped to the background network and set as disease associated genes, while 252 genes from QJSB and 233 genes from leucogen were mapped and set as drug's potential target genes. We calculated the Z-score as described in Materials and Methods. The results are listed in **Table 3**. As shown in **Table 3**, the Z-score for QJSB is 6.156 (larger than 3), and it is 10.823 for leucogen. These results suggest that QJSB and leucogen have significant effects against leucopenia in mice from perspective of network analysis.

### Differential Metabolites Identification and Metabolic Pathway Analysis

The plasma samples were subjected to LC/MS analysis in both the positive ion mode (ESI+) and negative ion mode (ESI−). To discriminate the metabolic profiles among normal, model control and QJSB group, we performed clustering analysis using PCA, and the supervised PLS-DA and OPLS-DA. The plasma sample from different groups tended to separated according to the PCA plots either in ESI+ or ESI-mode (**Figure 5A**). Furthermore, the PLS-DA score scatter plots further evidenced the significant separation among the normal, model and QJSB groups either in

marrow mononuclear cells (D). QJSB significantly reversed the increases of G-CSF (E) and IL-6 (F) induced by cyclophosphamide. Data are presented as means ± SD. ###Represent P < 0.001 vs. normal group and <sup>∗</sup> and ∗∗∗ represent P < 0.05 and 0.001 vs. model group, respectively.

TABLE 1 | Pathways enrichment analysis of differently expressed genes in bone marrow cells of QJSB-treated mice.

TABLE 3 | The effect scores of QJSB and leucogen on leukopenia.


TABLE 2 | GO enrichment analysis of differently expressed genes in bone marrow cells of QJSB-treated mice.


ESI+ or ESI-mode (**Figure 5B**). To further identify the significant metabolites contributing to the classifications among these three groups, supervised OPLS-DA was adopted between two groups either in the ESI+ or ESI- modes together. The permutations plot was used to assess the OPLS-DA model and the results showed the model was highly significant and non-overfitting (**Supplementary Figure S1**). The quality of the OPLS-DA model was shown in **Supplementary Table S3**. The result suggested that there was the remarkable separation in model vs. normal and model vs. QJSB. There were 51 and 47 metabolites differently regulated in leucopenia (vs. normal) and QJSB treatment (vs.


leucopenia model) mice, respectively (**Supplementary Table S4**). Then, in order to identify the key metabolic pathway, we did metabolic pathway enrichment analysis using MetaboAnalyst 4.0 (**Tables 4**, **5**). As shown in **Figure 6A**, in leucopenia model (vs. normal), the differential metabolites primarily participate in glycerophospholipid metabolism, primary bile acid biosynthesis phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine metabolism. These metabolic anomalies were found to be primarily involved in lipid metabolism and amino acid metabolism. After QJSB treatment (vs. leucopenia model), the differential metabolites also were primarily enriched in lipid metabolism (glycerophospholipid metabolism, ether lipid metabolism, and linoleic acid metabolism) and amino acid metabolism (tryptophan metabolism) (**Figure 6B**). The results indicate that QJSB is likely involved in the modulation of the metabolic disorders.

#### Correlation Networks Construction of Differential Genes and Metabolites

In order to obtain a comprehensive view of the complex mechanisms of QJSB, we combined the transcriptomics-based network pharmacology and metabolomics data to obtain a system-wide view of the therapeutic mechanism of QJSB. Using the Metscape plugin of Cytoscape, we constructed the correlation network between the differential genes and differential metabolites regulated in QJSB-treated mice to analyze their potential relationships. The results suggested that a lot of metabolites and genes were in the same metabolic

TABLE 4 | Pathways enrichment analysis of differential metabolites in bone

plot among three groups either in ESI+ or ESI– mode, respectively.

marrow cells of model mice.


pathways, including glycerophospholipid metabolism, linoleate metabolism, squalene and cholesterol biosynthesis, glycine, serine, alanine and threonine metabolism, histidine metabolism, tryptophan metabolism, purine and pyrimidine metabolism, etc. As shown in **Figure 7**, these metabolic pathways mainly were TABLE 5 | Pathways enrichment analysis of differential metabolites in bone marrow cells of QJSB-treated mice.


mainly grouped into three classes: lipid metabolism, amino acid metabolism and nucleotide metabolism.

In the lipid metabolic pathways, the levels of metabolites such as phosphatidylcholine (lecithin) and 2-lysolecithin were elevated, and the expression of some genes encoding important metabolic enzymes like Pcyt1a, Phospho1, Cyp4f18, Alox5, and Ggt5 were also augmented by QJSB treatment. Amino acids and nucleotides are essential components of proteins and nucleic acids, respectively, and amino acid metabolism and nucleotide metabolism play an important role in biological synthesis and metabolism of amino acids and nucleotides (Gutteridge

and Coombs, 1977; Ballantyne, 2001; Bender, 2012). In these metabolic pathways, the level of the metabolite tryptamine and the expression of GGTLA1, HDC, NDST1, and ATP6V1C2 were increased, while the level of the metabolite deoxyadenosine and the expression of ATP6V1G2, ATP2B2, and POLR3H were decreased. These results suggested that QJSB might affect biological synthesis and metabolism of amino acids and nucleotides by regulating amino acid metabolism and nucleotide metabolic pathways.

#### QJSB Increased the Gene Expression of Ggt5, Cyp4f18, Pcyt1a, Alox5, and Phospho1 in Leucopenia Model Mice

To further investigate the effects of QJSB on the regulation of metabolic pathways, we measured the expression of five genes, Ggt5, Cyp4f18, Pcyt1a, Alox5, and Phospho1, according to our previous data. They are key genes encoding important metabolic enzymes. RT-qPCR was performed in bone marrow cells from leucopenia model mice induced by cyclophosphamide. Compared to the normal control, the expressions of Ggt5, Cyp4f18, Pcyt1a, Alox5, and Phospho1 were significantly decreased in leucopenia model groups. QJSB significantly increased the mRNA levels of these genes (**Figure 8**, P < 0.01). These results are consistent with our data from transcriptomics analysis.

### DISCUSSION

Leucopenia is a very common adverse reaction induced by chemotherapy (Xu et al., 2011; Cui et al., 2015). In China, TCM formulae have been widely used to treat leucopenia. Among them, QJSB is a newly developed TCM formula and has been used clinically. In this study, our experiments conclude that QJSB has protective effects against leucopenia. As the theory of pharmachemistry of TCM, more scientists accept the viewpoint that the effective constituents may be detected in serum (Wang, 2006). We have identified 24 prototype compounds in the serum of rats after the oral administration of QJSB (Wu et al., 2018), which revealed the potential active substances of the formula to some extent (**Supplementary Table S5**). The network pharmacology technology is a powerful tool to investigate the therapeutic effects and molecular mechanisms (Zhao et al., 2009; Zhang et al., 2017). We also employed a transcriptomicsbased network pharmacology approach to determine that the mechanism was involved in the cell proliferation and differentiation, metabolism response and immunologic function. Functional enrichment analysis was performed to explore the biological processes of the top 300 differentially expressed genes. Usually, the top 300 differentially expressed genes under the treatment of a drug are regarded as the drug's potential target genes, and the top 300 differentially expressed genes between the model and control groups are regarded as disease associated genes. The relevant parameters between the drug's potential target genes and disease associated genes and the Z-score were calculated to evaluate the effect of drugs. A Z-score value greater than 3 often indicates a statistically significant deviation between the actual value and the random ones (Fang et al., 2017). In this study, the Z-score was 6.156. Thus, QJSB might have significant effects against leucopenia disease. Using the transcriptomicsbased network pharmacology and metabonomics technology, we propose a model for QJSB multi-pathways treatment mechanism (**Figure 9**). We concluded that QJSB mainly participates in the metabolism response, cell proliferation and differentiation, and the immune response, etc.

The hematopoietic cell lineage is mainly involved in blood cells development progresses from a rare population of hematopoietic stem cells (HSCs). HSCs can undergo either self-renewal or differentiation into multilineage committed progenitor cells: common lymphoid progenitors (CLPs) or common myeloid progenitors (CMPs), and successively become more restricted in their differentiation capacity. They

finally generate functionally mature cells such as lymphocytes, granulocytes, monocytes, and erythrocytes, et al. Among them, lymphocytes, including B and T cells, constitute a major proportion (more than 80%) of leukocytes (Dintzis and Treuting, 2011; O'Connell et al., 2015). B and T cells are the primary effector cells during the adaptive immune response

(Medzhitov and Janeway, 1997; Iwasaki and Medzhitov, 2015). B cell receptor signaling pathway is involved in B lymphocyte proliferation, differentiation, survival and activation (Jacob et al., 2002; Schweighoffer et al., 2013; Reth and Nielsen, 2014). T cell activation is a vital event for immune system, and only the activated T cell can exert an efficient immune response. Cd3d and Tnfsf13b are key genes in these pathways. The protein encoded by Cd3d is part of the T-cell receptor/CD3 complex and is involved in T-cell activation and signal transduction. Cd3ddeficient patients show a complete block in T cell development. Deficiency of Cd3d also impairs T cell-dependent functions of B cells and causes severe immunodeficiency (de Saint Basile et al., 2004; Gil et al., 2011; Munoz-Ruiz et al., 2016). The protein encoded by Tnfsf13b plays roles in the survival and maturation of both of B and T cells (Pfister et al., 2011; Liu et al., 2016). Besides lymphocytes, both eosinophils and basophils are also involved in the immune response (Jacobsen et al., 2011; Voehringer, 2011). The imbalance of eosinophils and basophils might also affect the hematopoiesis (Tebbi et al., 1980; Enokihara et al., 1996; Schneider et al., 2010). In this study, the expression levels of Cd3d and Tnfsf13b were both up-regulated in QJSB group and recovered the abnormity of eosinophils and basophils induced by cyclophosphamide. These data indicate that QJSB might participate in the regulation of the immune effector process.

Although most HSCs normally exist in a quiescent or dormant state (Wilson et al., 2008), some of them divide and support the production of all mature blood cell types through multiple intermediate progenitor stages, during the steady state, and in response to urgencies to maintain blood cell number homeostasis (Busch et al., 2015; Sawai et al., 2016; Grinenko et al., 2018). Itgam is mainly involved in adhesion and migration of leukocytes. It is necessary for HSCs expansion in vitro and engraftment in vivo (Prashad et al., 2015). Patients with Itgam variants have reduced switched memory B-cell counts (Maggadottir et al., 2015). Ets1 is a key transcription factor required for CD8 T cell differentiation (Zamisch et al., 2009). It is a critical regulator of group 2 innate

lymphoid cells expansion and cytokines production (Zook et al., 2016). In this study, the different expression genes of Itgam and Ets1 are simultaneously enriched in QJSB group (vs. leucopenia group). These data indicate that HSCs expansion, lymphocyte differentiation and cytokines production may also be involved in the protective mechanism of QJSB.

Hematopoiesis-related cytokines are important factors for the regulation of hematopoietic function (Alexander, 1998). For example, IL-6 was first identified and characterized as a lymphocyte-stimulating factor according to its ability to promote the activation and population expansion of T cells, the differentiation and survival of B cells, and the regulation of the acute-phase response (Hunter and Jones, 2015). G-CSF, also known as colony-stimulating factor 3 (Csf3), is the major hematopoietic growth factor involved in the control of neutrophil development. G-CSF supports the proliferation, survival, and differentiation of neutrophilic progenitor cells in vitro and provides non-redundant signals for the maintenance of steadystate neutrophil levels in vivo (van de Geijn et al., 2003). G-CSF also participates in the development of other myeloid lineages, the mobilization of HSCs and myeloid cell migration (Liongue et al., 2009). To determine how G-CSF was regulated by QJSB, we constructed a subnetwork by extracting the links between G-CSF and differentially expressed genes under the treatment of QJSB from our background PPI network, i.e., the STRING network (**Supplementary Figure S2**). This network shows that G-CSF interacts with a group of differentially expressed genes, including Itgam, Il7r, Il18, Ccr2, Dpp4, Jun, and Ltf. Among these genes, Itgam and G-CSF receptor (CSF3R) are two markers of granulocyte differentiation and it was found that G-CSF could decrease Itgam expression (Lantow et al., 2013; Lin et al., 2015). IL-18 (interleukin-18) is involved in the hematopoietic progenitor cell growth and stimulates the secretion of IL-6 and the expression of G-CSF mRNA in splenic adherent cells (Ogura et al., 2001). Additionally, IL-18 treatment increases the serum G-CSF level in C57BL/6 mice (Kinoshita et al., 2011). It was reported that when immortalized bone marrow progenitors are induced by G-CSF to differentiate into mature neutrophils, the

CCR2 gene is strongly activated and CCR2 play a critical role in monocyte recruitment (Iida et al., 2005). G-CSF also increases CCR2 protein expression of THP-1 monocytes (Chen et al., 2008). As our data shows, Itgam was up-regulated while Il18 and Ccr2 were down-regulated by QJSB, a finding that was consistent with the decrease in the serum G-CSF level. Excessive activation and release of cytokines impair the hematopoietic function, and exhaust the production of hematopoietic factor (Hara et al., 2004). QJSB reversed the excessive exhaustion of certain cytokines induced by cyclophosphamide, which might be beneficial for the recovery of leucopenia.

Qi-Jing-Sheng-Bai granule also modulates the metabolism response, including lipid metabolism, amino acid metabolism and nucleotide metabolism. In lipid metabolism, the levels of metabolites such as phosphatidylcholine (lecithin) and 2 lysolecithin are elevated, and the expression of some genes encoding important metabolic enzymes like Pcyt1a, Phospho1, Cyp4f18, Alox5, and Ggt5 are also augmented by QJSB treatment. Additionally, RT-qPCR was performed to verify that QJSB upregulated their mRNA levels. Phosphatidylcholine participates in a series of biological activities such as biological membranes synthesis, cell proliferation and platelet activation (Ridgway, 2013; Li et al., 2014; O'Donnell et al., 2014). Metabolic enzymes encoded by Pcyt1a regulate the biological synthesis of phosphatidylcholine (Haider et al., 2018). The high expression of Pcyt1a causes elevated levels of phosphatidylcholine, which may result in accelerated biological membranes synthesis and cell proliferation of WBCs and platelets. Additionally, Cyp4f18, Alox5, and Ggt5 are involved in the processes of generation, transformation and degradation of leukotriene (Christmas et al., 2006; Rådmark et al., 2015). Thus, QJSB may promote lipid production by regulating lipid metabolism, and regulate immune and inflammatory responses by affecting the generation, transformation and degradation of leukotriene.

Amino acids and nucleotides are essential components of proteins and nucleic acids, respectively. They are indispensable for cell proliferation, survival and development. Leucogen and vitamin B4 are very commonly used for the treatment of leucopenia. Leucogen is an analog of cysteine while vitamin B4 is a precursor of adenine (Lecoq, 1957; Whelan, 2005; Zheng et al., 2006; Langhammer et al., 2011). Therefore, regulating amino acid metabolism and nucleotide metabolism have been confirmed to cure leucopenia. Our data indicate that QJSB participate in the biological synthesis and metabolism of energy, nutrition and genetic materials, which are essential for cell proliferation, development and maturation.

#### CONCLUSION

In summary, our data reveal the therapeutic mechanism of QJSB by integrative application of transcriptomics-based network pharmacology and metabolomics technologies. QJSB exerts protective effect against leucopenia in mice through participating in multi-pathways, mainly including accelerating cell proliferation and differentiation, regulating metabolism response pathways and modulating immunologic function.

#### ETHICS STATEMENT

All animal studies were performed according to the institutional ethical guidelines of animal care and were approved by the Committee on the Ethics of Animal Experiments of the Second Military Medical University, China.

#### AUTHOR CONTRIBUTIONS

ST, PH, and YG collected and analyzed the data, and drafted and revised the manuscript. JY, RW, and JZ collected and revised the manuscript. WZ and A-JL designed the study, collected the data, and revised the manuscript. All authors read and approved the final manuscript.

#### FUNDING

The work was supported by Professor of Chang Jiang Scholars Program, NSFC (81520108030 and 21472238), Shanghai Engineering Research Center for the Preparation of Bioactive Natural Products (16DZ2280200), the Scientific Foundation of Shanghai China (13401900103 and 13401900101), and the National Key Research and Development Program of China (2017YFC1700200).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2019.00408/full#supplementary-material

FIGURE S1 | Distinct discrimination by comparison between Normal vs. Model (A,C), Model vs. QJSB (B,D) either in ESI+ and ESI−. OPLS-DA was used to distinguish the cluster and its permutations plot was used to assess the current OPLS-DA model.

FIGURE S2 | The protein-protein network of Csf3 (G-CSF) with differentially expressed genes between QJSB and model group. Csf3 (G-CSF) was surrounded by differentially expressed genes based on the STRING database.

TABLE S1 | The primers of the key genes encoding important metabolic enzymes.

TABLE S2 | The top 300 differentially expressed genes were identified in Model vs. Normal, QJSB vs. Model and Leucogen vs. Model groups.

TABLE S3 | Summary of the LC-MS data sets used in OPLS-DA modeling. R2X (cum) represents the cumulative X-variation modeled after components, R2Y means the fraction of Y-variation modeled in the component, and Q2 expresses overall cross-validated R2Y for the component and is used to an estimate the model prediction. Cumulative values of R2X, R2Y, and Q2 close to 1 indicate an excellent model.

TABLE S4 | Differential metabolites of plasma in Model vs. Normal and QJSB vs. Model were identified by OPLS-DA on SIMCA software. RT is retention time on gas chromatograph. MZ is the ratio of protons and charge number. Fold change is the ratio of relative abundance of differential metabolites. The data were calculated by t test.

TABLE S5 | The active substances of absorbed prototype compounds in QJSB and theirs evidence.

#### REFERENCES

fphar-10-00408 April 23, 2019 Time: 19:30 # 15


differentiation of discrete proinflammatory gammadelta T cell subsets. Nat. Immunol. 17, 721–727. doi: 10.1038/ni.3424


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling Editor declared a shared affiliation, though no other collaboration, with several of the authors at the time of review.

Copyright © 2019 Tian, Huang, Gu, Yang, Wu, Zhao, Liu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

fphar-10-00408 April 23, 2019 Time: 19:30 # 16

# Target Identification of Active Constituents of *Shen Qi Wan* to Treat Kidney *Yang* Deficiency Using Computational Target Fishing and Network Pharmacology

*Jie Ying Zhang1‡, Chun Lan Hong1,2‡, Hong Shu Chen3, Xiao Jie Zhou1, Yu Jia Zhang1, Thomas Efferth2†\*, Yuan Xiao Yang4\*† and Chang Yu Li1†\**

*1 Department of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, China, 2 Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, Germany, 3 The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 4 School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, China*

Background: Kidney *yang* deficiency syndrome (KYDS) is one of the most common syndromes treated with traditional Chinese medicine (TCM) among elderly patients. *Shen Qi Wan* (SQW) has been effectively used in treating various diseases associated with KYDS for hundreds of years. However, due to the complex composition of SQW, the mechanism of action remains unknown.

Purpose: To identify the mechanism of the SQW in the treatment of KYDS and determine the molecular targets of SQW.

Methods: The potential targets of active ingredients in SQW were predicted using PharmMapper. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the Molecule Annotation System (MAS3.0). The protein–protein interaction (PPI) network of these potential targets and "components-targets-pathways" interaction networks were constructed using Cytoscape. We also established a KYDS rat model induced by adenine to investigate the therapeutic effects of SQW. Body weight, rectal temperature, holding power, water intake, urinary output, blood urea nitrogen (BUN), serum creatinine (Scr), adrenocorticotrophic hormone (ACTH), cortisol (CORT), urine total protein (U-TP), and 17-hydroxy-corticosteroid (17-OHCS) were measured. Additionally, the mRNA expression levels of candidates were detected by qPCR.

Results: KYDS-caused changes in body weight, rectal temperature, holding power, water intake, urinary output, BUN, Scr, ACTH, CORT, U-TP, and 17-OHCS were corrected to the baseline values after SQW treatment. We selected the top 10 targets of each component and obtained 79 potential targets, which were mainly enriched in the proteolysis, protein binding, transferase activity, T cell receptor signaling pathway, and focal adhesion. *SRC*, *MAPK14*, *HRAS*, *HSP90AA1*, *F2*, *LCK*, *CDK2*, and *MMP9* were identified as targets of SQW in the treatment of KYDS. The administration of SQW significantly suppressed the

#### *Edited by:*

*Yuanjia Hu, University of Macau, China*

#### *Reviewed by:*

*Weifeng Yao, Nanjing University of Chinese Medicine, China Leihong Wu, National Center for Toxicological Research (FDA), United States*

#### *\*Correspondence:*

*Thomas Efferth efferth@uni-mainz.de Yuan Xiao Yang yyx104475@163.com Chang Yu Li lcyzcmu@sina.com*

*†These authors have contributed equally to this work.*

*‡These authors have contributed equally to this work and share first authorship.*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 18 March 2019 Accepted: 20 May 2019 Published: 07 June 2019*

#### *Citation:*

*Zhang JY, Hong CL, Chen HS, Zhou XJ, Zhang YJ, Efferth T, Yang YX and Li CY (2019) Target Identification of Active Constituents of Shen Qi Wan to Treat Kidney Yang Deficiency Using Computational Target Fishing and Network Pharmacology. Front. Pharmacol. 10:650. doi: 10.3389/fphar.2019.00650*

**252**

expression of *SRC, HSP90AA1, LCK*, and *CDK2* and markedly increased the expression of *MAPK14, MMP9*, and *F2*. However, *HRAS* levels remained unchanged.

Conclusion: These findings demonstrated that SQW corrected hypothalamic–pituitary– target gland axis disorder in rats caused by KYDS. *SRC, MAPK14, HRAS, HSP90AA1, F2, LCK, CDK2*, and *MMP9* were determined to the therapeutic target for the further investigation of SQW to ameliorate KYDS.

Keywords: network pharmacology, gene ontology, potential targets, traditional Chinese medicine, phytotherapy, transcriptomics

#### INTRODUCTION

Kidney *yang* deficiency syndrome (KYDS) is a diagnostic pattern in traditional Chinese medicine (TCM) and was first documented in *Huang Di Nei Jing*, one of the four great classical textbooks of TCM (Nan et al., 2016). KYDS is characterized by warm dysfunction and a metabolic disorder of the body fluid, causing aversion to cold, cold limbs, cold of waist and back, soreness and weakness of waist and knee, tinnitus, fatigue, impairment of hearing, and looseness of teeth (Lu et al., 2011; Tan et al., 2014; Rong et al., 2016; Xiong et al., 2019). Modern studies have indicated that functional disorders with different degrees of hypothalamic–pituitary–target gland axis, including adrenal glands, thyroids, and gonads, are the crucial pathological mechanism leading to KYDS (Lu et al., 2011; Tan et al., 2014; Nan et al., 2016; Zhang et al., 2017; Tang et al., 2018). KYDS can be present in chronic diseases such as rheumatoid arthritis, hypertension, and diabetes, posing a considerable challenge to the medical system. A valid and classic rat model of KYDS has been developed *via* the administration of a high dose of adenine by oral gavage, which precipitates in renal tubules, leading chronic renal failure, and the animals exhibit the clinical characteristics of KYDS.

*Shen Qi Wan* (SQW) is a frequently used Chinese formula described by Zhang Zhongjing in *Synopsis of Prescriptions of the Golden Chamber* (also named *Jin Kui Yao Lue* in Mandarin). It can be traced back to nearly 2,000 years ago in ancient China (Xiong et al., 2015). The SQW formula is based on the combinatorial principle of "emperor-ministeradjuvant-courier" (*jun-chen-zuo-shi* in Chinese) to combine multiple herbs. The *jun* herb of SQW contains *Cinnamomum*  *cassia (L). J.Presl* and *Aconitum carmichaelii Debeaux* to treat the main cause or primary symptoms of KYDS. The *chen* herb of SQW is *Rehmannia glutinosa (Gaertn). DC.*, *Cornus officinalis Siebold & Zucc*., and *Dioscorea oppositifolia L.* assist the *jun* herb to enhance its therapeutic effects and relieve the accompanying symptoms. The *zuo shi* herb includes *Poria cocos (Schw). Wolf*, *Alisma plantago-aquatica L*., and *Paeonia × suffruticosa Andrews* to counteract the possible toxicity or side effects of other herbs and to ensure the absorption of the formula components and help deliver or guide them to the target organs (Qiu, 2007; Yao et al., 2013). For centuries, SQW has been effectively used in treating various diseases associated with KYDS. However, the therapeutic mechanism remains unknown, the complexity of multiple components, multiple targets, and multiple pathways involved in KYDS make it difficult to elucidate using classical pharmacological approaches.

Network pharmacology is a distinctive new approach based on advances in polypharmacology and network biology to shift away from the traditional "one drug, one target" strategy and move toward sub-network targets and systems, providing a more comprehensive understanding of the mechanism, targets, and pathways behind drug action (Tang and Aittokallio, 2014; Poornima et al., 2016). With the combination of the "medicines-targets" network and biological system network, network pharmacology is becoming more widely known and more frequently used in the field of drug research. PharmMapper (http://www.lilab-ecust. cn/pharmmapper/) is a freely accessed web server designed to identify potential target candidates for probe small molecules of interest using pharmacophore mapping approach (Liu et al., 2010). PharmMapper provides deeper insights and scientific evidence for TCM and helps identify potential targets of Chinese herbs and their underlying mechanisms.

In the present study, investigations based on the pharmacology database and previous studies were conducted to investigate the warm yang and the involvement of several compounds of interest. Potential targets of SQW were predicted by reverse docking to analyze the biological information of potential targets and associated pathways using the network pharmacology method. Moreover, we aimed to identify the potential therapeutic target genes and explore the effects of SQW on the mRNA expression levels of the candidate targets to preliminarily discuss the involvement of the candidate targets in KYDS.

**Abbreviations:** 17-OHCS, 17-hydroxy-corticosteroid; ACTH, adrenocorticotrophic hormone; AQP, aquaporin; BUN, blood urea nitrogen; CAS, Chemical Abstract Service; *CDK2*, cell division protein kinase 2; CNKI, China National Knowledge Infrastructure; CORT, cortisol; ECM, extracellular matrix; *F2*, prothrombin; GO, Gene Ontology; HPLC, high performance liquid chromatography; *HRAS*, GTPase HRas; *HSP90AA1*, heat shock protein HSP 90α; KEGG, Kyoto Encyclopedia of Genes and Genomes; KYDS, kidney *yang* deficiency syndrome; *LCK*, Lymphocytespecific protein-tyrosine kinase LCK; *MAPK14*, mitogen-activated protein kinase 14; *MMP9*, matrix metalloproteinase-9; PPI, protein–protein interaction; Scr, serum creatinine; SQW, *Shen Qi Wan*; *SRC*, proto-oncogene tyrosine-protein kinase SRC; STRING, (Search Tool for the Retrieval of Interacting Genes/Proteins; TCM, traditional Chinese medicine; TcmSP™, Traditional Chinese Medicine Systems Pharmacology Database; UPLC, ultra-performance liquid chromatography; U-TP, urine total protein.

#### MATERIALS AND METHODS

#### Compound Preparation

To collect the compounds of SQW, we combined the Traditional Chinese Medicine Systems Pharmacology Database (TcmSP™, http://lsp.nwu.edu.cn), a unique system pharmacology platform designed for Chinese herbal medicines (Liu et al., 2016) and the review of previous studies (Wang et al., 2016). In addition, we used China National Knowledge Infrastructure (CNKI) and PubMed to obtain information on the modern pharmacology of the compounds in SQW. CAS No. comes from the Chemical Abstract Service (http://www.cas.org/). We finally selected several compounds of each herb in SQW, every compound we chose has various pharmacological effects such as vascular and tracheal relaxation effect; anti-thrombotic, anti-apoptotic, anti-oxidative effects; and anti-inflammatory and immunomodulatory effects.

#### Preparation of Mol2 Format Files

Using the software ChemBioDraw Ultra 14.0 (Version 14, PerkinElmer Inc), we transformed the structures of active components into the sdf structure format. Then, we transformed the sdf structure format into mol2 format files using ChemBio3D Ultra 14.0 (Version 14, PerkinElmer Inc) to obtain the corresponding three-dimensional molecular ball-and-stick model.

#### Prediction and Screening of Targets

To predict the potential target candidates, we imported the mol2 format files into the freely accessed web server of the target database of pharmacophore PharmaMapper website (http:// www.lilab-ecust.cn/pharmmapper/) to perform reverse docking. Subsequently, we employed UniProtKB (http://www.uniprot. org/), which is the central hub for the collection of functional information on proteins, to correct the unstandardized drug target naming by converting the protein names with the species limited to "Homo sapiens" to its official symbol. We selected the top 10 targets of each active component for the subsequent study.

#### Construction of Protein–Protein Interaction Network for Potential Targets

By using the STRING (Version 10.0) database (http://version10. string-db.org//), we identified the direct physical interactions of proteins and their functional interactions (Wu et al., 2016). We uploaded the gene symbols of potential targets and drew a protein–protein interaction (PPI) network graph online to evaluate the interactions among the potential targets. Then, we imported the PPI data in text format into Cytoscape (http://www. cytoscape.org/) to visualize relationships and used its network analyzer plugin to calculate the degree of PPI network.

#### Investigation of Biological Information for Potential Targets of SQW

We imported the potential targets into the Bio database (http:// bioinfo.capitalbio.com/mas3/ Version 3.37) to perform the analysis for GO and KEGG pathway enrichment and then screened for pathways with a cut-off *p* < 0.05 (Wu et al., 2016).

### Construction of the Component-Target-Pathway Network

Based on the screening of pathways with their corresponding targets and components, we created a component-target-pathway illustration using Cytoscape, which not only applies to visualizing biological pathways and intermolecular interaction networks but also supplies a basic set of features for data integration, analysis, and visualization for complicated network analysis (Liu et al., 2016). In the network, the node stands for the constituents of SQW, chemical components, component targets, and component pathways. These constituents are connected by an edge when a target is a potential target of a compound. With this network, we studied the effects of multiple components, multiple targets, and multiple pathways of SQW, which ameliorates KYDS.

#### Animal Study and Sample Collection

The animal study was approved by the Ethics of Committee of Zhejiang Chinese Medical University. Thirty male Wistar rats (250 ± 30 g, animal license no. SCXK-2013-0033) were obtained from the Animal Center of Zhejiang Chinese Medicine University [Laboratory rearing room Permit No. SYXK (Zhejiang) 2013-0184]. All of the animals were housed at 22 ± 2°C with 50–60% relative humidity. A 12 h light/12 h dark cycle was set, and the animals had free access to standard diet and water. All animals were randomly divided into the control group (n = 10), KYDS model group (n = 10), and SQW group (n = 10). In the first 21 consecutive days, the control group was administered normal saline, the model group and the SQW group were administered adenine (Lot: 131203. Shanghai Bo'ao Biological Technology Co., Ltd) at 200 mg/kg per day. From the 22nd day, the control group was administered normal saline, the model group was administered adenine at 200 mg/kg and normal saline *via* gavage after 1 h per day in the next 21 consecutive days, and the SQW group was administered adenine at 200 mg/kg per day and SQW (Lot: 130904. Henan WanXi Pharmaceuticals Co., Ltd) at 3g/ kg *via* gavage after 1 h per day for the next 21 consecutive days. The body weight, rectal temperature, and holding power were detected every 4 days. Water intake and urinary output were observed every 7 days. The urine was obtained from metabolism cages and was used to detect the urine total protein (U-TP) by an automatic biochemical analyzer; 17-hydroxy-corticosteroids (17-OHCSs) were measured using ELISA kits (CAS: 14020809, Biovol Technologies Co. Ltd. Shanghai) after adenine administration at day 21. Blood samples were collected from the heart after pentobarbital sodium (45 mg/ kg, i.p.) anesthesia, and then the kidney tissues were rapidly excised, quickly frozen in liquid nitrogen, and stored at −80°C to perform the quantitative real-time PCR assays. Serum was separated by centrifugation at 3,000 rpm for 15 min at 4°C after standing for 30 min to detect blood urea nitrogen (BUN) and serum creatinine (Scr) with an automatic biochemical analyzer (Hitachi, Japan); adrenocorticotrophic hormone (ACTH) and cortisol (CORT) were measured using enzyme-linked immunosorbent assay (ELISA) kits (CAS: 140208, 14020807. Biovol Technologies Co. Ltd., Shanghai). Throughout the experimental period, no animals died before the experimental endpoint. Euthanasia was performed under sodium pentobarbital anesthesia followed by cardiac puncture/kidney removal for all animals.

#### Quantitative Real-Time PCR Analysis

Total RNA separation and extraction methods were performed according to the instructions of the TaKaRa MiniBEST Universal RNA Extraction Kit (TaKaRa, Clontech). Spectrophotometric measurements at 260/280 nm (Thermo Scientific, USA) were used to determine the purities and concentrations of the total RNA samples. Reverse transcription reactions were performed using 300 ng of RNA with PrimerScript™ RT Master Mix (Perfect Real Time) for cDNA. **Table 1** lists the primer sequences. SRC, MAPK14, HRAS, HSP90AA1, F2, LCK, CDK2, and MMP9 gene expression was investigated. The samples were exposed to pre-denaturation at 95°C for 30 s, followed by 40 cycles of denaturation at 95°C for 5 s, and annealing at 60°C for 30 s. The dissolution curve conditions were 65°C for 0.05 s and 95°C for 0.5 s using 5 µL 5× SYBR Green qPCR Mix, 0.4 µL 20 µmol/L forward primer, 0.4 µL 20 µmol/L reverse primer, and 1 µL cDNA. Water was added to achieve a total volume of 10 µL. β-Actin was used as the internal control, and the data were analyzed using the 2-ΔΔCt method. The experiment was repeated three times.

#### Standards and Chemicals

The standards of o-anisaldehyde (purity >96%), higenamine (purity >98%), and coryneine chloride (purity >98%) were purchased from Yuanye Biological Technology Co., Ltd (Shanghai, China). Salsolinol standard (purity >98%) was purchased from Tauto Biotech Co., Ltd (Shanghai, China). And cinnamic acid standard (purity >98%) was from Chinese National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China). HPLC-grade acetonitrile and methanol were purchased from Tedia (Fairfield, USA). HPLC-grade phosphoric acid was

#### TABLE 1 | Primers used for qPCR.


*SRC, proto-oncogene tyrosine-protein kinase SRC; MAPK14, mitogen-activated protein kinase 14; HRAS, GTPase HRas; HSP90AA1, heat shock protein HSP 90*α*; F2, prothrombin; LCK, proto-oncogene tyrosine-protein kinase LCK; CDK2, cell division protein kinase 2; MMP9, matrix metalloproteinase-9.*

supplied from Shanghai Aladdin Bio-Chem Technology Co., Ltd (Shanghai, China). Distilled water was used throughout the study.

#### Preparation of Reference and Sample Solutions

A mixed standard solution was obtained by dissolving the five standards (o-anisaldehyde, higenamine, coryneine chloride, salsolinol, and cinnamic acid) in methanol. The final concentration is 2 mg/ml. SQW was ultrasonically extracted by 10-fold volume pure water twice for 30 min each time. The solution was concentrated to 40 mg/ml then filtered by a 0.22-μm Millipore filter. The injection volume was 10 μl in the same.

#### Liquid Chromatographic Analysis

Samples were analyzed using the ACQUITY UPLC system (Waters Corp., Milford, MA, USA), equipped with a quaternary pump and a variable wavelength ultraviolet (UV) detector. Elution of analytes was achieved on an Agilent EC-C18 column (100× 3.0 mm, 2.7 m diameter) at a flow rate of 0.3 ml/min. The mobile phase was acetonitrile (A) and 0.1% phosphoric acid solution (B). The gradient program was as follow: 0–5 min, 0.5–2% A; 5–6 min, 2–30% A; 6–8 min, 30–50% A; 10–12 min, 50–70% A; 12–15 min, 70–0.5% A. The UV detection wavelength was at 208 nm. The column temperature was set at 25°C.

#### Statistical Analysis

The SPSS 22.0 statistical software package (SPSS, Chicago, IL, USA) was used for the analysis of variance followed by one-way ANOVA. Data were presented as the mean values ± standard deviation. Statistical significance was considered if *p* < 0.05 was observed.

#### RESULTS

#### Compound Information

A search of the TcmSP™ identified 1,345 items, including 130 in *A. carmichaelii* Debeaux, 200 in *C. cassia* (L.) J.Presl, 151 in *R. glutinosa* (Gaertn.) DC., 452 in *C. officinalis* Siebold & Zucc., 142 in *D. oppositifolia* L., 68 in *P. cocos* (Schw). Wolf, 92 in *A. plantagoaquatica* L., and 110 in *Paeonia* × *suffruticosa* Andrews. Wang et al. (2016), in "an integrated chinmedomics strategy for discovery of effective constituents from traditional herbal medicine," reported 84 compounds, among which 51 compounds in negative ion mode and 33 compounds in positive ion mode were identified from SQW. Moreover, 20 compounds absorbed into the blood, such as azelaic acid-O-glucuronide, jionoside D, azelaic acid, and poricoic acid, had a strong relationship with the therapeutic effect of SQW on KYDS. Database search and current studies were used to select the chemical components of SQW according to its pharmacological activities, such as a cardiac-stimulating effect, heightened adrenal cortex function, promoting diuresis and detumescence, invigorating spleen and dampness removal, as shown in **Table 2**. Higenamine, coryneine chloride, salsolinol, o-anisaldehyde, cinnamic acid, catalpol, acteoside, loganin, diosgenin, morroniside, pachymic acid,





acetophenone, paeoniflorin, alisol A, and alisol B were probably associated with warming yang attributes of SQW, which were used for further study.

#### Construction of the Interaction Network and Network Analysis

Ranked by fit score in descending order, three hundred potential targets were predicted by PharmMapper. We selected the top 10 targets of each component, if one gene symbol of a component with a different subunit remained. Subsequently, 79 potential targets were selected for further investigation. In the present study, the components of SQW could dock the same or different targets, implying that SQW had a therapeutic effect on the treatment of KYDS through a "multiple components-multiple targets" mechanism. We evaluated 79 potential targets by using the STRING version 10.0 database to identify the interactions between identified 68 proteins. Then, we constructed a PPI network (**Figure 1**) by using Cytoscape. We deleted the isolated pairs of linked nodes, which were not meaningful. The resulting network was composed of 68 nodes and 229 edges, with 27 as the maximum degree of connectivity of a node and 1 as the minimum. We evaluated a node with a degree, which denotes

the number of edges between a node and other nodes in a network. A high-degree node was the most influential node in the network, and a hub node was a component of a network with a high-degree node. The average degree of connectivity of the nodes in the network was 6.74, and the standard deviation was 5.84. In this study, we selected the hub nodes with a degree of connectivity set as ≥ the mean value + standard deviation. *SRC* (degree = 27), *MAPK14* (degree = 24), *HRAS* (degree = 21), *HSP90AA1* (degree = 20), *AR* (degree = 19), *F2* (degree = 17), *PRKACA* (degree = 14), *MMP9* (degree = 14), *IL2* (degree = 14), *MAP2K1* (degree = 13), *LCK* (degree = 13), *KDR* (degree = 13), and *CDK2* (degree = 13). The hub nodes maintain the stability of the network and show the occurrence of KYDS, which involves multiple genes and multi-dimensional regulation.

#### GO Enrichment and Pathway Analysis for Potential Targets of SQW

We imported the selected potential 79 target genes into the Molecule Annotation System for GO enrichment and pathway analysis. GO analysis results revealed that the functions of these potential targets are related to many biological processes that may be important for the occurrence and development of KYDS, such as proteolysis,

oxidation reduction, signal transduction, and metabolism. Binding (protein, nucleotide, zinc ion, metal ion) and activity (transferase, peptidase) are closely related in molecular function and the cellular components, including cytoplasm, nucleus, and cytosol, these proteins were ranked highly as potential targets (**Figure 2**).

A total of 105 pathways were obtained by GO analysis, from which we selected the top 76 pathways that met the criterion of *p* < 0.05. Numerous pathways for potential target genes were identified. Our study found that the ErbB signaling pathway, VEGF signaling pathway, and MAPK signaling pathway are associated with signal transduction, the insulin signaling pathway, metabolism of xenobiotics by cytochrome P450, drug metabolism—cytochrome P450, and the PPAR signaling pathway. Androgen and estrogen metabolism are associated with the endocrine system. The focal adhesion and the T cell receptor signaling pathway are also closely related to immunological stress or inflammation. Moreover, we found some disease-related pathways such as prostate cancer, non-small cell lung cancer, endometrial cancer, and thyroid cancer, which indicate that SQW has a potential application in other diseases (**Figure 3**). The results prompted that SQW ameliorated the imbalance of body by regulating the neurological, endocrine, and immune processes.

#### Pharmacology Network of SQW

We constructed a pharmacology network of SQW (**Figure 4**) using the Cytoscape software, which showed the relationships among the constituents, chemical components, and potential targets of SQW and the selected 76 pathways (*p* < 0.05). We obtained a preliminary understanding of the mechanism of SQW through this network. The potential targets of the effective components are distributed in different metabolic pathways to jointly affect the occurrence and development of KYDS.

#### Adenine-Induced KYDS

To validate the establishment of the animal model, the body weight, rectal temperature, and the holding power were measured on days 0, 4, 8, 12, 16, and 20. The results (**Figure 5A, B**, and **C**) demonstrated that the body weight, temperature, and the holding power values of the KYDS model rats gradually decreased as the time increased compared to those of the rats in the control groups (*p* < 0.01), whereas the water intake and urinary output of KYDS model rats were higher than those of the rats in the control groups (*p* < 0.01) on the 7th, 14th, and 20th days (**Figure 5D** and **E**). As shown in **Table 3**, the contents of BUN, Scr, ACTH, and CORT were determined in rat serum on the 21st day. The BUN and Scr of the KYDS model were significantly increased (*p* < 0.01), whereas the ACTH and CORT of the KYDS model rats were significantly lower than those of the rats in the control group (*p* < 0.01). Moreover, the concentration of 17-OHCS in the KYDS rats was decreased compared with that in the control group rats (*p* < 0.01), but the U-TP in the urine of KYDS model rats was significantly increased (*p* < 0.01). These results indicated that the rats presented symptoms such as sluggishness, languorousness, and a crouched posture, which are the typical pathological features of KYDS. The biochemical results indicated that the KYDS model was successfully established for subsequent experiments.

### Treatment of KYDS With SQW

Treatment group rats recuperated after intra-gastric administration of SQW. The body weight improved significantly in the SQWtreated rats compared to that in model group rats (*p* < 0.01) on the 12th day of intra-gastric administration of SQW (**Figure 6A**). The rectal temperature and holding power of the SQW-treated rats were ameliorated compared with that in the model groups (*p* < 0.01) at the beginning of the 8th day of intra-gastric administration of SQW (**Figure 6B** and **C**). The levels of water intake and urinary output in the SQW-treated rats gradually returned to the baseline levels (*p* < 0.01) of the control group (**Figure 6D** and **E**). The body weight, rectal temperature, holding power, water intake, and urinary output of the model groups showed significant differences compared with those in the control groups (*p* < 0.01) throughout the treatment period. As shown in **Table 4**, the SQW treatment obviously improved the numeral values of ACTH, CORT, and 17-OHCS (*p* < 0.01), while BUN, Scr, and U-TP showed a significant decrease compared with the model groups (*p* < 0.01), demonstrating that SQW could effectively ameliorate KYDS and had a therapeutic effect on the rat KYDS models.

#### Results of qPCR for Candidate Target Genes

The hub genes were identified in the PPI network with high degree of connectivity. Among them, *SRC, MAPK14, HRAS, HSP90AA1, F2, LCK, CDK2*, and *MMP9* were closely related to the emperor's constituents. Then, we explored the effect of SQW on mRNA expression in the kidney using qPCR, as shown in **Figure 7**. The mRNA expression levels of *SRC, HSP90AA1, LCK*, and *CDK2* in the SQW-treated group were significantly decreased compared to those in the model group (**Figure 7A–D**), whereas *MAPK14, MMP9*, and *F2* expression levels were significantly higher in the SQW-treated group than those in the model group (**Figure 7E–G**). Although the mRNA expression levels of *HRAS* showed no significant difference compared with the model group, there was a weak trend (**Figure 7H**). These results indicate that SQW treatment could effectively ameliorate KYDS *via* the synergy of multiple targets.

#### Results of Ultra-Performance Liquid Chromatography

The main five components (higenamine, coryneine chloride, salsolinol, o-anisaldehyde, and cinnamic acid), which belong to the *jun* herbs *Ramulus Cinnamomi* and *Radix aconiti lateralis preparata,* were detected by UPLC in SQW (**Figure 8**). The gradient program was as follow: 0–5 min, 0.5–2% A; 5–6 min, 2–30% A; 6–8 min, 30–50% A; 10–12 min, 50–70% A; 12–15 min, 70–0.5% A. The UV detection wavelength was at 208 nm. However, the peak of cinnamic acid seemed to be weak.

### DISCUSSION

KYDS is most prevalent in older men and women and increases with age (Chen et al., 2010; Rong et al., 2016). TCM considers KYDS to be a complex kidney disorder, and "kidney *yang*" activates the power of human vitality (Lu et al., 2011; Huang et al., 2013; Zhao et al., 2013). The primary cause of KYDS is a decline

in kidney-*yang* and transformative action, which is similar to a debilitating disease, such as chronic prostatitis, nephrotic syndrome, adrenocortical insufficiency, chronic nephritis, and diabetes mellitus in Western medicine. SQW is a typical TCM formula widely used for the treatment of chronic diseases associated with KYDS in China. Nevertheless, due to the complex pathological properties and multiple targets in KYDS, it is not easy to explore the mechanism of action of SQW using traditional methods. In this study, reverse pharmacophore docking and network pharmacology strategies were used to study the characteristics of "multiple components-multiple targets-multiple pathways" associated with SQW in the treatment of KYDS.

PharmMapper was designed to identify potential target candidates for given small molecules (drugs, natural products, or other newly discovered compounds with unidentified binding targets) by the mutual recognition of space and the ability to find the best mapping configurations (Liu et al., 2010). Ma et al. (2016) showed the "multiple components-multiple targetsmultiple pathways" mechanism of *Naoxintong* capsule with the PharmMapper database and network pharmacology. Tao et al. (2016) used PharmMapper and the KEGG bioinformatics websites to predict the target proteins and related pathways of *Chuanbei Pipa* dropping pills to clarify the anti-inflammatory and cough-suppressing mechanisms. Using a network pharmacology method provides a basis for understanding the mechanism of action of SQW and is indispensable in the study of complex drugs.

We successfully predicted the drug targets of 15 compounds in SQW. The results of PPI network suggested 13 hub genes, which play important roles in the PPI. Among them, *SRC, MAPK14, HRAS, HSP90AA1, F2, LCK, CDK2*, and *MMP9* were closely associated with higenamine, coryneine chloride, salsolinol, o-anisaldehyde, and cinnamic acid. These compounds were found in *R. Cinnamomi* and *Radix aconiti lateralis preparata*, the *jun* herb of SQW treating the main cause or primary symptoms of KYDS.

Proto-oncogene tyrosine-protein kinase Src (*SRC*) is a nonreceptor tyrosine kinase. Once *SRC* is activated, the intracellular signal transduction cascades are triggered and subsequently multiple cellular functions such as cell proliferation, differentiation, and metabolism are modulated. Further investigations revealed that *SRC* activation is critically involved in the development of chronic kidney disease. Yan et al. (2016) observed that *SRC* kinase is activated in cultured kidney fibroblasts, and the inhibition of *SRC* by PP1, a selective small-molecule inhibitor of *SRC* kinase, appeared to disrupt TGFβ1/Smad3 and epidermal growth factor receptor (EGFR) signaling. Another study demonstrated that the inhibitor of SRC kinase effectively blocked the expression of α-SMA, which is associated with the progression of renal fibrogenesis (Hu et al., 2014). Even more, *SRC* can be activated by autophosphorylation of Tyr416, which is induced in response to a wide variety of cytokines/growth factors/transmembrane receptor proteins, including receptor tyrosine kinases, cytokine receptors, TGF-β1, and EGF (Yan et al., 2016; Zhou and Liu, 2016). Thus, *SRC* may be a potential therapeutic target for the treatment of chronic renal fibrosis with KYDS.

Mitogen-activated protein kinase 14 (*MAPK14*) encodes P38 mitogen-activated protein kinase and can be activated by various environmental stressors and pro-inflammatory cytokines (Han et al., 2015). *MAPK14* regulates the activation of several transcription factors responses, including gene expression, growth, inflammation, metabolism, and apoptosis (Umasuthan et al., 2015). *MAPK14* activity-deficient mice had

less kidney dysfunction, inflammation, and apoptosis in acute folate nephropathy, while *MAPK14* siRNA targeting decreased inflammation and cell death in cultured tubular cells (Ortiz et al., 2017). We conclude that *MAPK14* promoted kidney injury through the promotion of inflammation and cell death and that it is a putative novel therapeutic target of SQW to ameliorate KYDS.

*HRAS*, a small GTPase from the Ras family, encodes the GTPase HRas, which is also known as the transforming protein p21 (Sugita et al., 2018). *HRAS* plays a role in regulating the growth, differentiation, and death of endothelial cells while enhancing the effects of the growth factor (Burgoyne et al., 2012). Moreover, *HRAS* participates in focal adhesion and the MAPK pathway by relieving inflammation (Tao et al., 2016).

Heat shock protein (HSP) is a highly conserved protein that is synthesized in response to physical, chemical, biological, and/or mental stimulation. Heat shock protein HSP 90α (*HSP90AA1*)


KYDS model group (n = 20) versus the control group (n = 10). Values are presented as mean values ± SD. The *p-*values were calculated using a one-way ANOVA.

*The values are presented as the means ± SD. \*\*p < 0.01 KYDS model group (n = 20) compared with control group (n = 10).*

belongs to the HSP90 protein superfamily, which is a molecular chaperone of numerous oncoproteins and a mediator of cellular homeostasis to maintain cell survival under stimulation (Trepel et al., 2010). Hsp 90 inhibition represses the TLR4-mediated NF-κB activity primarily through IKK to reduce renal ischemiareperfusion acute injury (O'Neill et al., 2015). Moreover, inhibiting HSP90 activation prevents the development of renal fibrosis through the degradation of TβRII depending on Smurf2 mediation (Noh et al., 2012). These intriguing findings suggest that the kidney-protective functions of SQW may occur by regulating the expression of HSP90AA1.

Coagulation factor II (*F2*) encodes the prothrombin protein, which functions in blood homeostasis, inflammation, and wound healing. *Qi* deficiency and blood stasis are the key factors of KYDS, which is characterized by decreased gasification, a disorder of vital energy and blood, and cold limbs. We speculate that blood rheology abnormalities cause the deficiency of heat production and the ability of the kidney-yang to transfer body fluid into energy. However, the function of *F2* in KYDS should be further studied.

Tubular epithelial cells (TECs) play an important role in renal diseases, especially in tubulointerstitial inflammation and fibrosis, which is a pathological process involved in a variety of cytokines and inflammatory mediators. Lymphocyte-specific protein-tyrosine kinase (*LCK*) and a SRC family protein-tyrosine kinase are located in the cytoplasm of TECs and form the key signal transduction molecule in the process of intracellular signal transduction (Singh et al., 2017). Li et al. have investigated the effect of the LCK pathway activation on the IL-12 signal



*The values are presented as mean values ± SD. \*\*p < 0.01 compared with the control group (n = 10);* ▲▲*p < 0.01 SQW group (n = 10) compared with the model group (n = 10).*

transduction of TECs and found that *LCK* may regulate the LCK c-Jun signaling pathways in TEC, while the inflammation of TEC mediated by the activation of the LCK pathway is related to the expression of c-Jun promoted by IL-12 (Li et al., 2001).

Glomerular mesangial cell proliferation is a common pathological feature of many glomerular diseases (Lin et al., 2017). Cyclin-dependent kinase 2 (*CDK2*) is a serine/threonineprotein kinase involved in the control of the cell cycle. Yu et al. (2007) observed that the proliferation of mesangial cells is directly related to the high expression of *CDK2*, which indicates that SQW probably improves KYDS by depressing the expression of *CDK2*.

Renal fibrosis is a common disease with pathological characteristics of the accumulation of extracellular matrix (ECM) and also strongly associated with the progression of chronic kidney disease to end-stage renal disease. Matrix metalloproteinases (MMPs) are renal, physiological regulators of ECM degradation. Matrix metalloproteinase 9 (*MMP*9), a 92 kDa type IV collagenase, can specifically degrade type IV and V collagens and gelatin to maintain homeostasis of the ECM in the kidney (Lenz et al., 2000). ECM components accumulate due to an imbalance in ECM production and defective ECM degradation by proteolytic enzymes during renal fibrosis (Tsai et al., 2012). The results of GO enrichment showed that protein hydrolysis has an important role, which is consistent with the function of *MMP9*. The preliminary results in our research demonstrated that the medicated serum of 3.0 and 6.0 g/kg SQW significantly increased the expression of MMP9 protein in NRK-52E cells. Target prediction also showed that salsolinol is associated with *MMP9, MMP3*, and *MMP8*, suggesting an interaction relationship between SQW and the matrix metalloproteinases (MMPs) family. These findings suggest

or ▲ *p* < 0.05 compared with the model group.

(2 mg/ml) (A). The UV detection wavelength was at 208 nmOs.

that SQW reduces the accumulation of EMC in renal epithelial cells *via* the metalloproteinases.

Moreover, the effect of SQW on AQPs, the aquaporin channel family, and on the relation between *AQP1* and MMP9 showed a trend of enhancement to promote the migration of renal TECs for renal injury repair. Moreover, SQW has a therapeutic effect on water metabolism disorder by promoting the mRNA and protein expression levels of *AQP2*. Furthermore, SQW significantly increased the ACTH, while CORT regulated the hypothalamicpituitary-adrenal axis to exploit the *R. Cinnamomi* and *Radix aconiti lateralis preparata* role in tonifying the kidney yang (Xu et al., 2014). These results are consistent with the therapeutic effect of SQW observed in the present paper.

Our study showed that SQW treatment dramatically improved the common physiological symptoms of KYDS and had protective effects on the hypothalamic-pituitary-adrenal axis in KYDS model rats. The potential targets of SQW were identified using PharmMapper, bioinformatics, and PPI network analysis. We found 79 potential target genes and identified *SRC, MAPK14, HRAS, HSP90AA1, F2, LCK, CDK2*, and *MMP9* as the key potential therapeutic targets of SQW. The 79 target genes mainly related to the metabolism of xenobiotics by cytochrome P450, prostate cancer, and the T cell receptor signaling pathway. Wang et al. (2016) also reported 14 important potential targets associated with the aldosterone-regulated sodium reabsorption and adrenergic signaling pathways. However, further studies are required to confirm the results of this study. We explored the potential targets and pathways of SQW from a different perspective and using novel methods, and we conclude that multiple components, multiple targets, and multiple pathways of SQW led to a therapeutic effect on KYDS. This study shows that cell proliferation, differentiation, apoptosis, migration (*SRC, HRAS, HSP90AA1, CDK2*), and ameliorating chronic kidney disease (*MAPK14, F2, LCK, MMP9*) appear to play important roles in the therapeutic effect of SQW.

In this study, SQW ameliorated KYDS characteristics in rats presumably by eight target genes. Further studies are needed to analyze the protein levels of these targets. Moreover, other species should be considered for further verification.

#### CONCLUSION

In summary, SQW has a therapeutic effect on the treatment of KYDS through the "multiple components-multiple

#### REFERENCES


targets-multiple pathways" mechanism. We found that *SRC, MAPK14, HRAS, HSP90AA1, F2, LCK, CDK2*, and *MMP9* genes were highly involved and may be potential targets in the treatment of KYDS.

#### DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

#### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of the Ethics of Committee of Zhejiang Chinese Medical University (permit number: SYXK 2013-0115). The protocol was approved by the Ethics of Committee of Zhejiang Chinese Medical University (permit number: SYXK 2013-0115). All procedures were performed under sodium pentobarbital anesthesia, and all efforts were made to minimize suffering.

#### AUTHOR CONTRIBUTIONS

JZ, CH, YY and CL conceived and designed the experiments. JZ and CH performed the experiments. HC, XZ and YZ analyzed the data. JZ, HC, TE, YY and CL contributed reagents/materials/ analysis tools. JZ and TE wrote and edited the paper. JZ and CH contributed equally to this work. TE, YY and CL contributed equally to this work.

#### FUNDING

This study was supported by grants from the National Natural Science Foundation of China (Nos. 81673839 and 81373507), the Project of National Great New Drug Research and Development (No. 2012ZX09503001-001), and Science and Technology Innovation Project of Zhejiang Province College Students (Grant No. 2017R410049) and Zhejiang Province Administration of Traditional Chinese Medicine (no. 2015ZA073). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

determination of salsolinol. *Yao Xue Xue Bao* 17, 792–794. doi: 10.16438/ j.0513-4870.1982.10.011


triterpene production. *Plant Physiol. Biochem.* 97, 378–389. doi: 10.1016/j. plaphy.2015.10.031


pharmacology perspective based on ma-huang decoction. *J. Ethnopharmacol.* 150, 619–638. doi: 10.1016/j.jep.2013.09.018


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Zhang, Hong, Chen, Zhou, Zhang, Efferth, Yang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Effects of Chinese Herbal Medicines on the Risk of Overall Mortality, Readmission, and Reoperation in Hip Fracture Patients

*Chi-Fung Cheng1,2†, Ying-Ju Lin1,3†, Fuu-Jen Tsai1,3,4, Te-Mao Li3, Ting-Hsu Lin1, Chiu-Chu Liao1, Shao-Mei Huang1, Xiang Liu5, Ming-Ju Li2, Bo Ban6, Wen-Miin Liang2\* and Jeff Chien-Fu Lin7,8\**

#### *Edited by:*

*Yuanjia Hu, University of Macau, China*

#### *Reviewed by:*

*Qi Wang, Harbin Medical University, China Fang-Pey Chen, National Yang-Ming University, Taiwan*

#### *\*Correspondence:*

*Wen-Miin Liang wmliang@mail.cmu.edu.tw Jeff Chien-Fu Lin cflin@mail.ntpu.edu.tw*

*†These authors have contributed equally to this work.*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 18 October 2018 Accepted: 15 May 2019 Published: 11 June 2019*

#### *Citation:*

*Cheng C-F, Lin Y-J, Tsai F-J, Li T-M, Lin T-H, Liao C-C, Huang S-M, Liu X, Li M-J, Ban B, Liang W-M and Lin JC-F (2019) Effects of Chinese Herbal Medicines on the Risk of Overall Mortality, Readmission, and Reoperation in Hip Fracture Patients. Front. Pharmacol. 10:629. doi: 10.3389/fphar.2019.00629*

*1 Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, 2 Graduate Institute of Biostatistics, School of Public Health, China Medical University, Taichung, Taiwan,3 School of Chinese Medicine, China Medical University, Taichung, Taiwan, 4 Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan, 5 National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States, 6 Chinese Research Center for Behavior Medicine in Growth and Development, Jining, China, 7 Department of Statistics, National Taipei University, Taipei, Taiwan, 8 Department of Orthopedic Surgery, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan*

Hip fracture is a major public health concern, with high incidence rates in the elderly worldwide. Hip fractures are associated with increased medical costs, patient dependency on families, and higher rates of morbidity and mortality. Chinese herbal medicine (CHM) is typically characterized as cost-effective and suitable for long-term use with few side effects. To better understand the effects of CHM on hip fracture patients, we utilized a population-based database to investigate the demographic characteristics, cumulative incidence of overall mortality, readmission, reoperation, and patterns of CHM prescription. We found that CHM usage was associated with a lower risk of overall mortality [*P =* 0.0009; adjusted hazard ratio (HR): 0.47, 95% confidence interval (CI): 0.30–0.73], readmission (*P =* 0.0345; adjusted HR: 0.67, 95% CI: 0.46–0.97), and reoperation (*P =* 0.0009; adjusted HR: 0.57, 95% CI: 0.40– 0.79) after adjustment for age, type of hip fracture, surgical treatment type, and comorbidities. We also identified the herbal formulas, single herbs, and prescription patterns for the treatment of hip fracture by using association rule mining and network analysis. For hip fracture patients, the most common CHM coprescription pattern was Du-Zhong (DZ) → Xu-Duan (XD), followed by Du-Huo-Ji-Sheng-Tang (DHJST) → Shu-Jing-Huo-Xue-Tang (SJHXT), and Gu-Sui-Bu (GSB) → Xu-Duan (XD). Furthermore, XD was the core prescription, and DZ, GSB, SJHXT, and DHJST were important prescriptions located in cluster 1 of the prescription patterns. This study provides evidence for clinical CHM use as an adjunctive therapy that offers benefits to hip fracture patients.

Keywords: hip fracture, Chinese herbal medicine, overall mortality, readmission, reoperation

### INTRODUCTION

Hip fracture is a major public health concern with a high incidence rate, especially in elder patients worldwide (Friedman and Mendelson, 2014; Lin and Liang, 2017). An estimated 6.26 million hip fracture patients will exist worldwide by 2050 (Gullberg et al., 1997). Half of these, about 2.5 million hip fractures, will occur in Asia (Dhanwal et al., 2011). In Taiwan, among the elderly, hip fracture patients increased from 3% of the population in 1964 to 10.7% in 2011 (Wang et al., 2013). Patients with hip fractures incur increased costs of medical care, increased dependency on families, and have higher morbidity and mortality outcomes. Surgery, including hemiarthroplasty and internal fixation of fractures, is frequently used for the management of hip fractures. However, patient outcomes of morbidity and mortality and their relationships to current treatments require further scrutiny (Wang et al., 2013; Lin and Liang, 2017). To reduce the incidence of hip fracture and to reduce the outcomes of overall mortality, readmission, and reoperation of hip fracture patients, numerous approaches have been proposed and pursued including improved osteoporosis screening, diagnosis and medications, fracture prevention programs, and research-supported integrative, alternative, and complementary nutrition and medicine.

Chinese herbal medicine (CHM) is typically characterized as cost-effective, suitable for long-term use, and associated with relatively few side effects. It has been extensively used as a complementary therapy for the treatment of many diseases and ailments in Taiwan (Shih et al., 2012; Liao et al., 2015; Tsai et al., 2017a; Li et al., 2018a; Tsai et al., 2018). CHM has also been used to treat bone-related diseases including osteoporosis and bone fractures (Shih et al., 2012; Mukwaya et al., 2014; Liao et al., 2015). CHM is believed to maintain bone health, including: inhibition of inflammation, promotion of fracture healing, osteopenia prevention, and antiosteoporotic activities (Chow et al., 1982; Chen et al., 2005; Li et al., 2011; Ma et al., 2011; Xiang et al., 2011; Wong et al., 2013; He and Shen, 2014; Zhang et al., 2016; Hsiao et al., 2017; Wang et al., 2018c; Xi et al., 2018; Lee et al., 2019). These studies have encouraged the search for complementary therapy for the better management of bonerelated diseases. As such, an investigation into the clinical use of CHM in combination with regular therapy in hip fracture patients is appropriate and necessary.

To better understand the incidence and effects of CHM as treatment in hip fracture patients, we utilized a populationbased database to investigate the demographic characteristics, cumulative incidence of overall mortality, readmission, reoperation, and patterns of CHM prescription for hip fracture patients. Through this retrospective population-based case– control analysis, we were able to investigate whether the use of CHM as adjunctive therapy offers benefits to hip fracture patients.

### MATERIALS AND METHODS

#### Data Source

To examine whether CHM use is associated with a lower risk of overall mortality, readmission, and reoperation after hip fracture, a population-based retrospective cohort study was conducted. Subjects were identified based on the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM). This population was part of a database comprising all individuals 40 years of age or older who received surgery for hip fracture based on a) first discharge disease codes of hip fracture: ICD-9-CM: 820, 820.0, 820.00, 820.01, 820.02, 820.09, 820.8, 820.03, 820.2, 820.20, and 820.21; and b) procedure codes with surgery of internal fixation or hemiarthroplasty (based on ICD-9-CM: 79.15, 79.35, and 81.52) during the period from 2000 to 2010 who were included in the National Health Insurance Research database (NHIRD; http://nhird.nhri.org.tw/) of the National Health Insurance (NHI) program. This program includes the total population of patients in Taiwan (23 million individuals) and includes 99% of the general population; it is only used for research purposes by scientists in Taiwan. All personal data were decoded for identity, so we were unable to obtain an informed consent. This database provides detailed medical records including information on age, gender, diagnoses, prescriptions, records of clinical visits and hospitalizations, inpatient orders, ambulatory care, and sociodemographic factors. This database also offers longitudinally linked data for the period from 1996 to 2012. The study was approved by the Institutional Review Board of China Medical University Hospital.

#### Subjects

The first admission date due to a hip fracture was defined as the diagnostic day of the hip fracture. The exclusion criteria included subjects with cancers (ICD-9-CM 140–172, 174–195.8, and 200–208), which occurred before hip fracture or those with pathological fractures (ICD-9-CM: 733.14 and 733.15) before hip fracture. Subjects who underwent surgery for injuries to the pelvis, femur, or hip region before the index day were also excluded to avoid confounding effects. Individuals with more than 28 cumulative CHM treatment days within the first year after a diagnosis of hip fracture were defined as CHM users (*n* = 650, **Figure 1**). The study subjects who did not receive any CHM were defined as nonusers of CHM (*n* = 5,355). In addition, to reduce bias due to confounding variables, nonusers were selected at a 1:1 ratio with CHM users *via* individual matching for age, gender, year of hip fracture diagnosis, and physical therapy. In total, 556 and 556 subjects were selected as CHM and nonusers, respectively (**Figure 1** and **Table 1**). The day on which the 28 cumulative days within 1 year of CHM treatment were completed was designated as the index date. In this study, distribution of the cumulative period of CHM treatment of CHM users within 365 days after the index date is shown in **Table S5**. The study endpoint for overall mortality was defined as the date of death, the date of withdrawal from the NHI program, or the date of termination of follow-up (December 31, 2012) (**Tables S3** and **S4**).

The study endpoint for readmission was defined as the date of the first medical readmission due to medical complications within 365 days after index date. Readmission within 365 days after index date may be caused directly or indirectly by the surgery itself. Readmission included medical complications occurring within 365 days after which extra days of hospital stay or readmission

to the hospital was required for additional treatment including stroke, acute myocardial infarction, pulmonary embolism, acute renal failure, or acute respiratory failure.

The study endpoint for reoperation was defined as the date of the first reoperation due to surgical complications within 365 days after index date. Reoperation included conversion to or revision of an arthroplasty, surgical site infection, removal of an implant due to complications, mechanical complications (including loss reduction, screw back-out or cut-out, skin irritation, and implant failure), dislocation, avascular necrosis of the femoral head, second hip fracture, and malunion/nonunion during the follow-up period.

The patient demographic characteristics are shown in **Table 1**, including age, gender, physical therapy, type of hip fracture, surgery type of hip fracture treatment, and comorbidities. We identified comorbidities that had been diagnosed in the study subjects before or at the time of the index day, including hypertension (ICD-9-CM 401–405), diabetes (ICD-9-CM 250.0–250.3, and 250.7), heart diseases (ICD-9-CM 410–414), chronic obstructive pulmonary disease (ICD-9-CM 490–496), cerebrovascular diseases (ICD-9-CM 430–438), chronic liver diseases (ICD-9-CM 571.2, 571.4–571.6, 070.4, 070.5, and 070.7), and chronic renal diseases (ICD-9-CM 582, 583–583.7, 585, 586, and 588).

#### Chinese Herbal Medicine

There are two kinds of Chinese herbal medicine (CHM) products: herbal formulas and single herbs. Herbal formulas are composed of a combination of two or more herbs provided by knowledgeable traditional Chinese medicine (TCM) practitioners based on TCM or ancient medical books (**Table S1** and **Table S2**). Single herbs may be from plants, animals, or mineral sources. The codes for herbal formulas and single herbs were collected, grouped, and listed on the Taiwan NHI website (http://www.nhi.gov.tw/webdata/webdata. aspx?menu=21&menu\_id=713&webdata\_id=932). These CHM products in Taiwan are personally prescribed to patients for many kinds of ailments by experienced TCM doctors and are all manufactured by pharmaceutical manufacturers with Good Manufacturing Practice certifications. The main pharmaceutical manufacturers are Sun Ten Pharmaceutical Co. Ltd. (http://www.sunten.com.tw/), Chuang Song Zong Pharmaceutical Co. Ltd. (http://www.csz.com.tw/), Shang Chang Pharmaceutical Co. Ltd. (http://www.herb.com.tw/ about\_en.php), KO DA Pharmaceutical Co. Ltd. (http:// www.koda.com.tw/), and Kaiser Pharmaceutical Co. Ltd (http://www.kpc.com/). For CHM products, the frequency of prescriptions, frequency of users, person-years, percentage of people using that CHM, average drug dose (per day), and average duration of the prescription were calculated from the index date to the study end (**Table S1**).

#### Statistical Analysis

Categorical data are expressed as numbers and percentages. These include age, gender, physical therapy, type of hip fracture, surgery TABLE 1 | Demographic characteristics of total subjects and individual matched subjects of hip fracture patients.


*n, number; CHM, Chinese herbal medicine.* 

*Age, gender, physical therapy, type of hip fracture, surgery type of hip fracture, and comorbidities were expressed as categorical variable [number (%)].* 

*P values were obtained by chi-square test. Significant P values (P < 0.05) were highlighted in bold italic.*

type, and comorbidities including hypertension, diabetes, heart diseases, chronic obstructive pulmonary disease, cerebrovascular diseases, chronic liver diseases, and chronic renal diseases (**Table 1**). The significance of the differences of the categorical data was calculated using a chi-squared test (**Table 1**). A Cox proportional hazard model was applied to assess the hazard ratio (HR) of mortality for CHM users when compared with nonusers with adjustment for age, type of hip fracture, surgery type of hip fracture, and comorbidities (**Table 2**). Furthermore, a Fine and Gray's hazard model was performed to assess the hazard ratio (HR) of the risks of readmission and reoperation for CHM users when compared with nonusers with adjustment for age, type of hip fracture, surgery type of hip fracture, and comorbidities (**Tables 3** and **4**). The frequency and usage patterns of the 10 most common herbal formulas and single herbs used are shown in **Table S1**. Coprescriptions of single herbs and herbal formulas for hip fracture patients were shown by using the association rules (Yang et al., 2013) (**Table 5**). Association rule mining was computed using the ''arules\_1.6'' package of the R software (version 3.4.3). The Kaplan–Meier method, the log-rank test, and the Gray's test were performed to estimate the 365-day cumulative incidence of mortality, readmission, and reoperation according to CHM use (**Figure 2A–C**). Furthermore, for the risk of overall mortality, hip fracture patients were stratified according to age, physical therapy, type of hip fracture, and surgery type (**Figure 3A**). For the risk of readmission, the hip fracture patients were stratified according to age, physical therapy, type of hip fracture, and surgery type (**Figure 3B**). For the risk of reoperation, the hip fracture patients were stratified according to age, physical therapy, type of hip fracture, and surgery type (**Figure 3C**). The network analysis using Cytoscape (http://manual.cytoscape.org/ en/stable/Network\_Analyzer.html) was applied to explore the CHM network and the core treatments for these hip fracture patients from the NHIRD database in Taiwan. The red color indicates the herbal formula, and the green color indicates a single herb. The size of the circle represents the user number of each CHM. Larger circles mean higher frequencies of user numbers. The connection between CHMs represents user numbers for the CHM combinations. A more important connection between CHMs is indicated by a thicker and darker connection line. All *P* values less than 0.05 were considered to be statistically significant. All data management and statistical analyses were performed using SAS software (version 9.4; Statistical Analysis Software [SAS] Institute, Cary, NC, USA).

#### RESULTS

#### Demographic Characteristics of Study Patients

Overall, 19,803 hip fractures were diagnosed between 2000 and 2010 (**Figure 1**). Of these, 17,120 hip fracture patients 40 years

#### TABLE 2 | Cox proportional hazard models for overall mortality of hip fracture patients.


*HR, hazard ratio; 95% CI, 95% confidence interval; ND, not determined; COPD, chronic obstructive pulmonary disease; aHR, adjusted hazard ratio.*

*Age (years) was expressed as a continuous variable. Therefore, the number (%) for age was not determined. The risk of overall mortality increased with age (HR 1.09/year) in our study. Models adjusted for age, CHM use, type of hip fracture, surgery type of hip fracture, and comorbidities.*

*p value < 0.05 shown in bold italic.*

of age or older were enrolled between 2000 and 2010. Patients were further excluded due to cancers that occurred before the hip fracture (*n* = 1,800) and pathological fracture before the hip fracture (*n* = 12). These exclusions left 650 patients assigned to the CHM user group and 5,355 patients regarded as nonusers who did not use CHMs during the study period. As shown in **Table 1**, there were differences in age, gender, physical therapy, type of hip fracture, and comorbidities (hypertension, heart disease, chronic obstructive pulmonary disease, cerebrovascular diseases, and chronic renal diseases) between these two groups (total subjects; *P* < 0.05; **Table 1**). After individual matching of subjects in the CHM user group and nonuser group for age, gender, hip fracture diagnosed years, and physical therapy, 556 and 556 patients were included in the two groups, respectively (**Figure 1**). There were no significant differences between the two matched groups (**Table 1**; *P* > 0.05).

#### Cumulative Incidence and Cox Proportional Hazard of Overall Mortality Between Chinese Herbal Medicine and Non-Chinese Herbal Medicine Users in Hip Fracture Patients in Taiwan

The 365-day cumulative incidence of overall mortality was shown using the Kaplan–Meier survival curve (**Figure 2A**). A difference was identified in the probability of overall mortality between these two groups (log-rank test, *P* < 0.0001). The cumulative incidence of overall mortality was significantly lower in CHM users than in nonusers. A multivariate Cox proportional hazard model was performed to estimate the hazard ratio (HR) and 95% confidence interval (CI) of overall mortality associated with the CHM users and covariates among hip fracture patients. Compared with hip fracture patients who did not receive CHM treatment, those who did had a lower risk of overall mortality after adjustment for age, type of hip fracture, surgery type, and comorbidities TABLE 3 | Fine and Gray's hazard models for readmission risk in hip fracture patients.


*Age (years) was expressed as a continuous variable. Therefore, the number (%) for age was not determined.*

*Models adjusted for age, CHM use, type of hip fracture, surgery type of hip fracture, and comorbidities.*

*Death was used as a competing risk.*

*p value < 0.05 shown in bold italic.*

(aHR: 0.47, 95% CI: 0.30–0.73, *P =* 0.0009; **Table 2**). Compared with hip fracture patients who had hemiarthroplasty surgery, those who had internal fixation of fracture surgery had a higher risk of overall mortality (aHR: 2.47, 95% CI: 1.01–6.05, *P =*  0.0475; **Table 2**).

The HRs for overall mortalities of these hip fracture patients following division into subgroups according to age, physical therapy, type of hip fracture, and surgery type of hip fracture are shown (**Figure 3A**). Among these subgroups, the HRs for overall mortality risk among CHM users were lower than those of non-CHM users. Subgroup analysis showed that the use of CHM was associated with a protective effect in those who were aged 60 years or older (HR: 0.45, 95% CI: 0.27–0.74, *P =*  0.002), in those without physical therapy (HR: 0.54, 95% CI: 0.37–0.80, *P =* 0.002), in those with intracapsular fracture of the femoral neck (HR: 0.40, 95% CI: 0.19–0.83, *P =* 0.014), and in those who had hemiarthroplasty surgery (HR: 0.13, 95% CI: 0.04–0.48, *P =* 0.002).

#### Cumulative Incidence and Fine and Gray's Hazard for Readmission Risk Between Chinese Herbal Medicine and Non-Chinese Herbal Medicine Users in Hip Fracture Patients in Taiwan

The 365-day cumulative incidence of readmission was illustrated by the Kaplan–Meier survival curve (**Figure 2B**). The readmission outcome was observed by using death as the competing risk. The cumulative incidence of readmission was significantly lower in CHM users than in nonusers (Readmission: Gray's test, *P =* 0.0288). A multivariate Fine and Gray's proportional hazard model was also applied to estimate the hazard ratio (HR) and 95% confidence interval (CI) of readmission associated with the CHM users and covariates among the hip fracture patients using death as the competing risk (**Table 3**). Compared with the hip fracture patients who did not receive CHM treatment, CHM users had a lower risk of readmission than nonusers after adjustment for age, type of hip fracture,

#### TABLE 4 | Fine and Gray's hazard models for reoperation risk in hip fracture patients.


*Age (years) was expressed as a continuous variable. Therefore, the number (%) for age was not determined.*

*Models adjusted for age, CHM use, type of hip fracture, surgery type of hip fracture, and comorbidities.*

*Death was used as a competing risk.*

*p value < 0.05 shown in bold italic.*

TABLE 5 | Ten most commonly used pairs of CHM products for hip fracture patients in Taiwan.


*CHM, Chinese herbal medicine; LHS, left-hand side; RHS, right-hand side.*

*Support (X) (%) = Frequency of prescriptions of X and Y products/total prescriptions × 100%.*

*Confidence (X* → *Y) (%) = Frequency of prescriptions of X and Y products/Frequency of prescriptions of X product × 100%.*

*P (Y) (%) = Frequency of prescriptions of Y product/total prescriptions × 100%.*

*Lift = Confidence (X* → *Y) (%)/P (Y) (%).*

surgery type, and comorbidities (aHR: 0.67, 95% CI: 0.46–0.97, *P =* 0.0345; **Table 3**). Compared with the hip fracture patients who had intertrochanter fracture of the femur, patients who had an intracapsular fracture of the femoral neck had a higher risk of readmission (aHR: 2.34, 95% CI: 1.23–4.43, *P =* 0.0094; **Table 3**). Compared with the hip fracture patients who had hemiarthroplasty surgery, patients who underwent internal fixation had a higher risk of readmission (aHR: 2.38, 95% CI: 1.13–5.01, *P =* 0.0228; **Table 3**).

The HRs for readmission of these hip fracture patients following division into subgroups according to age, physical therapy, type of hip fracture, and surgery type are shown (**Figure 3B**). Among these subgroups, the HRs for the risk of readmission among CHM users were lower than those of non-CHM users. Subgroup analysis for the HR for readmission showed that use of CHM was associated with a protective effect in those who were aged 60 years or older (HR: 0.59, 95% CI: 0.42–0.83, *P =* 0.003), in those without physical therapy (HR: 0.56, 95% CI: 0.40–0.78, *P* < 0.001), in both types of hip fracture (HR: 0.52, 95% CI: 0.28–0.97, *P =* 0.039 and HR: 0.53, 95% CI: 0.34–0.83, *P =* 0.005, respectively), and in those who had hemiarthroplasty surgery (HR: 0.32, 95% CI: 0.16–0.64, *P =* 0.002) (**Figure 3B**).

#### Cumulative Incidence and Fine and Gray's Hazard for Reoperation Risk Between Chinese Herbal Medicine and Non-Chinese Herbal Medicine Users in Hip Fracture Patients in Taiwan

The 365-day cumulative incidence of reoperation was illustrated by the Kaplan–Meier survival curve (**Figure 2C**). The reoperation outcome was observed by using death as the competing risk. The cumulative incidence of reoperation was significantly lower in CHM users than in nonusers (Reoperation: Gray's test, *P* < 0.0001). A multivariate Fine and Gray's proportional hazard model was also applied to estimate the hazard ratio (HR) and 95% confidence interval (CI) of reoperation associated with the CHM users and covariates among the hip fracture patients using death as the competing risk (**Table 4**). Compared with the hip fracture patients who did not receive CHM treatment, CHM users had a lower risk of reoperation than nonusers after adjustment for age, type of hip fracture, surgery type, and comorbidities (aHR: 0.57, 95% CI: 0.40–0.79, *P =* 0.0009; **Table 4**). Compared with the hip fracture patients who had intertrochanter fracture of femur, the patients who had intracapsular fracture of the femoral neck had a higher risk of reoperation (aHR: 1.93, 95% CI: 1.01–3.66, *P =* 0.0456;


**Table 4**). Compared with the hip fracture patients who had hemiarthroplasty surgery, the patients who underwent internal fixation had a higher risk of reoperation (aHR: 2.31, 95% CI: 1.20–4.44, *P =* 0.0118; **Table 4**). There were significantly higher risks of reoperation among the hip fracture patients who had comorbidities such as diabetes (aHR: 2.11, 95% CI: 1.20–3.73, *P =* 0.0098; **Table 4**) and cerebrovascular diseases (aHR: 2.58, 95% CI: 1.41–4.69, *P =* 0.0020; **Table 4**).

The HRs for reoperation of these hip fracture patients following division into subgroups according to age, physical therapy, type of hip fracture, and surgery type are shown (**Figure 3C**). Of these subgroups, the HRs for the risk of reoperation among CHM users were lower than those of non-CHM users. Subgroup analysis for the HR for reoperation showed that use of CHM was associated with a protective effect in those who were aged 60 years or older (HR: 0.67, 95% CI: 0.45–0.98, *P =* 0.040), in



FIGURE 3 | Subgroup analysis for the risk of overall mortality, readmission, and reoperation in hip fracture patients. (A) Hazard ratios (HRs) and 95% confidence intervals (CI) for overall mortality were adjusted for confounding factors and stratified by age, physical therapy, type of hip fracture, and surgery type. The event and total number of each subgroup between CHM and non-CHM users are also shown. (B) HRs and 95% CI for readmission were adjusted for confounding factors and stratified by age, physical therapy, type of hip fracture, and surgery type. The event and total number of each subgroup between CHM and non-CHM users are also shown. (C) HRs and 95% CI for reoperation were adjusted for confounding factors and stratified by age, physical therapy, type of hip fracture, and surgery type of hip fracture. The event and total number of each subgroup between CHM and non-CHM users are also shown.

those without physical therapy (HR: 0.52, 95% CI: 0.36–0.76, *P =* 0.001), and in those who had hemiarthroplasty surgery (HR: 0.31, 95% CI: 0.12–0.81, *P =* 0.016) (**Figure 3C**).

#### Most Commonly Prescribed Chinese Herbal Formulas and Single Herbs by Traditional Chinese Medicine Doctors for the Treatment of Hip Fracture Patients

The 10 most commonly prescribed herbal formulas and 10 single herbs used for the treatment of hip fracture patients are listed (**Table S1**). The compositions of these CHM products are also presented (**Table S2**). According to the frequency of prescription, Shu-Jing-Huo-Xue-Tang (SJHXT) (40.8%) was the most commonly prescribed herbal formula. The second and third most common formulas were Du-Huo-Ji-Sheng-Tang (DHJST) (37.2%) and Ma-Zi-Ren-Wan (MZRW) (25.2%). Yan-Hu-Suo (YHS) [*Corydalis yanhusuo* (Y.H. Chou and Chun C. Hsu) W.T. Wang ex Z.Y. Su and C.Y. Wu, 36.2%] was the most commonly prescribed single herb, followed by Dan-Shen (DS) (*Salvia miltiorrhiza* Bunge, 31.1%) and Niu-Xi (NX) (*Achyranthes bidentata* Blume, 35.6%).

The coprescription patterns of the most commonly used CHM products were also studied in hip fracture patients by using association rules (**Table 5**). The support (%), confidence (%), and lift of the association rules of these 10 most commonly used pairs were explored. The coprescription patterns with higher values of support, confidence, and lift were more strongly correlated in hip fracture patients. As shown in **Table 5**, for hip fracture patients, the CHM coprescription pattern (Du-Zhong (DZ) → Xu-Duan (XD); support: 2.5%, confidence: 39.8%, lift: 6.3) had the highest value of support data, which suggested that this coprescription pattern had the most significant association for the treatment of hip fracture, followed by Du-Huo-Ji-Sheng-Tang (DHJST) → Shu-Jing-Huo-Xue-Tang (SJHXT) (second coprescription; support: 2.4%, confidence: 24.1%, lift: 2.2) and Gu-Sui-Bu (GSB) → Xu-Duan (XD) (third coprescription; support: 2.1%, confidence: 38.2%, lift: 6.0).

To further explore the CHM network for hip fracture patients, their coprescription patterns and networks were identified. These networks highlight the complicated relationships among the CHM products (**Figure 4**). There were 556 hip fracture patients who used CHM products and 20,326 prescriptions were provided by TCM doctors (**Table 4**). In addition, two clusters were identified by the association rule and network analysis (**Table 5** and **Figure 4**). Cluster 1 was the largest CHM cluster, and the major CHM in this cluster was different compared with cluster 2. In cluster 1, XD was the core CHM, and DZ, GSB, SJHXT, and DHJST were important CHMs. Among cluster 1, DZ, XD, and GSB had significant associations with each other according to the support, confidence, and lift values (DZ → XD: support: 2.5%, confidence: 39.8%, lift: 6.3; GSB → XD: support: 2.1%, confidence: 38.2%, lift: 6.0) (**Table 5** and **Figure 4**). In cluster 2, XFZYT was the core CHM, and DS, DH, GLY, and BXXXT were important CHMs. Among cluster 2, DH, DS, and XFZYT had significant associations with each other according to the support,

confidence, and lift values (DH → DS: support: 1.4%, confidence: 21.2%, lift: 2.9; XFZYT → DS: support: 1.3%, confidence: 29.0%, lift: 4.0) (**Table 5** and **Figure 4**).

#### Discussion

In this retrospective, population-based, case–control study, we investigated the demographic characteristics, cumulative incidence of overall mortality, readmission, reoperation, and patterns of CHM prescription in hip fracture patients in Taiwan. We found that CHM usage was associated with lower risks of overall mortality, readmission, and reoperation after adjustment for age, type of hip fracture, surgery type, and comorbidities. We also identified the herbal formulas, single herbs, and prescription patterns for the treatment of hip fracture by using association rule mining. Therefore, this study provides evidence of clinical CHM use as adjunctive therapy benefiting hip fracture patients.

We recruited hip fracture patients, 40 years of age or older, who underwent surgeries for hip fracture. Notably, about 85% of these patients were more than 60 years of age and about 56% were female. The risk of hip fracture is greater in postmenopausal women and seniors and is probably related to osteoporosis (Metcalfe, 2008). Osteoporosis is one of the most common types of bone diseases, resulting from an imbalance between bone formation and resorption (Infante and Rodriguez, 2018). It is characterized by a degeneration of the bone microstructure, reduction of bone mass, and higher fracture risks. As CHM is cost-effective with relatively few side effects and has been widely applied for clinical use in Asian countries, it has been previously used for the clinical treatment of osteoporosis and bone fracture in Taiwan (Shih et al., 2012; Liao et al., 2015). Indeed, there are several Chinese herbs that help maintain bone health by regulating bone metabolism (Chow et al., 1982; Chen et al., 2005; Li et al., 2011; Ma et al., 2011; Xiang et al., 2011; Wong et al., 2013; He and Shen, 2014; Zhang et al., 2016; Hsiao et al., 2017; Wang et al., 2018c; Xi et al., 2018). Our pharmacoepidemiologic results have demonstrated that for the patients who were above 60 years old, there was a significant distribution difference in the cumulative overall mortality between CHM and non-CHM users (**Table S3** and **Table S4**). Our results showed the protective effects of clinically used CHM on mortality and outcomes after surgeries in hip fracture patients.

Among the most commonly used pairs of CHM products for hip fracture patients, the CHM coprescription pattern Du-Zhong → Xu-Duan (support: 2.5) resulted in the highest support, followed by Du-Huo-Ji-Sheng-Tang → Shu-Jing-Huo-Xue-Tang (second coprescription; support: 2.4), and Gu-Sui-Bu → Xu-Duan (third coprescription; support: 2.1). Du-Zhong (DZ; *Eucommiae cortex*) is the dried trunk bark of *Eucommia ulmoides* Oliv., of the Eucommiaceae family. Du-Zhong (DZ) has been used for the treatment of fractures, osteoporosis, and rheumatoid arthritis (Shih et al., 2012; Gao et al., 2013; Liao et al., 2015; Wu et al., 2017; Qi et al., 2018; Wang et al., 2018a). Studies have reported that extracts of Du-Zhong exhibit anti-inflammatory, antitumor, collagen synthesizing, and antiosteoporotic properties (Li et al., 2000; Ha et al., 2003; Kim et al., 2012; Kang et al., 2013; Tan et al., 2014; Li et al., 2016; Wang et al., 2016a; Zhou et al., 2016; Koh et al., 2017). Natural compounds of Du-Zhong, including 5-(hydroxymethyl)-2-furaldehyde and chlorogenic acid, show antiosteoporotic activity *via* promoting osteoblast-like cell proliferation and osteoclast inhibition (Tan et al., 2014; Zhou et al., 2016).

Xu-Duan (XD; *Radix Dipsaci*) is the dried root of *Dipsacus asperoides* C.Y. Cheng and T.M.Ai of the Teasel family. Xu-Duan (XD) has been used for the treatment of fractures, osteoporosis, and rheumatoid arthritis (Liu et al., 2009; Peng et al., 2010; Jung et al., 2012; Liu et al., 2012; Shih et al., 2012; Liao et al., 2015; Ke et al., 2016; Li et al., 2016). Treatment of Xu-Duan extracts have exhibited anti-inflammatory, antiarthritic, and antiosteoporotic activities (Wong et al., 2007; Liu et al., 2009; Kim et al., 2011; Jung et al., 2012; Niu et al., 2015a). Natural compounds of Xu-Duan, including asperosaponin VI and saponins, are involved in bone metabolism (Niu et al., 2012; Niu et al., 2015b; Ke et al., 2016). Asperosaponin VI promotes osteogenic differentiation through the phosphoinositide-3-kinase/AKT serine/threonine kinase (PI3K/AKT) signaling pathway in bone marrow stromal cells (Ke et al., 2016). Saponins from Xu-Duan exert an effect on osteoblastic maturation and differentiation through the bone morphogenetic protein (BMP)-2/mitogen-activated protein kinase/Smad1/5/8-dependent Runx2 signaling pathways in MC3T3-E1 mouse osteoblast precursor cells (Niu et al., 2015b).

Gu-Sui-Bu (GSB; *Drynariae rhizoma*) is the dried rhizome of *Drynaria fortunei* (Kunze ex Mett). J.Sm. of the Polypodiaceae family. Gu-Sui-Bu (GSB) has been used for the treatment of fractures, osteoporosis, rheumatoid arthritis, and head injuries (Shih et al., 2012; Wang et al., 2012; Saravanan et al., 2013; Liao et al., 2015). Studies have reported that extracts of Gu-Sui-Bu exhibit immune-promoting, anti-inflammatory, antiosteoporotic, and neuroprotective activities (Anuja et al., 2010; Chen et al., 2011b; Wang et al., 2012; Saravanan et al., 2013; Kang et al., 2014; Wang et al., 2016b). The natural compounds of Gu-Sui-Bu include naringin and flavonoids (Wang et al., 2008; Chen et al., 2011b). Naringin from Gu-Sui-Bu increases the proliferation and differentiation of MC3T3-E1 osteoblastic cells (Chen et al., 2011b). Flavonoids from Gu-Sui-Bu show proliferative activity in UMR106 osteoblast-like cells (Wang et al., 2008).

Du-Huo-Ji-Sheng-Tang (DHJST) is composed of 15 single herbs. DHJST has been used for the treatment of fractures, osteoporosis, osteoarthritis, aging in the elderly, rheumatoid arthritis, and stroke in type 2 diabetes (Chen et al., 2011a; Shih et al., 2012; Chen et al., 2014; Liao et al., 2015; Yang et al., 2015; Chen et al., 2016; Tsai et al., 2017a; Wang et al., 2017). Studies have reported that DHJST extracts promote osteogenic differentiation, antiaging, anti-inflammatory activities, and therapeutic effects in osteoarthritis (Chen et al., 2011a; Yang et al., 2015; Chen et al., 2016; Wang et al., 2017). The natural compound *Ligusticum chuanxiong* from DHJST increases osteogenic activity in human mesenchymal stem cells by up-regulating BMP-2 and RUNX2 expression *via* SMAD 1/5/8 and ERK signaling and also delays the cell aging process by decreasing cell senescence in human mesenchymal stem cells (Wang et al., 2017).

Shu-Jing-Huo-Xue-Tang (SJHXT) is composed of 17 single herbs. SJHXT has been used for the treatment of fractures, osteoporosis, adjuvant arthritis, prostate cancer, breast cancer, hypertension, and type 2 diabetes (Kanai et al., 2003; Shu et al., 2010; Lin et al., 2012; Tsai et al., 2014; Liao et al., 2015; Tsai et al., 2017a, Tsai et al., 2017b, Tsai et al., 2017c). SJHXT extracts showed antihypersensitivity and pain relief effects by increasing blood circulation (Kanai et al., 2003; Shu et al., 2010). The natural compounds constituting SJHXT include ferulic acid and paeoniflorin. Ferulic acid promotes osteogenesis in bone marrow mesenchymal stem cells and suppresses osteoclast differentiation (Du et al., 2017; Doss et al., 2018). Paeoniflorin has a significant anti-inflammatory effect on rheumatoid arthritis (Lai et al., 2018; Xu et al., 2018). Paeoniflorin also shows antiosteoporosis activity and regulates osteoclastogenesis and osteoblastogenesis (Li and Chent 2018b; Wang et al., 2018b).

In conclusion, this study demonstrated that the CHM users had lower hazard ratios for the risk of overall mortality, readmission, and reoperation when compared with CHM nonusers among hip fracture patients. Based on association rule mining, Du-Zhong → Xu-Duan were most strongly associated with each other for the specific treatment of hip fractures. The use of CHM as an adjunctive therapy may reduce the risks of overall mortality, readmission, and reoperation; therefore, further clinical and experimental studies should be performed to optimize the safety and efficacy of CHM use in these patients.

### ETHICS STATEMENT

This database also offers longitudinally linked data for the period from 1996 to 2012. All personal data were decoded for identity, so we were unable to obtain informed consent. The study was approved by the Institutional Review Board of China Medical University Hospital.

## AUTHOR CONTRIBUTIONS

C-FC, JC-FL, F-JT, W-ML, and Y-JL conceived and designed the experiments. C-FC, T-HL, C-CL, and S-MH performed the experiments. C-FC and M-JL analyzed the data. T-ML, XL, BB, and Y-JL contributed with reagents/materials/analysis tools. W-ML and Y-JL wrote the manuscript. All of the authors have read and approved the final manuscript.

## FUNDING

This study was supported by grants from China Medical University (CMU107-S-13 and CMU107-S-15), China Medical University Hospital (DMR-107-042, DMR-108-113, DMR-108- 114, and DMR-108-118), and the National Science Council, the Ministry of Science and Technology, Taiwan (MOST 105-2314-B-039-037-MY3, MOST 106-2320-B-039-017-MY3).

#### ACKNOWLEDGMENTS

This study was based in part on data obtained from the National Health Insurance Research Database (NHIRD) provided by the Bureau of National Health Insurance, Department of Health and managed by the National Health Research Institutes (NHRI). The interpretation and conclusions contained herein do not represent those of the National Health Insurance Administration, Department of Health, or NHRI. The authors wish to thank the Aim for Top University Plan of the Ministry of

#### REFERENCES


Education, Taiwan, at China Medical University. We also thank Dr. Kuan-Teh Jeang and Willy W.L. Hong for their technical help and suggestions.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.00629/ full#supplementary-material

RANKL-induced NF-kappaB and NFATc-1 signaling pathways. *BMC Complement Altern. Med.* 17, 245. doi: 10.1186/s12906-017-1737-9


rats involving enhanced activation of the AC10/cAMP/PKA/CREB pathway. *J. Ethnopharmacol.* 223, 76–87. doi: 10.1016/j.jep.2018.05.023


implication for antiosteoporotic drug discovery. *J. Ethnopharmacol.* 189, 61–80. doi: 10.1016/j.jep.2016.05.025

Zhou, R. P., Lin, S. J., Wan, W. B., Zuo, H. L., Yao, F. F., Ruan, H. B., et al. (2016). Chlorogenic acid prevents osteoporosis by Shp2/PI3K/Akt pathway in ovariectomized rats. *PLoS One* 11, e0166751. doi: 10.1371/journal. pone.0166751

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Cheng, Lin, Tsai, Li, Lin, Liao, Huang, Liu, Li, Ban, Liang and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# A Novel Antiplatelet Aggregation Target of Justicidin B Obtained From *Rostellularia Procumbens* (L.) Nees

*Yan-Fang Yang1,2,3†, Song-Tao Wu1\*†, Bo Liu1,2,3, Zhou-Tao Xie1, Wei-Chen Xiong1, Peng-Fei Hao1, Wen-Ping Xiao1, Yuan Sun1, Zhong-Zhu Ai1,2,3, Peng-Tao You1,2,3 and He-Zhen Wu1,2,3\**

*1 Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China, 2 Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Wuhan, China, 3 Collaborative Innovation Center of Traditional Chinese Medicine of New Products for Geriatrics Hubei Province, Wuhan, China*

*Edited by: Yuanjia Hu, University of Macau, China*

#### *Reviewed by:*

*Jiachun Chen, Huazhong University of Science and Technology, China Qun Wei, Beijing Normal University, China Zhou Benhong, Wuhan University, China*

#### *\*Correspondence:*

*Song-Tao Wu songt1wu@163.com He-Zhen Wu hezh\_wu@163.com*

*†These authors have contributed equally to this work.*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 22 January 2019 Accepted: 27 May 2019 Published: 14 June 2019*

#### *Citation:*

*Yang Y-F, Wu S-T, Liu B, Xie Z-T, Xiong W-C, Hao P-F, Xiao W-P, Sun Y, Ai Z-Z, You P-T and Wu H-Z (2019) A Novel Antiplatelet Aggregation Target of Justicidin B Obtained From Rostellularia Procumbens (L.) Nees. Front. Pharmacol. 10:688. doi: 10.3389/fphar.2019.00688*

The present study explored the possible bioactive ingredients and target protein of *Rostellularia procumbens* (L.) Nees. Firstly, we found that the ethyl acetate extraction obtained from *R. procumbens* could inhibit platelet aggregation. Then, gene chip was used to investigate differentially expressed genes and blood absorption compounds were investigated using high performance liquid chromatography-mass spectrometry characterization (LC-MS). Depending on the results of gene chip and LC-MS, the targets of blood absorption compounds were predicted according to the reverse pharmacophore matching model. The platelet aggregation-related genes were discovered in databases, and antiplatelet aggregation-related gene targets were selected through comparison. The functions of target genes and related pathways were analyzed and screened using the DAVID database, and the network was constructed using Cytoscape software. We found that integrin αIIbβ3 had a highest degree, and it was almost the intersection of all pathways. Then, blood absorption compounds were screened by optical turbidimetry. Western blot (WB) revealed that justicidin B separated from the ethyl acetate fraction may inhibit the expression of integrin αIIbβ<sup>3</sup> protein. For the first time, we used Prometheus NT.48 and MST to detect the stability of this membrane protein to optimize the buffer and studied the interaction of justicidin B with its target protein. To our best knowledge, this is the first report to state that justicidin B targets the integrin αIIbβ3 protein. We believe that our findings can provide a novel target protein for the further understanding of the mechanism of *R. procumbens* on platelet aggregation.

Keywords: justicidin B, integrin **αII**b**β**3, platelet aggregation, gene chip, LC-MS, network pharmacology, Prometheus NT.48, microscale thermophoresis

#### INTRODUCTION

Integrin αIIbβ3 is a major platelet-surface receptor for the regulation of platelet aggregation and thrombosis. Fibrinogen is attached to the integrin αIIbβ3 protein of one platelet and is linked to the integrin αIIbβ3 protein of another platelet. At the same time, the platelets are grouped together to cause a platelet aggregation cascade. Therefore, integrin αIIbβ3 is a key protein for platelet aggregation (Coller and Shattil, 2008; Yun et al., 2016; Andrews and Gardiner, 2017; Bender et al., 2017; Chatterjee and Gawaz, 2017).

*Rostellularia procumbens* (L.) Nees (Acanthaceae) is widely distributed in the Taiwan Province and the southwest provinces and has been proven to have a huge potential for the development of Chinese medicine owing to its plant resources, chemical constituents, pharmacological action, and clinical application. It has complex chemical composition (Savithramma et al., 2007; Joshi and Joshi, 2000; Committee, 2011).

In addition, reports have shown that *R. procumbens* has significant pharmacological properties such as anti-viral and anti-tumoral (Chen et al., 1995; Corrêa and Alcântara, 2012). It has also been noted that aqueous extracts of *R. procumbens* decrease platelet aggregation (Chen et al., 1996). According to preliminary experiments, ethyl acetate extract is the active fraction. Animal experimentation conducted analyze this fraction. Then, gene chip was used to investigate differentially expressed genes. Blood absorption compounds were investigated using LC-MS. Targets of blood absorption compounds were predicted according to the reverse pharmacophore matching model. The platelet aggregation-related genes were found in databases, and antiplatelet aggregation-related gene targets were selected through comparison. The functions of target genes and related pathways were analyzed and screened using the DAVID database, and the network of antiplatelet aggregation effect of blood absorption compounds was constructed using Cytoscape software. However, the detailed molecular interaction between justicidin B and its target is still unknown. Firstly, blood absorption compounds were screened by optical turbidimetry. Prometheus NT.48 is used to detect protein stability and screen buffer (Maschberger et al.). Then, we compared the two models of microscale thermophoresis (MST) and used NT.115 to verify the interaction between the compound and the integrin αIIbβ3 protein (van den Bogaart et al., 2012; Batoulis et al., 2016; Sparks and Fratti, 2019; Vinothkannan Ravichandran et al., 2018).

In this study, according to preliminary experiments, blood absorption compounds of *R. procumbens* were screened by serum pharmacological. Gene chip and network pharmacology were used to find the target. Then, the interaction between justicidin B and the membrane protein integrin αIIbβ3 was verified by Western blot (WB) and MST. It would lay the groundwork for understanding the molecular mechanism involved in the inhibition of platelet aggregation by *R. procumbens*.

#### MATERIALS AND METHODS

#### Chemical and Materials

The ethyl acetate extract was obtained from the Key Laboratory for Traditional Chinese Medicine Resources and Chemistry of Hubei Province (Chen, 2012; Su, 2014; Wu et al., 2013) [ethyl acetate (EtOAc), Lichrosolv, purity ≥ 99.9%]. Acetonitrile (ACN) was of LC-MS grade and was purchased from Thermo-Fisher (Pittsburgh, PA, USA). Deionized water was made available in-lab using a Milli-Q purification instrument (Millipore, Bedford, MA, USA). 5-Hydroxytryptophan was obtained from National Institutes for Food and Drug Control (China). Thrombin was purchased from BioMed Lublin, Poland. Arachidonic acid (AA), bovine serum albumin (BSA), adenosine diphosphate (ADP), β-acetyl-γ-O-hexadecyl-L-α-phospharidylcholine hydrate (PAF), 12-O-tetradecanoylphorbol-13-acetate (PMA), and dimethyl sulfoxide (DMSO) were purchased from Sigma (St. Louis, MO, USA). RNeasy Mini Kit and RNase-free DNase I were purchased from QIAGEN. Pico Reagent Kit, GeneChip90 Hybridization, Wash, and Stain Kit were purchased from Affymetrix. Integrin αIIbβ3 monoclonal antibody was purchased from SAB (Maryland, USA), and β-actin and anti-mouse IgG were purchased from Cell Signaling (Boston, USA).

#### Experimental Animals

SD male rats (190–230 g) were randomly divided into two groups of 16 animals per group, treated orally as follows: control group received 0.5% CMC-Na, groups of ethyl acetate extract (97.20 g/ml) (fasting for 12 h before intragastric administration, administered by intragastric administration at 1 ml/200 mg twice a day for 3 days). After the last administration for 1.5 h, we took 5–8 ml of blood from the femoral artery. All experimental procedures were approved by Animal Care and Use Committee of Institute of Materia Medica, People's Republic of China.

#### Extraction and Detection of Total RNA in Two Groups of Platelets

The platelet of the control group and ethyl acetate group at 4°C and 12,000 rpm was centrifuged for 10 min using RNeasy Mini Kit separation of total RNA. The RNA concentration and A260/A280 ratio were determined using an SMA 3000 microspectrophotometer (Meriton, China). The results showed that the ratio was 2.00:2.05, indicating a high purity of the extracted RNA, which was deemed suitable for subsequent analysis. After this, a total of 1 µg RNA was used for 1% agarose gel electrophoresis. The ratio of 28S/18S was then determined using a JS-380A Bioanalyzer (Shanghai, China) to determine the quality of RNA. If the RNA integrity number (RIN) > 7.0 and 28S/18S > 0.7, samples were transcribed by a Pico Reagent Kit. Following this, samples were prepared using a GeneChip90 Hybridization, Wash, and Stain Kit (Affymetrix, Thermo Fisher Scientific, Inc.). Then, chips were scanned using a GeneChip Scanner 3000 (Affymetrix, Thermo Fisher Scientific, Inc.).

### Detection of Blood Absorption Compounds

Justicidin B, chinensinaphthol methyl ether, and 6'-hydroxy justicidin B were weighed accurately, and 1 mg/ml control solution with methanol was prepared. The control solution was stored at 4°C for use. The supernatant that was treated by solid phase microextraction cartridge was collected and dried by nitrogen after centrifugation (12,000 r/min, 10 min). Before the liquid injection, 200 μl acetonitrile was used for redissolution and the supernatant was determined by HPLC-DAD-ESI-MS after filtration.

The chromatography analysis was performed on an Agilent XDB-C18 column (150 mm × 4.6 mm, 4 µm) at 25°C; the mobile phase was a mixture of aqueous solutions containing water (A) and acetonitrile (B). The gradient elution procedure was made as follows: 0–14 min, 26% B; 14–51 min, 26–38.5% B; 51– 80 min, 38.5% B. The injection volume was 100 μl, and the flow rate was 1 ml/min. The wavelength of 190–690 nm was used for the detection. Mass spectrometry detection was set as follows: capillary temperature, 200°C; source voltage, 4.5 kV for the positive ion mode. The mass range was from 50 to 1,600. Blood absorption compounds were identified by accurate mass, MS/MS ion fragment pattern, and retention time of LC and then were validated by available standard.

#### Prediction and Analysis of Differentially Expressed Target Genes

Based on the results of the gene chip and LC-MS, we conducted a network pharmacology study. The PharmMapper Server (http:// lilab.ecust.edu.cn/pharmmapper/index.php) is a freely accessed web server used to identify potential target candidates for given probe blood absorption compounds using a pharmacophore mapping approach. GeneCards (https://www.genecards.org/) and MalaCards (http://www.malacards.org/) were used for potential target screening from the results of gene chip. All potential target genes were collected and uploaded to the DAVID (https://david.ncifcrf.gov/summary.jsp) database. Following this, the functions and signaling pathways of target genes as well as pathway enrichment were investigated *via* Gene Ontology (GO; http://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses (http://www.genome.jp/ kegg/ko.html).

#### Construction of the Network

The blood absorption compounds and target genes were imported into Cytoscape 3.6.1 software to build an active compound/ target gene/pathway network and an active compound/platelet aggregation-related target gene network. The blood absorption compounds and target genes were input as the node. If there was a connection between two nodes, edge was used to show the connection.

The network was then analyzed with the network analyze function. High degree gene targets in the protein interaction network were analyzed. According to the results of KEGG enrichment, the pathways with higher counts were selected to analyze their key targets. Meanwhile, the genes suitable for the analysis of the targets were obtained through comparative analysis from the literature and database.

#### Screening of Active Compounds Inhibiting Platelet Aggregation

Platelet-rich plasma (PRP) was prepared by centrifugation of fresh blood at 200×*g* for 10 min at room temperature and aspirating the supernatant. Platelet-poor plasma was then sedimented by centrifugation of residual blood at 800×*g* for 10 min at room temperature. Blood platelet aggregation was monitored by platelet turbidity, with 0% aggregation calibrated as the absorbance of platelet-poor plasma and 100% aggregation as the absorbance of PRP. PRP was incubated with the drug at 37°C for 20 min and then stimulated with AA, ADP, thrombin,

PAF, and PMA. The aggregation of PRP (preincubated with the tested plant fraction) in response to 10 µM inducer was recorded using an aggregometer (LBY-NJ4).

### Western Blotting to Detect Integrin **αII**b**β**<sup>3</sup>

Just like 2.7, we got PRP what were split into four groups: blank control group, inducer group, justicidin B induction group, and the last group received aspirin. Different groups were cultured 1.5 h, and the incubator was set to 5% CO2 at 37°C. Then, platelets were washed with PBS twice, harvested, and lysed in RIPA buffer containing protease inhibitors on ice for 60 min. The protein concentration was determined by the BCA method. Next, protein samples (18 μg) were equally loaded onto SDS-PAGE and electrotransferred to PVDF membranes. Subsequently, the membranes were blocked with 5% non-fat milk for 1 h and incubated overnight with primary antibodies. The membranes were washed with TBST buffer three times and then incubated with secondary antibodies for 1 h at 25°C. The membranes were then rinsed three times with blocking solution and visualized by the ECL detection system.

#### NT. LabelFree Analysis

Since integrin αIIbβ3 is a membrane protein, we used NT. LabelFree to prevent the label from affecting the results. Titration series with ligand concentrations varying between 0 and 1,000mM were prepared in the Prometheus NT.48 optimized buffer (**Figure S1**). SD-test (the automatic pre-detection of MST to detect the stability of protein signal) was required before measurement.

Approximately, 3 µl was loaded into NT.LabelFree standardtreated capillaries (Nanotemper). MST experiments were performed at 40% MST (infra-red laser) power and 60% LED power at 25°C using the Monolith NT.LabelFree Instrument (Nanotemper). Ratios between normalized initial fluorescence and after temperature-jump and thermophoresis were calculated and averaged from five to nine independent runs. Means of fluorescence intensity obtained by the MST measurements were fitted, and the resultant Kd values were given together with an error estimation from the fit by the built-in formula of the analysis software.

#### NT.115 Analysis

The fluorescence of the ligand interfered with the result. This was further exacerbated when using label-free thermophoresis owing to the additional noise present in measuring fluorescence in ranges where inherent fluorescence of the protein itself is measured. After the SD-test experiment verified that the label had little effect on the protein, we used NT.115 for measurement.

All the compounds were analyzed with the concentration gradient of 50 µM with 20 µM of CviR that was labeled Monolith NTTM Protein Labeling Kit RED–NHS (Cat Nr: L001) before instrumental analysis. LED power was 20% and Prometheus NT.48 optimized buffer was used only for the analysis.

Analysis was performed on Monolith Nano Temper (NT)115 and its accessory, i.e., standard-treated 4-µl volume glass capillaries were employed to measure the molecular interaction (Nano Temper Technologies GmbH, Munich, Germany). Means of fluorescence intensity obtained by the MST measurements were fitted, and the resultant Kd values were given together with an error estimation from the fit by the built-in formula of NT1.5.41 analysis software.

### RESULTS

#### Microarray Results

Compared with the control group, hundreds of genes were revealed to be differentially expressed in platelet in EtOAc extract group (**Table 1**). In EtOAc extract group, PLCB2, PRKCA, GNAQ, MAPK10, MAPK8, MAPK11, MAPK14, GNAI2, PIK3CG, and PIK3R1 were markedly down-regulated in the model group compared with the control group.

#### The Blood Absorptions Compounds of EtOAc Extract

All samples were analyzed by LC-MS according to chromatographic conditions. By comparing the relative retention time and UV absorption spectra of the chromatographic peaks of the test solution and the mixed control solution, the information of the mass spectrometry fragmentation, combined with the preliminary research results and literature of the laboratory, we can confirm that 6'-hydroxy justicidin B, justicidin B, and chinensinaphthol methyl ether are the prototype compounds of the ethyl acetate extract in the blood (**Figure 1**, **Table 2**).

#### Prediction and Analysis of the Target Genes

All potential target genes were synthesized and uploaded to the DAVID database for KEGG pathway annotation and GO enrichment. The threshold was set as P ≤ 0.05, and the pathways or gene functions with higher count were analyzed. The top 10 pathways were graphed by GraphPad Prism 6 (**Figure 2**).

KEGG pathway annotation showed that 30 of the 31 potential target genes were enriched (96.8%) and involved 62 pathways, and 27 of these pathways were significantly correlated with the target genes (P ≤ 0.05). The following pathways had the largest number of genes involved: Platelet activation (11, 35.5%), Rap1 signaling pathway (12, 38.7%), Focal adhesion (9, 29.0%), Proteoglycans in cancer (8, 25.8%), PI3K-Akt signaling pathway (7, 22.6%), and Pathways in cancer (7, 22.6%). GO enrichment analysis showed that the number of genes involved in the CC (Cell Components), MF (Molecular Function), and BP (Biological Process) targets was 31 (100%). CC enrichment was mainly involved in following target genes: extracellular exosome (19, 61.3%), plasma membrane (16, 51.6%), and cytosol (14, 45.2%). MF enrichment was mainly involved in the following target genes: protein binding (25, 80.6%), ATP binding (9, 29.0%), and enzyme binding (8, 25.8%). BP enrichment was mainly involved in the following target genes: platelet aggregation (5, 16.1%), platelet activation (6, 19.4%), and blood coagulation (6, 19.4%).



TABLE 2 | HPLC-DAD-ESI-MS data and identification of the compounds *in vitro* and *in vivo.*


#### Construction of the Network

According to the results of gene chip, target genes in the top 10 pathways and compounds were selected to construct an active compound/target gene/pathway network and an active compound/platelet aggregation-related target gene network (**Figure 3**). The network diagram shows the synergistic effect of various compounds on multiple targets when *R. procumbens* plays a role in antiplatelet aggregation effects.

The network analyze tool was used to analyze the network, and genes with higher degree were associated with more genes. It can be considered that the corresponding protein of the genes plays an important role in central correlation when *R. procumbens* plays an antiplatelet aggregation role. The integrin αIIbβ3 (integrin αIIbβ3 is a protein that in humans is encoded by the ITGA2B and ITGB3 gene) had the highest degree; it was the intersection of all platelet-related pathways.

The results were compared with those of the KEGG pathway analysis and combined with the literature study. Integrin αIIbβ3 was selected as the target for the binding validation experiment.

### Screening of Active Compounds

Platelet with four kinds of inducers was stimulated, and platelet aggregation was detected after being incubated by these isolated blood absorption compounds. The platelet aggregation inhibition rate (MIR) was calculated to indicate the inhibitory activity of the drug. Results showed that those justicidin B groups had higher maximum aggregation compared with others. The remaining compounds showed different degrees of antiplatelet aggregation (**Table 3**, **Figure 4**).

## Integrin **αII**b**β**3 Expression in PRP

We investigated whether justicidin B had a regulatory effect on integrin αIIbβ3 expression. As shown in **Figure 4**, the integrin αIIbβ3 level was significantly increased on the inducer group

(P < 0.01, versus control group). More remarkably, justicidin B produced a dramatically inhibited effect on integrin αIIbβ3 level (versus inducer group, P < 0.01, **Figure 5**).

#### NT. LabelFree

MST experiments were performed to detect the molecular interaction between inhibitor and integrin αIIbβ3. Owing to the influence of ligand fluorescence, we obtained unsatisfactory results under the SD-test pass condition, accompanied by large errors (**Figure 6**).

#### NT.115

The SD-test verified that the label had less effect on the protein. We used NT.115 for the experiment. As differences in

#### TABLE 3 | MIR (%) of different compounds.


different justicin B concentrations (ranging from 0.00748 to 245µM). (D) Dependence of the MST signal on the justicin B concentration (measured 30 s after turning

on heating; data from C).

normalized fluorescence of the bound and unbound state allow determination of the fraction bound, the dissociation constant was thus calculated. All values were multiplied by a factor of 1,000, which yielded the relative fluorescence change in per thousand. The Kd values of justicin B were 35.017 ± 5.9014 μm (**Figure 7**).

### DISCUSSION

*R. procumbens* has been used in herbal medicines for promoting blood circulation and pain relief. Modern pharmacological studies have shown that *R. procumbens* has a good antiplatelet aggregation effect (Chen et al., 1996). According to preliminary

experiments, ethyl acetate extract is the active fraction. However, the chemical composition of this plant material is quite complex. Therefore, it was difficult to determine the active ingredient and target protein by using traditional methods.

The animal experiment was utilized for further verification. Results of gene chip illustrated that ethyl acetate extract could inhibit Gq–PLC–PKC pathway and Gi–PI3K–MAPK pathway. The down-regulation of GNAI1 gene related to the regulation of AC kinase was also observed, indicating that the ethyl acetate site can reduce inhibitory effect of Gi and enhance the activity of AC kinase. This result indicated that the ethyl acetate site could regulate the GI–AC–CAMP signaling pathway. Above all, ethyl acetate might inhibit platelet aggregation by inhibiting Gq–PLC– PKC, Gi–PI3K–MAPK, and other signaling pathways.

In order to further explore the material basis of *R. procumbens*, the blood absorption compounds of *R. procumbens* were screened by serum pharmacological method. Based on the results of the gene chip and LC-MS, GO enrichment analysis found that the targets involved extracellular exosome, plasma membrane, extracellular space, cytosol, and other cell compartments. At the molecular level, the targets were involved in protein binding, enzyme binding, ATP binding, and other molecular activities, and they were related to platelet aggregation, platelet activation, and platelet degranulation. It indicated that the drug may inhibit platelet aggregation by binding to membrane proteins, affecting its energy utilization and activation. The pathway enrichment results also indicated that *R. procumbens* may play an antiplatelet aggregation role by inhibiting the key targets of the platelet activation signaling pathway, such as integrin αIIbβ3. It was the intersection of all platelet-related pathways. Proteoglycans in cancer, PI3K–Akt signaling pathway, and so on also suggest that justicidin B may affect the expression and activity of cancerrelated proteins by binding integrin αIIbβ3.

Then, we use optical turbidimetry to measure the activity of three isolated blood absorption compounds. We found that justicidin B is the most active compound. Then, WB experiment verified this fact. It is well known that membrane proteins are less stable, and this has shown to have a massive impact on the MST (NT.LabelFree) experiment (Chasis and Mohandas, 1986). To identify optimal test and storage conditions for the membrane protein integrin αIIbβ3, the protein was subjected to a thermal unfolding formulation screen of the Prometheus NT.48 (supplementary material). NanoTemper's on-the-fly technology allows to measure 48 samples in parallel, providing more than 10 data points per minute. Formulation developments benefit from ultra-high resolution that are not compromised by aggregation but, at the same time, offer ease of use. Prometheus NT.48 results suggest that Hepes is the best detergent. The onset for ratio of Hepes-DDM is 46.1°C, which is higher than other buffers. This attempt was not reported before.

MST is a powerful technique to measure biomolecular interaction that are based on thermophoresis—the movement of molecules in a temperature gradient. This technique was reported to be highly sensitive such that it allows precise quantification of molecular interaction (Jerabek-Willemsen et al., 2014). Because of the influence of ligand fluorescence, we obtained unsatisfactory results, accompanied by relatively

large errors. The results of the Prometheus NT.48 experiment showed that the protein stability was relatively good, and the labeled protein SD-test was qualified; hence, we experimented further with NT.115 and obtained better results. MST results suggest that the selected compound has potential molecular interaction with integrin αIIbβ3. The Kd value of justicin B was 35.017 ± 5.9014 μm. These data suggest that justicin B has a good interaction with integrin αIIbβ3. According to Seidel et al. (2013), the fitting curve may be either S-shaped or mirror S-shaped. The standard symbol of MST amplitude (change in normalized fluorescence) depends on the chemistry of the compound that is titrated, its binding site, and the conformational change induced upon binding. Justicin B shows a positive slope suggesting a strong conformational change induced upon complex formation. Probably its interaction plays a major role in conformational change. We speculate that it might interfere with the platelet aggregation mechanism by negatively influencing the conformational changes required for the integrin αIIbβ3 activation.

Taken together, our results demonstrate that the ethyl acetate extract plays an anti-platelet aggregation role through integrin αIIbβ3, and justicin B, which is the most active blood absorption compound, targets it. We believe that our findings would provide a better foundation for further understanding of the mechanism of *R. procumbens* intervention in platelet aggregation.

#### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of Animal Care and Use Committee of Institute of Materia Medica, China.

### AUTHOR CONTRIBUTIONS

H-ZW conceived the study. H-ZW and Y-FY designed the study. S-TW performed the experiments and the data analysis, and wrote the manuscript. Y-FY, W-CX, BL, P-FH, W-PX, YS, Z-ZA, P-TY, and Z-TX revised the manuscript. All the authors read and approved the final version of the manuscript.

### FUNDING

This research was funded by the National Natural Science Foundation of China, grant number 31570343.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.00688/ full#supplementary-material

FIGURE S1 | Thermal unfolding curves. (A) Thermal unfolding curves in presence of Hepes and pbs. (B) Thermal unfolding curves in presence of Ttis. Insets show the detergent dependence of the first unfolding transition midpoint (Tm1).

#### REFERENCES


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Yang, Wu, Liu, Xie, Xiong, Hao, Xiao, Sun, Ai, You and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery

#### *Wenjuan Zhang1,2,3, Ying Huai1,2,3, Zhiping Miao1,2,3, Airong Qian1,2,3\* and Yonghua Wang4\**

*1 Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an, China, 2 Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi'an, China, 3 NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi'an, China, 4 Lab of Systems Pharmacology, College of Life Sciences, Northwest University, Xi'an, China*

#### *Edited by:*

*Ruiwen Zhang, University of Houston, United States*

#### *Reviewed by:*

*Jianxin Chen, Beijing University of Chinese Medicine, China Rolf Teschke, Hospital Hanau, Germany Yanqiong Zhang, Institute of Chinese Materia Medica (CACMS), China*

#### *\*Correspondence:*

*Airong Qian qianair@nwpu.edu.cn Yonghua Wang yhwang@nwu.edu.cn*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 19 February 2019 Accepted: 07 June 2019 Published: 11 July 2019*

#### *Citation:*

*Zhang W, Huai Y, Miao Z, Qian A and Wang Y (2019) Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery. Front. Pharmacol. 10:743. doi: 10.3389/fphar.2019.00743*

As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.

Keywords: bioactive compounds, target identification, systems pharmacology, synergistic effect, drug discovery

### INTRODUCTION

Traditional Chinese medicine (TCM) plays important roles in the prevention and treatment of complex diseases, which has been developed in China for thousands of years (Tang et al., 2009). In recent decades, TCM has been widely used as the complementary and alternative medicine in Western countries. Generally, Chinese herbal prescriptions or formulae (also called "Fangji") are

**Abbreviations:** TCM, traditional Chinese medicine; ADME, absorption, distribution, metabolism, and excretion; SOD, superoxide dismutase; CK, creatine kinase; cAMP, cyclic adenosine monophosphate; cTnI, cardiac troponin I; ALOX5, arachidonate 5-lipoxygenase; TOP2A, topoisomerase 2-alpha; ADCY1, adenylate cyclase type 1; SCD, stearoyl-coenzyme A desaturase; BCHE, butyrylcholinesterase.

used in clinical practice, and they can exhibit coordinating roles through the rational combination of multiple herbs to achieve good efficacy and few side effects for various diseases' prevention and treatment (Li et al., 2009). Despite the widespread use of TCM in clinical practice, proving its effectiveness *via* scientific trials and dissecting the molecular mechanisms are still big challenges.

Indeed, TCM and Chinese medicine formulae are designed under the principle of "syndrome differentiation" according to the combination rule of medicinal properties in TCM with obvious multiple-compound characteristics (Li, 2009). In ancient times, ancestors usually tested poison to identify effective herbs; for example, Li Shizhen was a famous physician and pharmacologist in the Ming Dynasty, who tested drugs and tried poison in the spirit of dedication to science. There is no doubt that viewing humans as the testers would be risky, but knowledge on common herbs is the achievement of an Ancient Chinese medical scientist who tried poison. Nowadays, great efforts have been made to extract and isolate compounds in herbs and prescriptions, resulting in the emergence of numerous newly identified ingredients (Zhang et al., 2018). In addition, the absorption, distribution, metabolism, and excretion (ADME) properties are defined as the dynamic changes in drugs within an animal or the human body, such as oral bioavailability (OB), drug-likeness, and half-life, which are critical in drug discovery and development (Su et al., 2007). It has been reported that nearly 95% of lead compounds fail in the drug development in clinical trials each year, and approximately 50% of these failures are due to poor ADME properties (Kassel, 2004). Therefore, the optimization of the ADME properties of lead compounds may be a critical factor that determines whether the drug can be successfully developed (MacCoss and Baillie, 2004). Many clinical studies including randomized controlled trials (RCTs) of the herbs have been conducted, some demonstrating hepatotoxicity and toxicity (Hu et al., 2017). However, because the extraction and isolation of compounds derived from herbs are costly and time-consuming, as well as only a few of them have satisfactory ADME properties and less side effects, there is an urgent need to develop a fast and effective novel strategy for identifying potential active compounds.

Furthermore, the identification of compounds derived from TCM is also an important process for drug development and an essential factor for the dissection of the holistic mechanisms of action of TCM (Cao et al., 2012). Currently, the ligand-based virtual screening (LBVS), structured-based virtual screening (SBVS), and the text mining-based approach are widely used to predict the target–ligand interactions (Ballesteros and Palczewski, 2001; Byvatov et al., 2003; Krejsa et al., 2003). In addition, several chemical genomics approaches, such as the ligand-based, targetbased, or target–ligand methods, are more effective to predict the compound–protein interactions (Balakin et al., 2003; Frimurer et al., 2005; Nagamine and Sakakibara, 2007; Rognan, 2007; Xia et al., 2009; He et al., 2010; Yamanishi et al., 2011). For example, Frimurer et al. have established a target-based approach to divide the receptors and the known ligands into clusters and further to discover each cluster with shared ligands (Frimurer et al., 2005). However, the target–ligand approach integrates the ligand chemical space, target space, and the available known drug– target network information to construct a complex predictive model to predict ligands or targets. For example, the *in silico* models integrated the amino acid sequences, two-dimensional chemical structures, and mass spectrometry data, as well as the chemical functional groups and biological features, for predicting the drug–target interactions (Nagamine and Sakakibara, 2007). However, all these approaches only focused on limited receptor space with certain protein families or the limited chemical space of US Food and Drug Administration (FDA)-approved drugs, and maybe they are not suitable for the unknown compounds of TCM. Therefore, novel approaches to identify the drug targets of TCM are valuable for understanding the mechanisms of TCM.

More importantly, TCM views the human body as a complex dynamical system and focuses on the balance of the human body, both internally and with its external environment (Ma et al., 2016). Previously, the researchers could only focus on the human body's reaction to herbal medicines, such as alleviating cough, reducing heat, and limiting bleeding. However, how these active molecules combine with each other to assemble as a whole to exert their therapeutic effects is still unclear, and it is of great significance to understand the molecular mechanisms of TCM. Therefore, efficient approaches to dissecting the mechanisms of drug combinations in TCM are of great significance to understand the underlying mechanisms of action of TCM.

Fortunately, the advent of systems pharmacology has provided the opportunity and methodologies for the development and modernization of TCM. In the recent year, systems pharmacology has been used to identify active natural products and investigate the mechanism of natural products (Li et al., 2015a, Li et al., 2012b; Zhang et al., 2016; Fang et al., 2017; Wang et al., 2017; Yang et al., 2017). Also, systems pharmacology provides new strategy for discovering novel drug combinations for the treatment of complex diseases. Integrated TCM for treatment of various diseases based on syndrome differentiations is one essential factor of the compatibility principles contributing to the drug efficacy (Zhang and Wang, 2015; Wang and Li, 2016a; Zhu et al., 2018). However, in contrast to Western medicine, TCM is overly dependent on the experiences of patients and practitioners and lacks systematic research methods. Therefore, there are many issues that need to be resolved in the development of TCM, for example: 1) TCM focused on the overall efficacy and clinical safety, but there is a lack of precise analysis and monitoring, including few studies on the pharmacodynamic and toxicological mechanisms; 2) the quality of herbs is one of the most important factors for the modernization of TCM, which has a major effect on the efficacy of TCM, but the quality is difficult to control; 3) the synergistic, additive, or antagonistic effects of TCM depend on the different properties of absorption, distribution, metabolism, excretion, and the toxicity of the pharmacodynamic components, which remain unclear; 4) the active ingredients and the mechanisms of action of TCM are unclear, which restrict the acceptance and development of TCM and seriously hinder the modernization processes. Owing to its complex composition and multiple systems, it is difficulty to dissect the underlying mechanisms of TCM at the systems level. Furthermore, the methodology often leads to controversy. Therefore, there is an urgent need to develop a new systematic and holistic research method.

In this review, we first introduced the concept and principle of systems pharmacology, and then we reviewed the computational methods of systems pharmacology for bioactive compound screening, target fishing, drug combination, and network analysis. In addition, we detailed the applications of systems pharmacology, including the elucidation of the mechanisms of action of herbal formulae, the design of multi-target drugs or drug combinations, and the theoretical analysis of Chinese medicine to guide the development of herbal medicine.

#### CONCEPT AND PRINCIPLE OF SYSTEMS PHARMACOLOGY

The exploration of the mechanism of action of the multiple compounds within a TCM prescription is the inevitable requirement for the modernization of TCM. In addition, to uncover the mechanism of actions of TCM, the modern scientific and technological methods need to propose for the foundation to promote the global development of TCM. Owing to its complexity, the holistic concept, and syndrome differentiation of TCM theory, the dissection of mechanisms of action of TCM is difficult. Therefore, we proposed the systematic research approach of systems pharmacology, based on the dynamic interaction of TCM with the human body from different levels, such as cellular, molecular, tissue, organ, and holistic levels (Wang and Yang, 2013) (**Figure 1**).

Systems pharmacology is an emerging discipline that focuses on the interaction between drugs and the body and the rules and mechanisms of drugs at a systems level. More specifically, the interactions between drugs and the body are illustrated from the microscopic levels (molecular and biochemical network levels) to the macroscopic levels (tissue, organ, and holistic levels). Systems pharmacology aims to investigate the changes in the functions and reactions in the human body induced by drugs, thus providing new strategies and tools to achieve precise control of the complex biological networks inside cells, thus altering disease pathophysiology, improving drug efficacy, and reducing adverse reactions (Wang and Yang, 2013; Zhang and Wang, 2015). To enhance the systems pharmacology platform, theoretic calculations and experimental methods were integrated into the models for the discovery of bioactive molecules, the identification of new drug targets, the prediction of adverse drug reactions, the exploration of therapeutic mechanisms, and the elucidation of the rules of drug combination (Huang et al., 2013a). This platform allows the large-scale analysis of simulation methodology and optimization algorithms, which can be applied to determine the molecular mechanisms of TCM and to assist the development of novel drugs.

### METHODOLOGY OF SYSTEMS PHARMACOLOGY IN TCM

#### ADME Screening Methods of Bioactive Ingredients in TCM

The ADME properties consist of drug solubility, permeability, protein binding ability, oral bioavailability, drug-likeness, blood– brain barrier (BBB) permeability, small intestine absorption, and half-life. TCM is a multifaceted system consisting of numerous compounds, of which only a few exhibit favorable ADME properties. Therefore, the screening and analysis of bioactive components in TCM are extremely challenging. To solve this problem, in the following section, we have focused on the introduction of an *in silico* ADME system (SysADME) (**Figure 2**), which is a rapid, efficient, and cost-effective strategy to explore the potential bioactive compounds of herbal medicines.

First, from the structure of the compounds and the help of system theory and artificial intelligence, the SysADME system integrates more than 20 models, including P-glycoprotein substrate inhibitor (Pgp) recognition, small intestine absorption, BBB permeability, and a mathematical forecast of plasma protein binding (Ai et al., 2009; Wang et al., 2009; Ai et al., 2010). In addition, we have built a series of predictive toxicity analysis (toxicology) models through the integration of modern statistics, chemical informatics, and other techniques (Hao et al., 2011; Xu et al., 2011a; Xu et al., 2011b). In the following part, we will review three representative models in details.

#### The Prediction of Human Oral Bioavailability (OB)

Because the predominant and most convenient way to deliver drugs of TCM is the oral route, the good OB of a new drug candidate is one of the essential pharmacokinetic parameters of ADME properties. Recently, multiple large-scale experiments have been conducted to evaluate the OB values of drugs, but they are labor-intensive and time-consuming. At first, Lipinski's "rule of five" has been qualitatively used to predict the absorption and permeability of drugs to guide the prediction of OB (Lipinski et al., 2012). And then many *in silico* models have been established to predict OB of drug molecules in the early stages of drug discovery (Aller et al., 2009). Quantitative structure–property relationship (QSPR), rule of thumb (RoT), and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the OB prediction (Agoram et al., 2001; Cabrera-Pérez et al., 2018). Since 2000, numerous QSPR models have been developed to predict OB; for example, Andrews et al. constructed a regression model to predict OB based on a dataset of 591 molecules by applying 85 structural descriptors (Aller et al., 2009). Compared to Lipinski's "rule of five," the falsenegative rate was reduced from 5% to 3%, and the false-positive rate decreased from 78% to 53%. In addition, Yoshida et al. used the multiple linear regression model for predicting OB with 15 structural descriptors (Yoshida and Topliss, 2000). However, the correct accuracy of this model can only achieve 60% for the test compounds. As for PBPK models, Yu and Amidon have established a compartmental model of absorption and transit (CAT) to predict the fraction of absorbed dose of different drugs (Yu, 1999). These integrated models were established based on seven transit compartments, which represent different anatomical regions of the small intestine. The limitation of the CAT model is that it ignored several properties that affect drug absorption, such as rate of dissolution, pH dependence on drug solubility, absorption in the stomach and/or colon, first-pass metabolism, and drug degradation in the intestine and liver, leading to the prediction of absorption with low solubility or permeability (Cabrera-Pérez et al., 2018). Up to now, there are no reliable and efficient models for prediction of OB based on simple descriptors.

In our previous work, given the multiple compounds, multiple targets, and synergetic effects of TCM, we have proposed a mathematical model called prediction of oral drug bioavailability (PreOB), which integrated the effects of Pgp efflux and P450 metabolism to ensure the accuracy of OB prediction of drugs (Xu et al., 2012). The PreOB was carried out by the following steps: first, 805 drug and drug-like molecules and their OB values were collected from the bioavailability database (Hou and Xu, 2002), and all the OB values were transformed into the common logarithm of log (oral bioavailability) (logB). Besides, a total of 1,536 dragon descriptors were calculated by Professional 5.4, 2006 (Talete, 2011). Then, all the 805 drugs were divided into several statistical subsets according to the geometry-based algorithm and iterative self-consistent approach (Jain, 2003). Next, by self-organizing map (SOM) (Vesanto, 2002), the compounds in each subset were split into training and independent validation sets based on their distribution in the chemical space. The two linear methods including multiple linear regression (MLR) and partial least squares regression (PLS), and the non-linear method support vector regression model (SVR) were available to perform prediction with five-fold cross-validation and independent


TABLE 1 | The comparisons between the tools of the prediction of oral bioavailability (OB) developed by the other groups and prediction of oral drug bioavailability (PreOB) model.

external tests. The results showed that all the performance of SVR is slightly better than that of MLR and PLS, with its determination coefficient (*R*<sup>2</sup> ) of 0.80 and standard error of estimate (SEE) of 0.31 for test sets. The prediction abilities of the MLR and PLS are relatively weak, exhibiting 0.60 and 0.64 for the training set with SEE of 0.40 and 0.31, respectively. Our results showed that MLR-, PLS-, and SVR-based *in silico* models have good potential in the prediction of OB and may facilitate the drug design. Generally, the compounds meeting the criteria of OB ≥ 30% are considered as potential active compounds with satisfactory pharmacological properties. The comparisons between the tools of the prediction of OB developed by the other groups and PreOB model are summarized in **Table 1**. More importantly, the PreOB model has been successfully applied for material-based analysis of many Chinese medicines (Li et al., 2012b; Liu et al., 2013a).

#### Systematic Identification of Multiple Toxin–Target Interaction (SysTox)

For the novel drug development, many efforts are being devoted to evaluate the toxicity properties of drugs. Due to the vastness of chemical space (toxins) and the diversity of biological systems (targets), the prediction of the toxin–target interface remains difficult. Recently, several novel approaches have been proposed to achieve this goal. For example, a chemical genomics approach that focuses on how similar ligands may interact with similar proteins has been applied to predict novel bioactive compounds of a target (Klabunde, 2010; Yamanishi et al., 2010). In addition, Yu et al. have used the network method to explore ligand–target interactions from high-dimensional biological data (Yu et al., 2012). However, the prediction of toxicity information of a variety of compounds by experimental methods remains difficult, and a systems-level analysis of multiple toxin–target associations is still lacking up to now. Therefore, in our previous study, we

established a novel systems toxicology approach SysTox (Zhou et al., 2013) to predict the toxin targets and their related networks, which is based on a large-scale database of 33,800 poison–target interactions through the integration of chemical, genomic, and toxicological information and systems biology technologies. The procedures of SysTox are as follows: 1) a systematic model integrating the extracted chemical and genomic features has been developed to predict the multiple toxin–target interactions with its reliability and robustness estimated by support vector machine (SVM) and random forest (RF) methods. And according to the phenotypic diseases, the qualitative classification of targets has been applied to further explore the biological significance of targets, as well as to validate the robustness of the *in silico* models. 2) As an example, a genome-scale toxin–target–disease network of cardiovascular disease is constructed. 3) The topological analysis of the network is implemented to identify drug targets that are most susceptible to attracting the most critical toxins, as well as to uncover the toxin-specific mechanisms. The advantage of our SysTox approach is that it can be used to predict the toxin– target interactions even for targets with unknown 3D structure. It is worth to note that the toxin–target interaction network can help us to identify new toxins and new target proteins simultaneously and infer novel links from the information of known links. The limitation of the SysTox approach is that the drug targets involving DNA or RNA were not integrated into the model due to the insufficiency of toxin–target information. So the prediction of toxins that target RNA or DNA may be an extension in the following work. The approaches to evaluate the toxicity properties of drugs are listed in **Table 2**.

#### The Prediction of Half-Life (HL)

The biological half-life of a drug is defined as the time required for the human body to metabolize or eliminate 50% of an initial

TABLE 2 | The approaches for the prediction of the toxicity properties of drugs.


drug dosage. It is noteworthy that measuring and predicting the half-life of a given drug are important for the safe and accurate dosage of the drug (Berezhkovskiy, 2013). At present, several models were proposed to predict the half-lives of drugs. For example, Sharma et al. have proposed the prediction model for peptide half-life (HLP) in intestine-like environment based on 10mer (HL10) and 16mer (HL16) peptides dataset, which helps in estimating half-lives of peptides relatively rather than in absolute terms (Sharma et al., 2014). With the help of seven machine learning methods and molecular descriptors, Lu et al. have proposed an approach to predict elimination of half-life in humans (Lu et al., 2016). In addition, Turner et al. predicted human half-lives of 20 cephalosporins by integrating constitutional, topological, and quantum-chemical descriptors (Turner et al., 2010). Moreover, Arnot et al. developed two halflife prediction models in humans based on molecular fragments and an automated iterative fragment selection method (Arnot et al., 2014). In summary, most models of prediction of halflife were based on drug structures, while the PreHL model was constructed on only eight molecular descriptors of drugs by principal component analysis (PCA). However, it is difficult and time-consuming to predict the half-life of a specific drug.

In a previous study, we have proposed the PreHL model for TCM injection systems (Yang et al., 2014), which is a systematic decision-making model to predict long or short half-lives of drugs by the C-partial least square (C-PLS) algorithm (Boulesteix, 2004; Kidron et al., 2012). More specifically, the PreHL model was built in three steps: 1) *Dataset collection:* One hundred sixty-nine drugs (injection formulation) with their half-life values, DrugBank ID, chemical name, and Chemical Abstracts Service (CAS) number were collected from DrugBank database (Knox et al., 2011), and they were divided into two subsets: a training set (*n* = 126) used to build the model and an independent test set (*n* = 43) to validate the accuracy of the model. 2) *Descriptor calculation and selection:* Molecular descriptors were first calculated to construct the model, and then 43 objective features were selected based on forward stepwise algorithm. Finally, by PCA, only eight of them were applied for C-PLS modeling process. 3) *Model performance:* For internal validation, the model was evaluated by the leaveone-out (LOO) methodology. Bedsides, external validation was performed by all models. The performance of the model was evaluated by short half-life and long half-life accuracies. For internal validation and external validation, the overall accuracy, long half-life accuracy, and short half-life prediction accuracy are all approximately 85–87%. According to the PreHL model, a half-life higher than 4 h is considered as a satisfactory metabolism property of drugs. Furthermore, the PreHL model was successfully used to assess the half-lives of the potential bioactive components of reduning injection (Yang et al., 2014). The models or approaches of half-life are listed in **Table 3**. Compared with other models, PreHL is a more systematic decision-making model addressing the plasma protein binding, active transport across the membrane, absorption, BBB permeability, drug metabolism, and half-life in the body.

#### Identification of Drug Targets

The identification of drug targets of TCM is a basic problem in the processes of drug development, as well as an essential factor for the dissection of the holistic mechanisms of action of TCM (Cao et al., 2012). Currently, the LBVS, SBVS, and the text miningbased approach are widely used to predict the target–ligand interactions. In brief, LBVS aims to identify novel compounds by comparing candidate ligands with the known drugs of a target protein (Byvatov et al., 2003; Krejsa et al., 2003). Nevertheless, if the number of known active compounds for a target is small, the performance of LBVS is poor. In addition, it is difficult to identify drugs with novel structural scaffolds that differ from the known molecules. As for SBVS, it is constrained by the available crystallographic structure of target, thus hampering the prescreening process of drugs. And it is particularly limited for those membrane proteins, like the GPCRs (G-protein coupled receptors), whose 3D structure information is still unavailable up to now (Ballesteros and Palczewski, 2001).

Therefore, to predict the drug–target interactions, we have developed three models, including systematic drug–target identification technology (SysDT) (Yu et al., 2012), weighted ensemble similarity (WES) (Zheng et al., 2015) method, and Pred-binding method (Shar et al., 2016). All the methods of the prediction of drug targets are listed in **Table 4**. In the following part, we will review these methods.

#### The SysDT Model

The SysDT model was developed as a systematic approach for the prediction of the drug–target interactions that integrated artificial intelligence computing methods systems biology, chemical genomics, and structural genomics, which are based on two powerful methods, RF and SVM (Yu et al., 2012) The model was constructed by 6,707 drugs and 4,228 targets with known drug–target interactions in the DrugBank


TABLE 3 | The models or approaches for the prediction of half-life.

database, which constructed the positive samples. The negative samples were obtained by three steps: I) re-coupling all drugs and targets in the benchmark dataset into pairs, II) discarding those drug–protein pairs that appeared in the positive samples and keeping the remaining pairs to represent the non-interaction space, and III) randomly selecting the negative pairs from the non-interaction space to ensure the same number as the positive pairs. Then, by SVM, numerical vectors of the drug–target pairs (for both positive and negative samples) by concatenating chemical descriptors and protein descriptors were mapped into a higher dimensional feature space, which is a maximal margin hyper-plane that separates the positive from the negative samples by using a kernel function. Another method, RF, was also used to build a model, which is an ensemble of unpruned classification or regression tree. Finally, the performance of the models was evaluated by internal five-fold cross-validation and four external independent validations with the known drug–target interactions.

Our results showed that the optimal models by SVM showed impressive prediction performance, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%. Both SVM and RF demonstrate the reliability and robustness of the obtained models. Compared with the structure-based simulation methods, the SysDT approach is not restricted by the 3D structure of targets. More importantly, the advantage of the SysDT model is that it enables to identify the unrelated targets that may share structure similarity of a chemical with ligands. Moreover, it can promote the multi-target drug discovery by recognizing the proteins targeted by a particular ligand. Therefore, the SysDT approach may provide a reliable analysis tool for drug target identification of the herbal molecules on human proteins. Although the SysDT model is effective for the prediction of the drug-target interactions, it is limited by the information of the 3D structure features of the ligand-binding domains. Therefore, novel optimal approaches are still needed to be proposed in further research.

#### The WES Method

The available computational approaches mainly focus on the prediction of indirect targets of drugs or direct targets of drugs in a small scale. To further improve the drug target prediction systems, we have successfully developed two optical mathematical models: 1) a WES method and 2) a Pred-binding approach (**Figure 3**) to identify the direct targets of drugs based on large scale of drug–target interactions.

The WES approach was proposed on the theory that the systematic features of ligands could accurately reflect the ligand– receptor binding pattern. The WES method was constructed based on over 900,000 drug–target relations, including three steps: 1) identifying the key ligand structural features that strongly related to the pharmacological properties in a framework of ensemble; 2) confirming the targets of drugs by the evaluation of the overall similarity (ensemble) rather than a single ligand judgment; 3) obtaining the overall similarity with the ligand set by integrating the standardized ensemble similarities (*Z* score) by Bayesian network and multi-variate kernel approach; and 4) evaluating and validating the performance of the approach by leave-oneout cross-validation (LOOCV) and the ligand-binding assay test experiments. The WES method exhibits good reliability with a good specificity and sensitivity [Area Under The Curve (AUC) = 0.85] and external [both the binding (positive sample) and nonbinding data (negative sample)] and experimental test (ligandbinding assay test) accuracies of 70% and 71%, respectively. Notably, it is able to distinguish the direct binding or indirect binding relationships between drugs and targets, which is of great benefit for drug repositioning and discovery (Zheng et al., 2015).

The advantages of WES includes the following: 1) the structural features based on statistical tests and optimization analysis were integrated into a framework of ensemble to reduce dimensionality of dataset and eliminate data noise. 2) The ensemble concept was proposed to ensure the model to predict the target of the drug based on the drug's similarity with the whole feature of an ensemble. The one nearest neighbor (1NN) model evaluates the probability of drug targets based only on the maximum similarity to the known ligands


TABLE 4 | The methods for the prediction of drug targets.

of the target. Compared with the 1NN model, WES is better in predicting drug targets for various structurally diverse compounds.

#### Pred-Binding Approach

Drug–target interactions are important for exploring biological activities of these proteins. In fact, some drugs may bind to multiple target proteins and sometimes improperly bind to unwanted offtargets (Wang et al., 2013), leading to severe harmful side effects. Therefore, identifying the satisfactory targets of drugs is an urgent task for drug development. In our previous study, we have developed the Pred-binding model to accurately predict the binding strength between drugs and targets (Shar et al., 2016). The Predbinding model includes the following: 1) *Dataset construction:* The ligand and target dataset information with known binding affinity abstracted from Psychoactive Drug Screening Program (PDSP) Ki database was used to build the model (Roth et al., 2000). After the exclusion of ligand–target–Ki entries with the repeat number of Ki of more than 70, finally, a dataset consisting of 9,948 ligand– target–Ki pairs was constructed. And 1,589 Dragon descriptors of ligands and 1,080 protein descriptors were obtained for further analysis. 2) *Training set and test set construction:* The dataset was split into training (used to build the model) and test (used to validate the model's accuracy) sets, and they were randomly split into five subsets with equal number, and one subset was selected as the test set, and the others were considered as the training set. 3) *Model building:* Two *in silico* models based on SVM and RF were proposed to predict the binding affinity. 4) *Model validation:* As mentioned above, first, each subset was selected as the test set, and the other four subsets serve as the training set for validating model. The processes were repeated five times. Second, five external independent validations were performed for all models using different test sets. Third, the comparison of the performance of RF model and SVM model by *F* test was performed. The results showed that the cross-validation coefficient was 0.6079 for SVM and 0.6267 for RF, exhibiting a good potent Ki predictability. In conclusion, the Pred-binding approach may contribute to the prediction of novel potential targets, further guiding the drug development. The limitation of the model is the robust and efficient features; therefore, a better regression model needs to be developed.

In summary, the above three models have provided new approaches for the identification of drug targets, which may benefit the drug design and promote the drug development.

#### Drug Combination Prediction and Dynamic Analysis Approach Probability Ensemble Approach (PEA) for the Prediction of Drug Combination

Drug combination has been a promising strategy for the treatment of complex diseases with higher efficacy and fewer side effects than has the single-drug treatment (Zimmermann et al., 2007; Al-Lazikani et al., 2012; Roemer and Boone, 2013). *In vitro* approaches, such as the high-throughput screening method (Borisy et al., 2003; Lehár et al., 2009) and the "multiplex screening for interacting compounds" (MuSIC) (Tan et al., 2012), have been proposed to investigate the synergistic drug pairs. However, these methods are time-consuming and cost intensive. Alternatively, several computational approaches have been developed to identify novel synergistic drug pairs by integrating network analysis and chemical biology data (Chou, 2010; Zhao et al., 2011; Tang et al., 2013). The majority of these methods are limited to dissect the molecular mechanisms or identify combinatorial drugs based on targets with multiple diseases. In addition, some attention has been focused on pharmacokinetic properties of the compound, pharmacodynamic constants, or both pharmacokinetics and pharmacodynamics to predict the drug–drug interactions. But the systematic analysis for predicting the efficacy and side effects of the known or novel drug pairs is still lacking.

To clarify the issue, we have proposed the probability ensemble approach (PEA model) (Li et al., 2015b), by the integration of the molecular chemical space, the pharmacological space, the gene annotations, and the biological networks, for the prediction of drug combinations (**Figure 4**). First, by the integration of drug molecular and pharmacological phenotypes, a Bayesian network model based on a similarity algorithm was developed for the prediction of both clinical efficacy and adverse effects. The performance of PEA showed that the combination efficacy of drugs with high specificity and sensitivity (AUC = 0.90), which was further verified by independent data derived from the literature or novel experimental assays. Second, PEA also assesses the adverse effects (AUC = 0.95) quantitatively and predicts the potential therapeutic indications of drug combinations. Finally, the PreDC (Predict Drug Combination) database was constructed with 1,571 known and 3,269 predicted optimal drug combinations associated with their therapeutic indications and potential side effects. In addition, the standalone software and web server of the PreDC are freely available at http://lsp.nwu. edu.cn/predc.php.

Compared with the simple feature-enrich method proposed by Zhao et al. (2011), the PEA algorithm exhibited good advantages with high training efficiency and extensive applicability (the comparison of the methods for the prediction of drug combination is shown in **Table 5**). More particularly, PEA shows similar performances as the wholefeature model by integrating the weakly predictive features, such as target sequence and chemical structure, to improve the performance, making it convenient and easy to understand. Generally, owing to the unknown underlying molecular mechanisms of combination therapies, drug combinations are predicted based on clinical rules derived from clinical experience or randomized clinical trials. Therefore, the drug combinations were predicted only with the similar functions. Notably, PEA has shown that 43% of our high-confidence predictions (with P1 ≥ 0.9 and P2 ≤ 0.1) are predicted as effective drug combinations with different Anatomical Therapeutic and Chemical (ATC) classes (the first level), indicating that PEA is not restrained by the rule. Moreover, PEA model was experimentally validated by 10 novel effective drug combinations that are a combination of antibacterial and anticancer drugs, showing that 80% pairs are synergistic to cancer models. Moreover, the PEA algorithm has incorporated the clinical efficacy and adverse effect evaluation to identify the potential drug combinations effectively. The limitation of the PEA is that the dosage was not integrated into the model; therefore, it should be taken into account to improve the prediction of drug combinations.



#### Network Elementary Subgraphs and Dynamic Modeling Analysis (NetSyner)

TCM is a complex system with multiple compounds and multiple targets; particularly, natural products derived from TCM with weak binding affinity have been proved to have satisfactory therapeutic efficacy through the regulation of the coordination equilibrium of the whole biological network (Zhu and Xu, 2003; Tan, 2007; Huang et al., 2013a). Recently, nearly ~110,000 small molecules with low binding affinity have been reported in the public database (Liu et al., 2007). However, a suitable strategy to discover the low-binding-affinity molecules is yet to be constructed.

In a previous study, we have developed a systematic approach NetSyner, which is based on the dynamics of target networks and the dynamics of formula structure to predict the response of perturbation of multiple nodes by cell signaling networks (**Figure 5**) (Wang et al., 2016b). The approach includes three steps: First, dynamic models for a series of three-component elementary subgraphs were built, and 33 elementary subgraphs were performed to determine the desired topology and dynamic parameters among targets. And elementary subgraphs were modeled by a set of ordinary differential equations (ODEs) including the rate laws of mass action and the complete Michaelis– Menten reaction kinetics. The combination index (CI) was used to evaluate whether the two targets in an elementary subgraph can have a synergistic effect. Specially, the mitogen-activated protein kinase (MAPK) pathway is an evolutionarily conserved and wellstudied signaling pathway involved in regulating fundamental cellular processes in response to stress and inflammation (Johnson and Lapadat, 2002; Sabio and Davis, 2014). As an example, through the application of the elementary subgraphs to the MAPK pathway, several optimal target combinations were predicted. Then, all the targets of the formula were mapped into the elementary subgraphs; both the modes (synergistic, antagonistic, or unrelated) and extent (synergistic index) of interactions between the bioactive compounds were calculated by the dynamic analysis. Moreover, molecular dynamics simulation and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) methods were employed to evaluate the binding free energies between the compound and the targets. Furthermore, to experimentally validate the prediction of NetSyner, analyses of the inhibitory effects of the two natural products (luteolin and tanshinone IIA) and the four known selective inhibitors on IL-6 and TNF-α production were carried out. The results indicated that multi-weak perturbations of luteolin and tanshinone IIA against the MAPK signaling pathway can potentially decrease the inflammatory response. In conclusion, weak-binding drugs exhibit favorable efficiency and few adverse reactions, which may offer a promising future for novel drug discovery. Nevertheless, due to the parameter independent model of NetSyner, it is applicable to those pathways that must be satisfied by two conditions: 1) pathways must be evolutionarily conserved and 2) the parameters of the pathway must be intact.

#### APPLICATION OF SYSTEMS PHARMACOLOGY IN TCM

#### Construction of TCM Systems Pharmacology Software and Databases

At present, several databases have been established for the investigation of TCM from different aspects. (The database of TCMs were listed in **Table 6**.) For example, TCM Database@ Taiwan (Chen, 2011) and TCM-ID (Chen et al., 2010) have provided a large number of herbal ingredients with 3D structures and functional properties. TCMID (Xue et al., 2013) consists of TCM formulae, herbs, ingredients, and their related targets and diseases. Both ChemTCM (Ehrman et al., 2007) and HIT (Ye et al., 2011) focus on herbal ingredients and their corresponding targets. The CVDHD database (Gu et al., 2013) focuses on natural products associated with cardiovascular diseases and targets. But there is lack of systematic network pharmacology analysis among these databases.

Therefore, our team proposed a unique systems pharmacology platform of TCM-TCMSP (Ru et al., 2014; Liu et al., 2016) (http:// lsp.nwu.edu.cn/tcmsp.php). The database consists of more than 36,000 chemical molecules and forms a complete library of Chinese medicine ingredients. In addition, the database integrated 12 ADME key properties like human oral bioavailability, halflife, drug-likeness, Caco-2 permeability, blood–brain barrier and Lipinski's rule of five, and the drug-likeness analysis of compounds, with more than 4,000 targets and 1,000 types of disease information. More importantly, "drug–target–disease" network pharmacology analysis tools were developed as a novel tool for the identification of the specific targets and the specific diseases of active molecules/groups in TCM. In summary, the particular strengths of TCMSP are the large number of herbal ingredients with ADME properties and their ability to analyze

#### TABLE 6 | The database of TCMs.


drug–target networks and drug–disease networks, thus providing a platform to dissect the mechanisms of action of TCM, uncover nature of TCM theory, and develop novel herbal-oriented drugs. Moreover, the related software can be used to search the information in the database conveniently. Recently, two novel databases, SymMap (Wu et al., 2019) and ETCM databases (Encyclopedia of Traditional Chinese Medicine) (Xu et al., 2019), were built. SymMap is an integrative database of TCM enhanced by symptom mapping. SymMap is an integrative database, consisting of the information of TCM symptoms and related herbs, diseases and associated symptoms, herbal ingredients, and gene targets. Furthermore, SymMap could be applied to predict component pairwise relationships by statistical tests to filter promising results to guide drug discovery. Actually, SymMap was focused on TCM symptoms and their relationships to herbs and diseases, which provides both candidate leads and screening directions for phenotypic drug discovery. As for the ETCM database, it contains comprehensive and standardized information of 403 TCM herbal species, 3,962 TCM formulae, 7,274 herbal ingredients, 2,266 validated or predicted drug targets, and 3,027 related diseases. ETCM is convenient to obtain the information of the herbs' basic property and quality control standard, formula composition, ingredient drug-likeness, the gene targets of the ingredients, and related pathways or diseases.

Compared with SymMap, TCMSP is a more comprehensive database that integrated all the herbs and their related compounds, the compound ADME properties, potential targets, and diseases, which can automatically establish the compound–target and target– disease networks to analyze the drugs' mechanisms of action and promote the TCM drug development. The limitation of TCMSP is that it lacks some medicinal and pharmacological data, the dose– effect relationship of ingredients, and the drug action modes: stimulation or inhibition, drug combination for various diseases, and tissues and organs that the compounds target. To improve these limitations, the ETCM database provides the habitat and quality control information of herbs, which may become a major data warehouse for TCM to promote TCM drug development.

Although tremendous efforts have been made in the past to provide databases containing cancer-related information, to our knowledge, no such dedicated comprehensive repository of anticancer herbs and anticancer herb-originating natural products has been developed currently as yet. Some databases like CancerDR (Kumar et al., 2013) and CancerPPD (Tyagi et al., 2015) have been made in the past to provide comprehensive data involved in anticancer ingredients. However, the CancerDR mainly focuses on FDA-approved and experimental drugs, and CancerPPD is a database of anticancer peptides and proteins. Considering the bleak situation of cancer and absence of systematic database for anticancer herbal products, for the first time, we have developed a comprehensive repository named anticancer herbs database of systems pharmacology (CancerHSP). The CancerHSP database provides information of 2,439 anticancer herbs, 2,439 anticancer active compounds, the molecular structure of each compound, and antitumor activity data based on 492 different cell lines (Tao et al., 2015). Furthermore, the database also consists of natural products with anticancer effects, their related ADME properties, antitumor activity, and target information, which not only helps to dissect the underlying molecular mechanisms of anticancer drugs but also provides basic data support for the development of anticancer drugs.

### Synergistic Effects of the Active Components in TCM

#### Multi-Target Synergistic Effects of TCM

Based on network pharmacological methods, scientists discovered that TCM exhibits multi-target synergistic effects. For example, Violeta et al. have built a computer multiphase pharmacology fingerprint (CPF) based on the Gauss integration screening method (GES) to encode the corresponding multiple target fingerprint atlas of drugs. Besides, the approach successfully found that drugs can interact with multiple targets, which provides a novel method for the discovery of new preclinical and clinical drug candidates (Violeta et al., 2014). In fact, if one drug could act on multiple targets, the drug molecules may exhibit better therapeutic effects through targeting on multiple targets under the synergistic effects (Hopkins, 2007; Hopkins, 2008). Recently, Huang et al. successfully dissected the molecular mechanisms of TCM with multiple targets for the treatment of depression; for example, several antidepressant drugs acted on more than 20 targets (Huang et al., 2013a). In addition, Liu et al. illustrated the mechanisms of action for the herb licorice, and the potential bioactive components were identified by the systems pharmacology. For instance, liquiritigenin, licochalcone B, naringenin, and kaempferol were considered as the bioactive compounds that acted on 22 targets related to cough, including ADRB1 (β-1 adrenergic receptor), ADRB2 (β-2 adrenergic receptor), CALM1 (calmodulin-1), PDE4B (cAMP-specific 3′,5′-cyclic phosphodiesterase 4B), PDE4D (cAMP-specific 3′,5′-cyclic phosphodiesterase 4D), HSP90AA1 (heat shock protein HSP 90-α), HSP90AB1 (heat shock protein HSP 90-β), PPARG (peroxisome proliferator-activated receptor γ), and THRB (thyroid hormone receptor β). The flavonoids, including isoliquiritigenin, liquiritigenin, and liquiritin, exerted synergistic therapeutic effects on thrombosis through the regulation of the proteins F2 (prothrombin), F10 (coagulation factor X), and PTGS2 (prostaglandin G/H synthase 2), which are closely involved in the processes of thrombosis. In addition, licochalcone A and licoisoflavanone acted on the proteins 5-hydroxytryptamine 1A receptor (HTR1A), ADRB1, cell division protein kinase 5 (CDK5), D opioid receptor (OPRD1), GSK3B, and HRH1; therefore, they may exert synergetic effects to achieve antiischemic effects to treat ischemic heart disease (Liu et al., 2013a).

Moreover, we identified some novel targets, 5-hydroxytryptamine 2A receptor (5-HT2A) and aldose reductase (AKR1B1), which are associated with diabetes. Also, several bioactive compounds in licorice could target proteins of the nervous system, such as monoamine oxidase type B (MAOB), D2 and D3 dopaminergic receptors, and mitogen-activated protein kinase 10 (MAPK10) (Liu et al., 2013a). Notably, we dissected the detoxification mechanism of licorice; for example, the compounds liquiritin and licochalcone G can target the metalloelastase to destroy bacteria and strengthen the tissue macrophages, thus defending against external invasions. In summary, with the aid of systems pharmacology, we generated a novel perspective for better understanding of single herbal medicine for treating various diseases from the molecular level to the systems level. More importantly, it also explained why licorice is a popular herb, as well as the mechanisms of detoxification of the licorice (Liu et al., 2013a).

#### Multi-Pathway Interactions of Herbs

To comprehensively investigate the interactions between herbal ingredients and their related biological processes, a drug–target– pathway network was generated (Chen et al., 2009). The most important pathways are the cellular signaling pathways, which can interact with each other. In addition, various stimuli appear to activate the same downstream targets, thus exhibiting the same cellular functions. For example, Gong et al. identified alternative pathways based on experimental data, which are involved in regulating cell functions (Gong and Zhang, 2005). In the target– pathway network, targets that appear in multiple pathways are often considered as potential key targets for the treatment of complex diseases.

In addition, by a systematic genetic analysis of 24 types of cancers, scientists found that 67–100% of tumor cells were involved in 12 cellular signaling pathways and related carcinogenesis processes (Jones et al., 2008). Li et al. found that multiple compounds were involved in multiple pathways in Compound Danshen Formula: 58 compounds were associated with the glucocorticoid and inflammatory signaling pathways; 56 compounds acted on the l-arginine/NO signaling pathways; 35 compounds disturbed the renin–angiotensin–aldosterone pathways; and 31 compounds regulated signaling pathways associated with platelet aggregation. Interestingly, all these signaling pathways are closely related to inflammation and coagulation, indicating that Compound Danshen Formula may synergistically regulate these signaling pathways to treat cardiovascular diseases effectively (Li et al., 2012b). Therefore, multi-target drugs of TCM are likely to be involved in alternative pathways or biological processes to treat complex disease effectively instead of single-target drugs.

#### Combinations of Herbal Compounds Acting on Multiple Organs

TCM is a part of holistic medicine, which concentrates on systematic health care for the whole human body rather than one part of the body (**Figure 6A**) (Ventegodt et al., 2014); however, to understand the mechanisms of action of TCM at a systems level is still difficult. Indeed, there are two key problems: 1) If the compound and the person are considered as whole entities, how do they interact with each other? 2) How do the molecules, tissues, and organs of the body respond to different molecules or molecule groups in a formula under holistic frameworks? To solve these problems, in the previous studies, we have examined the molecular basis of some diseases associated with different organs, such as cardio-cerebral diseases and cardiovascular diseases (CVDs) and gastrointestinal disorders (GIDs). The systems pharmacology model consists of four modules (**Figure 6B**): 1) an ADME evaluation model, including oral bioavailability prediction, drug-likeness evaluation, human intestinal absorption, half-life, and BBB permeability prediction; 2) network target fishing and pathway analysis; 3) compound–pathway analysis; and 4) drug– organ enrichment and interaction model (Wang et al., 2015).

Take Xinnaoxin Pill and Sanhe Decoction as examples; with the help of systems pharmacology, we dissected the scientific connotations of simultaneous treatment for cardio-cerebral diseases, and CVDs and GIDs. More specifically, we found that several components in Xinnaoxin Pill exhibited good BBB permeability, suggesting that it may be beneficial for the cardiovascular system. Besides, it could act on several organs involved in multiple biological processes and multiple pathways associated with multiple functions, such as inflammation, myocardial contraction, and angiogenesis, thus allowing the simultaneous treatment of cardio-cerebral diseases.

Moreover, by ADME system evaluation, we identified 59 potential active compounds in Sanhe Decoction (Zhang et al., 2016). Seventy target proteins of these compounds were predicted by target fishing. The compound–pathway network analysis revealed that multiple drugs were simultaneously involved in several pathways, such as calcium ion signaling pathway, cGMP– dependent protein kinase (PKG) signaling pathway, and vascular smooth muscle contractions (**Figure 7A**), suggesting that these drugs tend to exhibit multi-target synergetic or additive effects. The target tissue distribution network indicated that the compounds of Sanhe Decoction acted on multiple tissues or organs simultaneously, the majority of which were associated with heart and stomach, thereby achieving therapeutic effects on CVDs and GIDs (**Figure 7B**). Furthermore, Sanhe Decoction significantly alleviates the myocardial conditions compared with those of the control group in a rat model of myocardial ischemia, verifying the reliability of the theoretical model (Zhang et al., 2016).

In conclusion, the systems pharmacology approach provides a holistic strategy for rational drug design for complex associated diseases, promoting the drug development.

#### Bidirectional Regulation of TCM for the Treatment of Diseases

Reduning injection, derived from the experience of ancient Chinese medicine doctors, consists of three herbs: *Artemisia annua* L. (genus *Artemisia*, Asteraceae), *Gardenia jasminoides* J.Ellis (genus *Gardenia*, Rubiaceae), and *Lonicera japonica* Thunb. (genus *Lonicera*, Caprifoliaceae), which are mainly used for the treatment of influenza-like diseases, including viral infections, fever, respiratory diseases, and inflammation (Yang et al., 2014). The target network indicated that different diseases may have the same symptoms and can be cured by the same combination of herbs (Lin, 1998). The mechanisms of reduning injection were illustrated by systems pharmacology. The compound–target network of reduning injection is shown in **Figure 8**. We noticed that arachidonate 5-lipoxygenase (ALOX5) is one of the key enzymes in the formation of proinflammatory eicosanoids from arachidonic acid (Albert et al., 2002), which transforms essential fatty acids into leukotrienes (such as leukotriene B4, C4, D4, and E4). Actually, leukotriene B4 is an effective activator of the chemotactic reaction in white blood cells. In the network, ALOX5 is a common pharmaceutical target against various diseases that interacted with several compounds, such as quercetin and luteolin. Moreover, reduning injection might also control the virus infection by directly targeting viral proteins, such as DNA topoisomerase 2-alpha (TOP2A) to inhibit the virus replication (Wang et al., 2012). The cell experiments also showed that the herbal ingredients reduced the inflammatory response through the regulation of inflammatory cytokines and proinflammatory mediators, such as IL-6, IL-8, TNF-α, and COX2. More importantly, the bioactive compounds in reduning can directly kill the virus through the inhibition of virus expression. In summary, the systems pharmacology-based analysis revealed that the dual regulation of reduning injection not only inhibited virus replication but also exerted anti-inflammatory activities to promote body recovery.

#### Application of Systems Pharmacology in the Examination of "Jun-Chen-Zuo-Shi" in the Combination Principles of Formula "Jun-Chen-Zuo-Shi" Combination Principle of Mahuang Decoction and Yujin Formula

"Jun-Chen-Zuo-Shi" is one of the basic principles of herbal formulae. It has been found that there was a clear difference in the structure and biological activity of each ingredient in different herbs and even ingredients in the same herb; however, only some bioactive compounds exhibit therapeutic activities (Zhao et al., 2010). Given the numerous components of a TCM, the interpretation of the rules of combination is difficult. In the previous study, taking Mahuang Decoction as an example, we explored the scientific connotation of the combination principle of TCM (Yao et al., 2013). Mahuang

Decoction consists of four herbs: ephedra, cinnamon, almond, and licorice. By the developed systems pharmacology model, the different roles of the four herbs in the prescription were deciphered through the integration of pharmacokinetic interactions, the drug–target network, and the target–disease network from the molecular level to the systems level (**Figure 9**). The main findings were as follows: 1) 45 active compounds were screened by ADME system; among these, 14 potential bioactive compounds belonged to ephedra, including ephedrine, pseudoephedrine, N-methyl ephedrine, and quercetin; 10 compounds were from cinnamon, including cinnamic aldehyde, cinnamic acid, and coumarin; and 9 compounds were from almonds, such as bitter amygdalin and soybean sterol. Licorice has 12 active molecules, which include glycyrrhizic acid, 18-betaglycyrrhizic acid, and glycyrrhizin; 2) the herb ephedrine plays a prominent role as the "Jun" herb, which mainly stimulates the body heat and asthma through targeting on epinephrine receptor; 3) the "Chen" herb cinnamon can act on the same targets as the "Jun" herb ephedrine, which enhances therapeutic effects. For example, the

(round, purple) and 11 disease nodes (square, green), and the size of the circle is the degree of the node (Yang et al., 2014).

herb cinnamon also acted on both the beta 1-adrenergic receptor and the beta 2-adrenergic receptor, thus reducing the dose of the "Jun" herb ephedrine required. 4) The "Zuo" and "Shi" herbs almond and licorice helped to improve the bioavailability of the "Jun" and "Chen" herbs and to coordinate all the drug activities to promoting synergistic effects of four herbs.

Moreover, we have dissected the famous prescription Yujin Formula for treating cardiovascular diseases to clarify the "Jun-Chen-Zuo-Shi" combination principle in Chinese medicine (Li et al., 2012a). From the Yujin Formula, 58 potential bioactive compounds were identified by ADME screening. The compound– target network indicated that the "Jun" herb *Curcuma aromatic* possessed the most bioactive compounds, which acted on the targets associated with CVDs; the "Chen" herb *Fructus Gardeniae* has fewer bioactive compound and targets and shared 15 targets with the "Jun" herb *C. aromatic* to enhance the therapeutic effects; both the "Zuo" "Shi" herbs musk and borneol play assistant

roles by decreasing the toxicity and targeting the ingredients to corresponding organs. In the Yujin Formula, target–disease network (**Figure 10B**) showed that most targets were associated with CVDs (44/147); moreover, they were distributed in tumors (40/147), neurological diseases (13/147), and nutritional metabolic diseases (9/147). These results indicated that Yujin Formula may be applied not only for the treatment of CVDs but also for tumors, nervous system diseases, nutritional or metabolic disease, and other diseases. In summary, the scientific connotations of the "Jun-Chen-Zuo-Shi" combination principle were illustrated, which are of great significance for understanding the mechanisms of TCM.

#### Pathogenesis of Vitiligo and Its Treatment by Qubaibabuqi Formula

Vitiligo is an acquired, pigmentary skin disease that is disfiguring and difficult to treat. Clinically, many TCM prescriptions possess significant effects on vitiligo. Previously, we examined the

potential pathogenic mechanisms of vitiligo and its treatment by Qubaibabuqi formula by the systems pharmacology (Pei et al., 2016). Fifty-six active ingredients were identified as the active compounds, including buritin, bubonin, kaempferol, and cholesterol, which played important roles in the treatment of vitiligo. They acted on 83 target ADCY1 (adenylate cyclase type 1), SCD (stearoyl-coenzyme A desaturase), and BCHE (butyrylcholinesterase) to enhance immune response, increase melanin synthesis, and equilibrate the nervous system. In addition, the analysis of the target network and integration of vitiligo pathways showed that the Qubaibabuqi formula may be involved in modules such as immune-related modules, nervous system-related modules, and melanin synthesis-related modules, exhibiting synergistic effects on vitiligo. The study systematically analyzed the potential molecular mechanisms of Qubaibabuqi formula and pathogenesis of vitiligo from the molecular, network, and pathway levels, deepening our understanding of vitiligo and extending the application of TCM in modern medicine.

### DISSECTION OF SYNDROME DIFFERENTIATION THEORY AND QI-BLOOD THEORY

TCM is derived from ancient medical practices that integrate the integrity of the body and the natural environment. The concept of entirety and the method of treatment with syndrome differentiation in TCM is distinctive, which provides a basis for the diagnosis and treatment of diseases (Jiang et al., 2012). More importantly, syndrome differentiation has always been an important pharmacological principle to guide the prescription. For example, Liuwei Dihuang Pill and Jinkui Shenqi Pill were developed under the guidance of the syndrome. However, owing to little evidence of the link between diseases and efficacy, the therapeutic strategies under syndrome are still lacking.

With the aid of systems pharmacology, the "drug–gene– targets–disease subtype" network associated with CVDs was established. Therein, the drugs, targets, and multi-level interactions were illuminated, and the complex interactions between disease genes and CVDs' subtypes were discovered (Li et al., 2014). To uncover the biological basis of CVDs' syndrome, "CVDs syndrome of qi stagnation, blood stasis, qi deficiency, and blood deficiency" were implemented. Combined with the related TCM and refined prescription, the "syndrome–gene–target–drug" network was established to clarify the molecular network and pathways in coronary heart disease with the characteristic of qi stagnation and blood stasis (Zhou and Wang, 2014).

Furthermore, we identified that the qi-tonifying medicines were involved in the enhancement of immunity, the promotion of energy metabolism, and blood circulation, whereas blood-tonic Chinese herbs tended to improve and promote the function of hematopoietic stem cells (**Figure 11**). A computational method was built to distinguish the molecular characteristics of qi-tonifying and blood-tonic molecules, with a prediction accuracy higher than 80%, providing a new

tool for the material-based analysis of qi-blood theory and the discovery of new drugs (Liu et al., 2013b).

### CONCLUSIONS AND PROSPECTS

TCM is a complex mixed system with multiple components and multiple targets; thus, the identification of the potential bioactive molecules and the dissection of the underlying mechanisms of action to establish the optical drug combinations are the essential tasks of TCM. Fortunately, the advent of systems pharmacology framework provides powerful tools for TCM studies: 1) new methods for identification of active components/groups of TCM from the whole perspective. More than 10 mathematical models, including PreOB and PreHF, have been developed, which overcome the limitation of TCM in pharmacokinetic and pharmacodynamic experiments, providing convenient approaches for the discovery of effective substances; 2) large-scale target prediction systems of TCM, with three approaches (SysDT, WES, and Pred-binding) as new tools for drug target discovery; 3) the probability ensemble approach (PEA) model as a novel tool for the dissection of mechanisms of action and the prediction of new indications of TCM; and 4) a novel network of elementary subgraphs and a dynamic model was proposed for the large-scale screening of weak-binding compound in TCM.

With the aid of the systems pharmacology method above, we have built a systems pharmacology database and analysis platform for TCM, which has been applied for the illustration of the synergetic effects of drug combinations, the synergetic effects of multiple targets, pathways and organs, and the bidirectional regulation of Chinese medicine. Moreover, the systems pharmacology of TCM provides methodological guidance for the dissection of the combination principle and syndrome differentiation of herbal formulae, as well as the interpretation of the qi and blood basis of TCM from the molecular level to the systems level. It is of great significance to both the modernization of TCM and the development of modern medicine.

Although the systems pharmacology approach has achieved certain applications and results, the theory and methods require further improvements in the future; for example, the dose of herbs should be added into the model because the efficacy of the same herb obviously differs with different dosages. Therefore, it is necessary to integrate the drug dosage into the systems pharmacology models to provide guidance for clinical applications and further validation. In addition, the systems pharmacology models were constructed predominantly based on computer predictions; however, the reliability and validity of these models still need to be verified by experiments and clinical practice. Besides, the quality of TCM is one of the most important factors for modernization of Chinese medicines, so the study on its genuineness is of great significance for the efficacy of TCM. How to assess the quality of TCM and integrate it into the in silico model should be considered. Furthermore, the development of precision medicine, TCM combination, or a combination of TCM and Western medicine (WM) has obvious advantages. Mass clinical data showed that the complementary advantages of combined TCM and WM can significantly improve the efficacy of treatment for

FIGURE 10 | Dissection of the "Jun-Chen-Zuo-Shi" combination principle of Yujin Formula. (A) The potential molecule–target networks constructed by 58 potential active components (triangles) and 32 potential targets associated with cardiovascular diseases (CVDs) (round). (B) Target–disease network, linked by 32 potential targets (the middle circles were marked with a variety of colors, as in Figure 2) and 147 kinds of diseases (red squares), which were divided into 16 types (black triangles) (Li et al., 2012a).

Systems Pharmacology to Investigate the Mechanisms of TCM

many diseases and contribute to the development of precision medicine (Wang and Zhang, 2017). Therefore, how to predict the drug combination of these TCM and WM and assess the efficacy and side effects is valuable for novel drug design. At present, RCTs have been generally used to assess the clinical efficacy of TCM (Hu et al., 2017). For example, a metaanalysis of RCTs has shown that TCM significantly improved analog scale, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and total effectiveness rates of knee osteoarthritis. In addition, TCM showed a lower risk of adverse events than did standard western treatments (Chen et al., 2016). Studies have shown that TCM is effective in treating atrial fibrillation and has relatively few side effects, but the mechanism of action is still unclear (Wang et al., 2011; Liu et al., 2014; Cai et al., 2017). However, respective RCTs of TCM are limited, because there has been no English meta-analysis of TCM treatment for some diseases.

In the future, we will further improve systems pharmacology and provide extended guidance for the modernization of Chinese medicine and the development of new drugs.

## AUTHOR CONTRIBUTIONS

WZ wrote the first draft of the manuscript. YH drew the figures. ZM, AQ, and YW helped revise the manuscript.

## FUNDING

This work was supported by Fund of the Fundamental Research Funds for the Central Universities (no. 3102017OQD050), China's Post-doctoral Science Fund (no. 2017M623249), the National Natural Science Foundation of China (no. 31570940), and the Key Research and Development Project of Shaanxi Province (no. 2018SF-363).

### REFERENCES


treatment by Qubaibabuqi formula. *J. Ethnopharmacol.* 190, 272–287. doi: 10.1016/j.jep.2016.06.001


multicenter trial. *Zhonghua Yi Xue Za Zhi* 91, 1677–1681. doi: 10.3760/cma.j.i ssn.0376-2491.2011.24.006


diseases in an animal model. *ACS Chem. Bio.* 12, 1363–1372. doi: 10.1021/ acschembio.6b00762


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Zhang, Huai, Miao, Qian and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# *In Silico* Study of Anti-Insomnia Mechanism for Suanzaoren Prescription

*Jian Gao1, Qiming Wang1, Yuwei Huang1, Kailin Tang1, Xue Yang2\* and Zhiwei Cao1\**

*1 Shanghai 10th People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China, 2 Department of Traditional Chinese Medicine, Yangpu Hospital, School of Medicine, TongJi University, Shanghai, China*

Insomnia is a common and widespread sleeping disorder caused by various risk factors. Though beneficial, conventional treatments of insomnia have significant limitations. As an alternative treatment, Chinese herbal formula Suanzaoren prescription (SZRP), composed of Suanzaoren [seeds of *Ziziphus jujuba* var. *spinosa* (Bunge) Hu ex H.F.Chow] and four additional herbs, has been reported with significant anti-insomnia effects. Yet the antiinsomnia mechanism of the herb formulae remains unknown. In this study, we attempted to extrapolate the holistic anti-insomnia mechanism of SZRP through herbal targeting and network pharmacology. The results indicated that the ingredients of Suanzaoren can target multi-neurotransmitter receptors at synapse interface, which was reported to be associated with sedative and hypnotic effects, while the four additional herbs can hit multiple pathways downstream of membrane neurotransmitters. Furthermore, the four additional herbs showed highly cooperative targeting patterns in the paralleled and cross-talked pathways related to inflammatory regulation and endocrine system, which may contribute to the additional relief of insomnia caused by inflammation, anxiety, or endocrine disorder. The interesting complementary mechanism we found among the herbal groups of SZRP may provide an example to study Chinese herbal formula and offers clues to future design of anti-insomnia strategy.

Keywords: network pharmacology, suanzaoren prescription, insomnia, bioinformatics, systematic analysis

#### INTRODUCTION

Insomnia is a common sleep disorder in which people have difficulty falling asleep or staying asleep (Punnoose et al., 2012). It can sap quality of life and even cause damage to health. Shortterm insomnia can cause daytime sleeping and low energy (Lande and Gragnani, 2010), while long-term insomnia may lead to serious problems including driving accident, anxiety, chronic pain, cardiovascular disease, and heart failure (Taylor et al., 2007). According to its etiology, insomnia can be classified as primary or secondary (National Heart, 2016). Primary insomnia is not directly associated with any other health conditions or problems, and its pathogenesis remains unclear. On the contrary, secondary insomnia is related to those obvious triggers, and it is generally caused by the following reasons: 1) emotional disorder including psychological stress, depression, and anxiety; 2) health conditions of chronic pain, such as arthritis, headache, and other inflammations; 3) hormone disorder including menstruation, menopause, and hyperglycemia (Pepin et al., 2014; Santoro et al., 2015); and 4) some medicines and substances.

#### *Edited by:*

*Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China*

#### *Reviewed by:*

*Yun K. Tam, Sinoveda Canada Inc., Canada Feng Zhu, Zhejiang University, China Lin Tao, Hangzhou Normal University, China*

#### *\*Correspondence:*

*Xue Yang yxkxl@163.com Zhiwei Cao zwcao@tongji.edu.cn*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 16 January 2019 Accepted: 22 July 2019 Published: 22 August 2019*

#### *Citation:*

*Gao J, Wang Q, Huang Y, Tang K, Yang X and Cao Z (2019) In Silico Study of Anti-Insomnia Mechanism for Suanzaoren Prescription. Front. Pharmacol. 10:925. doi: 10.3389/fphar.2019.00925*

**318**

Gao et al. Anti-Insomnia Mechanism for Suanzaoren Prescription

Anti-insomnia treatment often includes lifestyle change, cognitive-behavioral therapy, and prescription medicines (Buysse, 2013). Nonpharmacologic treatments are usually recommended as the first-line treatment (Trauer et al., 2015), while pharmacologic treatments including benzodiazepines and benzodiazepine-like medications are applied as additional treatment if nonpharmacologic treatments fail (Asnis et al., 2015). Though pharmacologic treatments can be effective, patients may not tolerate their side effects such as rebound withdrawal effects, disruption in sleep architecture, grogginess, memory impairment, and undesired behaviors during sleep (Tiller et al., 2003; Wilt et al., 2016). In addition to those drugs mentioned above, antidepressants, antipsychotics, and antihistamines may also be applied for sedative effect. Yet they are usually not recommended in the absence of corresponding disease symptoms. Due to the present limited treatment, a substantial number of patients worldwide have started to seek their sleeping aids in herbal medicine as an alternative approach (Chen et al., 2009; Frass et al., 2012). It has been reported that Chinese herbal formulae "Xiao Yao Wan," "liquorice, wheat, and jujube soup," "Tian Wang Bu Xin Dan," and "Suanzaoren prescription (SZRP)" are clinically effective against insomnia (Chen et al., 2011; Liu et al., 2015; Ko et al., 2016); among them, SZRP is the most widely used and well documented for refractory insomnia. It was first recorded in *Shennong Bencao Jing*, which is the earliest authoritative monograph on pharmacy in China (Gu, 2007). In addition to the clinical practice, highquality randomized controlled trials have confirmed its efficacy and safety (Wang et al., 2010; Wang et al., 2013; Yuan et al., 2013). Moreover, a meta-analysis study covering 1,454 patients showed that both SZRP monotherapy and combinational treatment can improve sleep quality significantly with minimal side effects (Zhou et al., 2018).

SZRP contains the main herb seeds of *Ziziphus jujuba* var. *spinosa* (Bunge) Hu ex H.F.Chow (Suanzaoren) and four additional herbs: *Anemarrhena asphodeloides* Bunge (Zhimu), *Wolfiporia extensa* (Peck) Ginns (Fuling), *Ligusticum sinense* Oliv. (Chuanxiong), and *Glycyrrhiza uralensis* Fisch. (Zhigancao). According to the TCM theory and Chinese Pharmacopeia (China, 2015), Suanzaore is the *Emperor* (*Jun*), Zhimu and Fuling are the *Ministers* (*Chen*), Chuanxiong is the *Adjuvant* (*Zuo*), and Zhigancao is the *Courier* (*Shi*) (WHO, 2007). The functions of herb are usually believed to be multiple because of the vast chemical diversity. Previous literatures have reported that Suanzaoren is anti-insomnia (Shergis et al., 2017), Zhimu is laxative and anti-inflammatory (Park et al., 2018; Ji et al., 2019; Li et al., 2019), Fuling is diuretic (Zhao et al., 2012), and Chuanxiong is anti-migraine (Shan et al., 2018). However, current studies about the pharmacological mechanism of SZRP are mainly focused on the main herb Suanzaoren. Photochemical analysis indicated that Suanzaoren contains flavonoids, saponins, and triterpenes. Jujuboside A, sanjoinine A, and flavonoids in Suanzaoren were reported to have sedative and hypnotic effects. Further study showed that jujuboside A affected GABAergic and serotonergic system in rat through glutamate-mediated excitatory signal pathway (Cao et al., 2010). The hydrolysis product of jujuboside A, jujubogenin,

was predicted to have high potential of blood–brain barrier penetration ability (Chen et al., 2008). In addition, sanjoinine A was found to be able to prolong sleeping time through increasing chloride influx and GABA synthesis (Zhang et al., 2003). In addition, the flavonoid 6-hydroxyflavone in Suanzaoren showed GABA agonistic action by binding to GABAA receptors (Ren et al., 2010). These aforementioned studies provide a glimpse of the partial mechanism of anti-insomnia effects of Suanzaoren herb. The holistic mechanism of SZRP formulae remains unclear. What is the mechanism of additional herbs in treating insomnia? How can they help to improve the therapeutic effects of the main herb? No systematic studies have been published so far. In this work, first, we conducted a global analysis of all known targets of herbal ingredients for five herbs of SZRP to elucidate the overall anti-insomnia mechanisms of SZRP, and then we analyzed the targeting network patterns herb by herb for better understanding of their potential roles in TCM formulae.

### METHODS

#### Dataset

The information of herbal ingredients and targets was collected from online TCM databases Traditional Chinese Medicine Systems Pharmacology (Ru et al., 2014), Herb Ingredients' Targets (HIT) (Ye et al., 2011), and Natural Product Activity & Species Source Database (NPASS) (Zeng et al., 2018). The following key words were used to search in these databases: *Ziziphus jujuba* var. *spinosa* (Bunge) Hu ex H.F.Chow OR Suanzaoren; *Anemarrhena asphodeloides* Bunge OR Zhimu; *Wolfiporia extensa* (Peck) Ginns OR Fuling; *Ligusticum sinense* Oliv. OR Chuanxiong; and *Glycyrrhiza uralensis* Fisch. OR Zhigancao. Therapeutic targets were collected from Therapeutic Targets Database (TTD) (Li et al., 2018). The validated information of plants was collected from Kew Royal Botanic Garden (https://mpns.science.kew.org/ mpns-portal/) and The Plant List (http://www.theplantlist.org).

#### Functional Annotation and Enrichment Analysis

Statistical analysis of KEGG function enrichment of the target profile was performed by Metascape (Tripathi et al., 2015) (http://metascape.org). The pathways significantly enriched were selected (*p*-value < 0.01). Then only terms with both −Log (*p*-value) > 5 and more than 5% targets falling into the category were retained. The retained terms were mapped into bubble graph by "pyplot" of matplotlib. Finally, bubble graphs of each herb were combined and labeled with Adobe Photoshop software (Adobe, San Jose, California).

#### Network Construction for SZRP

To better elaborate the holistic mechanism of SZRP, three subnetworks were compiled by following the procedures: 1) All targets of SZRP were submitted to an online tool KEGG Search Pathway (https://www.genome.jp/kegg/tool/map\_ pathway1.html). 2) All result maps of KEGG were downloaded and integrated. 3) Maps related to "nervous," "immune," or "endocrine" were reserved. 4) For each label of "nervous," "immune," and "endocrine," multiple pathways were integrated and overlapped according to cross-talk targets in these maps. Detailed information such as targets names and integrated pathways are shown in **Table S3**. 5) Subnetworks were drawn with Adobe Illustrator software, where intermediate genes were hidden for better display.

#### RESULTS

#### Overall Ingredients and Targets of SZRP

To understand the potential anti-insomnia mechanism of SZRP holistically, known targets for herbal ingredient in the formula were collected and analyzed. Five medicinal species were validated by botanical documentation (**Figure 1**). *Z. jujube*, *A. asphodeloides*, *L. sinense*, and *G. uralensis* were validated by Kew Database and TPL. *W. extensa* was reported as an edible fungus (Esteban, 2009; Wei et al., 2016). Through database searching, 497 unique targets were collected for five herbs in SZRP (**Table 1**); 24% of proteins (119) were reported as therapeutic targets in TTD (Li et al., 2018). Three known therapeutic targets of anti-insomnia drugs were covered by SZRP targets including gamma-aminobutyric acid type A receptor (GABAR), 5-hydroxytryptamine receptor (HTR), and histamine receptor (HRH). All information of targets is shown in **Table S1**. The distribution of targets of five herbs is shown in **Figure 2**. Eighty known targets were retrieved for Suanzaoren, among which 26 were shared by all five herbs. In terms of target abundance, *A. asphodeloides* retrieved the most targets, while *L. sinense* had the most abundant unique targets.

TABLE 1 | Number of ingredients and known targets for each herb in SZRP.


#### Functional Analysis of Separated SZRP

To investigate the functional relationship among the five individual herbs of SZRP, targets of each herb were mapped to KEGG for functional enrichment analysis. The top 5 functional pathways for each herb in SZRP are listed in **Table 2**. It can be seen that apart from the main herb, the top enriched pathway profiles were highly similar for the four additional herbs. In **Table S2**, the overall pathway enrichment of total formulae is shown with their targets combined. Aside from cellular processes, metabolism, and signal transduction terms, organismal systems of nervous, endocrine, immune infections (NEIs) were significantly enriched. As our aim is to interpolate the potential anti-insomnia mechanism, we tentatively focused on organismal systems of NEIs, which are known to closely relate with insomnia pathology (Santoro et al., 2015; Xiang et al., 2019). To display the functional similarity and difference among herbal groups of SZRP, significant pathway terms of KEGG were mapped into a bubble graph in **Figure 3**.

The big and higher bubbles in **Figure 3** represent those highly significantly enriched pathway terms. It can be seen that the main


FIGURE 1 | The validated information of five herbs in SZRP, including botanical documentation (voucher specimen deposited in herbarium), location (the collection location of voucher specimen), and used part (used part of medicinal species).



herb of SZRP significantly regulates the function of nervous system, such as dopaminergic and serotonergic synapse pathways, suggesting that the *Emperor* (*Jun*) herb may enable hypnotic effects through modulating excitement in nervous system. In contrast, four additional herbs concentrated more significantly on the endocrine system, immunology system, and infections, offering complementary effects against insomnia. Specifically, the *Minister* (*Chen*) herb significantly targets virus infection pathways and immune cell pathways, while the *Adjuvant* (*Zuo*) and *Courier* (*Shi*) herbs affected signaling regulation of oxidative stress (AGE–RAGE) and hormone secretion (Prolactin).

#### Subnetwork of SZRP on NEI System

For further investigation, subnetworks were constructed based on enriched pathways for multi-neurotransmitter regulation, inflammatory regulation, and endocrine regulation (**Figures 4**  and **5**). Each target in the pathways was labeled according to the different targeting patterns of the four herbal groups.

**Figure 4** shows the targeting patterns of SZRP herbs in a multi-neurotransmitter regulation network. These targets can be roughly divided into three classes: 1) interacted directly with multi-neurotransmitter receptors and downstream pathways; 2) related to the synthesis, secretion, and recycling

of neurotransmitters; and 3) related to the sedative or hypnotic effects, such as opioid receptor (OPR) and adenosine receptor (ADORA). Of interest is that all herbal groups can target transporter proteins of dopamine and serotonin in the presynaptic membrane. When being examined from the perspective of herbal groups, the *Emperor* (*Jun*) herb significantly targeted on interfacial neurotransmitter receptors (**Figure 4**). The four additional herbs not only targeted those receptors but also targeted those in downstream, such as Raf, Akt, PKC, and CaM. Specifically speaking, the *Courier* (*Shi*) herb mainly regulated major downstream targets of multi-neurotransmitter regulation pathway. The synapse formation, axonal outgrowth, synaptic plasticity, and neuroprotection medicated by targets nAChR, DR, and HTR may be beneficial to maintain circadian rhythm; in addition, targets of SZRP such as GABRA, ADORA, and HRH have been reported to participate in sleep induction (Huang et al., 2014; Wisden et al., 2017).

Besides, SZRP can regulate immune-related inflammatory pathways and their upstream targets (**Figure 5**). The *Emperor* (*Jun*) herb mainly influenced COX2, while the four additional herbs took part in the regulation of inflammatory factors such as IL-1, IL-2, IL-6, IL-8, and IL-10. And the *Courier* (*Shi*) herb covered the largest area of inflammation pathways. Two compounds from the *Adjuvant* (*Zuo*) herb *L. sinense* have been reported to inhibit inflammation through downregulating IL-1 and IL-8 (Diodovich et al., 2003; Medeiros et al., 2007) and upregulating IL-10 (Sarkar et al., 2006). Inflammation and chronic pain are widely reported with insomnia (Cheatle et al., 2016), while inhibition of inflammation and chronic pain was shown to improve sleep quality (Trenkwalder et al., 2017; Wells et al., 2017).

Endocrine disorder associated with premenstrual and menopausal syndromes in women shows outstanding effects on secondary insomnia (Santoro et al., 2015). **Figure 5** shows the targeting patterns of SZRP herbs in endocrine regulation pathways. These targets were related to the regulation of blood sugar, blood pressure, and hormone system. All herbal groups mainly exerted a wide range of hormonal regulating effect by targeting the key receptors such as estrogen receptor (ER), progesterone receptor (PR), and adrenergic receptor (ADR). The whole formula played holistic and complementary roles in blood pressure and hormone regulation, while other herbal groups of *Minister* (*Chen*), *Adjuvant* (*Zuo*), and *Courier* (*Shi*) showed unique effects on blood sugar regulation by targeting insulin (INS), insulin receptor (INSR), and glucagon-like peptide 1 receptor (GLP-1R). Menstruationand menopause-related insomnia is often caused by endocrine disorder (Santoro et al., 2015); moreover, hyperglycemia and hypertension are the high-risk factors of insomnia.

To understand the mechanistic relationship within TCM formulae, SZRP was disassembled into individual herbal groups. The *Emperor* (*Jun*) herb was found to mainly target receptors in synapse membrane, which might relieve the primary insomnia. The *Minister* (*Chen*) herbs can target not only the nervous system

helping the *Emperor* (*Jun*) herb but also inflammation and endocrine pathways, providing potential assistance to secondary insomnia. Zhimu has been reported to act on HRH, an effective target for sedative effect (Krystal et al., 2013). Among the herbs, only the Adjuvant (Zuo) herb can upregulate IL-10, the wellknown anti-inflammatory cytokine (Ouyang et al., 2011; Iyer and Cheng, 2012). It is noted that no unique function has been found for the *Courier* (*Shi*) herb in the network we constructed. Its function may be relates to the pharmacokinetics. Previous study showed that the *Courier* (*Shi*) herb was able to facilitate the adsorption of Suanzaoren (Shen et al., 2012; Bi et al., 2014). These results agree with the TCM theory that *Jun*, *Chen*, *Zuo*, and *Shi* perform their own functions and cooperate with each other (Fan et al., 2006). In summary, the holistic mechanism of SZRP was first studied in a systematic way by network pharmacology.

### DISCUSSION

In this study, the anti-insomnia mechanism of SZRP was first studied in a distinctive way by networking pharmacology. As a prevalent sleeping disorder, insomnia can be induced by various risk factors. Our research found that multiple pathways were involved in the anti-insomnia effects of SZRP. Five herbs in SZRP seem to play different roles in a complimentary way. The ingredients of the main herb mainly targeted multi-neurotransmitter proteins at the pre-synapse and post-synapse interface exerting sedative and hypnotic effects. This effect was likely enhanced by additional herbs through holistically regulating the nervous system related to insomnia symptoms. On top of that, the additional herbs were suggested to intensely regulate the immune system, particularly inflammation cytokines, which were often reported as an important influential factor for insomnia (Quartana et al., 2015; Irwin et al., 2016; Fernandez-Mendoza et al., 2017). Moreover, the whole formula can target the endocrine system to balance the hormone, blood pressure, and blood sugar, enhancing the sedative and hypnotic effects of the main herb.

By decomposing the formula, the *Emperor* (*Jun*) herb, *Z. jujuba*, was found to target synapse membrane, which may fight the main symptom of insomnia. The *Minister* (*Chen*) herbs, *A. asphodeloides* and *W. extensa*, not only can regulate the nervous system helping *Jun* herb in relieving the main symptom but also can densely target inflammation and endocrine pathways treating the likely pathological causes or secondary symptoms. *A. asphodeloides* is reported to act on HRH, which is an effective sedative target (Krystal et al., 2013). Interestingly, only the *Adjuvant* (*Zuo*) herb can uniquely upregulate IL-10, the wellknown inflammation-inhibiting factor (Ouyang et al., 2011; Iyer and Cheng, 2012) that seems to counteract the inflammationinhibiting effects of other herbal groups. It is noted that no unique function has been found for the *Courier* (*Shi*) herb *G. uralensis* from current MOA networking. We suggest that its possible role in ADME processes cannot be detected in the current analysis (Shen et al., 2012; Bi et al., 2014). The most interesting is to see the targeting patterns in the subnetworks among the herbal groups, such as targeting multi-points of the same pathway, parallel

pathways, and cross-talked pathways. These patterns have been reported to have beneficial synergistic effects (Jia et al., 2009) and have been applied to design synergistic drug combinations for cancer (Sun et al., 2015).

Meanwhile, the above results seem to support the TCM theory that the *Emperor* (*Jun*) herb often deals with the main symptoms of a disorder, while the *Minister* (*Chen*) assists the *Emperor* (*Jun*) herb to fight the main symptoms and also to remove those accompanying symptoms and signs. The *Adjuvant* (*Zuo*) herbs usually coordinate the effects of the *Emperor* (*Jun*) and *Minister* (*Chen*) herbs by counteracting their toxic or side effects, and the *Courier* (*Shi*) herb helps to deliver or guide the other herbs in the prescription to the target organs (Fan et al., 2006). As such, the future design of anti-insomnia drugs may cover not only the nervous system but also the endocrine and immune systems holistically for better clinical efficacy.

Herbal medicines are usually believed to be multi-functional because of their vast chemical diversity. In this work, we only focused on the widely reported anti-insomnia effects of SZRP. This does not exclude the possibility of additional functions of the formulae. In the pathway enrichment analysis, cancer, cardiovascular disease, signal transformation, and lipid metabolism were also detected for the whole formulae, inferring potential functions to be discovered. In fact, one is often involved with multiple functions and labeled with many different pathways. For instance, NFKB1 was labeled by 67 pathways in KEGG, suggesting its important role as a cross-talk gene. In our case, the ratio of NEI targets chosen for anti-insomnia analysis is about 51.7% of total unique targets.

It is realized that our study aims to explain the global mechanism of traditional formula by decomposing the herbal formula. The current conclusion may be limited by the data quality and quantity, as well as clinical evidence. To avoid noise induction, we started with a widely acknowledged formula as an example, and their targets with literature support, instead of predicting targets. In the future, more efforts should be devoted to mutual interactions between herbal constituents and the dosage effects. Therefore, we here recommend our method of TCM formula with clear clinical effects and validated targets at the current stage.

#### AUTHOR CONTRIBUTIONS

JG wrote the manuscript and contributed to the overall design. QW, YH, and JG analyzed and interpreted the results. YH, KT, and QW modified the manuscript. XY and ZC supervised the project. All authors read, critically reviewed, and approved the final manuscript.

#### FUNDING

This work was supported in part by the National Key R&D Program of China (2017YFC1700200 and 2017YFC0908400) and the National Natural Science Foundation of China (31671379).

#### REFERENCES


#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.00925/ full#supplementary-material

TABLE S1 | 497 targets of 5 herbs in SZRP.

TABLE S2 | Top 50 pathways of functional enrichment analysis.

TABLE S3 | Original information of targets in Figure 4-5.

studies and experimental sleep deprivation. *Biol. Psychiatry* 80 (1), 40–52. doi: 10.1016/j.biopsych.2015.05.014


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Gao, Wang, Huang, Tang, Yang and Cao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Erxian Decoction Attenuates TNF-**α** Induced Osteoblast Apoptosis by Modulating the Akt/Nrf2/HO-1 Signaling Pathway

*Nani Wang1,2, Hailiang Xin3, Pingcui Xu1,2, Zhongming Yu1 and Dan Shou1\**

*1 Department of Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, China, 2 School of Pharmacy, Zhejiang Chinese Medical University, China, 3 School of Pharmacy, Second Military Medical University, China*

Erxian decoction (EXD), a traditional Chinese medicine formula, has been used for treatment of osteoporosis for many years. The purpose of this study was to investigate the pharmacological effect of EXD in preventing osteoblast apoptosis and the underlying mechanism of prevention. Putative targets of EXD were predicted by network pharmacology, and functional and pathway enrichment analyses were also performed. Evaluations of bone mineral density, serum estradiol level, trabecular area fraction, serum calcium levels, and tumor necrosis factor (TNF)-α levels in ovariectomized rats, as well as cell proliferation assays, apoptosis assays, and western blotting in MC3T3-E1 osteoblasts were performed for further experimental validation. Ninety-three active ingredients in the EXD formula and 259 potential targets were identified. Functional and pathway enrichment analyses indicated that EXD significantly influenced the PI3K-Akt signaling pathway. *In vivo* experiments indicated that EXD treatment attenuated bone loss and decreased TNF-α levels in rats with osteoporosis. *In vitro* experiments showed that EXD treatment increased cell viability markedly and decreased levels of caspase-3 and the rate of apoptosis. It also promoted phosphorylation of Akt, nuclear translocation of transcription factor NF-erythroid 2-related factor (Nrf2), and hemeoxygenase-1 (HO-1) expression in TNF-α-induced MC3T3-E1 cells. Our results suggest that EXD exerted profound anti-osteoporosis effects, at least partially by reducing production of TNF-α and attenuating osteoblast apoptosis *via* Akt/Nrf2/HO-1 signaling pathway.

Keywords: network pharmacology, Erxian decoction, osteoporosis, Akt, tumor necrosis factor

### INTRODUCTION

Osteoporosis is a skeletal disease characterized by imbalanced bone homeostasis, which leads to an increase in bone fragility and fracture risk (Liu et al., 2017). Development of osteoporosis is mainly due to the production of a large number of immune and hematopoietic factors in the bone microenvironment (Yu and Wang, 2016). These complex and interacting factors influence the formation and absorption of bone (Wei and Frenette, 2018). One of the most important factors in osteoporosis is tumor necrosis factor-alpha (TNF-α), which is the strongest bone resorption enhancer and also inhibits bone formation (Zhou et al., 2017a; Zhou et al., 2017b).

*Edited by:*

*Yuanjia Hu, University of Macau, China*

#### *Reviewed by:*

*Lu Yan, Chinese Academy of Sciences, China Runyue Huang, Guangzhou University of Chinese Medicine, China*

> *\*Correspondence: Dan Shou shoudanok@163.com*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 05 October 2018 Accepted: 31 July 2019 Published: 10 September 2019*

#### *Citation:*

*Wang N, Xin H, Xu P, Yu Z and Shou D (2019) Erxian Decoction Attenuates TNF-α Induced Osteoblast Apoptosis by Modulating the Akt/Nrf2/HO-1 Signaling Pathway. Front. Pharmacol. 10:988. doi: 10.3389/fphar.2019.00988*

Wang et al. EXD Attenuates Apoptosis via Akt/Nrf2/HO-1

Erxian decoction (EXD) is a traditional Chinese medicine (TCM) formulation comprising six herbs: *Epimedium sagittatum*  (Siebold & Zucc.) Maxim. (ES), *Curculigo orchioides* Gaertn. (CO), *Angelica sinensis* (Oliv.) Diels. (AS), *Phellodendron chinense* Schneid. (PC), *Anemarrhena asphodeloides* Bge. (AR), and *Morinda officinalis* How (MO). EXD has been used to treat osteoporosis for several decades (Wang et al., 2016). We previously reported that some components of EXD, such as icariin, curculigoside, and berberine, displayed inhibitory effects on osteoclastic bone resorption and positive effects on osteoblast proliferation (Wang et al., 2017a; Wang et al., 2017b). However, potential effects of EXD on TNF-α production and TNF-αinduced bone loss have not been investigated.

Recently, network pharmacology analyses have been used to investigate TCM formulas to predict the molecular targets and pathways of different diseases (Zhao and He, 2018). As a systems biology-based methodology, network pharmacology provides an effective approach for evaluating the multi-pharmacological effects of traditional medicines at the molecular level and for evaluating the interactions of chemical molecules and target proteins (Liu et al., 2016). In our previous study, network pharmacology was used to predict the mechanism for the effects of CO in the prevention and treatment of osteoporosis (Wang et al., 2017a; Wang et al., 2017b). In the current study, network pharmacology was combined with experimental validation to study the effects of EXD on TNF-α-induced bone loss and clarify the underlying mechanism.

### MATERIALS AND METHODS

#### Instruments and Reagents

Double distilled water of at least 18.2 MΩ was purified by an ultrapure water system (Millipore Corporation, Boston, Massachusetts, USA). α-Modified minimum essential medium (α-MEM), phosphate buffered saline (PBS), trypsin, and fetal bovine serum (FBS) were purchased from Gibco (Gaithersburg, Maryland USA). TNF-α (purity >98%) was obtained from Sigma (St Louis, MO, USA).

Orcinol glucosid (>98%), palmatine (>99%), jatrorrhizine (>94%), berberine (>98%), protodioscin (>98%), baohuoside I (>99%), timosaponin BII (>99%), icariin (>98%), obacunone (>8%), curculigoside (>98%), anhydroicaritin (>98%), mangiferin (>98%), epimedin C (>98%), epimedin B (>98%), epimedin A (>98%), magnolflorine (>98%), and phellodendrine (>98%) standards were purchased from Aoke Biological Technology Co., LTD (Beijing, China). Ferulic acid (>98%) and naringin (>98%) were purchased from the National Institutes for Food and Drug Control (Beijing, China). Anemarsaponin B (>98%) was purchased from Yuanye Biological Technology Co. Ltd. (Shanghai, China).

The aerial parts of *E. sagittatum* (Siebold & Zucc.) Maxim*.* (Lot No: 170420, Drug name: Epimedii Folium) were obtained from Huadong Medicine Co. Ltd. (Zhejiang, China). The rhizomes of *C. orchioides* Gaertn. (Lot No: 1702074, Drug name: Curculiginis Rhizoma), the roots of *M. officinalis* How (Lot No: 1711067, Drug name: Morindae Officinalis Radix), the bark of *P. chinense* Schneid. (Lot No: 1710100, Drug name: Chinensis Cortex), and the rhizomes of *A. asphodeloides*  Bge. (Lot No: 1710006, Drug name: Anemarrhenae Rhizoma) were obtained from Quzhou Nankong Chinese Medicine Co. Ltd. (Zhejiang, China). The roots of *A. sinensis* (Oliv.) Diels (Lot No: 1802011, Drug name: Angelicae Sinensis Radix) were obtained from Zhejiang Conba Pharmaceutical Co. Ltd. (Zhejiang, China).

#### Chemical Components of Herbs in Erxian Decoction

Chemical components of each herb in EXD were determined from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) (Ru et al., 2014), TCM database @taiwan (Sanderson, 2011), Herbal Ingredients Targets (HIT), Traditional Chinese Medicine Integrated Database (TCMID) (Xue et al., 2013), and previous literature (Bian et al., 2013; Yu et al., 2013). The molecular properties of the herbs, including molecular weight (MW), Moriguchi octanol-water partition coefficient (AlogP), oral bioavailability (OB), drug-likeness (DL), number of donor atoms for H-bonds (nHDon), and number of acceptor atoms for H-bonds (nHAcc) were compared in **Table S1**.

#### Predication of Active Components and Targets

OB was used to monitor drug convergence during the ADME process, representing the percentage of an orally administered dose of unchanged drug that reached the systemic circulation (Simpson et al., 2009). DL was used for estimating how "druglike" prospective compounds were a parameter that is applied in drug design to optimize pharmacokinetic and pharmaceutical properties (Gozalbes et al., 2010). EXD components were considered active if OB ≥30% and DL ≥0.18 (Wang et al., 2015). Validated targets of potential active components were determined from Therapeutic Target Database (Chen et al., 2002), TCMSP, Drug bank (Law et al., 2014), and STITCH (Kuhn et al., 2008).

#### Network Pharmacology Analyses

Gen Ontology (GO) function enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis were carried out using The Database for Annotation, Visualization and Integrated Discovery (DAVID) database (Huang et al., 2007). The interaction network of the potential active components and identified targets of EXD, as well as of enriched KEGG pathways, were visualized with Cytoscape v3.4.0 software (**Figure 1**).

### Herbal Preparation

EXD extract was prepared by hot water extraction as previously described (Wang et al., 2017a; Wang et al., 2017b) with some modifications. Specifically, herbs were pulverized and 20 g of the powder of each herb (120 g total) was placed in

5-L triangular flasks. Samples were extracted twice with 2-L water (1 h each extraction) at 100°C. Extracted solutions were concentrated to 1 g (crude extract)/ml.

### Animals and Treatment

Twelve-month-old female Wistar rats (weighting 350–400 g) were supplied by the Animal Center of the Zhejiang Academy of Traditional Chinese Medicine (Hangzhou, China). The rats were maintained in air-conditioned quarters at 24 ± 2°C and a relative humidity of 60 ± 5%. All protocols for animal experiments were approved in accordance with the Guide for the Care and Use of Laboratory Animals and were approved by the Bioethics Committee of Zhejiang Academy of Traditional Chinese Medicine.

Animals were body-weight matched and randomly assigned into several groups: 1) sham-operated group (Control group); 2) ovariectomy (OVX) group (Model group); and 3) three OVX operated groups with oral administration of EXD extract (EXD groups). The doses of EXD administered were 2, 4, and 6 g/kg/ day in the low, middle, and high level to EXD groups, respectively. EXD administration was initiated 4 days after OVX operation and provided for 12 weeks.

### Liquid Chromatographic Analysis

High-performance liquid chromatography (HPLC) was performed on a Thermo Fisher ultra-high-performance liquid chromatography 3000 system (Thermo Fisher Technologies, Waltham, MA, USA), comprising a dual pump, auto-sampler, ultraviolet (UV) detector and Chromeleon software equipped with a 4.6 × 250 mm ZORBAX Eclipse XDB C18 column (Agilent, Santa Clara, CA, USA). The column temperature was set at 30°C. The mobile phases of HPLC were composed of acetonitrile (A) and water (B). The analytical column was SHISEIDO Capcell Pak C18 (Tokyo, Japan). The gradient condition was as follows: 0–15 min, 95–80% B; 15–30 min, 80–60% B; 30–60 min, 60–40% B. The column flow rate was 1.00 ml/min. The wavelength of UV detection was set at 285 nm. The injection volume was 10 μl. The EXD extract was air-dried and the residue was dissolved to a final concentration of 5 mg/ml in a 1:9 mixture of acetonitrile and water. The diluted solution was passed through a 0.22-µM pore filter prior to HPLC analysis.

### Histomorphology Assay

Left femurs were fixed in 10% formalin, embedded in paraffin, cut into 5-μm-thick sections, and finally stained with hematoxylin–eosin (HE) for histopathological analysis. At least 10 independent fields were assessed per sample in each treatment group. Trabecular area was measured according to the previous literature (Kloefkorn and Allen, 2016). Bone mineral density (BMD) was determined by Lunar dual-energy X-ray absorptiometry (GE, Boston, MA, USA) using the small animal scan mode.

#### Serum Biochemical Analysis

Blood was drawn from rats and centrifuged at 4°C, 5,000 × g for 10 min to collect the upper serum, and stored at −80°C. The concentrations of serum calcium (Ca), estradiol (E2), TNFα, and caspase-3 were detected with commercial detection kits (Boster Biological Technology Co. Ltd, California, USA) in accordance with the manufacturers' protocols.

#### Immunohistochemistry Analysis

Osteocalcin expression in the right femur was examined by immunohistochemistry. Briefly, tissue sections were treated with 3% H2O2 to remove endogenous peroxidase, incubated with anti-osteocalcin antibody (ab13420, Abcam, Cambridge, UK) at 4°C overnight prior to addition of added secondary antibody (PV-6002, ZSGB-Bio, Beijing, China), incubated at 37° C for 30 min, then colored with DAB (diaminobenzidine) reaction staining. Finally, sections were evaluated by microscopy in a blinded manner.

MT3T3-E1 (5 × 106 ) cells were seeded in six-well plates and then treated with EXD extract or TNF-α as indicated. Cells were fixed with 4% paraformaldehyde in PBS (0.1 M, pH 7.4) for 15 min, permeabilized with 50 μg/ml digitonin in PBS for 5 min, blocked with 0.1% (v/v) gelatin in PBS for 30 min, and then incubated with primary antibodies for 1 h. After washing, cells were incubated with Alexa Fluor 488-conjugated goat antiguinea pig and Alexa Fluor 647-conjugated goat anti-rabbit IgG secondary antibodies (Invitrogen, Waltham, MA, USA) for 30 min. Cells were imaged using a laser-scanning microscope (LSM510 META, Carl Zeiss, Oberkochen, Germany) with a Plan Apochromat 63 × NA 1.4 oil differential interference contrast objective lens.

#### Cell Culture and Treatment

MC3T3-E1 cells were cultured in α-MEM containing 10% FBS. All cells were washed with PBS before incubation with EXD or TNF-α. After reaching 85% confluence, the cells were treated with medium containing TNF-α and EXD according to the experimental design.

#### Cell Proliferation Assay

Cell viability was evaluated with commercially available 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) detection kits (Bio-Rad, Foster, California, USA) in accordance with the manufacturer's protocol. Briefly, treated cells were incubated 10% MTT solution at 37°C for 4 h, supernatants were removed, and the formazan crystals were dissolved in 150 μl DMSO (dimethyl sulfoxide). Absorbance was recoded at a wavelength of 490 nm.

#### Assay of Annexin V-EGFP/PI Apoptosis

Apoptosis was determined using the Annexin V-EGFP/ PI Apoptosis Detection Kit (Jiancheng, Nanjing, China). MT3T3-E1 cells (5 × 106) were seeded in six-well plates and then treated with LY294002, EXD extract, or TNF-α according to the experimental design. Detection was performed using flow cytometric analysis (ACCURI C6, BD Bioscience, Franklin Lakes, NJ, USA).

#### Western Blot Analysis

MT3T3-E1 cells (5 × 106 ) were seeded in six-well plates and then treated with EXD extract or TNF-α according to the experimental design. Cells were extracted with lysis buffer for 30 min at 4°C and the supernatant containing total protein was harvested. Aliquots containing 50 μg of protein were separated by sodium dodecylsulfate polyacrylamide gel electrophoresis and transferred to a polyvinylidine difluoride membrane (Millipore, MA, USA). Membranes were soaked in blocking buffer (5% skimmed milk) for 2 h. The proteins were detected with primary antibodies overnight at 4°C, and then probed with HRP (horseradish peroxidase)-conjugated secondary antibody for 1 h at room temperature. Detection was performed with an enhanced chemiluminescence system (ECL, Beyotime, Haimen, China). The relative content of each protein of interest was calculated as the ratio of its optical density to that of β-actin in the same sample.

#### Statistical Analysis

All experiments were performed in triplicate and results are presented as mean ± SD. One-way ANOVA was used to assess statistical significance of differences between group means. A value of p < 0.05 was considered statistically significant.

#### RESULTS

#### Physicochemical Properties of Active Components in EXD

A total of 93 compounds present in EXD met the criteria for inclusion (OB ≥ 30% and DL index ≥ 0.18). MW, AlogP, HDon,

TABLE 1 | Properties of potential active ingredients from Erxian decoction.


*aEpimedium sagittatum (Siebold & Zucc.) Maxim. (ES), Curculigo orchioides Gaertn. (CO), Angelica sinensis (Oliv.) Diels. (AS), Phellodendron chinense Schneid. (PC), Anemarrhena asphodeloides Bge. (AR), Morinda officinalis How (MO).*

#*P* < 0.05, ##*P* < 0.01 compared to ES; \**P* < 0.05, \*\**P* < 0.01 compared to AR.

nHAcc, OB, and DL of all 93 compounds were determined to identify similarities and differences in the physicochemical properties of the components in each herb (**Table 1**). Herbal sources of these EXD components were determined from TCMSP, TCM database @taiwan, HIT, TCMID, and previous reports, as shown in **Table S1**. The structures of these potentially active components are presented in **Table S2**. The nHacc and DL of the components derived from ES were significantly different to those derived from PC, and the nHacc of compounds derived from AR were different from those from all other herbs.

#### Network Pharmacology Analysis of EXD

A total of 259 targets corresponding to active components in EXD were collected. Cytoscape was used to establish the chemical component-targets-pathway regulatory network of EXD, which showed correlation of 42 compounds, 150 targets, and 32 pathways, as shown in **Figure 1**. The network among PI3K-Akt pathway, the targets, and ingredients were presented in **Figure 1C**. According to the degree ranking in the network, the top 10 nodes were AKT1 (degree 108), TP53 (degree 102), IL6 (degree 99), VEGFA (degree 95),

CASP3 (degree 89), JUN (degree 88), PTGS2 (degree 86), MAPK8 (degree 85), MAPK1 (degree 83), and EGFR (degree 80). AKT1 appeared with the highest frequency and was correlated with 132 nodes, including 5 components, 19 KEGG pathways, and 108 targets. Among the 42 ingredients, luteolin from ES had the highest degree (degree 31). PI3K-Akt had the highest degree (40) in the signaling pathway. Six EXD components would have a close relationship with Akt signaling pathway. The network analysis suggested that the Akt pathway may play an important role in the antiosteoporosis mechanism of EXD.

#### EXD Attenuated Osteoporosis in Ovariectomy Rats

Twenty dominant components in the EXD extract were quantified by HPLC-UV (**Figure S1**). These components ranged in concentration from 0.23 to 34.38 mg/L (**Table S3**), with icariin from ES, orcinol glucoside from CO, and epimedin B from ES being the most abundant compounds. As compared with the model group, BMD was significantly higher in the EXD group (**Figure 2A**). EXD treatment also increased the fraction of trabecular area of OVX rats, E2 level, serum Ca, and bone formation (**Figures 2B**–**E**). As shown in **Figure 2F**, osteocalcin was negatively correlated with BMD, trabecular area, and E2 level. It is possible that a high transformation type of osteoporosis was induced by OVX resulting in bone absorption being higher than bone formation (Zhou et al., 2017a; Zhou et al., 2017b). After EXD treatment, osteocalcin expression markedly reduced.

The level of TNF-α in the serum of the model group was 170% higher than in the control group (**Figure 3**). Compared with the model group, the EXD groups had an average 44% decrease in the TNF-α level.

#### EXD Protects Osteoblasts Against TNF-**α** Induced Injury Through AKT/Nrf2/HO-1 Pathway

To investigate the potential effects of EXD on TNF-α-induced cytotoxicity in osteoblasts, MC3T3-E1 osteoblastic cells were treated with TNF-α or EXD in a range of concentrations (0.1–10 μM). Cell viability decreased after exposure to TNF-α and EXD treatment significantly protected MC3T3-E1 cell against TNF-α-induced injury in a dose-dependent manner (**Figure 3**).

EXD treatment reduced apoptosis after TNF-α exposure (**Figure 4**) and increased phosphorylation of AKT. It also significantly activated the expression of nuclear Nrf2 and HO-1, which were the essential downstream targets of Akt (**Figure 5**). PI3K inhibitor (LY294002) reduced both p-Akt and nuclear Nrf2 expression. A HO-1 inhibitor (ZnPP-IX) reduced the expression of HO-1 in the EXD-treated osteoblasts. Taken together, the above results indicated that EXD reduced TNF-α production and cell apoptosis, probably *via* activation of the Akt/Nrf2/HO-1 signaling pathway in TNF-α induced osteoblast.

with water or EXD extract at a dose of 2 g/kg/day (low level), 4 g/kg/day (middle level), 6 g/kg/day (high level), which started on day 4 after OVX operation for 12 weeks. #*P <*0.05, ##*P* < 0.01 compared to the control group. \**P* < 0.05, \*\**P* < 0.01 compared to the model group. (B) Effects of EXD on MC3T3-E1cell viability. Cells were induced by TNF-α. #*P <*0.05, ##*P* < 0.01 compared to the control group. \**P* < 0.05, \*\**P* < 0.01 compared to the TNF-α group (10 ng/ml).

## DISCUSSION

EXD, a traditional Chinese medicinal formula, has been used widely for treating osteoporosis (Tong et al., 2012). However, the effect of EXD on osteoblast injury induced by TNF-α induced underlying mechanism has not been fully identified. In the current study, EXD treatment attenuated TNF-α production in OVX rat and protected osteoblasts against TNF-α-induced apoptosis. Our results suggest that EXD might attenuate osteoporosis at least partially by regulating the Akt/Nrf2/HO-1 signaling pathway (**Figure 6**).

*In vivo* investigation showed significantly decreased TNF-α serum levels after EXD treatment. Similarly, we showed that TNF-α reduced osteoblast activity and ALP activity *in vitro,* resulting in increased apoptosis and activation of caspase-3. The cleavage of caspase-3 is the final step of the process that initiates the apoptotic signaling (Wei and Frenette, 2018). However, EXD attenuated the increase of caspase-3 and TNF-α compared to the cell model. Thus, EXD treatment not only attenuated TNF-α production in the serum, but also protected osteoblasts against TNF-α-induced apoptosis.

FIGURE 4 | Effect of EXD and selective inhibitor LY294002 on TNF-α-induced apoptosis in MC3T3-E1 cells. Cells were incubated with or without 20 μM LY294002, 100 μg/ml EXD, and 10 ng/ml TNF-α for 24 h. (A) Cell apoptosis rate. (B) Caspase-3 level. (C) Annexin V/PI staining results. #*P <* 0.05, ##*P* < 0.01 compared to the control group. \**P* < 0.05, \*\**P* < 0.01 compared to the TNF-α group.

Molecular networks were constructed to demonstrate interactions between EXD ingredients, targets, and enriched pathways. Pathway and functional enrichment analyses indicate that EXD was primarily associated with the PI3K-Akt signaling pathway, which in turn was associated with 29 targets (**Figure 1**). Investigation of the correlation between PI3K-Akt activation and EXD-regulated osteoblast protection in the presence of TNF-α found results consistent with results from the network pharmacology study, i.e., EXD administration activated the PI3K-Akt pathway. A PI3K inhibitor (LY294002) reduced EXD-mediated protection of cell death and apoptosis (**Figure 4**), in accordance with the previous reports that activation of PI3K-Akt pathway attenuates osteoblast injury in osteoporosis (Vanella et al., 2010).

An increasing number of studies have indicated that TNF-α exposure significantly effects HO-1 expression, which is an essential anti-inflammatory molecule that regulates proinflammatory mediators (Chiu et al., 2016). Since the master upstream regulator of HO-1, Nrf2, is affected by the PI3K-Akt signaling pathway, we examined whether the Akt/Nrf2/HO-1 signaling pathway was involved in EXD mediated regulation of osteoblast apoptosis. EXD was found to increase the level of AKT phosphorylation and also promoted nuclear translocation of Nrf2, further increasing HO-1 expression. LY294002 inhibited nuclear translocation of Nrf2, and a HO-1 inhibitor (ZnPP-IX) reversed the EXD-mediated increase of HO-1 expression in TNFα-induced osteoblasts. These observations suggest that EXD protected osteoblasts against TNF-α-induced apoptosis at least partially by activating the Akt/Nrf2/HO-1 signaling pathway.

Furthermore, the molecular network analysis predicted that six EXD components would have a close relationship with Akt signaling pathway, including kaempferol, luteolin, quercetin, beta-sitosterol, diosgenin, and rutaecarpine. The above six EXD compounds were derived from the traditional medicinal parts of six herbs in EXD. Three flavonoids (luteolin, kaempferol, and quercetin) could be isolated from the aerial parts of *E. sagittatum*  (Siebold & Zucc.) Maxim*.* (Chen et al., 1996; Wang et al., 2010a; Wang et al., 2010b). Diosgenin was a spirostanol steroidal saponin, which could be obtained from the rhizomes of *A. asphodeloides*  Bge. (Xia et al., 2017). These ingredients showed anti-osteoporosis effects *via* promotion of osteogenic differentiation and stimulation of mineralization in osteoblasts (Pang et al., 2006; Nash et al., 2015). Luteolin and kaempferol also had anti-apoptotic properties (Pang et al., 2006; Im et al., 2018). Beta-sitosterol existed in four herbs, including bark of *P. chinense* Schneid (Wang et al., 2009; Xue et al., 2015), roots of *M. officinalis* How. (Hong et al., 2009; Zhang et al., 2018), rhizomes of *A. asphodeloides* Bge. (Bian et al., 1996), and rhizomes of *C. orchioides* Gaertn. (Cao et al., 2009). Beta-sitosterol could promote the proliferation and mineralized nodule formation of osteoblasts (Chauhan et al., 2018). Rutaecarpine could be found in the fruit and bark of *P. chinense* Schneid. (Yan et al., 2017). Future investigations will evaluate the relationship between the above ingredients and the Akt/Nrf2/HO-1 signaling pathway.

#### CONCLUSIONS

In the current study, we showed that EXD protects osteoblasts against apoptosis following exposure to excess TNF-α and used network modeling to elucidate the underlying mechanism of this protection. EXD ameliorated osteoporosis and reduced TNF-α level in OVX rats in the dose range of 2–6 g/kg/day. Further studies will be carried out in order to explore the potential of EXD at a lower concentration range. Combining network pharmacology analysis with experimental verification *in vivo*  and *in vitro*, we found that EXD may attenuate osteoporosis at least partially by reducing TNF-α production and regulating the Akt/Nrf2/HO-1 signaling pathway.

#### ETHICS STATEMENT

Twelve-month-old female Wistar rats (weighting 350-400 g) were supplied by the Animal Center of the Zhejiang Academy of Traditional Chinese Medicine (Hangzhou, China). The rats were maintained in air-conditional animal quarters at a temperature of 24 ± 2 °C and a relative humidity of 60 ± 5%. All protocols for animal experiments were approved in accordance with the regulations of experimental animal administration issued by the state commission of science and technology of the People's Republic of China.

#### AUTHOR CONTRIBUTIONS

NW designed and performed experimental procedures, including network pharmacology analysis and *in vitro* experiments. PX developed the osteoporosis rat models. HX helped with *in vivo* experiments and provided professional consultancy about pharmaceutical use. ZY assisted in preparing samples and provided medical materials. DS revised the manuscript critically for important intellectual content.

#### ACKNOWLEDGMENTS

The project was sponsored by the National Natural Science Foundation of China (No. 81603252, U1603283, 81873062), Zhejiang Provincial Natural Science Foundation of China (No. LQ17H280002), and the Open Fund Project of First-Class Discipline for Science of Chinese Pharmacy, Zhejiang Chinese Medical University (Ya2017008).

#### REFERENCES


#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.00988/ full#supplementary-material.

FIGURE S1 | (A) Typical chromatograms of the EXD extract and the standards. (B) The chemical structure of the peaks. Other information of the peaks was presented in Table S3.

method of network pharmacology. *Acta Med. Mediterr.* 32 (4), 877–882. doi: 10.19193/0393-6384\_2016\_4\_104


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Wang, Xin, Xu, Yu and Shou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Elucidation of the Mechanisms and Molecular Targets of Yiqi Shexue Formula for Treatment of Primary Immune Thrombocytopenia Based on Network Pharmacology

#### *Edited by:*

*Shao Li, Tsinghua University, China*

#### *Reviewed by:*

*Wentzel Christoffel Gelderblom, Cape Peninsula University of Technology, South Africa Zhongju Shi, Tianjin Medical University General Hospital, China Bo Zhang, Tianjin International Joint Academy of Biomedicine, China*

#### *\*Correspondence:*

*Xiaomei Hu huxiaomei\_2@163.com Dennis Chang D.Chang@westernsydney.edu.au Jianxun Liu jianxun\_liu@163.com*

*†These authors have contributed equally to this work*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 04 July 2018 Accepted: 03 September 2019 Published: 01 October 2019*

#### *Citation:*

*Jiang Y, Liu N, Zhu S, Hu X, Chang D and Liu J (2019) Elucidation of the Mechanisms and Molecular Targets of Yiqi Shexue Formula for Treatment of Primary Immune Thrombocytopenia Based on Network Pharmacology. Front. Pharmacol. 10:1136. doi: 10.3389/fphar.2019.01136*

*Yunyao Jiang1,2,3†, Nan Liu4†, Shirong Zhu1, Xiaomei Hu1\*, Dennis Chang5\* and Jianxun Liu1,3\**

*1 Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China, 2 School of Pharmaceutical Sciences, Institute for Chinese Materia Medica, Tsinghua University, Beijing, China, 3 Beijing Key Laboratory of TCM Pharmacology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China, 4 Department of PK- PD, Beijing Increase Research for Drug Efficacy and Safety Co., Ltd, Beijing, China, 5 NICM Health Research Institute, Western Sydney University, Westmead, NSW, Australia*

Yiqi Shexue formula (YQSX) is traditionally used to treat primary immune thrombocytopenia (ITP) in clinical practice of traditional Chinese medicine. However, its mechanisms of action and molecular targets for treatment of ITP are not clear. The active compounds of YQSX were collected and their targets were identified. ITP-related targets were obtained by analyzing the differential expressed genes between ITP patients and healthy individuals. Protein–protein interaction (PPI) data were then obtained and PPI networks of YQSX putative targets and ITP-related targets were visualized and merged to identify the candidate targets for YQSX against ITP. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out. The gene-pathway network was constructed to screen the key target genes. In total, 177 active compounds and 251 targets of YQSX were identified. Two hundred and thirty differential expressed genes with an *P* value < 0.005 and |log2(fold change)| > 1 were identified between ITP patient and control groups. One hundred and eighty-three target genes associated with ITP were finally identified. The functional annotations of target genes were found to be related to transcription, cytosol, protein binding, and so on. Twenty-four pathways including cell cycle, estrogen signaling pathway, and MAPK signaling pathway were significantly enriched. MDM2 was the core gene and other several genes including TP53, MAPK1, CDKN1A, MYC, and DDX5 were the key gens in the gene-pathway network of YQSX for treatment of ITP. The results indicated that YQSX's effects against ITP may relate to regulation of immunological function through the specific biological processes and the related pathways. This study demonstrates the application of network pharmacology in evaluating mechanisms of action and molecular targets of complex herbal formulations.

Keywords: Yiqi Shexue formula, primary immune thrombocytopenia, network pharmacology, mechanism, target gene, pathway

## INTRODUCTION

Primary immune thrombocytopenia (ITP) is the most common autoimmune cytopenia characterized by transient or persistent decreased platelet count (Comont et al., 2017; Castro, 2017). The occurrence of ITP results from the generation of anti-platelet autoantibodies against platelet membrane glycoproteins finally leading to the destruction of platelets in the reticuloendothelial system, especially in the spleen (Zhang et al., 2015). ITP patients have an increased risk of bruising, cutaneous bleeding, and infrequently serious bleeding including intracranial hemorrhage (Lin et al., 2017). In addition, the quality of life of ITP patients is affected as a result of the physical and psychological symptoms, discomfort, fear, reduced social activity, and reduced ability to work (López et al., 2015). The standard therapy for newly diagnosed ITP patients is to stop bleeding and increase platelet counts using pharmaceutical medicines (Kühne, 2015). However, these treatments are often accompanied by harmful side effects, which tend to be more evident with the time of treatment. In addition, the high treatment costs cast a heavy financial burden to ITP patients, especially those with severe forms of the disease (Khellaf et al., 2011).

In recent years, traditional Chinese medicine (TCM) has been regarded as a potential effective auxiliary strategy to treat the chronic diseases, including ITP (He et al., 2015). Huang et al. (2013) reported that a modified Chinese herbal formula, Zi-Ying-Jiang-Huo-Tang (Phellodendri Combination) cured a 4-year-old ITP patient who did not respond to a 7-month first-line conventional treatment of steroids and intravenous immunoglobulin, and no recurrence of the disease or side effects of the treatment were found during the 12-month follow-up period. Yang et al. (2017) reported that imbalance of Th1/Th2 and Th17/Treg cells play a crucial role in the pathogenesis of chronic ITP and that Yiqi Tongyang Decoction significantly regulated the dynamics of Th1/Th2 and Th17/ Treg equilibria.

Yiqi Shexue formula (YQSX), an improved formula of Bazhen decoction (BZD), is a mixture of 9 Chinese medicine extracts including *Ginseng Radix et Rhizoma* (GRR, the dried root and rhizome of *Panax ginseng* C. A. Mey.), *Poria* [P, the dried sclerotium of *Poria cocos* (Schw.) Wolf], *Atractylodis Macrocephalae Rhizoma* (AMR, the dried rhizome of *Atractylodes macrocephala* Koidz.), *Glycyrrhizae Radix et Rhizoma* (RRG, the dried root and rhizome of *Glycyrrhiza uralensis* Fisch), *Angelicae Sinensis Radix* [ASR, the dried root of *Angelica sinensis* (Oliv.) Diels], *Chuanxiong Rhizoma* (CR, the dried rhizome of *Ligusticum chuanxiong* Hort.), *Paeoniae Radix Alba* (PRA, the dried root of *Paeonia lactiflora* Pall.), *Rehmanniae Radix Praeparata* (RRP, the dried root of *Rehmannia glutinosa* Libosch.), and *Asini Corii Colla* (ACC, the product of hide of *Equus asinus* L.). In TCM, BZD is frequently used to treat the deficiency of *qi* and *blood* which is characterized by many symptoms, including anemia, asthenia, dizziness, chronic abscess, fatigue, irregular menstruation, palpitations, fatigue of the muscles, and pale complexion (Song et al., 2014). It has been reported that BZD could substantially promote the proliferation of bone marrow hematopoietic cells in anemic mice (Tian et al., 2016). YQSX is formulated based on BZD with one additional medicine (ACC). ACC is obtained from *Equus asinus Linnaeus* and has been widely used to promote health in China for life cultivation and clinical hematic antanemic therapy as a health food and TCM for more than 2,000 years. And early evidence has shown that ACC possesses a therapeutic effect in treating various hematologic diseases, such as anemia, aleucocytosis, and thrombopenia (Wang et al., 2014). In TCM, YQSX has been suggested to be able to invigorate *spleen*, replenish *qi*, nourish *blood*, and promote blood circulation and is traditionally used to treat ITP in clinical practice. However, the mechanisms of action and molecular targets of YQSX for treatment of ITP are not clear, which is the main factor limiting its wider use.

In TCM, complex herbal formulations that consist of multiple herbs are used and these complex chemical mixtures include numerous potential bioactive components that can interact with multiple therapeutic targets. This multicomponent, multi-target, and multi-pathway approach may be ideal for the treatment of diseases with complex pathophysiology and therapeutic targets, but also present a tremendous challenge in understanding of the interactions between various components, their mechanisms of action and molecular targets. Liu et al. (2016) proposed the concept of Network Pharmacology in an attempt to solve these problems. Network pharmacology is a novel approach that combines system network analysis and pharmacology. It could be used to elucidate the synergistic effects among compounds and potential mechanisms of multi-component and multiple target drugs at the molecular level through the networks of compound–compound, compound–target, and target–disease. Network pharmacology would facilitate the understanding of the interactions among the compounds, genes, proteins, and diseases and is suitable for the study of complex TCM formulations (Xu et al., 2017; Zeng et al., 2017). Chen et al. (2018) explored potential mechanism of Jiawei Foshou San on endometriosis using a network pharmacology approach. The underlying action mechanism of Wu-Tou decoction in rheumatoid arthritis was expounded by network pharmacology prediction and experimental verification (Guo et al., 2017). The potential mechanism between Dangguishaoyao-san and neurodegenerative disorders was deciphered through a network pharmacology approach (Luo et al., 2016). The research group of Shao Li elucidated anti-rheumatic mechanisms of Qing-Luo-Yin and investigated its possible interactions with methotrexate using an integrating strategy coupled with network pharmacology and metabolomics techniques (Zou et al., 2018).

In the present study, a network pharmacology approach was used to explore the mechanisms of action and molecular targets of YQSX for treatment of ITP. The active compounds of YQSX and their targets were firstly identified using drugbank database. Then ITP-related targets were obtained by analyzing the differential expressed genes between ITP patients and healthy individuals. The mechanisms of action underlying YQSX for the treatment of ITP were analyzed by gene ontology (GO) and pathway analysis.

### MATERIALS AND METHODS

#### Active Ingredients Screening

We identified the chemical composition of YQSX from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (Ru et al., 2014) (TCMSP, http://lsp.nwu.edu.cn/ tcmsp.php) and select candidate compounds which has oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 (Li et al., 2015). One hundred and sixty eligible compounds were obtained, 22 in GRR, 15 in P, 7 in AMR, 92 in RRG, 2 in ASR, 7 in CR, 13 in PRA, and 2 in RRP. However, 8 compounds including ferulic acid, ligustilide, senkyunolide C, and leonuride which were not found in the database have been selected as they have the pharmacological activity on ITP treatment. Additionally, 19 amino acids including aspartic acid, threonine, and serine in ACC have been reported to process relevant pharmacological properties and were also included (Wang et al., 2014). Eventually, 177 candidate compounds were obtained in total after the duplications were removed.

#### Identification of Potential Targets

The 177 candidate compounds were imported into the DrugBank database (Law et al., 2014) (https://www.drugbank.ca/) to identify the corresponding targets of YQSX. One hundred and forty-eight compounds were finally selected after removing 29 compounds which did not link to any targets. And the targets of 148 compounds were collected. Two thousand one hundred and seventy-seven targets were identified, 204 in GRR, 28 in P, 20 in AMR, 1272 in RRG, 106 in ASR, 36 in CR, 99 in PRA, 58 in RRP, and 354 in ACC. A total of 251 targets were collected after removing duplication.

#### ITP-Related Targets

The differential expressed genes of ITP patients were obtained from GEO database (https://www.ncbi.nlm.nih.gov/geo/, Series: GSE574, Samples: GSM8814, GSM8815, GSM8816, GSM8817). Genes with a *P* value < 0.005 and |log 2(fold change)| > 1 were considered to be of significantly differential expression and ITPrelated targets.

#### Network Construction

The compound-target network of YQSX was constructed and visualized using Cytoscape 3.5.1 software. PPI data were obtained from Database of Interacting Proteins (DIP™), Biological General Repository for Interaction Datasets (BioGRID), Human Protein Reference Database (HPRD), IntAct Molecular Interaction Database (IntAct), Molecular INTeraction database (MINT), and biomolecular interaction network database (BIND) using the plugin Bisogenet (Martin et al., 2010) of Cytoscape 3.5.1 software. The PPI networks of YQSX putative targets and ITPrelated targets were visualized with Cytoscape software.

#### Network Merge

The PPI networks of YQSX putative targets and ITP-related targets were merged with Cytoscape software. And the nodes with topological importance in the interaction network were screened by calculating Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Eigenvector Centrality (EC), Local average connectivity-based method (LAC), and Network Centrality (NC) with the Cytoscape plugin CytoNCA. These parameters represent the topological importance and they have been reported about their definitions and computational formulas and used in network pharmacology and systems pharmacology (Tang et al., 2015).

#### Bioinformatic Analysis

GO analysis with the biological process, cellular component, and molecular function was carried out using the Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov, v6.8) (Huang et al., 2009). Functional categories were enriched within genes (FDR < 0.05) and the top 20 GO functional categories were selected. DAVID that assigned Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used for pathway analysis. Pathways that had significant changes of FDR < 0.05 were identified for further analysis. The genes that significantly regulated pathways were selected for gene-pathway network analysis. The gene-pathway network was constructed to screen the key target genes that YQSX treated ITP.

### RESULTS

#### Compound-Target Network Analysis

One hundred and forty-eight compounds of YQSX (**Table 1**) were finally selected as the candidate compounds. And 230 ITPrelated targets were identified from GEO database. As shown in **Figure 1**, a volcano plot was created to show the distribution of differentially expressed genes, which were represented by the red dots in the plot.

The Compound-target network of YQSX was constructed using the screened compounds and their targets as shown in **Figure 2**. The network contained 399 nods (148 compounds in YQSX and 251 compound targets) and 2,177 edges which indicated the compound-target interactions. One hundred and forty-eight candidate compounds had a median of 12 degrees, which suggested that most compounds of YQSX affected multiple targets. Kaempferol, glycine, and stigmasterol acted on 118, 99 and 87 targets, respectively. And the OB of kaempferol, glycine, and stigmasterol is 41.88, 48.74, and 43.83%, respectively. Therefore, they might be the crucial active compounds of YQSX by reason of their considerable positioning in the network.

#### PPI Networks Analysis

PPI operates large-scale biological processes, such as cell-to-cell interactions, metabolic control, and developmental control, and is increasingly regarded as the primary objectives of system biology (Rao et al., 2014). Therefore, PPI networks of YQSX putative targets and ITP-related targets were visualized using PPI data.

#### TABLE 1 | The final selected compounds in YQSX for analysis.


*(Continued)*


*OB, oral bioavailability; DL, drug-likeness; GRR, Ginseng Radix et Rhizoma; P, Poria; AMR, Atractylodis Macrocephalae Rhizoma; RRG, Glycyrrhizae Radix et Rhizoma; ASR, Angelicae Sinensis Radix; CR, Chuanxiong Rhizoma; PRA, Paeoniae Radix Alba; RRP, Rehmanniae Radix Praeparata; ACC, Asini Corii Colla.*

The PPI network of YQSX putative targets contained 5,959 nodes and 148,332 edges, which represented 5,959 interacting protein and 148,332 interactions. The PPI network of ITP-related targets contained 5,163 nodes and 127,564 edges.

#### Identification of Candidate Targets for YQSX Against ITP

In order to reveal the mechanisms of action underling YQSX's effects on ITP, the PPI network of YQSX putative targets and the PPI network of ITP-related targets were merged to identify the candidate targets for YQSX against ITP. This network consisting of 3,232 nodes and 95,775 edges was presented in **Figure 3A**. The median degree of all nodes was 37 and the nodes with more than 74 degrees were identified as significant targets according to the previous research (Zhang et al., 2013). A network of significant targets for YQSX against ITP was constructed and it contained 780 nodes and 35,907 edges (**Figure 3B**). The median values of DC, BC, CC, EC, LAC, and NC were 117, 6,403.125, 0.318309, 0.024411, 19.61376, and 27.984, respectively. The candidate targets were further screened and 183 targets with DC > 117, BC > 6,403.125, CC > 0.318309, EC > 0.024411, LAC > 19.61376, and NC > 27.984 were identified (**Figure 3C**). One hundred and eighty-three target genes were eventually identified for YQSX against ITP.

### GO and Pathway Enrichment Analysis

DAVID was used to perform GO and KEGG pathway analysis of the 183 candidate targets identified. GO of candidate targets was analyzed based on biological process, cellular component, and molecular function. One hundred seventy-two GO terms were significantly enriched (FDR < 0.05), 98 in biological process, 29 in cellular component, and 45 in molecular function. The data of GO analysis were shown in **Supplementary Table 1.** Top 20 terms were shown in **Figure 4**. The highly enriched GO terms in biological process, cellular component, and molecular function included regulation of gene silencing, regulation of gene expression, nucleoplasm, nucleus, protein binding, and ubiquitin protein ligase binding.

The pathways that were significantly influenced by YQSX in the process of treating ITP were identified by KEGG pathway analysis. Twenty-four significantly enriched pathways (FDR < 0.05) including Epstein-Barr virus infection, cell cycle, estrogen signaling pathway, pathway in cancer, and MAPK signaling pathway were identified.

centrality; CC, closeness centrality; EC, eigenvector centrality; LAC, local average connectivity-based method; NC, network centrality.

The data of KEGG pathway analysis were shown in **Supplementary Table 2**. As shown in **Figure 5**, size of the spot represented number of genes and color represented FDR value.

#### Gene-Pathway Network Analysis

The gene-pathway network was constructed based on the significantly enriched pathways and genes that regulated these pathways, which was presented in **Figure 6**. The topological analysis of 24 pathways and 115 genes was carried out with BC. The squares represented target genes and the V-shapes represented pathways in the network. The network diagram suggested that MDM2 had the most maximum BC and was the core target gene. Other several genes also had larger BC, such as TP53, MAPK1, CDKN1A, MYC, and DDX5. They might be the key target gens for YQSX against ITP.

### DISCUSSION

The unique medical theory of TCM has been formed and developed over thousands of years in China for the treatment and preventions of diseases. Multiple compatible herbs are usually used as complex herbal formulations to improve therapeutic effect through synergism (Li et al., 2016). In TCM, ITP is thought to be a disease caused by the failure of *spleen* to manage blood. Guipi decoction and BZD were the most common prescribed formulas in the treatment of ITP in TCM. YQSX, the improved formula of BZD, is an empirical formula to treat ITP in TCM clinical practice, and has demonstrated significant clinical effects. Compared with BZD, ACC is added to YQSX. ACC has been shown to enrich the *blood* and to improve hemorheology, hemostasis, and immunological regulation, and its addition has further strengthened the BZD's

effects for the treatment of ITP. TCM adopts a holistic approach focusing on overall functional recovery and elimination of the cause of the disease. The concept of network pharmacology is comparable to TCM theory and is therefore appropriate to be used for the research on unknown components and mechanism of action of complex TCM herbal formulations supported by a variety of databases and software available.

In the present study, a compound-target network of YQSX was constructed using the 148 compounds and 251 compound targets. The results suggested that most compounds of YQSX affected multiple targets, for example, kaempferol, glycine, and stigmasterol acted on 118, 99, and 87 targets, respectively. Therefore, they were very likely to be the crucial pleiotropically active compounds for YQSX. Although the number of putative targets in each single herb was different,

the overlapping targets in different herbs were numerous. In another word, multiple compounds of YQSX may have the same target providing synergistic effects. Kaempferol is a representative flavonoid and has been shown to exert multiple pharmacological activities, such as antioxidant, antiinflammatory, anti-cancer, anti-diabetic, anti-osteoarthritic, and immunomodulatory properties (Tsai et al., 2018; Wang et al., 2018). Lin et al. (2011) reported that kaempferol might be a potent immunosuppressant to decrease the harmful immune responses, including chronic inflammation and autoimmunity. Glycine is an important amino acid contributing to metabolism, growth, development, immunity, cytoprotection, and survival owning to its anti-inflammatory and immunomodulatory properties (Lu et al., 2017; Heidari et al., 2018). Stigmasterol also showed anti-cancer, anti-inflammatory and anti-allergic properties as well as the modulatory effects on immune responses (Antwi et al., 2017). TCM is a highly complex system and contains a large number of constituents. Researchers try to verify even more effective chemical components from TCM through various approaches including network pharmacology. But there has not been a way to recognize the total effective constituents of TCM up till the present moment. It is well known that the effects of TCM on treating diseases are the result of the combination effects of many constituents. In the present study, kaempferol, glycine, and stigmasterol regulated the most targets associated with ITP and all of them have immunomodulatory properties. Although kaempferol, glycine, and stigmasterol are ubiquitous and widely known compounds, there is some evidence for their immunomodulatory effects. In addition, they have high oral bioavailability and kaempferol and stigmasterol came from 3 herbs of YQSX. Therefore, they might be identified as the representative compounds for YQSX.

The PPI networks of YQSX putative targets and ITP-related targets were structured and merged to obtain the candidate targets for YQSX against ITP. In order to get the more accurate targets, 6 parameters including DC, BC, and CC were set to screen nodes and structure a new network. One hundred and eightythree targets were finally identified and used to carry out the bioinformatic analysis to elucidate the mechanisms underlying the anti-ITP effects of YQSX.

The targets of YQSX against ITP were enriched in biological processes, cellular components, and molecular function by GO enrichment analysis. Results suggested that YQSX regulated some biological processes, such as gene silencing, gene expression, apoptotic process, and signal transduction by p53 class mediator. ITP is an autoimmune disease characterized by an abnormality in T cell immunity and T cell mechanism has been proved to be an important pathophysiologic mechanism in ITP (Jernås et al., 2013; Ji et al., 2014). It has been shown that allogenic T cell responses could be inhibited through the production of immunoregulatory dendritic cells resulted by silencing RelB (Zhang et al., 2010). The expression of CD72 and IL-2 was decreased whilst the IFN-γ/IL-4 expression was increased in ITP patients (Zhou et al., 2012). Apoptotic process plays an important role in maintenance of normal immune system development, and a failure of apoptotic function has been shown to be associated with the pathogenesis of ITP (Qian et al., 2018). It has been found that mesenchymal stem cells from ITP patients showed increased apoptosis and a defect in immunoregulation and the apoptotic rate was decreased by inhibiting the expression of p53 (Zhang et al., 2016). Therefore, YQSX may help to regulate immunological function through intervening these biological/pathological processes. It has been suggested that the pathogenesis of ITP was associated with gene expression, regulation of apoptosis, regulation of cell proliferation, nucleoplasm, transcription factor binding, histone deacetylase binding, protein kinase binding, and core promoter binding (Deng et al., 2017; Zuo et al., 2017), all of which were significantly enriched in the present study. Therefore, YQSX may exert regulatory function in the pathogenesis of ITP and may also affect some cellular components and molecular function including nucleoplasm, nucleus, cytosol, protein binding, enzyme binding, and DNA binding in the treatment of ITP. Studies have found that the ultrastructural abnormalities in cytoplasmic vacuolization, mitochondrial swelling, abnormal chromatin condensation, and increased staining for activated caspase-3 in megakaryocytes also occur in ITP patients (Kistanguri and McCrae, 2013).

TCM is multi-component, multi-target, and multipathway. YQSX, as a TCM formula, also has the same characteristic. Therefore, it can be sure that YQSX treats ITP through multi-pathway. In the present study, a total of 24 KEGG pathways including MAPK signaling pathway and PI3K-AKT signaling pathway were significantly enriched. MAPKs can regulate gene expression, immune response, cell proliferation, apoptosis, and response to oxidative stress, which is one of the mechanisms of immune regulation (Li et al., 2017). Research suggested that PI3K-AKT signaling pathways played a role in reducing excessive innate immune responses and crosstalk between MAPK, which was one of the mechanisms to balance the innate immune responses (Rohani et al., 2010). Eltrombopag is a thrombopoietin receptor agonist and has been used to treat the thrombocytopenia of ITP. The signaling mechanisms of eltrombopag are involved in AKT and MAPK pathways, which is similar to that of thrombopoietin (Kim et al., 2018). Therefore, YQSX may regulate immunological function through the related pathways in the process of ITP treatment. In this study, several pathways related to viral also were significantly enriched. The viral infection relates to the genesis of ITP. The body's autoimmune response is activated when infected with virus, such as Human Immunodeficiency Virus, Hepatitis C Virus, Epstein-Barr virus, Cytomegalovirus, Herpes simplex virus, and Varicella zoster virus, and the autoimmune response will perpetuate itself despite viral clearance (Audia et al., 2017). The autoimmune response triggered by viral infection might be regulated by YQSX through specific pathways, such as Epstein-Barr virus infection, viral carcinogenesis, Hepatitis B, HTLV-I infection, and Herpes simplex infection. In a human study, plasma samples from 74 ITP patients and 58 healthy controls were collected and bioinformatic analysis was carried out. The results indicated that the occurrence of ITP was associated with proteoglycans in cancer, prostate cancer, glioma, thyroid hormone signaling pathway, and estrogen signaling pathway (Zuo et al., 2017). In the present study, the aforementioned pathways were also significantly enriched, which suggested that the regulation of pathways associated with the occurrence of ITP might be one of the mechanisms of YQSX for treatment of ITP. In addition, YQSX may function by regulating other pathways, including cell cycle, and neurotrophin and ErbB signaling pathways. Inhibition of cell cycle caused by selective inhibition of lymphocyte proliferation was found to be beneficial for treating refractory ITP (Grace and Neunert, 2016).

Gene-pathway network was constructed to investigate the core and key target genes for YQSX against ITP. Results suggested that MDM2 had the maximum BC and it might be the core target gene. Other top 5 genes (TP53, MAPK1, CDKN1A, MYC, and DDX5) were selected as the key target gens. MDM2 can negatively regulate p53 which is a central cell cycle regulator and has a negatively regulatory effect on autoimmunity (Liu et al., 2017). MDM2 can block the transactivation domain of p53 and affects gene transcription by inhibiting the ability and then block the progression of cell cycle and promote apoptosis (Wang et al., 2012). MDM2 can regulate a functional autologous immune response; therefore, it is linked to the development of autoimmunity (Mayr et al., 2006). Studies on the role of MDM2 in immune regulation illustrated that inhibition of MDM2 promoted T cell proliferation induced by dendritic cells (Gasparini et al., 2012). It is well-known that MAPKs are an essential regulator of immune responses. The TP53 gene provides instructions to make the p53 protein (Vickers, 2018). The expression and stability of p53 can be promoted by inhibiting PKA and p53 phosphorylation inactivates BCL-XL, leading to platelet apoptosis (Zhao et al., 2017). Research found that CDKN1A has a potential pro-apoptotic effect by reason of arresting cells at G1 or G2/M phases (Hui et al., 2014). CDKN1A protein is also a p53 transcriptional target and can activate cell cycle checkpoints, promote DNA repair, downregulate apoptosis, and trigger a senescence-like growth arrested response, all of which play an important role in the network of DNA damage surveillance (Mirzayans and Murray, 2016). MYC has been suggested to directly coordinate the immune response through regulating the immune checkpoints expression (Casey et al., 2017). It has been widely accepted that the role of Th17 cells in ITP is a possible pathogenic factor and a potential therapeutic target of ITP (Ye et al., 2015). DDX5 was found to control the differentiation of Th17 cells at steady state and autoimmunity (Huang et al., 2015).

The mechanisms of action and molecular targets of YQSX for ITP were explored using a network pharmacology approach in this study. Kaempferol, glycine, and stigmasterol regulated the most targets associated with ITP. YQSX may regulate immunological function through the specific biological processes including gene silencing, gene expression, apoptotic

#### REFERENCES


process, and signal transduction by p53 class mediator and the related pathways including MAPK signaling pathway and PI3K-AKT signaling pathway. In addition, YQSX may exert its regulatory function in the pathogenesis of ITP and the regulation of pathways including proteoglycans in cancer, prostate cancer, glioma, and thyroid hormone and estrogen signaling pathways which are associated with the occurrence of ITP. MDM2, TP53, MAPK1, CDKN1A, MYC, and DDX5 were the key target gens in the gene network of YQSX for treatment of ITP. The network pharmacology appears to be a suitable approach for the study of complex TCM formulations.

### AUTHOR CONTRIBUTIONS

YJ and NL performed main analysis and drafted the manuscript. JL and XH designed the research. SZ helped for introduction and discussion. DC assisted in the preparation of the manuscript. All authors wrote, read, and approved the manuscript.

#### FUNDING

This work was supported by the National Basic Research Program of China (973 Program, 2015CB554403 and 2015CB554405) and National Natural Science Foundation of China (81803772).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.01136/ full#supplementary-material.

TABLE S1 | The data of GO enrichment analysis including biological process, cellular component, and molecular function.


purpura unresponsive to intravenous immunoglobulin. *Complement. Ther. Med.* 21, 525–528. doi: 10.1016/j.ctim.2013.08.005


of neurodegenerative diseases. *J. Ethnopharmacol.* 178, 66–81. doi: 10.1016/j. jep.2015.12.011


**Conflict of Interest:** NL was employed by Beijing Increase Research for Drug Efficacy and Safety Co., Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling editor declared a shared affiliation, though no other collaboration, with one of the authors YJ at the time of the review.

*Copyright © 2019 Jiang, Liu, Zhu, Hu, Chang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Closing the Gap Between Therapeutic Use and Mode of Action in Remedial Herbs

*Joaquim Olivés1 and Jordi Mestres1,2\**

*1 Research Group on Systems Pharmacology, Research Programme on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute, Barcelona, Spain, 2 Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain*

The ancient tradition of taking parts of a plant or preparing plant extracts for treating certain discomforts and maladies has long been lacking a scientific rationale to support its preparation and still widespread use in several parts of the world. In an attempt to address this challenge, we collected and integrated data connecting metabolites, plants, diseases, and proteins. A mechanistic hypothesis is generated when a metabolite is known to be present in a given plant, that plant is known to be used to treat a certain disease, that disease is known to be linked to the function of a given protein, and that protein is finally known or predicted to interact with the original metabolite. The construction of plant–protein networks from mutually connected metabolites and diseases facilitated the identification of plausible mechanisms of action for plants being used to treat analgesia, hypercholesterolemia, diarrhea, catarrh, and cough. Additional concrete examples using both experimentally known and computationally predicted, and subsequently experimentally confirmed, metabolite–protein interactions to close the connection circle between metabolites, plants, diseases, and proteins offered further proof of concept for the validity and scope of the approach to generate mode of action hypotheses for some of the therapeutic uses of remedial herbs.

Keywords: ethnopharmacology, traditional medicine, network pharmacology, mechanism of action, phytochemicals, endogenous metabolites, plant metabolomics

#### INTRODUCTION

Plant leaves, roots, barks, and extracts have been used since the dawn of human history to treat various discomforts and maladies. The healing properties of remedial herbs were most likely identified through a long and serendipitous learning process that once acquired was carefully passed through generations. Still today, traditional medicines represent a well-established therapeutic alternative to synthetic drugs in vast parts of the world (Tao et al., 2014). However, there is still a profound lack of understanding about the specific chemical ingredient(s) and the exact mechanism(s) of action by which medicinal plants exert their therapeutic effect.

In recent years, global efforts to generate, collect, store, and make publicly available data connecting plants with their endogenous metabolites (phytoconstituents), interacting proteins, and disease indications have set the ground to develop novel systems approaches to unveiling the mode of action of remedial herbs (Liu et al., 2013; Lagunin et al., 2014; Chen et al., 2017). This is schematically illustrated in **Figure 1**. A number of publicly available well annotated databases on medicinal plants

#### *Edited by:*

*Yuanjia Hu, University of Macau, China*

#### *Reviewed by:*

*Pinarosa Avato, University of Bari Aldo Moro, Italy Vladimir Poroikov, Russian Academy of Medical Sciences (RAMS), Russia*

> *\*Correspondence: Jordi Mestres jmestres@imim.es*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 01 March 2019 Accepted: 30 August 2019 Published: 03 October 2019*

#### *Citation:*

*Olivés J and Mestres J (2019) Closing the Gap Between Therapeutic Use and Mode of Action in Remedial Herbs. Front. Pharmacol. 10:1132. doi: 10.3389/fphar.2019.01132*

**350**

in use in different regions of the planet exist already (Chen, 2011; James et al., 2013; Ntie-Kang et al., 2013; Tota et al., 2013; Pathania et al., 2015; Mohanraj et al., 2018). Once data connecting the different aspects of ethnopharmacological relevance are known, the circle is closed and mechanistic hypotheses emerge naturally. The problem arises when gaps of data exist and the circles cannot be closed. In this respect, most current ethnomedicinal studies still focus on which parts of the plant are used to treat common ailments (Chassagne et al., 2016). Initiatives to identify and isolate some of the chemical structures present in those parts of therapeutic interest are expensive and inefficient. This notwithstanding, at least 50,000 endogenous plant metabolites have been already identified (Hounsome et al., 2008).

However, *in vitro* affinity data between plant metabolites and human proteins are scarce to find in public repositories (Bolton et al., 2008; Gaulton et al., 2012). Therefore, more efforts are needed in this direction to close the gap between therapeutic use and mode of action in remedial herbs (represented as a dotted line in **Figure 1**). One option is to process large libraries of isolated small molecules from plants through *in vitro* high-throughput screening assays to identify affinities for therapeutically relevant proteins. This is a highly tedious and expensive endeavor if one wants to be comprehensive. Alternatively, modern state-ofthe-art computational methods to predict the affinity of small molecules across thousands of proteins can be used to prioritize any further *in vitro* testing of selected small molecules on particular proteins (Vidal et al., 2011; Garcia-Serna et al., 2015). Applications on predicting the targets of natural medicines are increasingly being reported (Keum et al., 2016; Fang et al., 2017; Sawada et al., 2018; Yi et al., 2018).

The last step involves connecting those confirmed interacting proteins with the actual disease for which the plant is prescribed. This task is now facilitated by the recent construction of databases connecting human genes with diseases (Piñero et al., 2017). The aim of this work is to collect and integrate all pieces of data and processes that allow for automatically generating mechanistic hypotheses for the known therapeutic uses of plants.

## RESULTS AND DISCUSSION

Among the 372 medicinal plants present in our integrated database, *Sambucus nigra* (black elder) is the plant associated with the highest number of therapeutic uses (31). It is recommended for bronchitis, migraine, diarrhea, nausea, hyperuricemia, and influenza, to name just a few. Genus *Sambucus* belongs to the Caprifoliaceae family of flowering plants, whose leaves, flowers, and berries are traditionally used worldwide for a wide variety of medicinal applications (Dulf et al., 2013; Mahmoudi et al., 2014). Following *Sambucus nigra* in the list of plants with widest therapeutic use are *Allium sativum* (24)*, Rosmarinus officinalis*  (22), *Mentha spicata* (21), *Urtica dioica* (21), *Salvia officinalis*  (21), and *Thymus vulgaris* (21), all of them found easily in many parts of the world and used as food and/or spice.

If we focus on cardiovascular diseases, a total of 171 plants were found to be associated with 46 different therapeutic uses. For illustrative purposes, the network of plants linked to cardiovascular diseases is shown in **Figure 2**. Among those, *Ginkgo biloba* is the plant with the most cardiovascular uses (with 14), followed by C*amellia sinensis* (with 8) and *Allium cepa, Crataegus monogyna, Olea europea, Urtica doica*, and *Vitis vinifera* (with 7). Among diseases, hypertension, hypercholesterolemia, hyperglycemia, and haemorrhoids are clearly the cardiovascular aspects being most addressed by remedial herbs.

*Ginkgo biloba* and *Camellia sinensis* are indigenous plants from Asia (Cybulska-Heinrich et al., 2012; Moore et al., 2009). The extracts of the leaves and nuts from *Ginkgo biloba* have been used for hundreds of years to treat a wide variety of disorders, such as asthma, vertigo, tinnitus, as well as general circulatory problems (Cybulska-Heinrich et al., 2012). *Camellia sinensis* is a plant from which green tea can be produced. This beverage has a long traditional use as social drink but also as medicine in the treatment and prevention of disorders, dysfunctions, or diseases in humans and other animals (Batista et al., 2009; Moore et al., 2009). *Aesculus hippocastanum* (horse chestnut) is native to the countries of the Balkan Peninsula, but it is cultivated worldwide for its beauty. Historically, seed extracts from this plant have been used as a treatment for many ailments (Anonymous, 2009). *Crataegus monogyna* (hawthorn) is known as a traditional medicinal plant in many countries, growing in shrub communities and decidious thin forests (Öztürk and Tunçel, 2011). *Vitis vinifera* (grapevine) is an indigenous plant from southern and Western Asia, but it is cultivated today in all temperature regions of the world (Nassiri-Asl and Hosseinzadeh, 2009). Finally, *Allium cepa* (onion) is one of the most important vegetables worldwide and is extensively cultivated. It is an herbaceous bulbous plant that has a long tradition of being beneficial against inflammation, general cardiovascular diseases, and cancer (Slimestad et al., 2007).

Regarding knowledge on the chemical composition of plants, *Camellia sinensis* (green tea) is the plant with the highest number of chemical structures identified (710), followed by *Zea mays* (677) and *Panax ginseng* (601). Other chemically well characterized plants are *Citrus sinensis* (orange tree), *Apium graveolens* (celery), and *Daucus carota* (carrot), with 589, 533, and 507 known

molecules, respectively. In contrast, many plants in the database have only one or very few endogenous metabolites identified, such as *Rhamnus alaternus* (Mediterranean blackthorn), *Lonicera etrusca* (honeysuckle), or *Hernaria glabra* (herniaria).

A detailed analysis of all the links between plants, metabolites, targets, and diseases in our integrated database (**Figure 1**) identified a total of 31,808 mechanistic hypotheses. At this stage, a mechanistic hypothesis is generated if a given plant known to have some therapeutic use contains at least one endogenous metabolite that is either known or predicted to interact with a human protein associated with its original therapeutic use. It ought to be stressed here that the concentration of any plant metabolite in a herbal preparation is very low and that, by any means, the results presented below imply that the metabolite assigned to the mechanistic hypothesis is the sole responsible of the therapeutic action of the plant but it will somehow contribute it. In this respect, a total of 893 mechanistic hypotheses for

its different therapeutic uses could be generated for *Glycine max* (soybean). Among the plants with the highest number of mechanistic hypotheses generated, we found *Ginkgo biloba* (793), *Camellia sinensis* (781), *Citrus limon* (578), and *Vitis vinifera* (563). Out of the total number of 31,808 mechanistic hypotheses generated, 14,308 involved known interactions between endogenous metabolites and protein targets, whereas the remaining 17,500 hypotheses emerged from predicted interactions (see *Materials and Methods*).

#### Retrospective Validation

Among the molecules involved in the mechanistic hypotheses generated with known metabolite–protein interactions, we identified some well-known single active principles, such as atropine, morphine, and digitoxin, as well as a mixture of active principles, such as the one composed of quercetin, luteolin, and apigenin (**Figure 3**).

Atropine is found mainly in *Atropa belladonna* and *Datura stramonium* (Kurzbaum et al., 2001; Caksen et al., 2003), both used commonly for their analgesic action (Duttaroy et al., 2002; Overington et al., 2006). This molecule is known to be active against the muscarinic acetylcholine receptor M4, a therapeutic target associated with some analgesics. Therefore, we have all links described in **Figure 1** confirmed and thus forming a mechanistic hypothesis for the analgesic action of these plants (Soni et al., 2012; Owais et al., 2014).

Another widely recognized molecule for its analgesic activity is morphine. It is found in *Papaver somniferum* (opium poppy), and it was the first active alkaloid extracted from this plant (Jurna, 2003). Opium has been used in traditional medicinal as sedative and analgesic (Calixto et al., 2001). According to all links established in our database, morphine would be directly identified as a candidate to contribute to the analgesic action of opium through its interaction with µ-type opioid receptor (Choi et al., 2006; Yamada et al., 2006), a receptor well known for its association with analgesia (Inturrisi, 2002).

Digitoxin is a glycoside with known activity against the sodium/ potassium-transporting ATPase subunit α-1, a protein associated with heart failure (Müller-Ehmsen et al., 2002; Hauck et al., 2009). Digitoxin is found in *Digitalis purpurea* (Chen et al., 2001), a plant used in traditional medicine for treating precisely this particular disease. Digitoxin has not only been proven to interact with the sodium/potassium-transporting ATPase subunit α-1, but it has also been shown to be indeed effective in heart failure (Belz et al., 2001).

Finally, we selected an example in which three compounds, namely, quercetin, luteolin, and apigenin, all confirmed endogenous metabolites of *Achillea millefolium* (yarrow), a plant used traditionally for treating depression, are known to have biologically relevant affinities for monoamine oxidase A (Lemmens-Gruber et al., 2006; Han et al., 2007; Benetis et al., 2008; Bandaruk et al., 2014), which, in turn, is one of the target proteins for depression (Thase et al., 1995).

A more systematic analysis of all the mechanistic hypotheses that could be derived directly from known data and associations revealed that, among all disease categories, the circulatory, respiratory, and musculoskeletal systems collectively represented over 47% of all mechanistic hypotheses generated. The plant– protein networks derived for some specific diseases within these categories are shown in **Figure 4**.

In the analgesia network (**Figure 4**, top), one can observe that plants have multiple connections with a diverse range of proteins. This reflects the fact that some of the endogenous metabolites found in those plants have biologically relevant affinities for many of those proteins. Among them, aldose reductase (Young et al., 1983), muscarinic acetylcholine receptors (Duttaroy et al., 2002), and opioid receptors (Inturrisi, 2002) are the ones being most targeted by metabolites from these plants. Altogether, the formation of this complex network is indicative of a variety of plausible mechanisms of action relevant to analgesia, although one cannot exclude the possibility that the proteins involved in this analgesia network are closely related by cross-pharmacology, that is, they interact with similar ligands (Keiser et al., 2007; Briansó et al., 2011).

In contrast, the catarrh and cough networks (**Figure 4**, bottom) show that plants indicated for these therapeutic uses have endogenous metabolites targeting very much the same proteins. For catarrh, all plants contain some chemical entity that is active on the dopamine D1B, D2, and D3 receptors, all of them well known to be associated with respiratory diseases (Birrell et al., 2002). For cough, all plants contain at least one chemical entity with affinity for the µ and δ opioid receptors (Kotzer et al., 2000).

In between these two limit situations, the plant–protein networks obtained for hypercholesterolemia and diarrhea (**Figure 4**, middle) are consistent with different plants linked to these therapeutic uses acting through a small number of different mechanistic hypotheses. In this respect, most plants used for treating hypercholesterolemia

seem to contain some chemical ingredient biologically active on the peroxisome proliferator-activated receptor α (56) and the hydroxycarboxylic receptor 2 (Karpe and Frayn, 2004). However, other plants may be exerting their therapeutic effect through interactions with some enzymes also associated with hypercholesterolemia, such as fatty acid synthase (Marseille-Tremblay et al., 2007) and squalene monooxygenase (Belter et al., 2011). Likewise, plants used for treating diarrhea contain chemical entities that have affinities for the µ and κ opioid receptors (Callahan, 2002), the 5-hydroxytryptamine 3A receptor channel (Sikander et al., 2009), and/or the calcium-activated potassium channel subunit α-1 (Deng et al., 2015). Even though many plants are targeting all of them, others seem to target only one or two.

It is important to highlight at this stage that the use of known data only is prone to the effects of completeness, and thus, several links may actually be missing in the networks discussed. In fact, looking at the distribution of the affinity values for those known interactions, we observe that in many cases these interacting chemicals are found also in plants that are not used for treating the disease associated with the interacting protein. Some of the reasons why these plants have not been used for these illnesses could be, for example, that the compound concentration is not enough in the plant or the plant really has this therapeutic action but it is simply not used for it. The same plant may have different uses in different parts of the world. Last but not least, it could well be that the action of some of these compounds requires the presence of some bioenhancer (Dudhatra et al., 2012), the therapeutic action being ultimately the result of multiple compounds acting synergistically. Overall, from the initial number of 372 plants associated with at least one therapeutic use, only 193 contain known data for all necessary links to derive a mechanistic hypothesis. Accordingly, the following section illustrates the use of high-confidence predictions as a means to enlarge the coverage of plants for which mechanistic hypotheses can be derived.

#### Prospective Evaluation

Before embarking into the analysis of some of the mechanistic hypotheses emerging from predicted interactions, we validated the expected accuracy of those predictions for which known data was available. Overall, a good correlation was found between known and predicted affinity values for the same molecule–protein interactions. The median of the difference in affinities was 0.332, with 25% and 75% quartiles being at −0.1 and 0.7 with respect to the median, respectively, with a standard deviation of 0.794. Then, for those predicted interactions only, we focused on those providing a balance between potency of the predicted affinity and novelty of the prediction, as regarded by the similarity to the closest molecule for which the affinity for the same protein is known already. Among those, we prioritised the confirmation of the proposed mechanistic hypotheses for two single compounds, namely, rybosylzeatin and isorhamnetin, and one compound mixture, composed of cyanidin, delphinidin, and malvidin. The results are compiled in **Figure 5**.

Ribosylzeatin is an endogenous metabolite present in *Ginkgo biloba* (gingko), *Glycine max* (soybean), and *Vitis vinifera* (grapevine). This small molecule was predicted to have low micromolar affinity for the adenosine A1 and A3 receptors and *in vitro* testing performed subsequently confirmed 57% and 65% binding, respectively, at 10mM concentration. Accordingly, a mechanistic hypothesis could be derived suggesting that the interaction of ribosylzeatin with the adenosine A1 and A3 receptors may be contributing to the beneficial effects of those plants in the treatment of a number of cardiovascular diseases where these receptors are known to play a role, namely, cardiac dysrhythmias, supraventricular tachycardia, acute ischaemic heart disease, and mycocardial ischemia (Kiesman et al., 2009; Fishman et al., 2012). In this respect, soybean is known as an important source of proteins in diet, widely used as herbal medicine for the treatment of several cardiovascular diseases. Also, the cardioprotector properties of grapevine have been exploited in folk medicine since

ancient times. In particular, the therapeutic action of grapevine against ventricular tachycardia was recently demonstrated in rats (Zhao et al., 2010). However, in this study, the authors used a proanthocyanidin grape seed extract. On the basis of the hypothesis generated here, we would suggest that ribosylzeatin is one of the active ingredients in grapevine participating in this therapeutic effect in synergy with other proanthocyanidins.

Isorhamnetin is a phytochemical present in multiple plants used for the treatment of hypertension. A low micromolar affinity between isorhamnetin and the dopamine D4 receptor was predicted. Upon *in vitro* testing, the experimental value obtained was only 28% binding. Despite this rather low affinity value, we could suggest that this compound may well be contributing to some extent to the effect on hypertension of the plants in which it is present. In fact, a similar chemical present in most of those plants listed and suggested to be partly responsible for their therapeutic action on hypertension is quercetin, reported to have also a low experimental affinity value of 5 µM against the same dopamine D4 receptor.

Finally, very much along the same lines reported above for the hypotheses generated from known metabolite–protein interactions, we were also keen on having a prospective mixture example. Accordingly, we predicted activity of cyanidin, delphinidin, and malvidin, all present in *Vaccinium myrtillus* (bilberry), on the arachidonate 5-lipoxygenase (ALOX5). *In vitro* testing of its mixture confirmed a 41% inhibition. This result provides a mechanistic hypothesis for the therapeutic use of bilberry for atherosclerosis and ischemic heart disease. It has been already suggested that quercetin is partially responsible for the therapeutic action of this plant due to its affinity for the ALOX5. We could add now delphinidin, cyanidin, and malvidin to the list of potential chemical effectors of this plant. Indeed, bilberry fruit contains high concentration of several anthocyanidins (Cassinese et al., 2007; Chu et al., 2011). In fact, other anthocyanidins present in bilberry, such as peonidin and petunidin, were also predicted to be active against this protein. So all these compounds may actually contribute synergistically to the therapeutic effect attributed to bilberry for the treatment of atherosclerosis and ischemic heart disease.

### CONCLUSIONS

An effort to integrate data linking metabolites, plants, diseases, and proteins has been shown to be useful to generate mechanistic hypotheses for some of the therapeutic uses of remedial herbs. In this respect, the use of predicted interactions largely increases our ability to generate mechanistic hypotheses for plants for which known data is scarce. This notwithstanding, one ought to admit that many computationally derived hypotheses may either be false positives or not truly contribute to the therapeutic effect exhibited by the medicinal plant. Unfortunately, it is impossible to pursue experimental confirmation of all hypotheses generated and offer general statistics of this limitation. Nonetheless, the examples presented offer clear potential for the use of this type of systems approaches to contribute to finding a scientific rationale for traditional medicines. There is much more to learn about

nature and its use for therapeutic purposes, and more research in this direction is certainly necessary.

### MATERIALS AND METHODS

**Linking plants to diseases.** A very first version of the database was created containing the therapeutic use of plants in traditional Catalan medicine (Gausachs, 2008). Plants were stored using their scientific name in Latin, whereas therapeutic uses were mapped to their corresponding disease identifier in ICD-10 (International Classification of Diseases Version 10). This initial database was complemented with additional therapeutic uses found for those plants in other public sources (Raja et al., 1997; Rigat et al., 2007; James et al., 2013). Data from the different sources was integrated using Latin names for plants and ICD-10 identifiers for diseases from 18 categories. In total, 372 medicinal plants associated with 187 therapeutic uses were collected at this stage (**Supplementary Material**).

**Linking plants to metabolites.** The endogenous metabolites identified at present for every single plant in the database were extracted from three different sources, namely, Dr Duke Phytochemical and Ethnobotanical Database (U.S. Department of Agriculture, Agricultural Research Service., 1992-2016), the KNApSAcK database (Nakamura et al., 2014), and Gausachs' work on Catalan remedial herbs (Gausachs, 2008). Among those, only KNApSacK contains chemical structures linked to chemical names. Structures for the chemical names available only in the other two sources were extracted from PubChem (Bolton et al., 2008). The final set of chemical structures from all sources was unified and stored using InChI Keys. In the end, a total of 7,443 unique chemical structures present in 322 of those 372 medicinal plants could be gathered and added to the database (**Supplementary Material**).

**Linking proteins to diseases.** Next, a list of both known and explored human proteins associated with diseases was extracted from the Therapeutic Target Database (Zhu et al., 2012). These data was complemented with curated protein−disease links, with focus on cardiovascular diseases, available in the literature (Cases and Mestres, 2009). The final list of proteins was stored and unified using their UniProt identifiers (The UniProt Consortium, 2015). A final number of 724 unique proteins known to be relevant for 166 out of the initial 187 therapeutic uses were ultimately entered into the database (**Supplementary Material**).

**Linking metabolites to proteins.** Finally, affinity data (pKi , pKd, pIC50, pEC50) between chemical structures and proteins was extracted from various public sources (Roth et al., 2004; Bolton et al., 2008; Gaulton et al., 2012; Gilson et al., 2016; Southan et al., 2016). Up to 3,171 known interactions between 705 phytoconstituents and 228 proteins were identified and collected into the database at this stage. In addition, since affinity data are well recognised to be suffering from completeness issues (Mestres et al., 2008; Cases and Mestres, 2009), known interactions between molecules and proteins were complemented with high-confidence predictions obtained using ligand-based computational models implemented in the CT-link software (Vidal et al., 2011; Garcia-Serna et al., 2015). Accordingly, 16,897 additional interactions predicted between 2,796 molecules, present in 297 plants, and 313 proteins were generated. However, the vast majority of the interactions predicted were assigned relatively low confidence scores (CScore). If only high confidence predictions (CScore ≥ 0.7) are considered, a total of 1,555 predicted interactions remain. In the end, affinity data for a total of 4,726 interactions between 1,157 endogenous metabolites and 320 therapeutically relevant proteins were assembled and stored in the database (**Supplementary Material**).

**Predicting metabolite–protein interactions.** All metabolite two-dimensional structures were processed with the CT-link software to obtain predicted affinities for a list of over 2,000 protein targets (Vidal et al., 2011; Garcia-Serna et al., 2015). Predictions are based on six independent ligand-based approaches that include similarity-based methods, pharmacophore clusters, quantitative structure–activity relationships, machine learning techniques, and cross-pharmacology relationships (Vidal et al., 2011). Ligand-based target models were constructed from small molecules for which *in vitro* affinity data was publicly available (Bolton et al., 2008; Gaulton et al., 2012). Each prediction is assigned a confidence score (CScore) that depends on the number and type of methods as well as on the affinity range of the prediction (see **Supplementary Material**). In the last decade, predictions from CT-link have been validated both retrospectively (Vidal and Mestres, 2010; Flachner et al., 2012; Spitzmüller and Mestres, 2013) and prospectively (Areias et al., 2010; Mestres et al., 2011; Antolin et al., 2012; Montolio et al., 2012; Antolin and Mestres, 2015; van Voorhuis et al., 2016; Ellis et al., 2019) in a wide range of applications and therapeutic areas.

**Experimental** *in vitro* **assays.** For the prospective validation, two molecules and one herbal extract were selected for testing with *in vitro* assays at Cerep (CEREP Inc). Ribosylzeatin was tested in binding assays to confirm the predicted interactions with adenosine A1 and A3 receptors. Cellular assays were used to confirm the predicted interactions between isorhamnetin and the dopamine D4 receptor, as well as between a compound mixture (containing cyanidin, delphinidin, and malvidin) and 5-lipoxygenase.

For the binding assay, ribosylzeatin was tested twice at a test concentration of 10 µM. The reference agonist ligands used to calculate the compound activity were CPA for the adenosine A1 receptor and IB-MECA for the adenosine A3 receptor, which have IC50 values of 0.75 nM and 0.31 nM, respectively. The adenosine A1 receptor assay was performed in the presence of 1 nM of 3H.CCPA. After 60 min of incubation with shaking, bound radioactivity was separated from free by vacuum filtration and determined by scintillation counting. A similar procedure was followed for the adenosine A3 receptor assay. In this case, it was performed in the presence of 0.15 nM of 125I.AB-MECA. It was incubated

#### REFERENCES


with shaking during 120 min. After that, bound radioactivity was filtered and measured with scintillation counting. For these binding assays the results are expressed as a percent of measured specific binding relative to control specific binding.

For the dopamine D4 receptor assay, isorhamnetin was tested at a concentration of 10 µM. The reference agonist ligand was dopamine, with an EC50 value of 28 nM. D4.4 was incubated for 10 min at 37ºC and, after that, cAMP was detected and measured with HTRF. Results are expressed as a percentage of measured response relative to control response.

Finally, for the testing of the compound mixture of cyanidin, delphinidin, and malvidin in the 5-lipoxygenase enzyme assay, the reference compound used was NDGA, which has an IC50 of 910 nM. 5-Lipoxygenase was incubated 20 min with shaking and 25 µM arachidonic acid as substrate. Thereafter, rhodamine 123 was measured using fluorimetry. Results are expressed as a percentage of measured specific binding relative to control specific binding.

Compounds showing an inhibition or stimulation higher than 50% were considered to be active for the proteins tested, whereas interactions showing activity values between 25% and 50% were considered to be indicative of at least weak to moderate effects.

#### DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the manuscript/**Supplementary Files**.

#### AUTHOR CONTRIBUTIONS

JM conceived and designed the study. JO acquired the data for the analysis. JO and JM analyzed and interpreted the data, discussed the results, and wrote the manuscript.

#### FUNDING

This research was supported by the Spanish Ministerio de Ciencia Innovación y Universidades (project SAF2017-83614-R).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.01132/ full#supplementary-material

SUPPLEMENTARY TABLE 1 | Collected data connecting diseases and plants, plants and metabolites, metabolites and proteins, and proteins and diseases. Also provided are the different subsets used to construct the plant-disease and plant-protein networks of Figures 2 and 4, respectively.


*Aspects* (Boca Raton (FL): CRC Press/Taylor & Francis). chapter 4. doi: 10.1201/b10787-5


the absence of mu opioid receptors. *Brain Res.* 1083 (1), 61–69. doi: 10.1016/j. brainres.2006.01.095


drug discovery. *Nucleic Acids Res.* 40 (Database issue), D1128–D1136 doi: 10.1093/nar/gkr797

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Olivés and Mestres. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Systems Pharmacology-Based Method to Assess the Mechanism of Action of Weight-Loss Herbal Intervention Therapy for Obesity

*Wei Zhou1,2, Ziyi Chen1, Yonghua Wang3, Xiumin Li1,2, Aiping Lu4, Xizhuo Sun1\* and Zhigang Liu1,2\**

*1 Department of Respirology and Allergy, The Third Affiliated Hospital of ShenZhen University, Shenzhen, China, 2 School of Basic Medical Sciences, Henan University of Traditional Chinese Medicine, Zhengzhou, China, 3 College of Life Sciences, Northwest University, Xi'an, China, 4 School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong*

#### *Edited by:*

*Shao Li, Tsinghua University, China*

#### *Reviewed by:*

*Bo Zhang, Tianjin International Joint Academy of Biomedicine, China Le Huang, The Chinese University of Hong Kong, China*

#### *\*Correspondence:*

*Xizhuo Sun xizhuomd@163.com Zhigang Liu lzg195910@126.com*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 15 March 2019 Accepted: 10 September 2019 Published: 14 October 2019*

#### *Citation:*

*Zhou W, Chen Z, Wang Y, Li X, Lu A, Sun X and Liu Z (2019) Systems Pharmacology-Based Method to Assess the Mechanism of Action of Weight-Loss Herbal Intervention Therapy for Obesity. Front. Pharmacol. 10:1165. doi: 10.3389/fphar.2019.01165*

Obesity is a multi-factorial chronic disease that has become a serious, prevalent, and refractory public health challenge globally because of high rates of various complications. Traditional Chinese medicines (TCMs) as a functional food are considered to be a valuable and readily available resource for treating obesity because of their better therapeutic effects and reduced side effects. However, their "multi-compound" and "multi-target" features make it extremely difficult to interpret the potential mechanism underlying the antiobesity effects of TCMs from a holistic perspective. An innovative systems-pharmacology approach was employed, which combined absorption, distribution, metabolism, and excretion screening and multiple target fishing, gene ontology enrichment analysis, network pharmacology, and pathway analysis to explore the potential therapeutic mechanism of weight-loss herbal intervention therapy in obesity and related diseases. The current study provides a promising approach to facilitate the development and discovery of new botanical drugs.

Keywords: obesity, weight-loss herbal intervention therapy (W-LHIT), multi-compounds, systems pharmacology, mechanism

### INTRODUCTION

Obesity is a multi-factor chronic disease involving an abnormal or excessive accumulation of fat in the body. Current trends predict that obesity prevalence rates for the global population will increase to 57.8% by 2030 (Pal et al., 2016). Obesity has become one of the leading health risk factors worldwide because it can induce various complications, particularly cardiovascular diseases, diabetes mellitus, fatty liver, and certain types of cancer (Haslam and James, 2005; Poulain et al., 2006).

The conventional therapeutic approaches for obesity are lifestyle changes, diet restriction, regular physical exercise, bariatric surgery, and pharmacological drugs (Li et al., 2015). However, because of the complex pathological mechanisms underlying obesity and obesity-related diseases, these strategies have proven to be far from satisfactory. Lifestyle changes, diet restriction, and regular physical exercise typically produce modest weight loss. It is a great challenge to sustain long-term behavioral modification, and this may cause unfavorable psychological changes (Li et al., 2015). Surgery is often considered for serious medical conditions, such as those with a high risk of obesity-related diseases and death (Shippey and Macedonia, 2003). In addition, although conventional western anti-obesity drugs are the dominant treatment used by obese patients, they are limited by serious side effects such as negative mood changes, possible liver damage, gastrointestinal or cardiovascular complications, and the potential for drug abuse and dependency for some people. These issues have resulted in a bottleneck in developing a safe and effective weight control strategy.

Given the drawbacks of conventional therapeutic methods, alternative treatments are needed. Traditional Chinese medicines (TCMs) have a long history, and for more than 2000 years, they have been favored by people all over the world for their unique advantages in preventing and treating various diseases and facilitating rehabilitation and health care (Xu and Chen, 2011). Multi-compound and multi-target TCMs have been used by the public at large and provide an alternative treatment for controlling weight and its related symptoms with reputable safety, potential efficacy, low cost, and few adverse effects. It is reported that pioneer investigations concerning clinical studies and animal models have been studied to explore the role of TCM in weight loss (Xiong et al., 2010; Lenon et al., 2012). In a previous study, we developed a TCM formula called weightloss herbal intervention therapy (W-LHIT) for the prevention of obesity which consists of six herbs: *Ganoderma lucidum* (Ling Zhi), rhizome of *Coptis chinensis* (Huang Lian), *Radix Astragali* (Huang Qi), *Nelumbo nucifera* Gaertn (He Ye), *Chaenomeles speciosa* (Mu Gua), and *Fructus aurantii* (Zhi Qiao). Despite the fact that W-LHIT has been proven to be effective in controlling weight (Yang et al., 2014), it is still difficult to clarify the active compounds, potential targets, the related pathways, and the underlying mechanisms of W-LHIT in the treatment of obesity using traditional experimental methods. Experimental evidence-based studies are not only labor-intensive, costly, and time-consuming but also lack a systematic explanation for the underlying mechanisms of W-LHIT in weight control. Therefore, it is imperative to develop a systematic approach to identify active ingredients and their related targets and clarify the mechanisms of W-LHIT in the treatment of obesity.

Fortunately, systems pharmacology has emerged as a novel strategy to elucidate therapeutic mechanisms and promote drug discovery and development (Zhou et al., 2016). Systems pharmacology involves the dissection of complex interrelationships between compounds, targets, pathways, and diseases at a systems level by combining absorption, distribution, metabolism and excretion (ADME) assessments, multiple drugtarget predictions, network pharmacology, and pathway analysis (Zhou et al., 2016). A growing number of evidences suggested that systems pharmacology approaches have been developed to explore the complex mechanism of TCM. For example, a network pharmacology framework was established to translate TCM from an experience-based medicine to an evidence-based medicine system in the past years (Li, 2007; Li and Zhang, 2013). A "network target"–based approach was proposed using network analysis to establish an algorithm termed NIMS (network target–based identification of multicomponent synergy) to screen synergistic drug combinations from TCM herbs or herbal formulae (Li et al., 2011). It is reported that herbal formula Qing-Luo-Yin and Liu-Wei-Di-Huang Pill were used as probes to decipher the combinatorial rule and the pharmacological mechanisms of TCM formulate at the point of network/systemic view (Zhang et al., 2013; Liang et al., 2014). Furthermore, in our previous studies, we have successfully elucidated the underlying mechanisms of TCMs in major types of coronary artery disease and deciphered the mechanisms of botanic drug pairs in treating different diseases based on systems pharmacology methods (Zhou and Wang, 2014; Zhou et al., 2016). The application of systems pharmacology in TCM may permit further understanding of the multiple mechanisms of action of TCMs in treating complex diseases.

Therefore, in the present study, a modified systemspharmacology framework which integrated an ADME evaluation, herb feature mapping, multiple targeting, gene ontology (GO) enrichment analysis, network pharmacology, and pathway analysis was proposed to clarify the pharmacological mechanism of W-LHIT in the treatment of obesity and related diseases. A comprehensive exploration of W-LHIT based on systems pharmacology not only provides an opportunity to further understand the potential mechanisms of W-LHIT in obesity therapy but also sheds light on a novel method to promote TCM drug discovery for the treatment of complex diseases. A flowchart of the systems pharmacology approach is shown in **Figure 1**.

### MATERIALS AND METHODS

#### Ingredients of Database Construction

The ingredients of the six herbs in the W-LHIT were obtained from the traditional Chinese medicines for systems pharmacology database and analysis platform (Ru et al., 2014), the traditional Chinese medicine integrative database (Xue et al., 2012), the TCM Database @Taiwan (Chen, 2011), and wide-scale literature mining. Finally, 541 chemical compounds from the six herbs in the W-LHIT were listed, including 225 in Ling Zhi, 48 in Huang Lian, 85 in Huang Qi, 87 in He Ye, 80 in Mu Gua, and 16 in Zhi Qiao. These compound structures were saved in mol2 format for further investigation.

#### ADME Screening

The major cause of costly late-stage failures during drug development is poor ADME properties (Wang and Urban, 2004). Therefore, ADME evaluation of a given compound in the early stages of drug discovery is extremely important. Multicomponent herbal medicine, such as a TCM formula, often contains hundreds or even thousands of ingredients, but only several bioactive compounds produce pharmacological effects in treating disease. The evaluation of pharmacokinetic profiles is a fundamental step for the accurate identification of the active ingredients in TCMs. In recent years, *in silico* methods have become more widely used to find active compounds that possess favorable pharmacokinetic properties, which are useful in assessing the therapeutic mechanism of herbs, since existing biological experimental techniques are generally more labor-intensive, costly, and time-consuming. Therefore, in the current study, two important pharmacokinetic parameters, oral

bioavailability (OB) and drug-likeness (DL), were used to screen potential active compounds in W-LHIT.

OB is an important pharmacokinetic indicator in drug screening cascades which represents the fraction of an orally administered dose that enters the systemic circulation to produce a pharmacological effect. In the current study, the OB value was used to identify the active compounds in W-LHIT, and the values were calculated using a robust *in silico* model OBioavail 1.1 (Xu et al., 2012). This model is based on a dataset of 805 structurally diverse drugs and is constructed using the multiple linear regression, partial least squares regression, and support-vector machine (SVR) methods (Hou and Xu, 2002). The SVR as the optimal model exhibits good performance with a determination coefficient (R2 ) of 0.80 and a standard error of estimate of 0.31 for test sets. Finally, the compounds with OB ≥50% were selected as candidate molecules since the values of OB are 30% on average with 10–50% variability based on clinical studies. The threshold determination is mainly based upon two careful considerations: (1) extracting as much as possible from the studied herbs in the W-LHIT with the least number of chemical ingredients; (2) the acquired model can be rationally interpreted using the published pharmacological data.

DL is important in drug design for discriminating "drug-like" molecules from an enormous number of chemical compounds, which helps to optimize pharmacokinetic and pharmaceutical profiles. Therefore, in the current study, a database-dependent model was performed to filter out the drug-like characteristics of the expected molecules from the W-LHIT based on the Tanimoto coefficient (Yamanishi et al., 2010). The formula of the DL index for a new compound is defined as follows:

$$f(A,B) = \frac{A \cdot B}{\left| \left| A \right|^2 + \left| B \right|^2 - A \cdot B \right|}$$

in which *A* represents the molecular descriptors of the compounds in the herbs, and *B* denotes the average molecular properties of 6,511 structurally diverse drugs and drug-like molecules in the Drugbank database (http://www.drugbank.ca). The compounds meeting the criteria of DL ≥0.18 were selected as potential bioactive compounds because the mean value of the DL index in DrugBank is 0.18 (Liu et al., 2013).

#### Multiple Target Fishing

The identification of compound-target interaction profiles has been increasingly necessary to interpret the mechanism of drug action. To predict the targets of active compounds in the herbs, a multiple targeting strategy which effectively integrated a systematic *in silico* prediction model and chemogenomic and data mining was proposed to identify target proteins of the active compounds. Initially, a robust multiple drug-target interaction prediction (DTpre) model that combines chemical, genomic, and pharmacological information based on support vector machine (SVM) and random forest (RF) values was developed to identify the potential drug-target interactions (Yu et al., 2012). The DTpre model performs well in predicting compoundtarget interactions, with a concordance of 82.83%, sensitivity of 81.33%, and specificity of 93.62%. In the current study, target proteins with SVM values and RF scores larger than 0.8 and 0.7, respectively, were chosen as the final predicted targets. Secondly, the virtual chemical Engerprint Similarity Ensemble Approach (SEA, http://sea.bkslab.org/) was used to identify the targets of active compounds based on the chemogenomic approach (Keiser et al., 2007). Thirdly, text mining was performed using the Therapeutic Target Database (TTD) (http://bidd.nus.edu.sg/ group/ttd/) (Chen et al., 2002) and DrugBank (Knox et al., 2010) to extract more accurate interactions between active compounds and targets, and all information was supported by published literature. Finally, to further investigate the mechanisms of W-LHIT in obesity therapy, the final obtained targets were mapped to the Comparative Toxicogenomics Database (CTD, http://ctdbase.org/) (Davis et al., 2012) and TTD to search for related diseases and construct target-disease relationships.

#### Gene Ontology Enrichment and Pathway Analysis

To investigate the meaningful biological functional annotation of the potential targets, GO enrichment analysis was introduced to extract the key GO terms (biological process and molecular function) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways based on a widely used Cytoscape v2.8.3 plugin ClueGO with a hypergeometric test (Bindea et al., 2009). The target proteins were added to ClueGO in simple text format or interactively derived from gene network graphs visualized in Cytoscape v2.8.3 to symbolize gene function and pathway information. The targets that organized and condensed into several functional groups denoted by their most significant leading term were visualized in the network. The criterion for difference screening included a p-value ≤0.05.

#### Network Construction and Analysis

To interpret the pharmacological mechanisms of W-LHIT in obesity at a systems level, a compound-target network (C-T network), target-disease network (T-D network), and target-pathway network (T-P network) were separately established to comprehensively clarify the complicated relationships among the compounds, targets, diseases, and related pathways. The C-T network was constructed by linking the active compounds and their potential targets, and the T-D network was generated by connecting relevant targets with their diseases. The T-P network was constructed by relating the targets to their related biological pathways. In these networks, compounds and targets, diseases, and pathways are marked as nodes, while the interactions between them are represented by edges. All networks were constructed using Cytoscape v2.8.3, which is a powerful bioinformatics package for biological network data visualization, integration, and analysis (Smoot et al., 2010).

#### RESULTS AND DISCUSSION

#### Active Compound Screening for Each Herb in W-LHIT

Generally, many orally administered drugs fail to reach to their target sites because of their poor pharmaceutical properties. Therefore, it is necessary to develop a method to overcome these barriers so as to identify the compounds with satisfactory pharmacokinetic properties. In the current study, two effective *in silico* models (OB and DL) (Yamanishi et al., 2010; Xu et al., 2012) were established to screen the active pharmaceutical compounds in the herbs. As a result, 51 active compounds from the six herbs in the W-LHIT were shown to have satisfactory properties with the filter criterion OB ≥40% and DL ≥0.18 (as shown in **Table S1**). Among them, 13 active compounds with good OB and DL values were from Ling Zhi, such as epoxyganoderiol B (OB = 42.30% and DL = 0.83), ganoderal B (OB = 42.19% and DL = 0.81), and lucialdehyde C (OB = 42.26% and DL = 0.81). For Huang Lian, nine bioactive components met the filter criteria, including corchoroside A (OB = 104.95% and DL = 0.78) and (R)-canadine (OB = 55.37% and DL = 0.77). A total of 14 active molecules were identified in Huang Qi, such as formononetin (OB = 69.67% and DL = 0.21), folic acid (OB = 68.96% and DL = 0.71), and isomucronulatol (OB = 67.67% and DL = 0.26). A total of nine active ingredients were obtained from He Ye, which include machiline (OB = 79.64% and DL = 0.24) and armepavine (OB = 69.31% and DL = 0.29). In addition, only three active compounds were obtained from Mu Gua and Zhi Qiao, respectively. For instance, betulinic acid (OB = 55.38% and DL = 0.78) was identified in Mu Gua, and hesperetin (OB = 70.31% and DL = 0.27) was identified in Zhi Qiao.

Interestingly, of the 51 active ingredients, most have been reported to be associated with various pathological processes including obesity, diabetes, cardiovascular disease, fatty liver, osteoporosis, gastrointestinal disease, inflammation, and cancer. For instance, ergosterol peroxide (OB = 44.39% and DL = 0.82) in Ling Zhi has the potential to suppress lipopolysaccharide (LPS)-induced inflammatory responses by suppressing the transcriptional activity of nuclear factor-κB (NF-κB) and CCAAT-enhancer binding protein β (C/EBPβ) and the phosphorylation of mitogen-activated protein kinases (MAPKs) (Kobori et al., 2007). Obacunone, as one of the oxygenated triterpenoids of Huang Lian, has been confirmed to be beneficial against obesity through the Takeda G-protein receptor 5 (TGR5) and peroxisome proliferator–activated receptor gamma (PPARγ) pathway (Horiba et al., 2015). The protoberberine alkaloid epiberberine (OB = 43.09% and DL = 0.78) from Huang Lian is a potential preventive and therapeutic agent for diabetes (Chen et al., 2018). Calycosin is the major active component in Huang Qi and exhibits beneficial effects against high-fat diet-induced nonalcoholic fatty liver disease (Duan et al., 2018). Betulinic acid (OB = 55.38% and DL = 0.78) may be a promising leading compound for obesity treatment via the regulation of fat and carbohydrate metabolism (de Melo et al., 2009). Moreover, isorhamnetin (OB = 49.60% and DL = 0.31) in He Ye has proved to be a specific antagonistic ligand of PPARγ that may be beneficial in preventing obesity induced by a high-fat diet (Zhang et al., 2016). Kaempferol (OB = 41.88% and DL = 0.24), which is a flavonoid, has been confirmed to be a potential therapy for cardiovascular diseases through inhibiting the migration of vascular smooth muscle cells (Kim et al., 2015). For Mu Gua and Zhi Qiao, the epicatechin (OB = 48.96% and DL = 0.24) in Mu Gua can suppress the expression of adipose tissue CCL19 so as to produce beneficial effects in diet-induced obesity (Sano et al., 2017). Naringenin (OB = 59.29% and DL = 0.21) in Zhi Qiao has shown good pharmacological effects in gastrointestinal disease (Kim and Kim, 2017).

#### Target Identification and Analysis

TCM formulas exert their pharmacological activity in various complex diseases through synergistic interactions between multiple compounds and targets. Therefore, the identification of target proteins is necessary in addition to the identification of active compounds. In the current study, several integrated *in silico* approaches, including SysDT, SEA, and TTD were employed to find potential targets of the active compounds in the W-LHIT.

As a result, 111 proteins were identified as the targets of the herbs in the W-LHIT (**Table S2**). The numbers of potential targets affected by active compounds from Ling Zhi, Huang Lian, Huang Qi, He Ye, Mu Gua, and Zhi Qiao are 14, 100, 103, 104, 81, and 15, respectively. Many active compounds exert their pharmacological effects through binding to more than one target simultaneously. For instance, stellasterol from Ling Zhi targets five proteins such as the glucocorticoid receptor (NR3C1). (R)-canadine, as an active molecule in Huang Lian, can interact with 28 targets, including dipeptidyl peptidase IV (DPP4) and peroxisome proliferator– activated receptor gamma (PPARG). Kaempferol (from Huang Qi) was found to be connected with 19 target proteins, such as acetylcholinesterase (ACHE) and prostaglandin G/H synthase 1 (PTGS1), while 27 targets were predicted for armepavine in He Ye, such as the mu-type opioid receptor (OPRM1). For Mua Gua and Zhi Qiao, epicatechin in Mua Gua showed interactions with 10 targets, such as nitric oxide synthase, inducible (NOS2), and naringenin in Zhi Qiao was also linked to 10 targets, such as glycogen synthase kinase-3 beta (GSK3B). The obtained targets may potentially be therapeutic targets for their related diseases. To elucidate the therapeutic mechanism of W-LHIT in various diseases, 111 potential targets were mapped to the PharmGkb, TTD, and CTD database to identify relevant diseases. The interactions between the targets and related diseases are presented in **Table S2**.

#### Gene Ontology Enrichment Analysis for Potential Targets

To further investigate the 111 potential targets in the network, GO term annotations, including molecular function and biological processes, were performed. As shown in **Figure 2A**, the results suggest that the potential targets are involved in various molecular functions which are closely associated with the pathogenesis of obesity and related diseases, such as adrenergic receptor activity, steroid hormone receptor activity, catecholamine binding, monoamine transmembrane transporter activity, estrogen receptor activity, tumor necrosis factor receptor superfamily binding, and insulin-like growth factor II binding. For instance, adrenergic receptor genes play important roles in regulating the lipid mobilization responsible for obesity and diabetes (Takenaka et al., 2012). Steroid hormone receptors, as ligand-dependent intracellular transcription factors, have been reported to be associated with various pathologies such as obesity, diabetes, cardiovascular disease, and inflammation (Kumar, 2016).

For biological process analysis (**Figure 2B**), the top 14 significant GO terms responsible for obesity and related diseases were enriched, including reactive oxygen species metabolic process, the vascular process in circulatory system, the response to estradiol,

enriched biological processes relative to the targets.

the regulation of fibroblast proliferation, lipid storage, and the regulation of generation of precursor metabolites and energy. For instance, reactive oxygen species are contributors to oxidative stress that can occur in obesity and related metabolic complications such as diabetes and cardiovascular disease (Le Lay et al., 2014). These results suggest that the targets were enriched and associated with the pathogenesis of obesity and related diseases.

#### Network Pharmacology Analysis

Generally, TCM formulas play a potential role in treating various diseases through multiple compounds, targets, and pathways. To elucidate these complex relationships at a systems level, C-T, T-D, and T-P networks were constructed.

#### Compound-Target Network

The C-T network consisted of 51 active compounds, 111 targets, and 676 C-T interactions (162 nodes and 676 edges) (**Figure 3**, **Table S2**). This is consistent with the multi-component multitarget characteristics of TCMs. Among the 51 active compounds in the six herbs, 22 demonstrate a high degree and are linked with more than 10 targets. For instance, in Huang Qi, 7 of 14 active compounds exhibit a high degree, including quercetin (degree = 70), 7-O-methylisomucronulatol (degree = 25), 3,9-Di-O-methylnissolin (degree = 20), kaempferol (degree = 19), astrapterocarpan (degree = 16), calycosin (degree = 12), and kumatakenin (degree = 11). Of the nine active molecules, five derived from He Ye: i.e., quercetin (degree = 70), armepavine (degree = 27), machiline (degree = 22), kaempferol (degree = 19), and roemerine (degree = 15). The remaining four compounds derived from Huang Lian, namely, quercetin (degree = 70), (R)-canadine (degree = 28), epiberberine (degree = 12), and palmatine (degree = 16). The two active ingredients in Zhi Qiao showed a high number of interactions with target proteins, including nobiletin (degree = 14) and hesperetin (degree = 11). Quercetin was identified in Mu Gua with a degree of 70, and ergosta-4,6,8 (14),22-tetraene-3-one was identified in Ling Zhi with a degree of 13.

Similarly, the results of a network analysis demonstrated that one target can be targeted by more than one compound from different herbs, which indicates the synergistic effects of TCM formulas. In the C-T network, 64 out of the 111 target proteins exhibited at least 2 interactions with the active compounds of the different herbs. For instance, prostaglandin G/H synthase 2 (PTGS2) is simultaneously targeted by 17 active ingredients from six herbs in the W-LHIT, including five from Huang Qi (3,9-di-O-methylnissolin, 7-O-methylisomucronulatol, astrapterocarpan, calycosin, kaempferol, kumatakenin) and He Ye (armepavine, epicatechin, kaempferol, machiline, roemerine), four from Huang Lian ([R]-canadine, epiberberine, palmatine, worenine), three from Zhi Qiao (hesperetin, naringenin, nobiletin), and one from Mu Gua (epicatechin) and Ling Zhi (ergosta-7,22-dien-3-yl linoleate). These results indicate that the therapeutic effects of W-LHIT in obesity and related diseases likely depend on synergistic interactions between multiple compounds and targets.

#### Target-Disease Network

To explore the pharmacological mechanism of W-LHIT in the treatment of diseases, a T-D network (**Figure 4**, **Table S2**) was constructed by employing 111 target proteins and eight corresponding diseases. As a result, 28 targets (24 in Huang Lian, 21 in Huang Qi, 26 in He Ye, 14 in Mua Gua, 4 in Zhi Qiao, 4 in Ling Zhi) affected by the 6 herbs in the W-LHIT were identified as being closely associated with obesity. For instance, the 5-hydroxytryptamine 2C receptor (HTR2C) targeted by Huang Lian was confirmed as an obesity-related treatment target because of its function in modulating the activity of neuronal pathways regulating energy balance (Burke and Heisler, 2015). PPARG (targeted by Huang Qi) is involved as a nuclear receptor in the pathological process of obesity (Motawi et al., 2017). It has been suggested that fatty acid synthase (FAS) is a potential therapeutic target for anti-obesity drugs with the highest level of enriched expression in human adipocytes (Liang et al., 2018). NR3C1, which is targeted by He Ye, is related to the etiology of obesity, the control of which will lead to an improvement in obesity-related disorders (Majer-Łobodzińska and Adamiec-Mroczek, 2017). Moreover, DDP4 (targeted by Zhi Qiao) and ACHE (targeted by Ling Zhi) play a significant role in obesity, and modulating their activity may be of great utility in obesity treatment (Valerio et al., 2017; Shenhar-Tsarfaty et al., 2019).

Obesity is a complex disease that often leads to other diseases. In the current study, 17, 20, 14, 4, 6, 15, and 11 target proteins are considered to have significant relationships with the pathological processes of cardiovascular diseases (15 in Huang Lian, 14 in Huang Qi, 16 in He Ye, 13 in Mua Gua, 3 in Zhi Qiao, 3 in Ling Zhi), diabetes (20 in Huang Lian, 20 in Huang Qi, 19 in He Ye, 16 in Mua Gua, 2 in Zhi Qiao, 1 in Ling Zhi), fatty liver (10 in Huang Lian, 11 in Huang Qi, 11 in He Ye, 10 in Mua Gua, 1 in Zhi Qiao, 1 in Ling Zhi), gastrointestinal disease (4 in Huang Lian, 4 in Huang Qi, 4 in He Ye, 3 in Mua Gua, 1 in Zhi Qiao), osteoarthritis (4 in Huang Lian, 6 in Huang Qi, 5 in He Ye, 5 in Mua Gua, 1 in Ling Zhi), inflammation (13 in Huang Lian, 12 in Huang Qi, 13 in He Ye, 11 in Mua Gua, 3 in Zhi Qiao, 2 in Ling Zhi), and cancer (10 in Huang Lian, 10 in Huang Qi, 10 in He Ye, 9 in Mua Gua, 1 in Zhi Qiao, 1 in Ling Zhi), respectively. This implies that W-LHIT may exert pharmacological effects not only in obesity but also in these related diseases.

For instance, nitric oxide synthase, endothelial (NOS3), targeted by Huang Lian has been reported to be a mediator of angiogenesis that is responsible for pathological processes in cardiovascular disease (Wang et al., 2015). It has been demonstrated that GSK3B (targeted by Huang Qi) is a novel target for the treatment of diabetes mellitus (Gao et al., 2012). He Ye was found to act on insulin-like growth factor II (IGF2), transient overexpression of which can cause fatty liver disease with the accumulation of free cholesterol, phospholipids, and lipid droplets (Kessler et al., 2016). Estrogen receptor beta (ESR2), which is targeted by Zhi Qiao, is expressed in the gastrointestinal tract and provides a therapeutic strategy to treat patients suffering from gastrointestinal diseases induced by excessive neuronal/glial cell damage (D'Errico et al., 2018). Compounds in Mu Gua target tumor necrosis factor (TNF), which is a well-known cytokine involved in inflammatory processes (López-Urrutia et al., 2017). The progesterone receptor (PGR) is targeted by Ling Zhi and plays a role in reducing the risk of osteoporosis (Zhong et al., 2017). Overall, the complicated interactions between multiple targets and diverse diseases achieved with TCM formulations imply that the synergistic and therapeutic effects of such an approach are better than highly targeted drugs in isolation.

#### Target-Pathway Network

To further decipher the underlying therapeutic mechanisms of W-LHIT for the treatment of obesity and related diseases, all predicted target proteins were mapped onto ClueGO to enrich their relevant pathways. As a result, 24 KEGG pathways were obtained including fluid shear stress and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, neuroactive ligand-receptor interaction, the IL-17 signaling pathway, the TNF signaling pathway, and the NF-kappa B signaling pathway (**Figure 5A**). The T-P network containing 111 targets and 24 corresponding pathways is shown in **Figure 5B**.

The results suggest that the enriched pathways are closely related to various pathological processes such as obesity, cardiovascular disease, diabetes mellitus, fatty liver, osteoporosis, inflammation, and certain types of cancer. For example, 27 targets are involved in mediating the pathways in cancer, the dysregulation of which is recognized as a diagnostic marker for various types of cancer (Koury et al., 2017). The 22 targets involved in fluid shear stress and atherosclerosis take part in the regulation of atherosclerosis, which is a major pathogenic factor in cardiovascular diseases (Baeyens et al., 2016). The AGE-RAGE signaling pathway (involving 19 targets) is a well-studied cascade and has been shown to play a role in the maintenance and regulation of the extracellular matrix in diabetes (Kay et al., 2016). A total of 16 targets participate in the IL-17 signaling pathway, which is a novel therapeutic target for the treatment of nonalcoholic fatty liver disease *via* the modulation of hepatocellular damage (Harley et al., 2014). The TNF signaling pathway (involving 14 targets) is one of the most studied pathways involved in the regulation of the inflammatory response (López-Urrutia et al., 2017).

Taken together, these results suggest that multiple targets could affect various pathways responsible for regulating the pathologic processes underlying obesity and related diseases, modulation of which may be a potent therapeutic approach for these diseases.

## CONCLUSION

Obesity is considered a metabolic disease characterized by an excess storage of body fat and has become a serious growing public health problem globally because of high rates of various complications including cardiovascular disease, diabetes, fatty liver, osteoarthritis, gastrointestinal disease, inflammation, and cancer. TCM has always been regarded as an alternative therapy that has been used to prevent and treat various diseases for thousands of years. However, the potential mechanisms of TCM in obesity have not been fully elucidated. To clarify

and diseases (square).

FIGURE 5 | (A) Pathway analysis of the targets. The *y*-axis represents the name of significantly enriched pathways related to the target genes, and the *x*-axis represents the target counts. (B) T-P network. The T-P network was constructed by linking active compounds and their related pathways. The nodes represent active compounds (rhombus) and pathways (square).

the pharmacological mechanisms of W-LHIT in obesity and related diseases, an integrated systems pharmacology model that incorporates ADME screening, target prediction, GO enrichment analysis, network technology, and pathway analysis was employed.

Through ADME screening, 51 active compounds with satisfactory pharmacokinetic properties were identified from the six herbs in the W-LHIT. These active compounds could bind to 111 target proteins involved in pathologic processes underlying obesity, cardiovascular disease, diabetes, fatty liver, osteoarthritis, gastrointestinal disease, inflammation, and cancer, suggesting that the herbs may exert pharmacological effects by regulating these targets. After target identification, GO enrichment analysis was performed for molecular function and biological processes to uncover a significant biological functional annotation for the obtained targets. Moreover, the constructed C-T, T-D, and T-P networks suggest that the herbs in W-LHIT play a significant role not only in obesity but also in treating related complications, which reveals that multiple diseases may be treated using a common herbal medicine. Furthermore, the pathway analysis suggests that the six herbs may simultaneously target several related signaling pathways, demonstrating the synergistic mechanism of W-LHIT in the treatment of obesity and related diseases at the pathway level.

In conclusion, the current study has provided a systems pharmacology framework to identify active compounds and potential target proteins and elucidate the pharmacological mechanism of W-LHIT for the treatment of obesity and related diseases. The present work offers a novel and reliable strategy to investigate the complex therapeutic mechanism of W-LHIT in obesity and related diseases at a systems level, which has the potential to facilitate drug discovery using TCMs and the identification of treatments for other complex diseases.

#### REFERENCES


#### DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

#### AUTHOR CONTRIBUTIONS

ZL and WZ conceived the research; WZ and ZC designed the research, analyzed the data and wrote the paper; WZ and YW developed the theoretical models and performed the computation; XL, AL and XS critically discussed the manuscript. All authors have read and approved the final manuscript.

#### ACKNOWLEDGMENTS

This research was supported by the National Natural Science Foundation of China Grant 31700805 and 81460252. This research was also supported by the Shenzhen Peacock Team Project (No. KQTD20170331145453160) and the Basic Research Project of Shenzhen Science and Technology Plan (No. JCYJ20170307163626362 and No. JCYJ20170307163506558). The authors thank Mr. Henry Ehrlich for reading this manuscript.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.01165/ full#supplementary-material.

TABLE S1 | The information of 51 active compounds in W-LHIT.

TABLE S2 | The interactions between the targets and related diseases.


potential for therapeutics and drug discovery. *J. Ethnopharmacol.* 151 (1), 66–77. doi: 10.1016/j.jep.2013.11.007

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Zhou, Chen, Wang, Li, Lu, Sun and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Intermodule Coupling Analysis of Huang-Lian-Jie-Du Decoction on Stroke

*Pengqian Wang1†, Li Dai1†, Weiwei Zhou1, Jing Meng1, Miao Zhang1, Yin Wu1, Hairu Huo1, Xingjiang Xiong2\* and Feng Sui1\**

*1 Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China, 2 Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China*

#### *Edited by:*

*Yuanjia Hu, University of Macau, China*

#### *Reviewed by:*

*Jun-Song Wang, Nanjing University of Science and Technology, China Pei Li, Huazhong Agricultural University, China Chao Huang, Northwest A&F University, China*

#### *\*Correspondence:*

*Xingjiang Xiong 5administration@163.com Feng Sui fsui@icmm.ac.cn*

*†These authors have contributed equally to this work*

#### *Specialty section:*

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

*Received: 04 January 2019 Accepted: 08 October 2019 Published: 05 November 2019*

#### *Citation:*

*Wang P, Dai L, Zhou W, Meng J, Zhang M, Wu Y, Huo H, Xiong X and Sui F (2019) Intermodule Coupling Analysis of Huang-Lian-Jie-Du Decoction on Stroke. Front. Pharmacol. 10:1288. doi: 10.3389/fphar.2019.01288*

Huang-Lian-Jie-Du Decoction (HLJDD) is a "Fangji" made up of well-designed Chinese herb array and widely used to treat ischemic stroke. Here we aimed to investigate pharmacological mechanism by introducing an inter-module analysis to identify an overarching view of target profile and action mode of HLJDD. Stroke-related genes were obtained from OMIM (Online Mendelian Inheritance in Man). And the potential target proteins of HLJDD were identified according to TCMsp (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform). The two sets of molecules related to stroke and HLJDD were respectively imported into STRING database to construct the stroke network and HLJDD network, which were dissected into modules through MCODE, respectively. We analyzed the inter-module connectivity by quantify "coupling score" (CS) between HLJDD-modules (H-modules) and stroke-modules (S-module) to explore the pharmacological acting pattern of HLJDD on stroke. A total of 267 stroke-related proteins and 15 S-modules, 335 HLJDD putative targeting proteins, and 13 H-modules were identified, respectively. HLJDD directly targeted 28 proteins in stroke network, majority (16, 57.14%) of which were in S-modules 1 and 4. According to the modular map based on inter-module CS analysis, H-modules 1, 2, and 8 densely connected with S-modules 1, 3, and 4 to constitute a module-to-module bridgeness, and the enriched pathways of this bridgeness with top significance were TNF signaling pathway, HIF signaling pathway, and PI3K-Akt signaling pathway. Furthermore, through this bridgeness, H-modules 2 and 4 cooperatively work together to regulate mitochondrial apoptosis against the ischemia injury. Finally, the core protein in H-module 4 account for mitochondrial apoptosis was validated by an *in vivo* experiment. This study has developed an integrative approach by inter-modular analysis for elucidating the "shotgun-like" pharmacological mechanism of HLJDD for stroke.

Keywords: Huang-Lian-Jie-Du decoction, stroke, inter-module analysis, pharmacological mechanism, network pharmacology

**Abbreviations:** HLJDD, Huang-Lian-Jie-Du Decotion; OMIM, Online Mendelian Inheritance in Man; TCMsp, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; CS, coupling score; MCODE, molecular complex detection; KEGG, Kyoto Encyclopedia of Genes and Genomes; stroke module, S-module; HLJDD module, H-module.

### INTRODUCTION

Stroke is a complex disease featured by various genetic variations and dysfunction (Matthew et al., 2012). The subsequent mess brought by gene interactions and pathway crosstalk makes it difficult to obtain a "magic bullet" acting on "single gene, single target" to achieve therapeutic efficacy (Roth et al., 2004; Frantz, 2005; Hopkins, 2009; Wang et al., 2018). These observations, coupled with the increasing failure rate of drug discovery based on reductionism (Khanna, 2012) have led to calls for a new science of "network medicine" to find a multi-target therapy modulating multiple genes and their interactions (Chandra and Padiadpu, 2013; Wang and Wang, 2013; Harvey et al., 2015; Greene and Loscalzo, 2017). Chinese herbal medicines, known as concoctions of numerous chemical ingredients, have been suggested to act on multiple pharmacological targets and therefore drew increasing attention in the latest decades. "Fangji" was a well-designed Chinese herb array according to principle of traditional Chinese medicine, in order to improve therapeutic efficacy and/or reduce toxicity and adverse reactions (Wang et al., 2011; Wang et al., 2013; Duan et al., 2015; Liu and Wang, 2015). Systematic prediction of multiple drug–target interactions from chemical, genomic, and pharmacological data was expected to accelerate the drug discovery processes (Yu et al., 2012). This may provide a potential avenue to multi-target therapy reversing the disease condition. As amount and sheer diversity of high throughput data generated are enormous in the post-genomic age, it is pertinent to explore underlying pathogenesis and pharmacological mechanism by taking a more overarching view of Fangji multi-target therapies on stroke (Hasan et al., 2012; Gu and Chen, 2014; Huang et al., 2014; Li et al., 2017).

Huang-Lian-Jie-Du Decoction (HLJDD), also known as Hwangryun-Hae-Dok Decoction or oren-gedoku-to in Japan, is an ancient traditional Chinese formula first described in Wang Tao's "Wai Tai Mi Yao" 2,000 years ago. It is composed of four herbs: Coptidis Rhizoma (*Coptis chinensis* Franch., rhizome), Radix Scutellariae (*Scutellaria baicalensis* Georgi., radix), Phellodendri Chinensis Cortex (*Phellodendron chinense* Schneid., cortex), and Gardeniae Fructus (*Gardenia jasminoides* Ellis., fructus) with the ratio of 3:2:2:3. HLJDD was widely applied as a complementary and alternative medicine to treat cerebral ischemia in Asian countries (Kondo et al., 2000; Xu et al., 2000; Zhang et al., 2017; Fu et al., 2019). It is reported that HLJDD could reduce ischemia-reperfusion brain injury (Hwang et al., 2002) and promote functional recovery in stroke (Zou et al., 2016) by alleviating the oxidative stress from reactive oxygen species (ROS), ameliorating inflammatory damage, enhancing cortical neurogenesis, inducing protective autophagy, and so on (Wang et al., 2013; Wang et al., 2014; Zou et al., 2016). The ingredients from HLJDD were also studied for their anti-ischemia effect. For instance, baicalin, an ingredient from *Rhizoma Coptidis*, was reported to reduce ischemic infarct volume by regulating apoptotic and neurophysiological processes (Liu et al., 2017), to protect brains against hypoxicischemic injury *via* the PI3K/Akt signaling pathway (Zhou et al., 2017); jasminoidin from *Fructus Gardeniae* could attenuate inflammatory response by suppressing ERK1/2 signaling pathway in brain microvascular endothelial cells (Li et al., 2016). Berberine, baicalin, and jasminoidin are major ingredients responsible for the effectiveness of HLJDD by amelioration of abnormal metabolism and regulation of oxidative stress, neuron autophagy, and inflammatory response (Zhang et al., 2017), and acted synergistically to exert protective effects (Zhang et al., 2016). It is reported that both of baicalin and jasminoidin could attenuate ischemia/reperfusion injury by suppressing mitochondrial apoptosis (Jiang et al., 2016; Zhao et al., 2016; Li, et al., 2017). And the process of mitochondrial apoptosis is largely consequent on the translocation of Bax and Bak of the Bcl-2 family to the mitochondrial outer membrane (Love, 2003; Zhang et al., 2015). However, how the mitochondrial apoptosis is attenuated by the ingredients of HLJDD remains unsettled.

All the above researches enriched the pharmacological targets of HLJDD. However, the next challenge arising may be how to grab the specific targeting community and action mode of HLJDD in the biological network. As a complex adaptive system, biological network constitutes a set of interacting units, "modules," which are suggested to be minimum functional entities (Sales-Pardo, 2017). The rewiring of these interacting modules can bring out the nonlinear phenomena: chaotic behaviors, synchronization, emergence, and subsequent phenotype alteration (Bandyopadhyay et al., 2010; Tang et al., 2014). Therefore, the interactions between these modules, not only the modules themselves, should be investigated to elucidate process of biological network response to perturbation (Bandyopadhyay et al., 2010; Hsu et al., 2011), especially to drugs and multitarget therapies. Accordingly exploring the target-on modules of HLJDD and how they work together to execute sophisticated function causing phenotypic alteration might be a promising opportunity to clarify the pharmacological mechanisms of this multi-ingredient herb array.

In this paper, we introduced the modularity analysis integrating inter-module connectivity calculation in protein to protein association network to identify the target profile and action mode of HLJDD (**Figure 1**). Firstly, we constructed the strokerelated network and HLJDD targeting protein network according to OMIM (Online Mendelian Inheritance in Man) databases and TCMsp (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform), respectively. Then we dissected the two networks into modules, respectively. The strokemodules (S-modules) and HLJDD-modules (H-module) were bridged by integrating the two-dimensional network based on protein–protein interaction background from STRING. Next, we analyzed the inter-module connectivity between S-modules and H-modules to explore the pharmacological acting pattern of HLJDD on stroke. Finally, we validated the conclusions by an *in vivo* experiment.

#### MATERIALS AND METHODS

#### Stroke-Related Data Source and Network Construction

Genes related to stroke were derived from OMIM (https:// www.ncbi.nlm.nih.gov/omim/), a database of human genes and

genetic disorders. We searched "stroke" as a keyword in OMIM and filtered the records for gene variations. As a result, all of genes related to stroke were identified and mapped to the background in STRING, which is an online database for functional protein association networks (https://string-db.org/cgi/input. pl), providing associations between proteins based on curated databases, experimentally determined, gene neighborhood, gene fusions, gene co-occurrence, textmining, co-expression, or protein homology. Finally, based on the proteins that correspond to imported stroke genes and protein-to-protein interactions from STRING, proteins association network related to stroke was constructed, in which proteins were represented as vertex, and interaction confidence more than 0.4 (a relatively low confidence to catch the broader scope of proteins related to stroke) was set as the edge connecting corresponding proteins.

### HLJDD Potential Targets and Network Construction

The potential targets of herbs from HLJDD were obtained from TCMsp (http://lsp.nwu.edu.cn/tcmsp.phpm), which is a systems pharmacology platform of Chinese herbal medicines that captures the relationships between drugs and targets (Ru et al., 2014). For we aimed to construct the holistic target landscape of HLJDD, we included all of the ingredients in the HLJDD and all of the potential targets of these ingredients. Then potential targets of the four herbs were merged as the target protein of HLJDD. The union of the HLJDD potential targets was also mapped to STRING background. Therefore, these targets were regarded as vertex, and their interaction from STRING was used as the edge, to construct the HLJDD target network. The weight of edges was equal to the interaction confidence, which is a parameter to evaluate the associations between protein in STRING. The cutoff of the edges was set as 0.7, a mediate confidence to obtain herbs targets interactions with high reliability.

### Module Identification and Inter-Module Analysis

Both the stroke network and the HLJDD network were clustered to find stroke-modules (abbreviated as S-module) and HLJDD-modules (abbreviated as H-module), respectively, by MCODE (molecular complex detection). MCODE was a cluster algorithmic-based software in cytoscape, which can cluster a given network based on topology to find densely connected regions. Pathways of these two groups of modules were enriched according to KEGG database (Kyoto Encyclopedia of Genes and Genomes). All of enriched pathways were classified according to KEGG pathway maps (https://www.kegg.jp/kegg/pathway.html) to conduct the heterogeneity analysis. The connectivity between S-modules and H-modules was bridged by merging the stroke network and HLJDD network to constitute a disease-drug bi-dimensional network. The proteins related to disease or herbs and their interactions can be simplified as a pair of networks D = (V, E) and H = (V, E), representing the disease-related and herbsrelated network. The two networks were merged based on the background of STRING database into a union graph, as G = (V, E), which contain the total of two sets of vertices and edges. Therefore, the S-modules and H-modules were all in G. The hypergeometric distribution was employed to calculate the significance of the interaction of a pair of modules.

$$p = \sum\_{k=\chi}^{n} \frac{\left(\frac{M}{k}\right)\left(\frac{N-M}{n-k}\right)}{\left(\frac{N}{n}\right)}\tag{1}$$

where x is observed inter-module connection; k and n represent the numbers of inter-module connections and all possible interactions between the pair of modules, respectively; M and N were the total numbers of inter-module connections and all probably existing inter-module interactions between any two modules in a network, respectively. We set *P ≤* 0.05 as significant. If the P-value of an inter-module connection was less than 0.05, the inter-module connectivity will be quantified by a novel parameter: coupling score (CS), which were introduced to evaluate the inter-module connectivity mediated by nodes and edges. The CS between any two modules was defined as follows:

$$\text{CS} = 2\text{t} + \sum\_{i \in M\_{x}, j \in M\_{\text{Y}}} a\_{ij} \tag{2}$$

where *Mx* and *My* denote a disease-module and herb-module connected by at least one edge; t is the total number of overlapping nodes between *Mx* and *My.* The symbols *i* and *j* represent a gene in *Mx* and *My*, respectively; *aij* is the weight of edge between genes *i* and *j*. Accordingly, based on this score, the modular map was constructed to include all inter-module connectivity relationships.

#### *In Vivo Experiment Validation*

To further validate our conclusion, we employed the middle cerebral artery obstruction (MCAO) animal model to examine the main ingredients' effect on the protein related to new identified mechanism of HLJDD. All the animal experiments were approved by the Ethics Committee of China Academy of Chinese Medicine. The experimental procedures were in accordance with the Prevention of Cruelty to Animals Act 1986 and NIH Guidelines for the Care and Use of Laboratory Animals for Experimental Procedures (National Research Council (US) Institute for Laboratory Animal Research, 1996).

A total of 24 Sprague-Dawley (SD) rats, weighing 200–220 g, were subjected to MCAO in order to induce a focal cerebral ischemia-reperfusion model. All the rats, except those in the sham-operated group, were subjected to MCAO procedure, according to the method described by Longa et al. (1989). Briefly, after being anesthetized with 2% pentobarbital (4 mg/kg, ip), the rats were exposed and the external carotid artery (ECA) was prepared, and an intraluminal filament was inserted from the ECA to ligate the left middle cerebral artery for 1.5. Then the intraluminal filament was withdrawn for reperfusion for 24 h. Rats in the sham group were also subjected to the same surgical preparation for the insertion of the filament as other groups, but no filament was inserted.

The standards of two major ingredients of HLJDD, baicalin (BA) and jasminoidin (JA), were obtained from the National Institutes for Food and Drug Control, and the purity was validated by fingerprint chromatographic methodologies. All compounds were dissolved in 0.9% saline just before the experiment. The rats were randomly divided into four groups: sham-operated group (0.9% saline), vehicle group (0.9% saline), BA (5 mg/ml) treated group, and JA (25 mg/ml)-treated group in this study. After the reperfusion, the rats received the responding treatment by intraperitoneal injection at 2 ml/kg body weight. After 24 h reperfusion and treatment, the rats were sacrificed, and the hippocampi of these rats were removed for western blotting.

The hippocampi were homogenized. After protein extraction and concentration adjustment, proteins were separated by sodium dodecyl sulfate (SDS)–polyacrylamide gel electrophoresis (PAGE) and transferred to nitrocellulose membranes (Hybond-C, Amersham, Buckinghamshire, UK) by electroblotting. Blots were stained with rabbit anti-Bak (Santa Cruz Biotechnology, Santa Cruz, CA, USA) and anti-β-actin (Abcam, Cambridge, UK) at a concentration of 1:1,000 and 1:5,000, respectively. After cyclic membrane wash and staining by goat anti-rabbit IgG with chemiluminescence (Amersham), the band density was determined with a GS-700 densitometer (Bio-Rad). Each measurement was taken in three replicates.

#### RESULTS

#### Two S-Module Community With Diverse Functions

As a result, 303 genes related to stroke were identified. A total of 267 out of 303 genes were found corresponding to proteins. The official symbols, domains, and annotations of the 267 proteins are shown in **Supplementary Table 1**.And 256 in 267 proteins were involved in the stroke-related network (named as stroke network), and the other 11 proteins were distributed individually; 1,502 edges were included in the network. According to MCODE, the stroke network was divided into 15 S-modules and many individual nodes (**Supplementary Figure 1**). The S-modules were interacting with each other to constitute a module map. S-module 1 and S-module 2 were in the center of the module map and possessed most neighbor modules: S-module 1 densely associated with S-modules 3, 4, 5, and 7; S-module 2 was densely interacting with S-module 10. The two module groups, led by S-module 1 and S-module 2, constituted two communities of stroke network. The other S-modules were sparsely connected (**Figure 2A**).

To investigate the biological function of S-modules, the KEGG pathway enrichment was conducted (**Supplementary Table 2**). As the center of the two community, S-module 1 and S-module 2 were enriched for 21 and 6 signaling pathways, respectively. The representative enriched annotation term with minimum P-value of S-module 1 was the calcium signaling pathway, and the representative term of S-module 2 was oxidative phosphorylation (**Figure 2A**). Additionally, there were 1, 3, 2, 6, 5, and 5 signaling pathways enriched in S-modules 3, 4, 5, 7, 8, and 9, respectively. Furthermore, the enriched pathways of

each S-module were classified. According to the categories of S-modules, the functions of two modular communities varied from each other. For the modular community 1, the top 4 pathway categories were signal transduction, immune system, and infectious diseases: parasitic and amino acid metabolism, accounting for 19%, 11%, 11%, and 11% of the total number of enriched pathways, respectively. These four sections accounted for 52% of the total pathways. For the modular community 2, categories showed more concentrated state: the top 3 pathway categories were global and overview maps, neurodegenerative diseases, and carbohydrate metabolism, accounting for 28%, 27%, and 18% of total enriched pathways. These three categories accounted for 73% of total pathways. Therefore, the pathological functions of the communities were on different aspects (**Figures 2B** and **3A**).

#### H-Modules Mainly Regulated Signal Transduction, Immune, Cancer, Infectious Diseases, Nervous System

As collected from the TCMsp database, a total of 105, 35, 102, and 66 compounds and 234, 228, 288, and 310 target proteins

of *Rhizoma Coptidis, Radix Scutellariae, Cortex Phellodendri*, and *Fructus Gardeniae* were identified respectively, and listed in **Supplementary Table 3**. After the target merging, there were 400 proteins that were regarded as the potential targets of HLJDD, and 335 out of 400 proteins were found as annotation in the STRING database (**Supplementary Table 4**). As a result, the ultimate HLJDD target network concluded with 300 proteins and 2,775 interactions. To investigate the targeting position in the intra-structure of this network, we also dissected the HLJDD network into modules. A total of 13 H-modules were identified by MCODE and 133 individual proteins not belonging to any module (**Supplementary Figure 1**).

According to KEGG pathway enrichment analysis of the H-modules (**Supplementary Table 5**), the most signaling pathways (76 pathways with significance) were enriched in H-module 1, among which the representative pathway with minimum P-value was TNF signaling pathway. In H-module 2, 72 pathways were enriched, and the minimum P-value pathway was cell cycle. H-modules 1 and 2 were concentrated on signal transduction, immune, cancer, and infectious diseases. The pathways related to the above aspects accounted for 81.58% and 77.78% of the total in H-modules 1 and 2,

FIGURE 3 | The number and distribution of enriched pathway of H-modules in each category. (A) The number of enriched pathways of H-modules in categories. The depth of the color was in proportion to the number of pathways. The letters emphasized by red represented categories with high enrichment frequency. (B) The radar chart of categories of pathways. The length of the line in each point position showed the number of the pathways in correspondent categories. The categories circled by red rings were frequently enriched.

respectively. Additionally, 13, 33, 6, 7, 53, 2, 2, 8, and 4 pathways were enriched for H-modules 3, 4, 5, 6, 7, 9, 11, 12, and 13, respectively. These pathways were also categorized based on the KEGG pathway maps (**Figures 3A, B**). It is showed that the function of H-modules mainly concentrated on five aspects: signal transduction, immune, cancer, infectious diseases, and nervous system. It is also remarkable that the frequently enriched category was signal transduction, which may indicate the principal function of HLJDD.

#### Overlapping Proteins Between HLJDD and Stroke Network Were the Direct Targets

As it is aimed to provide the targeting basis of HLJDD in stroke, we merged the HLJDD network and stroke network to construct the disease-drug bi-dimensional network. This merged network contained 515 proteins and 4,416 interactions, in which S-modules and H-modules were all involved. There were 28 overlapping proteins between stroke-related proteins and HLJDD targeting proteins. This may indicate that the 28 proteins are the potential targets of HLJDD, and the action mode of herbs was to directly regulate the disease gene. In the stroke network, nearly a half (12, accounting for 42.86%) of the 28 overlapping were located in S-module 1; 4 proteins were in S-module 4; and 1 protein was distributed in S-modules 5 and 7, respectively; the other 10 proteins were scattered around S-modules. This may indicate that S-module 1 and its neighbor S-module 4 were the major direct targets of HLJDD (**Figure 2A**), and the direct targets of HLJDD on stroke are mostly involved in stroke community 1 rather than stroke community 2.

Integrating with the pathway categories of stroke S-modules 1 and 4 in the above sections (**Supplementary Table 2**), we can infer that the priority direct regulation of HLJDD relies on the effect on signal transduction, immune, and infectious diseases, especially the signal transduction. For example, 24% pathways of S-module 1 focus on signal transduction, including calcium signaling pathway, TNF signaling pathway, sphingolipid signaling pathway, cGMP-PKG signaling pathway, and HIF-1 signaling pathway. This may be partly accounted for the pharmacological mechanism of HLJDD on stroke (**Figures 4A**, **B**).

#### Inter-Module Connectivity Between H-Modules and S-Modules Bridged More Holistic Target Profile

HLJDD, which as a formula constituted of multiple herbs and numerous ingredients, may act more like the "magic shotguns" mode. As the cellular components were organized in a wide interaction pattern to achieve mutual information propagation, these "shotguns" of HLJDD may affect the stroke network not only by overlapping targets but also by perturbing the fluctuation of this biological adaptive system. Therefore, we employ the inter-module CS to explore a more holistic landscape of the HLJDD action mode.

According to the modular map based on inter-module CS, we have also noticed that several H-modules formed an inter-module coupling connectivity with S-modules. In the modular map, H-modules 1, 2, 4, 7, 8, 9, 10, and 12 were densely connected with S-modules. H-modules 1, 2, and 8 surrounded the major targeted S-modules 1, 3, and and 4 to constitute a module-to-module coupling connectivity to bridge formula-related and diseaserelated network as a potential targeting pattern (**Figure 4C**).

In this module-to-module bridgeness, by comparing the pathways of S-modules 1, 3, and 4 with H-modules 1, 2, and 8, a total of 14 overlapping pathways were found. These 14 pathways mainly focused on signal transduction (42.86%) and infectious diseases (28.57%), as shown in **Table 1** and **Figures 4D, E**. The other pathways belong to the immune system, endocrine system, metabolic diseases, and cancers. Among these pathways, the TNF signaling pathways were enriched with a minimum p-value (1.52E-14). That also verified that these peripheral H-modules included the same pathways with S-modules; that means, HLJDD regulated these pathological pathways of stroke by forming the module-tomodule bridgeness in the biological system.

Furthermore, through this bridgeness, more H-modules were connected to more S-modules: H-modules 4, 7, 9, 10, and 12 could interact with S-modules through this bridgeness to constitute a more complete target profile on the disease network. For instance, viral carcinogenesis was a pathway enriched in H-module 2, involving the protein BAX. And there are also 12 proteins in H-module 4, one of which was Bak1, enriched in this pathway. Therefore, these H-modules could work together cooperatively to constitute a more comprehensive target profile.

#### WESTERN BLOT VALIDATION

To further validate the mechanism of HLJDD identified by inter-module coupling analysis, we selected a protein (Bak1) in H-module 4, which may act on S-modules by module-to-module bridgeness, and we employed western blot assays to compare the untreated and treated groups.

According to western blot, as shown in **Figures 5A**, **B**, the expression of protein Bak in hippocampi significantly increased in the vehicle group compared with the sham group (paired T-test, one-sided, *P* < *0.05*). Its expression significantly decreased in BA groups compared with the vehicle group (paired T-test, one-sided, *P* < *0.01*). There was no statistical significance between JA and the vehicle group.

### DISCUSSION

HLJDD, as a "fangji" formed by herb array, consists of numerous multi-target ingredients. Therefore, the targets of HLJDD were neither individual gene or protein, nor a single module, but "shotgunlike" target profiles. In this paper, we explored the H-modules, as well as drug- and disease-module inter-module coupling connectivity in stroke to investigate multiple targeting pathways of HLJDD and how they work together to cause phenotypic alteration.

#### S-Modules 1 and 4 Were the Core Pathological Module Targeted by HLJDD

In our stroke network, the S-module 1 was in the center of the modular map, circled by S-modules 3, 4, 5, and 7, and so on. Majority of the direct targets of HLJDD was distributed in S-modules 1 and 4. And the modular map also showed that the S-modules 1 and 4 were surrounded by H-modules (**Figure 6**). Therefore, S-modules 1 and 4 were the core targets regulated by HLJDD. Among the enriched pathways of S-module 1, the most frequently enriched category signal transduction, including calcium signaling pathway, TNF signaling pathway, sphingolipid signaling pathway, cGMP-PKG signaling pathway, and HIF-1 signaling pathway, exhibited a close relationship with stroke. All the above pathways showed close relationship with the stroke process. For example, the calcium signaling plays a critical role in the inflammation of stroke, associated with immune- and injury-related functions of astrocyte (Hamby et al., 2012). Ca2+ signaling showed beneficial effects on neuronal and brain protection and functional deficits after stroke (Li et al., 2015). TNF signaling is one of the key players in stroke inflammation progression: inhibition of TNF signaling can rescue functional cortical plasticity impaired in early post-stroke period (Liguz-Lecznar et al., 2015). HIF-1α signaling, which was involved in necroptosis, modulated blood–brain barrier integrity after focal ischemia (Geng et al., 2017). Sphingosine-1-phosphate, a key signaling molecule in the sphingolipid signaling pathway, is critical for sequelae after stressful stimulations: regulating glial cell activation, vasoconstriction, endothelial barrier function, and neuronal death pathways, which act as important components in many neurological conditions. Activation of sphingosine-1-phosphate receptor-1 by FTY720, a known sphingosine 1-phosphate receptor agonist, is neuroprotective after ischemic stroke in rats (Rosen et al., 2007; Hasegawa et al., 2010; Pfeilschifter et al., 2010; Maceyka and Spiegel, 2014; Prager et al., 2015; Sun et al., 2016). For pathways of stroke, S-module 4 also exhibited a close relationship to stroke. Also setting the signal transduction category as an example, the PI3K-Akt signaling pathway was enriched in S-module 4. It is reported that regulating PI3K/Akt signaling may induce ischemic damage attenuation in cerebral artery occlusion. In summary, the core pathological modules targeted by HLJDD were S-modules 1 and 4, which mainly include signal transductions related to neuroprotective effect.

#### Modular Connectivity Revealed the "Shotgun-Like" Action Pattern of HLJDD

S-modules 1, 3, and 4 and surrounding H-modules 1, 2, and 8 constitute a bridging relationship between disease network to herb



network. Among the overlapping pathways of this bridge structure, TNF signaling pathway was the top pathway with the most statistical significance. The "shotgun-like" action was exhibited in the TNF signaling pathway: four proteins (VCAM1, TNF, MMP9, and PIK3CA) in the S-module 1 were found in the TNF signaling pathway downstream; and 14 and 9 proteins of H-modules 1 and 2 were also found not only in the downstream, but also in the upstream of this pathway. The H-modules 1 and 2 constitute a comprehensive

targeting set to regulate this pathway. This pathway was suggested to play a role in cerebral ischemia and impaired functional cortical plasticity and to be a primary process of releasing of inflammatory cytokines (Liguz-Lecznar et al., 2015; McCoy and Tansey, 2015; Liu, et al., 2016; Hollander et al., 2017; Wang et al., 2017).

Another pathway with top statistical significance was HIF-1 signaling pathway, which was enriched in S-module 1 and H-modules 1 and 2. A total of 17 target proteins in H-modules 1 and 2 belong to the HIF-1 signaling pathway, including MAPK1, IL6, INS, RELA, BCL2, EDN1, VEGFA, IFNG, TLR4, STAT3, AKT1, EGFR, ERBB2, SERPINE1, NOS3, EGF, and TIMP, which were overlapping with three proteins in S-module 1 in this pathway, PIK3CA, NOS3, and NOS2. And the targets of H-modules 1 and 2 contained the downstream and upstream of the stroke-related proteins in this pathway. It has been reported that HIF-1 played an important role in the antioxidant's neuroprotection in ischemic stroke (Zhang et al., 2014). HIF-1α can be served as an upstream regulator of cerebral glycerol concentrations and brain edema (Higashida et al., 2011). That means HLJDD regulated the pathological proteins and neighboring proteins closely related to stroke process to constitute a targeting network in the HIF signaling pathway.

Other significant pathways were also regulated by HLJDD by the similar action mode. Activation of the PI3K-Akt pathway was reported to promote neuroprotection against cerebral ischemiareperfusion injury by decreasing nerve cell apoptosis (Lv et al., 2017; Li et al., 2019). The regulation mode on these overlapping pathways may provide a characteristic action pattern of multiple target ingredients, like many "shotguns" to form a regulating pathway profile, mainly concentrating on inflammatory response, antioxidant, and apoptosis.

Besides these overlapping pathways, the specific pathway of S-module 1 also has crosstalk with H-module enriched pathways. For example, inflammatory mediator regulation of TRP channels was a specific pathway in S-module 1. And this pathway can be regulated by MAPK signaling and calcium signaling, which were pathways in H-module 1. Therefore, the HLJDD regulated the upstream pathways of the stroke pathological pathway through the pathway crosstalk. Above all, HLJDD regulated the strokerelated core pathological pathways as well as their upstream and/or downstream pathways to constitute the waterfall of the pathway and to contribute a therapeutic effect.

Furthermore, through the bridgeness structure constituted by S-modules 1, 3, and 4 and H-modules 1, 2, and 8, more H-modules worked cooperatively on S-module. For instance, both the protein Bak1 in H-module 4 and BAX in H-module 2 were enriched in the viral carcinogenesis pathway. It is demonstrated that BAX and BAK are required for the initiation of apoptosis at the mitochondria (Ren et al., 2010). It is reported that the oligomerization of the Bax and Bak is an irreversible step leading to the execution of apoptosis, and inhibition of Bax/Bak oligomerization allowed cells to evade apoptotic stimuli and rescued neurons from death after excitotoxicity (Niu et al., 2017). Previous studies have demonstrated that both the ingredients baicalin and jasminoidin extracted from HLJDD could suppress mitochondrial apoptosis induced by ischemia/reperfusion injury, but the exact mechanism involved in these core proteins was far from clear. In our *in vivo* experiment, BA inhibited the protein expression Bak. This suggested that regulating mitochondrial apoptosis by inhibiting the Bak expression is an important mechanism of HLJDD in protecting against the neuronal injury. Therefore, through this module-to-module bridgeness, H-modules 2 and 4 cooperatively work together to regulate more comprehensive aspect of this pathway related to mitochondrial apoptosis against the ischemia injury. This also supported that it is the alteration of interaction between proteins from different H-modules that contributed to the phenotype reversion rather than a single target or a single module.

Accordingly, the core pathological modules were S-modules 1 and 4. We can infer that it is formatting an inter-module coupling connectivity between H-modules and stroke pathological modules, which contributed to pharmacological mechanism of HLJDD in stroke, mainly involving the TNF signaling pathway, the HIF signaling pathway, and the PI3K-Akt signaling pathway. Furthermore, through this bridgeness, H-modules 2 and 4 cooperatively work together to regulate mitochondrial apoptosis against the ischemia injury. These regulation targets were not a simple protein or a single module but constitute targeting pathway profiles, by pathway crosstalk, upstream and downstream and vertically converges to integral regulation.

#### CONCLUSIONS

Our integrative approach is a step toward elucidating the "shotgun-like" pharmacological mechanism of multi-target and

multi-ingredient "Fangji" by inter-modular analysis in complex diseases like stroke. Our methodology identified a subset of modules that can serve as potential targets of response mechanism to drug activity. Our findings offer a glimpse of the "tuning" from target entities to the relationship between these entities.

The targets in our study were based on the open database. It seems to be likely to produce some false positives that prevent us from a flawless work. And the validation experiment is still focusing on single targets. Therefore, one of our future works will be designing an integrative strategy using the effect-based multi-omics data from experiments to detect more mechanism with more accuracy and precision. In addition, we will try to design the further validation experiments for the inter-module connectivity.

#### ETHICS STATEMENT

All of the animal experiments were approved by Ethics Committee of China Academy of Chinese Medicine. The experimental procedures were in accordance with the Prevention of Cruelty to Animals Act 1986 and NIH Guidelines for the Care and Use of Laboratory Animals for Experimental Procedures [National Research Council (US) Institute for Laboratory Animal Research, 1996].

### AUTHOR CONTRIBUTIONS

PW and FS designed the research. PW performed the research and wrote the paper. LD, MZ, and YW performed the experiment validation. WZ, JM, HH and XX contributed to data and statistical analysis. PW and XX revised the manuscript.

### FUNDING

This work was supported by grants from the National Natural Science Foundation of China (81873024; 81773923; 81473372; 81373986)

#### REFERENCES


and Inheritance Program from Institute of Chinese Materia Media of China Academy of Chinese Medical Sciences (ZXKT18002).

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.01288/ full#supplementary-material

FIGURE S1 | Stroke-related protein-protein interaction network and modules; HLJDD targeting protein-protein interaction network and modules. (A) Strokerelated protein-protein interaction network. (B) HLJDD targeting protein-protein interaction network. (C) Modules detected by MCODE in stroke-related proteinprotein interaction network, abbreviated as S-modules. (D) Modules detected by MCODE in HLJDD targeting protein-protein interaction network, abbreviated as H-modules. Each vertex represents a protein, circles and triangles are the strokerelated proteins and HLJDD targeting proteins, respectively. The vertexes fulfilled by red represent the overlapping proteins between the stroke-related proteins and HLJDD targeting proteins.

TABLE S1 | The stroke-related proteins and annotation.

TABLE S2 | The ingredients and target of herbs from HLJDD.

TABLE S3 | Target proteins and annotation of HLJDD.

TABLE S4 | The enriched KEGG pathways and their categories of stroke-modules.

TABLE S5 | The enriched KEGG pathways and their categories of HLJDD-modules.


Genotoxic Cell Death and Promotes Neuroprotection. *Cell Chem. Biol*. 24, 493–506. doi: 10.1016/j.chembiol.2017.03.011


protein kinase B signaling pathway. *Neural. Regen. Res.* 12, 1625–1631. doi: 10.4103/1673-5374.217335

Zou, H., Long, J., Zhang, Q., Zhao, H., Bian, B., Wang, Y., et al. (2016). Induced cortical neurogenesis after focal cerebral ischemia-three active components from Huang-Lian-Jie-Du Decoction. *J. Ethnopharmacol.* 178, 115–124. doi: 10.1016/j.jep.2015.12.001

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Wang, Dai, Zhou, Meng, Zhang, Wu, Huo, Xiong and Sui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Comparative Network Pharmacology Analysis of Classical TCM Prescriptions for Chronic Liver Disease

*Zikun Chen1, Xiaoning Wang2, Yuanyuan Li3, Yahang Wang3, Kailin Tang1, Dingfeng Wu1, Wenyan Zhao1, Yueming Ma3\*, Ping Liu2,4\* and Zhiwei Cao1\**

#### Edited by:

Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China

#### Reviewed by:

Jianxin Chen, Beijing University of Chinese Medicine, China Yiyang Hu, Shanghai University of Traditional Chinese Medicine, China Yonghua Wang, Northwest A&F University, China

#### \*Correspondence:

Yueming Ma mayueming@shutcm.edu.cn Ping Liu liuliver@vip.sina.com Zhiwei Cao zwcao@tongji.edu.cn

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 19 February 2019 Accepted: 25 October 2019 Published: 22 November 2019

#### Citation:

Chen Z, Wang X, Li Y, Wang Y, Tang K, Wu D, Zhao W, Ma Y, Liu P and Cao Z (2019) Comparative Network Pharmacology Analysis of Classical TCM Prescriptions for Chronic Liver Disease. Front. Pharmacol. 10:1353. doi: 10.3389/fphar.2019.01353

1 Department of Gastroenterology, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China, 2 Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China, 3 Department of Pharmacology, Shanghai University of Traditional Chinese Medicine, Shanghai, China, 4 Key Laboratory of Liver and Kidney Diseases of Ministry of Education of China, Institute of Liver Diseases, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

Chronic liver disease (CLD) has become a major global health problem while herb prescriptions are clinically observed with significant efficacy. Three classical Traditional Chinese Medicine (TCM) formulae, Yinchenhao Decoction (YCHT), Huangqi Decoction (HQT), and Yiguanjian (YGJ) have been widely applied in China to treat CLD, but no systematic study has yet been published to investigate their common and different mechanism of action (MOA). Partial limitation may own to deficiency of effective bioinformatics methods. Here, a computational framework of comparative network pharmacology is firstly proposed and then applied to herbal recipes for CLD disease. The analysis showed that, the three formulae modulate CLD mainly through functional modules of immune response, inflammation, energy metabolism, oxidative stress, and others. On top of that, each formula can target additional unique modules. Typically, YGJ ingredients can uniquely target the ATP synthesis and neurotransmitter release cycle. Interestingly, different formulae may regulate the same functional module in different modes. For instance, YCHT and YGJ can activate oxidative stress-related genes of SOD family while HQT are found to inhibit SOD1 gene. Overall, our framework of comparative network pharmacology proposed in our work may not only explain the MOA of different formulae treating CLD, but also provide hints to further investigate the biological basis of CLD subtypes.

Keywords: network comparative analysis, TCM, chronic liver disease, Yinchenhao Decoction, Huangqi Decoction, Yiguanjian

#### INTRODUCTION

With increasing alcohol consumption and viral infection, chronic liver disease (CLD) has become a worldwide health concern. Chronic liver disease in the clinical context is a disease process of the liver that involves a process of progressive destruction and regeneration of the liver parenchyma leading to fibrosis and cirrhosis. "Chronic liver disease" refers to disease of the liver that lasts over a period of six months. It consists of a wide range of liver pathologies covering inflammation (chronic hepatitis), liver cirrhosis, and hepatocellular carcinoma. According to causes, chronic liver diseases can be classified as viral disease, toxic and drug-related disease, metabolic disease, autoimmune-response disease, and so on. While the nucleotide analogues (NUCs) and pegylated-interferon (Peg-IFN) therapies are effective, patients may not tolerate their adverse effects in lifelong treatment. In the meantime, most of CLD patients seek medical help from herbal medicine for its significant efficacy and low side effects. Recently, several meta-studies of randomized, controlled, clinical trials (RCTs) suggested that herbal remedies have a similar and even better effects compared to western drug therapy in dealing with CLD (Peng et al., 2016; Chen et al., 2019).

According to Guidelines for the diagnosis and treatment of liver fibrosis in integrative medicine practice of China, three classical formulae, Yinchenhao Decoction (YCHT), Huangqi Decoction (HQT), and Yiguanjian (YGJ) have been recommended to treat different subtypes of CLD according to "Zheng" differentiation, which classifies the pathological state of a patient at system level leading to individualized treatments. Syndrome Differentiation and Treatment (SDAT) is the basis of clinical application of traditional Chinese Medicine prescription. A chronic liver disease can be treated with the different efficacy TCM prescription based on its different TCM syndrome. Formula YCHT is suggested to treat "dampheat" type of patients featured by jaundice, inflammation, and abnormal fatty acids metabolism (Yan et al., 2017), and the therapeutic characteristic of Yinchenhao decoction is to clear heat and drain dampness. Clinically, it is applied mainly to treat patients who have acute jaundice hepatitis, hepatitis B, cholelithiasis, cholecystitis, leptospirosis, typhoid fever, pneumonia, hemolytic jaundice caused by favism, and so on (Yan et al., 2017), while formula HQT, with the proportion of Huangqi [*Astragalus mongholicus* Bge.] and Zhigancao [*Glycyrrhiza uralensis* Fisch.] at 6:1, is recommended for those "Qi-deficiency" patients with weak body, black and yellowish complexion, palpitations and vexations in the chest, dry lips and mouths, pale complexion and lack of appetite. The therapeutic characteristic of HQT is tonifying qi. HQT is applied clinically to treat CLD patients who have liver fibrosis or cirrhosis, heart failure, constipation, type 2 diabetes, and diabetic peripheral neuropathy with "Qi-blood deficiency" (Wang et al., 2015). Additionally, HQT alleviated DMN-induced liver fibrosis (Song et al., 2016). As the therapeutic characteristic of YGJ is nourishing liver and kidney, it is applied clinically to treat those who have chronic liver diseases, chronic gastritis, gastric and duodenal ulcer, intercostal neuralgia, and neurosis with "Yindeficiency" (Wang et al., 2015).

Studies have investigated the liver-protective activity of above formula. For instance, being composed of Yinchen [*Artemisia capillaris* Thunb.], Zhizi [*Gardenia jasminoides* J. Ellis], and Dahuang [*Rheum palmatum* L.], YCHT demonstrated variable ability of liver protection in treating cholestasis, liver fibrosis, hepatitis, biliary cirrhosis, and cholesteric liver diseases (Yan et al., 2017). The liver protection of YCHT was also confirmed by histopathology and biochemical experiments (Sun et al., 2019), and YCHD treatment could reverse the damage by DMN-induced in liver function (Cai et al., 2018). According to literature validation, pharmacological research revealed that YCHT showed function of anti-oxidative stress, inhibiting apoptosis. Also, it was validated to regulate cellular inflammation and lipid metabolism (Wang et al., 2015). Kupffer cells (KC) activation of inflammatory response and abnormal fatty acid metabolism were found to be the main pathological basis in the early inflammation for YCHT Syndrome (Xu-Dong et al., 2014).

Through literature searching, HQT decoction was reported to downregulate the expressions of PDGF genes, collagen genes (COL1A1, COL1A2, COL5A2), and THBS1, inhibiting TGF-beta and PDGF signaling pathways, followed by verification *via* qRT-PCR (Zhang et al., 2015). Also, it was noticed to promote Kupffer cell activation (Du et al., 2012; Liu et al., 2012; Zhang et al., 2015; Song et al., 2016), inhibit Notch signaling pathway (Wang et al., 2015), and alleviate oxidative stress and lipid peroxidation injury. In an animal experiment, HQT was found to significantly reduce alpha-naphthylisothiocyanate-induced cholestasis in mice (Wu et al., 2017). At herb level, herb Huangqi in HQT were reported with anti-oxidative and immune regulating effects (Shahzad et al., 2016; Li et al., 2019).

YGJ decoction has 6 herbs: Beishashen [*Glehnia littoralis* F. Schmidt ex Miq.], Maidong [*Ophiopogon japonicus* (Thunb.) Ker Gawl.], Danggui [*Angelica sinensis* (Oliv.) Diels], Shengdihuang [*Rehmannia glutinosa* (Gaertn.) DC.], Gouqizi [*Lycium barbarum* L.], and Chuanlianzi [*Melia azedarach* L.]. Research indicates the hepatoprotective, anti-fibrogenic, anti-angiogenesis effects of YGJ in hepatic injury mice models (Tian et al., 2016). In rat model of liver fibrosis, YGJ was found to alleviate the hepatic collagen hyperplasia and deteriorated hepatic function (Tao et al., 2009). Also, YGJ can reduce the liver oxidative stress and lipid peroxidation injury while inhibiting angiogenesis and induce cell differentiation (Zhou et al., 2015). In a rat model of liver cirrhosis, YGJ was reported with repairing function of liver cirrhosis, and the key mechanism was suggested as relating to the regulation of macrophage activation state (Xu et al., 2018).

Above studies showed the biological activity of formulae, but it would be interesting to know the detailed MOA difference or similarities between the three formulae under the same background of CLD disease. As TCM formulae are believed to produce efficacy in a holistic way, the difficulty in detecting the MOA similarities and differences lie in the inherent complexity of multi-ingredients and multi-targets for TCM formulae.

With the development of systems biology and accumulated herbal targets information, the emerging method of network pharmacology makes it possible for MOA analysis of herbal ingredients. Coupled with transcriptomics validation, a latest article provided an excellent model adopting network pharmacology to predict the active components and regulation mechanisms of YCHT against liver fibrosis (Cai et al., 2019). Until now, network pharmacology has been applied to study individual ingredients, herbs, or formulae. One reason causing the difficulty is the lack of effective bioinformatics methods. Here, we developed a computational framework aiming to compare the MOA between formulae for same disease, taking the YCHT, HQT, and YGJ as an example.

The results indicated that although different formulae have different molecular targeting profile, they are commonly involved in a group of functional modules, indicating the CLD disease background. In addition to that, each formula was detected with unique function modules in MOA analysis. More interestingly, even on the same functional module, different regulating modes were found for different formulae agreeing with observed "Zheng" classification of CLD subtyping.

### MATERIALS AND METHODS

#### Dataset

#### Formulae, Herbs, Ingredients, and Targets

For each of the three formulae, herb components, ingredients and targets of each ingredients were collected from HIT (Ye et al., 2011), NPASS (Zeng et al., 2018b), TCMDB (Chen, 2011) and TCM-ID (Huang et al., 2018) databases respectively.

### Method

#### Disease Network, Disease Modules and Function Annotation

Targets of herbal active ingredients were obtained from HIT (Ye et al., 2011) and NPASS (Zeng et al., 2018b) database for target network construction. Potential protein-protein interaction (PPI) network of disease is generated *via* Reactome database (Croft et al., 2014) based on combined unique targets from 3 formulae *via* step size 1 (one bridging node). Then, the disease network was modularized through ReactomeFIViz (Wu and Haw, 2017). Genecards database (Safran et al., 2010) was used for function annotation of each module because of its high level of function annotation. Disease network and modularization were displayed *via* Cytoscape (Shannon et al., 2003). KEGG (Kanehisa, 2002) and GO Biological Process (Ashburner et al., 2000; Mi et al., 2017; The Gene Ontology, 2017) databases were used for biological function annotation for targets of each formula.

#### Statistic Enrichment of Functional Modules

For the given number of formula targets, we tested whether it is significantly enriched in a specific module A, comparing to a randomly picked module with equal size of A, from nodes of disease network background. To make the variation comparable in statistics, we repeated the picking for 5000 times. And each time, half the number of given targets were randomly picked. T-test statistics was made between the hitting of tested targets on specific module and the random module equally sized. According to above, different number of targets for each formula were enriched respectively into the functional modules of disease.

#### Effect of Kaempferol on the Absorption of Astragalosides

To investigate the effect of kaempferol on the absorption of astragalosides, experiments of verted rat sac was performed as previously described (Yan et al., 2015). Briefly, the everted intestinal sacs were prepared by rapidly removing the small intestine from starved rats euthanized under carbon dioxide (CO2) anesthesia. Then, four intestinal segments (the duodenum, upper jejunum, bottom jejunum, and ileum) were excised, flushed several times with saline solution at room temperature, and then placed immediately into oxygenated buffer solution at 37°C. Intestine segments from four rats were divided into four groups treated with astragalosides without or with kaempferol at three different concentrations (1, 3, 10M) using a 4 × 4 Latin square design (Gressley et al., 2016).



A: Astragalosides + 1µM kaempferol;

B: Astragalosides + 3µM kaempferol;

C: Astragalosides + 10µM kaempferol;

D: Astragalosides.

The concentration of Astragaloside solutions was set at 8 mg/mL based on the gastrointestinal concentration after oral administration of Huangqi [*Astragalus mongholicus* Bge.] decoction to rats, and the concentration of the additional kaempferol was 1, 3, and 10 µM. Then, aliquots of the sac fluid sample (200 µL) were removed, and the same volume of buffer solution was added at 0, 0.5, 1, and 2 h. The area of each sac was calculated closely. Furthermore, each sac was weighed before and after fluid collection to calculate accurately the volume inside the sac. The samples were analyzed using UPLC-LTQ-Orbitrap in both positive and negative ionization mode as previously described. The proposed method could be used to determine the 10 compounds in astragalosides simultaneously with high precision, sensitivity, and accuracy (Zeng et al., 2018a). A multivariate analysis of variance (ANOVA) was performed using the SPSS ver. 13.0 software while p-values < 0.05 were considered statistically significant.

### RESULTS

#### Computational Framework of Comparative Network Pharmacology

The idea of comparative network pharmacology is to construct a background disease network covering all the targets of different formulae, and then analyze the common and different modules or regulations. The framework of comparative network pharmacology is proposed as below (**Figure 1**): 1. Disease network construction and modularization of combined targets; 2. Comparative analysis of formulae on disease module network; 3. Detailed regulation comparison between formulae.

Accordingly, the computational framework works like this: 1. Retrieve targets of each formula for same disease; 2. Construct the disease network based on combined targets of all formulae; 3. Partition and annotate the disease network into function modules; 4. Calculate the enriched modules for common targets among all formulae and unique targets for each formula; 5. Analyze the MOA similarities and differences.

### Disease Network and Function Modules

The known ingredients with experimental target evidence from literatures were retrieved from databases and summarized into **Supplementary Table 1** for each herb and formula. Though different formulae are composed of different number of herbs, the overall known targets for each formula is roughly similar ranging from 120 to 140 genes. A total of 293 non-redundant targets were combined for all 3 formulae.

Targets of each formula was firstly enriched into KEGG and GO database for function annotation. The results show that all the three formulae are primarily targeting pathway of Hepatitis B, suggesting their common therapeutic effects in liver disease, but the enriched pathway lists are heavily overlapping (**Supplementary Table 2**). Similarly, mapping to GO databases also shows similar pathways profiles and GO terms. This suggests that the commonly used bioinformatics analysis of KEGG and GO annotation is good at inferring the MOA similarities, rather than the MOA differences for different formulae.

After running the pipeline of comparative network pharmacology, we proposed the biggest connected subnetwork containing 244 targets (83.28% of total) as the CLD disease background network.

This CLD network was initially clustered into 16 modules by ReactomeFIViz (Wu and Haw, 2017). After removing the two modules fully composed of background bridging nodes, we kept the remaining 14 modules for further analysis.

#### Common and Unique Functional Modules for TCM Formulae

Since active ingredient list is often an arguable issue for herbs, consensus has not been reached particularly for those commonly found among herbs, such as kaempferol, vitamins, or quercetins. Despite the molecular activity *in vitro*, the low bioavailability was often reported (Barve et al., 2009). Under the complexity of multi-ingredients context of herbs, their potential ADME effect to known active ingredients deserves further investigation. Taking kaempferol as an example, we tested the effects of kaempferol on the absorption of astragalosides in Huangqi herb *via* verted rat sac method under 4 different dosages. In the preliminary test, it was found that there were significant differences in the absorption of astragalosides in different rats and different gut segments. Then, Latin square was adopted to exclude the influence of different rats and intestinal segments. As the detailed results shown in **Supplementary Table 3**, under the concentration range of 1-10µM, kaempferol was observed with no significant effect on the absorption of 10 components in astragalosides in verted rat sac model.

As such, the active ingredient list was perturbed from full to shortlist by removing kaempferol, vitamin C, and vitamin E one by one. The module enrichment results were summarized in **Supplementary Table 1** (Sheet name: results of enrichment). Despite the perturbation, all formulae share 3 consensus modules and one conditional module, and each formula own two unique modules. Ten modules covered 236 targets, and their functional annotation are shown in **Table 1** by GeneCards (Safran et al., 2010).

Through the computational framework, the MOA similarity and differences are illustrated in **Figure 2** as shown below in the background of disease modules. The four common modules cover 167 targets, mainly involving oxidative stress, drug metabolism, DNA damage, NFAT and Cardiac Hypertrophy, Jak-STAT signaling and inflammation, etc. Further details of modules are shown in **Figure 3**. These common processes of oxidative stress, inflammation, and corresponding signaling pathways are highly involved in the pathological basis of CLD. For instance, oxidative stress, caused by imbalance between production and consumption by antioxidants, is a prominent feature in the pathophysiology of CLD (Vuppalanchi et al., 2011). Once oxidative stress is initiated, continuous cycle of cellular damage and release of proinflammatory cytokines will move on leading to hepatic inflammation, fibrosis, and cirrhosis (Comporti et al., 2009).



In view of literature report, consistent mechanistic evidences have been found between our computing analysis and independent validation. Modules 8 and 7 are involved with antioxidative stress. All three formulae can target these two common modules, indicating their common anti-oxidative function. Furthermore, module 10 is mainly involved with inflammation, and all three formulae were predicted to interact with a wide variety of inflammation factors. For instance, YCHT can target IL1B, IL4, and IL6; HQT can target IL6 and IL10; and YGJ can target more factors

of IL1B, IL2, IL4, IL5, IL6, IL10, and IL13. At molecular level, our predictive results gained support from previous validation in that TGF-beta was targeted by HQT, and collagen genes (COL1A1, COL4A2, and COL7A1) were targeted by YGJ.

Meanwhile, each formula can significantly target two unique modules as illustrated in **Figure 2**. YCHT can significantly target modules 1 and 2. Module 1 is specifically involved with immune response of lectin induced complement pathway, where Dahuang herb can uniquely interact with it. Module 2 is related to leukocyte trans-endothelial migration and cell membrane interaction or adhesion, where all herbs in YCHT can target this module. HQT compounds can interact with CDK-mediated phosphorylation and removal of Cdc6, together with ATP binding in LKB1 signaling events (modules 3 and 4). Most interestingly, YGJ ingredients can directly and uniquely target the ATP synthesis (Kamanna et al., 2013) and neurotransmitter release cycle (modules 5 and 6), implying the potential modulation of ATP energy and neuron alertness.

#### Different Regulation Modes in the Same Functional Module

It is noted that, despite the unique modules for different formulae, the majority of targets are still involving the same functional modules. We investigated the regulation modes of herbal ingredients in each formula on the same module and found that a substantial number of targets are differently regulated by different formulae. It's realized that the information of regulation modes on targets is highly precious, where only a few databases such as HIT collected the sparse evidences. Details of regulation modes on three consensus modules can be found in **Table 2**.

For the module of DNA Damage and Oxidative Stress (Module 8), we can see that YCHT and YGJ ingredients can activate the antioxidant-related genes SOD families, while HQT compounds inhibit SOD1 genes. This actually agrees well with previous literature results for HQT (Wang et al., 2008). On Module 9 of NFAT and Cardiac Hypertrophy, YCHT ingredient can inhibit ESR1 target and HQT ingredient has inverse action, while YGJ shows mixed function. The 10th Module contains a list of inflammation factors. Generally, all formulae inhibit a wide range of pro-inflammation factors, such as IL1, IL2, IL4, IL5, etc.

Previous studies have shown that YCHT can regulate Kupffer cells; inhibit the release of proinflammatory cytokines, such as TNF-α, IL-1β, and IL-6; and promote the release of anti-inflammatory and anti-fibrogenic factors, IFN-γ and IL-10 (Zhang et al., 2013). Palmitic acid contained in herb Zhizi can inhibit the cytokines involved in various inflammatory processes as an antagonist of CASP1 (Xu et al., 2016). Further, the active ingredients in YCHT, such as salicylic acid, rottlerin, and garlic acid can also reduce the expression of NOS2, TNF, and RELA, and then inhibit the inflammation (Hee Sun et al., 2008). Among the cytokines, IL10 is well known for its balanced function of inflammation-inhibition. On this particular gene of IL10, HQT shows negative effects, while YGT ingredients show positive effects. It is noted that only individual ingredients were

ingredients can target unique module.

reported with activating/inhibiting effect on molecular targets; the final *in vivo* effects may depend on the relative content and bioavailability of multiple ingredients in formula.

### DISCUSSION

Highly personalized prescriptions of different formulae are frequently applied to treat one same disease in different states. Comparing the MOA similarities and differences between formulae for same disease will provide us a good opportunity to study the pathogenesis of disease subtypes. However, as shown in this study, the commonly simple adoption of KEGG or GO annotation often gives more similarities than differences because the pathway maps or GO terms have been strictly predefined while they are actually densely cross-talked. We thus set up a computational framework of comparative network pharmacology for this purpose and illustrated an example to reveal MOA details of 3 herbal recipes for CLD disease.

In applying this framework to other formulae, there are some suggestions to get better understanding of the method. First, instead of the predicted targets, we strongly support to collect those clearly validated ingredients and targets with regulation modes. One reason is that the prediction error may expand and mess up the modularization part during the subsequent analysis. Another reason is, regulation modes are normally deficient in the results of computational prediction. That is why we have to abandon several elegant TCM databases, such as SymMap (Wu et al., 2018), TCMSP (Ru et al., 2014), or YaTCM (Li et al., 2018). Secondly, other databases or household PPI list can also be adopted to generate PPI network if the information in Reactome is not enough. Lastly, the statistics of module enrichment is elaborated here by comparing with a randomly picked module with equal size of targeting groups in disease network background.

The comparative analysis finds the MOA difference correlates well with the Zheng symptoms between the formulae. As we know, YCHT is used to treat CLD patients with jaundice or inflammation. The unique function module of YCHT is just related to lectin-induced complement pathway and leukocyte trans-endothelial migration, which might mobilize extra antiinflammation factors to deal with "damp-heat" symptoms. Another interesting finding here is that YGJ can directly target ATP synthesis and neurotransmitter release, which may promote ATP supply and increase the alertness of neuro systems.


TABLE 2 | Different regulation modes of different formulae on the same functional module. Information of regulation modes is collected from HIT database (Ye et al., 2011).

In our previous study, YGJ was found with ability to modulate the abnormal energy metabolism (Yan et al., 2017). This unique MOA of YGJ agrees well with its clinical application to treat "Yin deficiency" patients with extreme fatigue and weakness.

Yet, most of the time, disease subtypes of TCM Zheng are classified by highly summarized concepts of tongue color, pulse feeling, and personal overall phenotypes. While the Zheng research is still at beginning stage, our comparison framework from formula's perspective may provide alternative views to study their material basis. In addition to above, more consistent evidence may be proposed in future with both the progress in herbal target information and the TCM Zheng study.

Lastly, interaction between compounds and protein targets plays an important role in modulating the biological process and activity. We collected only those compounds with target information and activity evidence for network analysis. Yet, what ingredients are actually pharmacologically active deserves further investigation for these formulae. Correspondingly, perturbation tests are necessary to reach consensus conclusion. In fact, the list of active compounds may be affected by multiple factors such as the quantities in herbs, herb dosage, conjugated forms, compound interactions, biotransformation, dietary usage, bioavailability, other ADME issues (Dabeek and Marra, 2019).

To summarize, the comparative network pharmacology can detect a list of functional modules inferring the potential mechanisms of different formulae and provide functional insights for the subsequent exploration for not only formulae, but also disease subtyping. It's aware that all the information in the framework is collected from public database. Thus, the analysis results may be influenced by the abundancy and accuracy of related information. In the future, with more herbal experimental evidences accumulated, MOA comparison between different formulae is expected to be refreshed quantitatively and highlighted.

### DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC3013727/.

## ETHICS STATEMENT

The experimental procedures involving the use of animals were approved by the Committee on the Use of Live Animals for Teaching and Research of the Shanghai University of Traditional Chinese Medicine and all experiments were performed in accordance with the guidelines of this committee.

## AUTHOR CONTRIBUTIONS

ZKC conducted the project and wrote the manuscript. YL and YW finished the experimental verification. XW, KT, and DW collected the data and interpreted the results. WZ modified the manuscript. YM, PL, and ZWC designed and supervised the project. All authors read, critically reviewed, and approved the final manuscript.

## FUNDING

This work was supported in part by National Key R&D Program of China (grant number 2017YFC1700200 and 2017YFC0908400), the National Natural Science Foundation of China (31671379, 81573873, 81774196).

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.01353/ full#supplementary-material

SUPPLEMENTARY TABLE 1 | The detailed results of targets and ingredients.

SUPPLEMENTARY TABLE 2 | The detailed results of Enrichment in KEGG and GO.

SUPPLEMENTARY TABLE 3 | The detailed results of the everted intestinal sacs experiment.

### REFERENCES


reactome pathways, and data analysis tool enhancements. *Nucleic Acids Res.* 45 (D1), D183–D189. doi: 10.1093/nar/gkw1138


**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Chen, Wang, Li, Wang, Tang, Wu, Zhao, Ma, Liu and Cao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

Edited by:

Yuanjia Hu, University of Macau, China

#### Reviewed by:

Pei Luo, Macau University of Science and Technology, Macau Sameer Goyal, R.C.Patel Institute of Pharmaceutical Education and Research, India Chanderdeep Tandon, Amity University, India Shreesh K. Ojha, United Arab Emirates University, United Arab Emirates Mohamed Fizur Nagoor Meeran, United Arab Emirates University, United Arab Emirates

> \*Correspondence: Shyamal K. Goswami

skgoswami@mail.jnu.ac.in

#### †Present address:

Santosh Kumar, Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 09 February 2019 Accepted: 12 November 2019 Published: 10 December 2019

#### Citation:

Kumar G, Saleem N, Kumar S, Maulik SK, Ahmad S, Sharma M and Goswami SK (2019) Transcriptomic Validation of the Protective Effects of Aqueous Bark Extract of Terminalia arjuna (Roxb.) on Isoproterenol-Induced Cardiac Hypertrophy in Rats. Front. Pharmacol. 10:1443. doi: 10.3389/fphar.2019.01443

# Transcriptomic Validation of the Protective Effects of Aqueous Bark Extract of Terminalia arjuna (Roxb.) on Isoproterenol-Induced Cardiac Hypertrophy in Rats

*Gaurav Kumar1, Nikhat Saleem1, Santosh Kumar1†, Subir K. Maulik2, Sayeed Ahmad3, Manish Sharma4 and Shyamal K. Goswami1\**

1 School of Life Sciences, Jawaharlal Nehru University, New Delhi, India, 2 Department of Pharmacology, All India Institute of Medical Sciences (A.I.I.M.S.), New Delhi, India, 3 Bioactive Natural Product Laboratory, Department of Pharmacognosy & Phytochemistry, School of Pharmaceutical Education & Research, New Delhi, India, 4 Peptide and Proteomics Division, Defence Institute of Physiology and Allied Sciences (DIPAS), Defence Research and Development Organisation, New Delhi, India

Aqueous extract of the bark of Terminalia arjuna (TA) is used by a large population in the Indian subcontinent for treating various cardiovascular conditions. Animal experiments have shown its anti-atherogenic, anti-hypertensive, and anti-inflammatory effects. It has several bioactive ingredients with hemodynamic, ROS scavenging, and anti-inflammatory properties. Earlier we have done limited proteomic and transcriptomic analysis to show its efficacy in ameliorating cardiac hypertrophy induced by isoproterenol (ISO) in rats. In the present study we have used high-throughput sequencing of the mRNA from control and treated rat heart to further establish its efficacy. ISO (5 mg/kg/day s.c.) was administered in male adult rats for 14 days to induce cardiac hypertrophy. Standardized aqueous extract TA bark extract was administered orally. Total RNA were isolated from control, ISO, ISO + TA, and TA treated rat hearts and subjected to high throughput sequence analysis. The modulations of the transcript levels were then subjected to bio-informatics analyses using established software. Treatment with ISO downregulated 1,129 genes and upregulated 204 others. Pre-treatment with the TA bark extracts markedly restored that expression pattern with only 97 genes upregulated and 85 genes downregulated. The TA alone group had only 88 upregulated and 26 downregulated genes. The overall profile of expression in ISO + TA and TA alone groups closely matched with the control group. The genes that were modulated included those involved in metabolism, activation of receptors and cell signaling, and cardiovascular and other diseases. Networks associated with those genes included those involved in angiogenesis, extracellular matrix organization, integrin binding, inflammation, drug metabolism, redox metabolism, oxidative phosphorylation, and organization of myofibril. Overlaying of the networks in ISO and ISO\_TA group showed that those activated in ISO group were mostly absent in ISO\_TA and TA group,

1 **395** suggesting a global effect of the TA extracts. This study for the first time reveals that TA partially or completely restores the gene regulatory network perturbed by ISO treatment in rat heart; signifying its efficacy in checking ISO-induced cardiac hypertrophy.

Keywords: Terminalia arjuna, cardiac hypertrophy, heart failure, transcriptomics, biological network

### INTRODUCTION

Pathological cardiac hypertrophy (CH) is a major risk factor for heart failure and mortality worldwide. It is characterized by an increase in length and breadth of the cardiac myocytes in association with other physiological/biochemical changes affecting myocardial blood flow, diastolic function, and diminished cardiac output (Shimizu and Minamino, 2016). Being multifactorial in nature, CH is characterized by fibrosis, pro-inflammatory milieu, autophagy, apoptosis, oxidative stress, altered mechanotransduction, and mitochondrial energetics etc. (Marian and Braunwald, 2017; Nakamura and Sadoshima, 2018). At the molecular level, CH is often associated with an extensive re-programing of gene expression in the heart (Raghow, 2016). Such extensive alterations in cardiac functions is presumably not caused due to the perturbation of a single pathway, but rather a cumulative alteration of many signaling modules including cAMP, PKA, PLC, MAPK, PI3K/Akt, m-TOR etc. (Liu and Molkentin, 2016; Wang and Cai, 2017; Gibb and Hill, 2018; Sciarretta et al., 2018). While these signaling networks are highly interactive and complex, the categorical goal of pharmacological science is to target the nodal points and the effectors (Walters et al., 2016; Philipson et al., 2017).

However, the current treatment option available for CH is limited to few drugs like β-blockers, ACE inhibitors, and angiotensin receptor blockers (Talan et al., 2011; Soliman and Prineas, 2017). Although these drugs are being used worldwide for managing CH, they are also associated with various side effects and their efficacy depends upon several variables including pharmacogenomics (Lainscak et al., 2016; Takezako et al., 2017).

In pursuit of finding novel therapy of CH and heart failure, various natural product based preparations are also empirically used due to the evidences of their efficacy, minimal side effects, affordability, and wide acceptability, especially for the people beyond the western hemisphere (Van Galen, 2014; Rastogi et al., 2016). A number of traditional Chinese medicines like QSYQ and Danshensu (*Salvia miltiorrhiza*) have shown cardio-protective effect in many experimental sets up (Zhu et al., 2016; Hao et al., 2017; Layne and Ferro, 2017).

One major impediment of testing the efficacy of phytochemicals in ameliorating diseases is the difficulty in identifying the active ingredient(s) from a complex mixture of bioactive molecules and their targets of actions. While the first can be addressed by standardization of the extracts used in various assays, the latter issue is more complex and ambiguous. It is now acknowledged that the ingredients of many Ayurvedic preparations, traditional Chinese medicines, and even modern pharmacological agents show synergistic as well as antagonistic effects when added together (Zegpi et al., 2009; Parasuraman et al., 2014; Zhang et al., 2014). Therefore, it is more prudent to test the Ayurvedic formulations as the practitioners use it. However, applications of the modern tools of genomics and proteomics that are broad based, quantitative, and largely unbiased, can help us establish the efficacy of phytomedicines in a more unambiguous manner.

In the Indian subcontinent, the stem barks powder of *Terminalia arjuna* (Roxb.) (TA) has been in use as a cardioprotective agent for centuries by Indian system of medicine (Ayurveda) (Mongalo et al., 2016; Amalraj and Gopi, 2017). Studies done in our laboratory have shown that the bark extract of TA has beneficial effects in experimentally induced myocardial ischemia, hypertrophy, fibrosis, and other cardiovascular disorders. It boosts anti-oxidant activities, prevents fibrosis, protects against ischemia reperfusion injury and has anti-hypotensive effects (Kumar et al., 2009; Maulik and Katiyar, 2010; Maulik et al., 2016; Meghwani et al., 2017). In a recent study, arjunolic acid, one of the constituents of the aqueous TA extract, was shown to ameliorate cardiac fibrosis by inhibiting TGF-β signaling (Bansal et al., 2017). Earlier, we had used limited proteomic approach (2D gel based) to establish that TA bark extract substantially modulates the rat cardiac proteome under adrenergic (ISO) stress (Kumar et al., 2017). To further establish the efficacy of TA extract we now have used more robust approach that is global transcriptomic analyses to establish the efficacy of TA extract in modulating various biological pathways and gene networks targeted by ISO. We demonstrate that TA extract reverses ISO induced reprogramming of gene expression in rat heart. Our study for the first time convincingly establishes that the effects of TA are far wider than that is expected for a modern drug usually having a single target.

### MATERIALS AND METHODS

### Animal Experiments and Ethics Statement

Laboratory bred Wistar male rats (150–200 g, 10–12 weeks) were employed for the study and maintained under standard laboratory conditions (temperature; 25°C ± 2°C, relative humidity; 50% ± 15% and 12-h dark/12-h light period). The study was conducted in accordance with the Institutional Animal Ethics Committee, All India Institute of Medical Sciences, New Delhi, India. All animal care and experimental protocols were performed in compliance with the National Institutes of Health (NIH) Guidelines for the Care and Use of Laboratory Animals (NIH Publication no. 85723, revised 1996).

### TA Extract

The material under investigation is a standardized aqueous extract of the bark of TA (made available to us from the Dabur Research Foundation, Ghaziabad, India). It has been is prepared by extraction as per the literatures of Ayurveda against its traditional claims and as it has been extensively used in the Indian System of Medicine for centuries. For its scientific validation, the extract has been standardized by the fingerprinting using UPLC. All the separated metabolites of the extract have also been characterized by metabolomic profiling using UPLC high resolution mass spectrometry which separated 26 compounds at different Rt with different molecular masses (**Supplemental Figures S1A, B**). The major peaks obtained were at Rt 8.01, 7.80, and at 7.53 with m/z 510.26, 893.46, and 488.25 respectively (**Supplemental Figure S1C**). The major peak at Rt 7.80 was identified as Arjunolic acid with m/z 488.25 (**Supplemental Figure S1D**) which is main marker compound of Arjuna (Kumar et al., 2017).

#### Administration of TA Extract

The animals were randomly divided into four groups according to drug treatment with5 animals in each group. The groups consisted of control, ISO + saline, ISO\_TA, and TA alone. In control group the animals were administered normal saline, 1.0 ml/kg body weight, s.c. once daily for 14 days. In ISO + saline group, rats received both ISO, 5.0 mg/kg body weight s.c. once daily followed by normal saline, 1 ml/kg body weight, orally once daily for 14 days. In the group designated with ISO\_TA, rats were administered with ISO, 5.0 mg/kg body weight, s.c. once daily along with the aqueous extract of TA, 125.0 mg/kg body weight, orally once daily for 14 days. In TA group, aqueous extract of TA, 125.0 mg/kg body weight, was administered orally once daily for 14 days. Lyophilized powder of aqueous extract of TA stem bark and ISO was freshly prepared in double distilled water before use.

## RNA Isolation

The rats were sacrificed at the end of treatment and the hearts were carefully excised followed by snap freezing at −80°C. The tissues samples from five animals were pooled for each group and total RNA was isolated utilizing TRI Reagent (Sigma Aldrich) as per manufacturer's instructions. The integrity of RNA was checked on 0.8% formaldehyde agarose gel and further purified for gene expression experiments employing RNeasy Mini Kit (Qiagen, Hilden, Germany) with on-column DNAase digestion.

## RNA Sequencing

#### Library Preparation and Sequencing

The respective cDNA libraries were prepared using TruSeq mRNA Sample Prep Kit (Illumina, Inc), as per manufacturer's instructions. Poly-A tail containing mRNA molecules were purified using oligo-dT attached magnetic beads (Illumina, Inc) using two rounds of purification. Purified mRNA were subjected to fragmentation, reverse transcription, end repair, 3′- end adenylation, and adaptor ligation, followed by PCR amplification and bead purification. The unique barcode sequences were incorporated in the adaptors for multiplexed high-throughput sequencing. The final product was assessed for its distribution of size and concentration using 2100 Bioanalyzer (Agilent Technologies), followed by cluster generation and sequencing on HiSeq 2000.

#### Read Filtering, Processing, and Alignment

The mRNA-Seq reads were aligned to the rat reference genome (downloaded from UCSC) utilizing "Bowtie 2" and "TopHat" as per published method (Langmead and Salzberg, 2012; Ghosh and Chan, 2016). The mapped reads were subsequently fed as input to "Cufflinks" (Langmead and Salzberg, 2012). The assembly files so generated were merged into a unified annotation with the reference transcriptome annotation for further analysis. The merged annotations were quantified using "Cuffdiff " to identify differentially expressed genes (Langmead and Salzberg, 2012). The "FPKM" (fragments per kilobase of transcript per million fragments) parameter was used to quantitate abundance of the transcripts (Li et al., 2017).

### Bioinformatics Analysis

Gene Ontology, Pathway Mining, Functional Annotation Clustering was done utilizing various data mining tools. David Bioinformatics resources, which use a previously published gene, list to statistically highlight the most overrepresented (enriched) biological annotation viz., Gene Ontology Terms (Huang et al., 2007; Xing et al., 2016). GeneMANIA, a Cytoscape plug-in were further used for data analysis (Montojo et al., 2010).GeneMANIA, was used to place the data obtained in a functional context and represent it as degree sorted circular view of key networks of statistically significant biological processes. The GeneMANIA integrates association networks from multiple sources into a single composite network using a conjugate gradient optimization algorithm.

### RESULTS

#### Treatment With TA Extracts Blunts the Activated β-Adrenergic Signaling in Heart

In order to understand the molecular mechanisms of cardioprotection by the TA extracts, we analyzed its effects on the overall signatures of β-adrenergic stimulation in rat heart. Rats were injected with ISO (5 mg/kg, once daily, s.c.) and fed with the aqueous TA extract (125 mg/kg, once daily, orally). After 28 days of treatment, the animals were sacrificed and the effects of TA treatment were confirmed at the gross morphological level by weighing the hearts. In reiteration of our previous reports (Kumar et al, 2017), while ISO treatment resulted in an increase in heart to body weight ratio, TA extracts reversed it significantly. As expected, TA alone had no effect on heart to body weight ratio (**Supplemental Figure S2**).

To determine molecular basis of the effects of TA on ISO treatment, we performed RNA sequence based differential gene expression analyses. This is a state of the art technology with tremendous potential for revealing multiple molecular cues associated with diseases and their amelioration by the drugs (Doostparast Torshizi and Wang, 2018). A schematic presentation of the experiment pipeline is shown in **Figure 1A**.

The overall number of differentially expressed genes in all the groups is represented as a bar plot in **Figure 1B** and differentially expressed genes in each group along with log2 fold change are tabulated (**Supplemental Tables S1, S2,** and **S3**). As expected, treatment with ISO generated a wide response with 1,333 genes differentially expressed of which 1,129 were downregulated and 204 were upregulated. In striking contrast, pretreatment of animals with the aqueous TA bark extracts led to a marked reduction of this expression signature, with only 97 genes upregulated and 85 genes downregulated. The TA alone group consisted of 88 upregulated and 26 downregulated genes. Notably, when we subjected the genes modulated by all the groups in principal component analysis, we observed ISO\_TA and TA alone groups clustered closely with control group while the ISO group was distinctively separated from this cluster (**Figure 1C**). This analyses showing higher similarity between control, ISO\_ TA, and TA alone groups thus reiterates our early report that TA suppresses the responses induced by ISO treatment (Kumar et al, 2017). Finally, the intersection of differentially expressed genes between ISO, ISO\_TA, and TA group is shown in **Figure 1D**. It is evident from the data that a large number of genes which were induced in ISO group, are missing from the ISO\_TA group. Taken together, these results indicate that there are unique gene expression signatures for all groups of animals treated with ISO, TA, or both.

#### TA Treatment Reverses the Metabolic Reprogramming Induced by ISO Treatment

To elucidate a discernible reversal of ISO-induced differential gene expression pattern by TA, we analyzed the repertoire of regulated genes in different treatment groups using various data mining strategies. We first used "DAVID bioinformatics resources" for systematic and integrative analysis of large data set and extracted key biological pathways (KEGG) that were significantly (*p* < 0.1) regulated. As shown in **Figure 2A** and **Table 1**, the pathways that are downregulated in ISO treated group includes those involved in the metabolism of carbohydrates (71 genes), fatty acids (40 genes), amino acids (34 genes), and nucleic acids (30 genes); receptor activation and cell signaling (105 genes); and cardiovascular and other diseases (160 genes). As shown in **Figure 2A**, among the 71 unique genes involved in carbohydrate metabolism were those associated with glycolysis and gluconeogenesis (Bar A: 15 genes); metabolism of starch and sucrose (Bar B: 10 genes), fructose and mannose (Bar C: six genes), and galactose (Bar D: six genes); TCA cycle (Bar E: 19 genes), pyruvate metabolism (Bar F: 15 genes); pentose phosphate pathway (Bar G: five genes); metabolism of mannose, glyoxylate, and dicarboxylate (Bar H: five genes); and oxidative phosphorylation (Bar I: 28 genes). Similarly, 40 downregulated genes involved in fatty acid metabolism included those associated

FIGURE 2 | KEGG pathways significantly enriched after multiple testing adjustments (p < 0.1) (A) Bar graph indicates KEGG pathway upregulated and downregulated after ISO treatment. Pathways under the same group (carbohydrate metabolism, fatty acid metabolism, etc.) are clustered together and marked by the same color. The number of unique genes modulated in each group are shown in parenthesis next to the respective group names. The total number of genes modulated in various pathways under each group is generally larger than the total number of unique genes mentioned in parenthesis. It is due to the occurrence of same genes in multiple pathways and accordingly, those were counted more than once. The individual pathways given in the bar graph (A-O1) is tabulated in Table 1. (B) Bar graph representing genes upregulated and downregulated by ISO + TA is shown. The total number of unique genes that were downregulated is 14. The total number of genes modulated in various pathways under each group is more than 14 as the same genes might be associated with multiple pathways, hence counted more than once. (C) Bar graph representing three different pathways involving 13 unique genes upregulated by TA alone is shown. The individual pathways given in the bar graphs in A and B is tabulated in Table 1. (D) Heatmap of significantly regulated genes in the ISO group compared to ISO\_TA group along with expression value. (E) Heatmap of significantly regulated genes in the ISO \_TA group compared to ISO group along with expression value. The genes, which are not significantly regulated, are indicated as ns (non-significant). A detail of expression value of heatmap along with p-value is tabulated in Supplementary Table S5.

TABLE 1 | List of pathways extracted from "DAVID bioinformatics resources." Codes as indicated in Figure 2 and respective gene counts are shown. Negative and positive sign indicates downregulation and upregulation respectively.


with the metabolism of propanoate (Bar J: 15 genes) and butanoate (Bar K: 11 genes); fatty acid (Bar L: 13 genes), glycerolipid (Bar M: eight genes); fatty acid elongation in mitochondria (Bar N: five genes); and glycerophospholipid metabolism (Bar O: eight genes). Noticeably, three genes involved in tyrosine metabolism (Bar P) and three genes involved in amino sugar and nucleotide metabolism (Bar Q) were upregulated under ISO treatment. Genes downregulated by ISO were also involved in the anabolism of valine, leucine, isoleucine, and lysine (Bar R: 23 genes); lysine degradation (Bar S: eight genes); and metabolism of beta-alanine (Bar T: five genes) and tryptophan (Bar U: seven genes). Five genes involved in purine metabolism were upregulated under ISO treatment (Bar V). Ten genes involved in aminoacyl-tRNA biosynthesis (Bar W) and seven genes in purine metabolism (Bar X) were also downregulated under ISO treatment. Out of seven genes involved in receptor activation and cell signaling upregulated by ISO, four genes were involved in ECM-receptor interaction (Bar Y) and three in cytosolic DNA-sensing pathway (Bar Z). The largest cluster of genes downregulated by ISO also included those involved in cell signaling and receptor activation. Among the downregulated genes included those involved in cardiac muscle contraction (Bar A1: 20 genes), PPAR signaling (Bar B1: 10 genes), proteostasis (Bar C1: eight genes), regulation of actin cytoskeleton (Bar D1: 19 genes), calcium signaling (Bar E1: 17 genes), and insulin signaling (Bar F1: 24 genes). Out of 160 genes involved in cardiovascular and other diseases modulated by ISO, seven genes involved in drug metabolism (Bar G1) and four genes involved in metabolism of xenobiotics by cytochrome P450 (Bar H1) were upregulated. Among the genes downregulated by ISO, were those associated with hypertrophic, dilated, and arrhythmogenic right ventricular cardiomyopathy (Bar I1: 19 genes, Bar J1: 17 genes, and Bar K1: 24 genes); Huntington's (Bar M1: 31 genes), Parkinson's (Bar N1: 27 genes), and Alzheimer's diseases (Bar O1: 29 genes); type II diabetes (Bar P1: eight genes).

As shown in **Figure 2B**, total of nine genes were upregulated by ISO\_TA of which three each were involved in nitrogen, retinol, and drug metabolism respectively (Bar A, B, and C). Total fourteen unique genes were downregulated by the ISO\_TA group. Those included genes involved in chemokine and cytokine signaling (Bar D: six genes), cytokine-cytokine receptor interaction (Bar E: five genes), cell adhesion (Bar F: seven genes), NOD-like receptor signaling pathway (Bar G: three genes), antigen processing and presentation (Bar H: three genes), circadian rhythm (Bar I: two genes), and the development of asthma (Bar J: two genes).

Only thirteen genes were upregulated by TA alone as shown in **Figure 2C**. Those were involved in drug metabolism (Bar A: four genes), MAPK signaling (Bar B: six genes), and complement cascade (Bar C: three genes). Taken together, total 316 unique genes were downregulated by ISO, and their levels were restored upon TA treatment (**Figures 2D**, **E**). This suggests a genome wide effect of TA extract in reversing the pathological effects of ISO.

#### TA Treatment Ameliorates the Pathological Networks Activated by the Adrenergic Stimulation

We next applied a widely used network extraction tool GeneMANIA to mine various gene networks and associated functions from these data sets (Montojo et al., 2010). Based on the functional similarity or shared topology in the published literature, this tool exhibit networks from the query gene list. The network thus derived were then visualized and analyzed using the visualization software Cytoscape 3.0.2. We derived networks of up- and downregulated genes in all the treatment sets as shown in **Figures 3** and **4** where the extent of modulation are represented by color codes as red: upregulation; green: downregulation; and gray: no significant change. These networks indicate modulation of various key genes, reflecting the diversity of ISO-induced reprogramming of cellular networks and its restoration by TA. In agreement with the data obtained from KEGG pathways, this analysis showed the upregulation of biological networks associated with angiogenesis, extracellular matrix organization, integrin binding, inflammation, drug metabolism, redox metabolism, and corticosteroid response in the ISO-treated group (**Figure 3**). ISO also downregulated the networks like those involved in oxidative phosphorylation, pyrimidine metabolism, myofibril, mitochondrial electron transport chain, and acetyl CoA metabolism (**Figure 4**). The details of the network along with key genes upregulated and downregulated by ISO is given in **Table 2**.

To characterize the impact of pretreatment with TA extracts (ISO\_TA) on the metabolic reprogramming elicited by ISO, we overlaid the genes regulated in this groups on the network derived from ISO groups. As shown in **Figures 3** and **4**, nodes in the overlaid network in ISO\_TA and TA group showed largely no significantly regulated genes (gray color). Clearly, the metabolic network activated in ISO group was completely absent in ISO\_ TA (and TA group), suggesting a global effect of the TA extracts. Moreover, TA treatment alone significantly perturbed only a

FIGURE 3 | Comparison of enriched non-redundant biological processes represented as "degree sorted circular view" networks of upregulated genes in ISO group extracted from "GeneMANIA" (Cytoscape plugins) along with ISO\_TA and TA group overlaid network. Upregulated, downregulated, and undifferentiated gene represented as red node, green node, and gray node respectively.

few networks as a limited number of genes were modulated by TA-treatment (**Figures 3** and **4**).

### DISCUSSION

Despite the advancements and availability of modern medicines, especially in past quarter of a century, a large population worldwide still relies upon the traditional medicines (Van Galen, 2014). Although traditional medicines are used worldwide, it is prevalent in the Asian subcontinent, especially in India and China (Jaiswal et al., 2016). Due to sustained support from the government, traditional medicines in China are better regulated and have more acceptability among its own population. Several ancient Chinese formulations have also been successfully exploited for developing modern drugs that are now widely used by the western community (Chao et al., 2017).

Under the ambit of clinical bioinformatics, high-resolution, high-throughput data analyses has enhanced opportunities for examining the efficacies of traditional medicinal preparations. Applications of sophisticated tools of microarray, next generation sequencing, pharmacogenomics etc. enhances our understanding of the mode of actions of various formulations. These advance tools of analytical biology thus provide an opportunity to validate or refute the therapeutic potential of phytomedicines (Beckmann and Lew, 2016). Although the TA extract is widely used for treating various types of cardiovascular ailments by traditional practitioners in the Indian subcontinent, any systematic study of its efficacy using the modern tools of biology has not been done till date (Maulik and Talwar, 2012).

ISO, an adrenergic agonist, is a widely used for studying cardiac pathobiology in experimental animals (Seifert, 2013). However, studies on its effects on gene expression in the heart have largely been restricted to a handful of genes of interest and fewer analyses of the total transcriptome from ISO treated heart have been reported till date (Li et al., 2003; Lu et al., 2012; Talarico et al., 2014). To establish the efficacy of TA in ameliorating CH, we undertook the total transcriptomic analyses of ISO/TA treated rat heart. Surprisingly, we found that total 1129 genes were downregulated and 204 genes were upregulated by the ISO treatment and most of them were restored by TA. Since adrenergic receptors play a critical role in cardiac functions, modulation of such a large number of genes upon sustained ISO treatment might not be unusual. It is likely that the ISO treated heart needs to reset its function through the readjustment of its genome wide expression profile. To be noted that Talarico et al. (2014) have reported change in expression of 493 genes in mouse heart following ISO treatment for an hour. Considering the species difference and experimental parameters like the dose and duration of treatment, our data is largely in tune with theirs (**Supplementary Table S4**).

TABLE 2 | List of networks extracted from GeneMANIA after ISO treatments along with key genes are indicated.


#### Network upregulated by ISO treatment

In our study, among the 1,333 genes that were modulated by ISO (**Table 2**, **Supplemental Data**) included those involved in metabolism (carbohydrate, fatty acid, and nitrogen; total 105 genes), cell signaling (105 genes), and cardiovascular and other diseases (106 genes). Changes in metabolism such as a shift

from oxidative phosphorylation to glycolysis and increased fatty acid oxidation are the hallmarks of heart failure (Fillmore et al., 2014; Fukushima et al., 2015). Since intermediary metabolism involves substantial number of enzymes, modulation of a large number of metabolic genes by ISO is expected, although its significance needs further study. Similarly, adrenergic signaling plays a major role in cardiovascular function and diseases. The mechanisms of adrenoceptor activation, its downstream targets, its signal specificity versus bias and receptor dynamics have been extensively studied for understanding the role of adrenergic system in cardiovascular pathophysiology (Lohse, 2015). Also, the cross talk between adrenergic signaling and that by other receptor viz., insulin, EGF and TGFβ play a critical role in cardiovascular function (Saha et al., 2012; Heger et al., 2016; Fu et al., 2017; Mohan et al., 2017). Signaling by these receptors in heart involve a plethora of kinases, phosphatases, second messengers and gene regulatory proteins. Therefore, changes in expression of more than hundred signaling genes by the sustained adrenergic activation is quite likely.

Although an exhaustive analyses of the role of these diverse group of genes in cardiovascular biology would be desired, it might as well lead to a voluminous but hypothetical compilation of experimental data accumulated over the past several decades. Therefore, we intend to focus onto the relevance of 54 genes that are modulated by ISO treatment and are involved in cardiac diseases. It was quite convincing to find that number of genes downregulated by ISO are involved in Redox-homeostasis/ oxidative stress and noted among them is glutathione-Stransferase (GST, **Table 3**). Glutathione-S-transferase family are Phase II detoxification enzymes that catalyze the covalent conjugation of glutathione (GSH) to electrophilic compounds such as peroxidized lipids, enabling their breakdown. Under oxidative stress, various enzymatic and other proteins susceptible to oxidative damage also undergo S-glutathionylation, a process of covalent conjugation of glutathione to cysteine sulfhydryl or sulfenic acid groups. In a recent study, a member of the GST family, i.e. GSTπ has been shown to catalyze protein S-glutathionylation *in vivo* (Townsend et al., 2009).Downregulation of a number of redox enzymes including GST thus suggest a reduced capacity of the heart to cope up with increased oxidative stress under ISO treatment (Saleem et al., 2018).

Another noticeable observation was the downregulation of expression of a number of contractile proteins viz., Troponin C1, β-Myosin heavy chain, Myosin light chain 2 etc. (**Table 3**). Modulation of expression of contractile proteins have been demonstrated in *ex vivo* cultured myocytes and in experimental rats treated with adrenergic agonists. While in *ex vivo* rat cardiac myocytes, the level of myosin light chain 2 mRNA increases upon treatment with phenylephrine; the mRNA for α-myosin heavy chain decreases in aged spontaneously hypertensive rats with heart failure (Shubeita et al., 1992; Boluyt et al., 1994). In male Sprague-Dawley rats, infusion of NEfor seven days reduce the expression of myosin heavy chain 11, myosin light chain 3 and troponin I. In patients with heart failure, the expression of a number of sarcomeric genes decreases, while their expression is restored in those having left ventricular assisted device (Rodrigue-Way et al., 2005). Taken together, our observation that ISO treatment reduces the mRNA levels of certain sarcomeric



genes are in general agreement with studies done by others and the restoration of their levels by TA treatment is significant.

Another set of genes whose expression were downregulated by ISO are involved in cell signaling in general and Ca++ signaling in particular. As summarized in **Table 3**, ISO treatment reduced the expression of Ryanodine receptor, Calsequestrin-2, Sarcoplasmic/ endoplasmic reticulum calcium ATPase 2 and cardiac phospholamban. Except that of calsequestrin-2, the expression of others decrease in various experimental models of CH and heart failure and in human patients (Nagai et al., 1989; Takahashi et al., 1992; Arai et al., 1996; Kawase and Hajjar, 2008). The mRNA levels of three other genes encoding signaling kinases viz., adenylate cyclase, PKA, and SSTK11 were also downregulated by ISO and restored by TA. The role of adenylate cyclase-PKA in CH has been extensively studied, especially in the context of adrenergic signaling. Available literature suggests that the compartmentalization of cAMP/protein kinase A (PKA) in different subcellular organelles is involved in pathophysiological outcomes under different stimuli (Fields et al., 2016). However, upon extensive literature search, we could not find any report on modulation of expression of these two key enzymes under any pathological context, hence we are unable to correlate our data with that of others. The downregulation of the third kinase, i.e. SSTK11 (Liver Kinase B1/LKB1) in ISO treated heart and its restoration by TA is notable as it prevents CH and dysfunction (Ikeda et al., 2009).

Taken together, above mentioned alteration in the profile of expression of genes involved in metabolism, signaling, and pathology of heart failure are well in conformity of available literature. Further, although substantial literature has shown the beneficial effects of TA in ameliorating various cardiovascular dysfunction, to date no information is available on the modulation of gene expression by TA. Therefore our observation that TA restores a vast majority of genes upregulated and downregulated by ISO in heart is of immense importance. However, TA extract is a mixture of about 26 constituents of which several have been shown to be bioactive (Saha et al, 2012; Kumar et al., 2017). Also, the effects of TA is pleiotropic, as it has anti-atherogenic, hypotensive, inotropic, anti-inflammatory, anti-thrombotic, and antioxidant actions (Maulik and Talwar, 2012). Till date, very little is known about the mechanism of actions of TA at the cellular and molecular levels. Thus, at this point we are unable to offer the mechanistic insight into the global reversal of the effect of ISO by TA. Nevertheless, our present study thus opens up a new window of understanding the beneficial role of TA extract in treating cardiovascular ailments.

### DATA AVAILABILITY STATEMENT

The RNA seq data generated in this study have been submitted to the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/ bioproject/) under accession number PRJNA525742.

### ETHICS STATEMENT

The study was conducted in accordance with the Institutional Animal Ethics Committee, All India Institute of Medical Sciences (AIIMS), New Delhi, India. All animal care and experimental protocols were performed in compliance with the National Institutes of Health (NIH) guidelines for the care and use of the Laboratory Animals (NIH Publication no. 85723, revised 1996).

### AUTHOR CONTRIBUTIONS

GK has done the data analyses and MS has supervised him. NS has done the animal experiments while SK had done the experimental work at the very early phase. SKM had conceived the entire project. SA had helped in analyzing the TA extract. MS and SKG wrote the manuscript.

### FUNDING

The authors thankfully acknowledge funding support from the DST-PURSE awarded by the Department of Science and Technology, Government of India to the Jawaharlal Nehru University. Also, the initial work was done with the support from the Department of Science and Technology under the grant SR/ SO/HS-0085/2012 awarded to SKG. GK is a recipient of a NPDF from SERB-DST, Government of India.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.01443/ full#supplementary-material

### REFERENCES


by Terminalia arjuna (Roxb.). *J. Pharm. Pharmacol.* 61, 1529–1536. doi: 10.1211/jpp.61.11.0013


**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2019 Kumar, Saleem, Kumar, Maulik, Ahmad, Sharma and Goswami. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Xuebijing Injection Maintains GRP78 Expression to Prevent Candida albicans–Induced Epithelial Death in the Kidney

*Ting Shang1,2, Qilin Yu3, Tongtong Ren3, Xin-Tong Wang1,2, Hongyan Zhu1,2, Jia-Ming Gao1,2, Guixiang Pan1,2, Xiumei Gao1, Yan Zhu1,2, Yuxin Feng1,2\* and Ming-Chun Li3\**

1 Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China, 2 Research and Development Center of TCM, Tianjin International Joint Academy of Biomedicine, TEDA, Tianjin, China, 3 Key Laboratory of Molecular Microbiology and Technology for Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China

#### Edited by:

Yuanjia Hu, University of Macau, China

#### Reviewed by:

JianLi Gao, Zhejiang Chinese Medical University, China Weifeng Yao, Nanjing University of Chinese Medicine, China

> \*Correspondence: Yuxin Feng fengyn@live.com Ming-Chun Li nklimingchun@163.com

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 23 January 2019 Accepted: 07 November 2019 Published: 06 January 2020

#### Citation:

Shang T, Yu Q, Ren T, Wang X-T, Zhu H, Gao J-M, Pan G, Gao X, Zhu Y, Feng Y and Li M-C (2020) Xuebijing Injection Maintains GRP78 Expression to Prevent Candida albicans–Induced Epithelial Death in the Kidney. Front. Pharmacol. 10:1416. doi: 10.3389/fphar.2019.01416

Sepsis and septic shock threaten the survival of millions of patients in the intensive care unit. Secondary fungal infections significantly increased the risk of mortality in sepsis patients. Chinese medicine Xuebijing injection (XBJ) has been routinely used as an add-on treatment to sepsis and septic shock in China. Our network pharmacology analysis predicted that XBJ also influences fungal infection, consisting with results of pioneer clinical studies. We conducted in vivo and in vitro experiments to verify this prediction. To our surprise, XBJ rescued mice from lethal Candida sepsis in a disseminated Candida albicans infection model and abolished the colonization of C. albicans in kidneys. Although XBJ did not inhibit the growth and the virulence of C. albicans in vitro, it enhanced the viability of 293T cells upon C. albicans insults. Further RNA-seq analysis revealed that XBJ activated the endoplasmic reticulum (ER) stress pathway upon C. albicans infection. Western blot confirmed that XBJ maintained the expression of GRP78 in the presence of C. albicans. Interestingly, key active ingredients in XBJ (C0127) mirrored the effects of XBJ. C0127 not only rescued mice from lethal Candida sepsis and prevented the colonization of C. albicans in kidneys, but also sustained the survival of kidney epithelial cells partially by maintaining the expression of GRP78. These results suggested that XBJ may prevent fungal infection in sepsis patients. Pre-activation of ER stress pathway is a novel strategy to control C. albicans infection. Network pharmacology may accelerate drug development in the field of infectious diseases.

Keywords: fungal infection, C. albicans, Xuebijing injection, endoplasmic reticulum stress, GRP78, Chinese medicine

### HIGHLIGHTS


## INTRODUCTION

Fungal infection causes an annual mortality of 1.5 million people worldwide (Wirnsberger et al., 2016). The cost of treating invasive fungal infection is over 2 billion dollars in the US (Pfaller and Diekema, 2010). As the leading pathogen in patients suffering invasive fungal infections, *Candida albicans* fostered 50% of candida sepsis cases (Pfaller and Diekema, 2010; Brown et al., 2012). Associated with a mortality rate exceeding 40%, past decades witnessed a dramatic rise in the incidence of invasive candidiasis (Kullberg and Arendrup, 2015).

Limited choices of antifungal drugs are available to treat fungal infections with only two non-toxic antifungal classes for candidiasis (Diekema et al., 2012). Azoles are applied in clinical practice to treat *C. albicans*–related infections (Sakagami et al., 2018). Nevertheless, invasive *C. albicans* infection still claims mortality of 45% to 75% (Brown et al., 2012). Emerging drugresistant fungal infections are also calling for novel management strategies to restrain fungal sepsis (Healey et al., 2016).

*C. albicans*–induced kidney failure is a major cause of mortality in *C. albicans* sepsis (Spellberg et al., 2005). Enhancing the function of the innate immune system rescued lethal *C. albicans* infections in murine models (Xiao et al., 2016; Dominguez-Andres et al., 2017). Other potential mechanisms remain elusive. Administrating mirR-124 and mirR-204 mimics prevented *C. albicans–*induced acute kidney injury (Li et al., 2014b; Li et al., 2018).

Secretory and membrane proteins are synthesized and modified in the endoplasmic reticulum (ER) of mammalian cells (Yu et al., 2015; Zhu and Lee, 2015). Activating ER stress signaling renders survival advantage for tissues and cells upon *C. albicans* infection. Glucose-regulated proteins (GRPs) are constitutively expressed in cells to maintain cellular homeostasis, belonging to the heat shock protein family as stress-inducible chaperones. Infections activate GRPs to translocate in the cells to assume functions such as regulating signaling transduction, proliferation and immunity (Zhu and Lee, 2015; Lewy et al., 2017). Conserved from yeast to human, GRP78 (BiP) is one of such proteins that regulate homeostasis of organs from endoderm, mesoderm, and ectoderm. Interestingly, GRP78 cross-talks with PI3K/AKT pathway, which sustains cell survival (Shani et al., 2008; Gray et al., 2013; Liu et al., 2013).

Xuebijing (XBJ) injection was prepared with extracts from five different Chinese herbs [*Carthamus tinctorius* flowers (Honghua), *Paeonia lactiflora* roots (Chishao), *Ligusticum chuanxiong* rhizomes (Chuanxiong), *Angelica sinensis* roots (Danggui), and Salvia miltiorrhiza roots (Danshen)] (Cheng et al., 2016; Li et al., 2016; Li et al., 2019; Zhang et al., 2018). Approved by the Food and Drug Administration of China in 2004, XBJ has been frequently used as an add-on therapy for multiple organ dysfunction syndromes, sepsis, and septic shock in China for over a decade (Chen et al., 2018a; Gao et al., 2015; Shi et al., 2017). It rendered a series of benefits for sepsis patients, including reducing 28-day mortality and incidence of complications, shortening dwelling time in the intensive care unit (Gao et al., 2015; Shi et al., 2017; Song et al., 2019). Preclinical studies indicated XBJ might be a treatment option for sepsis and septic shock individually (Jiang et al., 2013; Chen et al., 2018). Four classes of compounds from five different herbs in XBJ may be important for its antiseptic effect (Li et al., 2016). Intensive research is going on to identify major active compounds in XBJ that can effectively treat sepsis (Cheng et al., 2016; Li et al., 2016). Combining Xuebijing with anti-fungal agents or antibiotics had positive impacts on the quality of life of patients suffering invasive fungal infections in several clinical studies and may improve the survival of patients (Gao, 2010; Wang, 2010; Cao, 2017). However, it was not clear whether XBJ can influence fungal infection individually.

Our network pharmacology analysis predicted that XBJ not only affects therapeutic targets of sepsis but also influences fungal infection, suggesting XBJ may prevent fungal infection in sepsis patients. Here we reported that XBJ prevented systemic *C. albicans* infections. Notably, XBJ pretreatment protected 70% of mice from mortality after systemic *C. albicans* infection. It prevented the colonization of *C. albicans* in kidneys and enhanced the viability of kidney epithelial cells by sustaining ER stress signaling.

## METHODS

## Chemicals and Reagents

Xuebijing injection (catalog number: z20039833, batch number: 1606121) was manufactured by Tianjin Chase Sun Pharmaceutical Co., Ltd (Tianjin, China). This Chinese medicine is approved by the China Food and Drug Administration (CFDA) for treating sepsis and septic shock with the CFDA ratification number of GuoYaoZhunZi-Z20039833 for market approval as a drug product. It is routinely used as an add-on to conventional therapy for treating sepsis and septic shock in China (Jiang et al., 2013; Chen et al., 2018; Li et al., 2019). This injection contains extracts of five herbs, including *Carthamus tinctorius* flowers (Honghua in Chinese)*, Paeonia lactiflora* roots (Chishao in Chinese)*, Ligusticum chuanxiong* rhizomes (Chuanxiong in Chinese)*, Angelica sinensis* roots (Danggui in Chinese)*,* and Salvia miltiorrhiza roots (Danshen in Chinese).

Methods of extraction, preparation, and quality control of XBJ were the same as previously reported (Huang et al., 2011; Chen et al., 2016; Li et al., 2016; Zhang et al., 2018). Briefly, ingredients from *Carthamus tinctorius flowers* ("Honghua" in Chinese) were first extracted with ethanol then with water. Ingredients from the other four herbs were extracted with water. Finally, XBJ was standardized to contain 1.0 to 1.7 mg/ml of paeoniflorin and 0.2 to 0.5 mg/ml of hydroxysafflor yellow A as described (Huang et al., 2011; Chen et al., 2016; Li et al., 2016).

GRP78 inhibitor HA15 and other chemicals used in the experiments were ordered from Sigma-Aldrich (Shanghai, China) unless indicated. Paeoniflorin (Cas #: 23180-57-6), hydroxysafflor yellow A (Cas#: 78281-02-4), ferulic acid (Cas #:537-98-4), and protocatechualdehyde (Cas#:139-85-5) were purchased from Shanghai Yuanye Biotechnology Co., Ltd. (Shanghai, China). C0127 was prepared with paeoniflorin, hydroxysafflor yellow A, ferulic acid, and protocatechualdehyde according to reported concentrations in XBJ and manufacturer's quality control information (Liu et al., 2015; Zuo et al., 2017; Zuo et al., 2018). The structures of the four compounds in C0127 were presented in **Supplementary Figure 1**. Western blotting was performed using the GRP78 monoclonal antibody and tubulin monoclonal antibodies (Abcam, USA).

## C. albicans Strain and Growth Conditions

*C. albicans* strain SC5314 (ATCC, USA), which was routinely cultivated in YPD (1% yeast extract, 2% peptone, 2% glucose), was used for all experiments in this study. The *C. albicans* cells were cultured overnight at 30°C and washed twice with PBS for further use.

## Animal Experiments and Ethics Statement

This study was carried out in accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85-23, revised 1996, USA) and the recommendations in the Guidance for the Care and Use of Laboratory Animals issued by the Ministry of Science and Technology of China. All experiments were approved by the Institutional Animal Care and Use Committee of Nankai University and the Laboratory Animal Ethics Committee of the Tianjin University of Traditional Chinese Medicine (Tianjin, China) and were performed in accordance with its guidelines (Permit Number: TCM-LAE-20170017). Five to six week-old ICR female mice were used for *in vivo* experiments (Dong et al., 2017). The mice were provided with free access to food and sterile water and were caged under controlled temperature (23°C ± 2°C) and humidity (60% ± 5%) with an artificial 12 h light/dark cycle. The mice were randomly divided into six groups (n = 15 in each group) as follows: Control group injected with normal saline; CA group was infected with 5 × 106 colony-forming units of *C. albicans via*  tail-vein injection. XBJ group treated with XBJ (6 ml/kg; Chase Sun Pharmaceutical, Ltd., Tianjin, China) by subcutaneous injection; CA+XBJ group infected with *C. albicans* and co-treated with XBJ (6 ml/kg); C0127 group infected with C. *albicans* after 3 injections of C0127 (6 ml/kg). XBJ and C0127 were administered once/day from Day -3 to Day -1 before the C. albicans infection. The *C. albicans* strain SC5314 was cultivated in YPD (1% yeast extract, 2% peptone, and 2% dextrose) broth with overnight shaking at 30˚C. The systemic fungal infection and virulence assays were performed as described (Dong et al., 2017; Liang et al., 2018).

#### Database Construction and Network Analysis

Fungal infection-related targets were mainly integrated from literature mining, GeneCards (Stelzer et al., 2011) and Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com) database (Kramer et al., 2014). Repetitive genes were automatically identified and removed by IPA software. In addition, ingredients derived from XBJ were collected from literature mining (Huang et al., 2011; Jiang et al., 2013; Guo et al., 2014; Han et al., 2017; Zuo et al., 2017; Zuo et al., 2018) and several TCM databases, such as TCMID (Xue et al., 2013) and TCMSP (Ru et al., 2014). Compounds that had more than 10 targets in mammalians were selected for further analysis. The chemical name of each compound was transferred into PubChem CID or CAS number which could be recognized by the IPA software. Furthermore, corresponding targets of XBJ ingredients that were not recorded by the IPA database were supplemented by literature from PubChem and databases of TCMID and TCMSP. In total, three datasets including XBJ ingredients, fungal infection associated targets, and corresponding targets of XBJ's major ingredients were constructed and then uploaded into the IPA system to visualize the discovery. The relationships between fungal infection-related targets and XBJ ingredients were discovered by "Build-Path Explorer" module. "Build-Connection" module was implemented to interpret the relationship between targets. "Overlay-Canonical Pathway" module was used to generate the resulting canonical pathways. "Build-Diseases & Functions" module was exploited to build the targets-related diseases and functions. We utilized the "Core analysis" module to analyze the correlation of the established network to acquire top diseases, top functions, top pathways, and top upstream regulators. Certain top upstream regulators were defined by the "Upstream Regulator" module. The "Path designer" module was performed to clarify the network. Upstream regulators analyses were performed to elucidate the causal inference of upstream biological causes and probable downstream effects on cellular and organismal biology (Kramer et al., 2014). "Path designer" module was used to demonstrate the network. The algorithm of the network analysis was based on Fisher's exact test with the enrichment score of P-values in this study.

#### RNA Isolation, RNA-seq, and Quantitative Real-Time Polymerase Chain Reaction

Total RNA was extracted by the phenol-chloroform method as previously described (Dong et al., 2017). The overall quality of RNA was determined by A260/A280 and analyzed by agarose gel electrophoresis. Roche GS-FLX 454 pyrosequencing was carried out using Illumina HiSeq™ 2000 (Oebiotech Company, China). Reverse transcriptions were conducted with an Oligo (dT)-primed RT reagent Kit (Promega, USA). Quantitative real-time polymerase chain reaction (PCR) was performed in triplicate and repeated in three independent experiments with the Mastercycler ep realplex system. Independent reaction mixtures were carried out with the same DNA template for both the genes of interest and the GAPDH reference gene using the RealMasterMix (SYBR Green) Kit (Trans-Gen Biotech, China) according to the instructions. The relative fold changes in gene expression were determined by the 2-delta delta Ct method. Data were presented as means ± SD of three independent experiments.

## Hematoxylin and Eosin Staining

Hematoxylin and eosin (H&E) staining was conducted as described (Dong et al., 2017). Briefly, kidney tissues were collected 4 days after *C. albicans* infection and were fixed with 4% formalin at room temperature for 24 h, dehydrated with alcohol, and paraffin−embedded. The tissues were then cut into 5-µm-thick sections, which were stained with H&E at room temperature for 1 to 2 min and visualized under a microscope (BX53, Olympus, Japan).

### Western Blotting

Protein was extracted from 293T cells using the RIPA solution. The protein concentration of the lysates was measured with the Bradford assay. Western blotting was conducted as described (Fu et al., 2007).

### Statistical Analysis

Data were presented as the mean ± standard deviation for each group. All statistical analyses were performed using PRISM version 5.0 (GraphPad Software, Inc., La Jolla, CA, USA). Inter-group differences were analyzed using one-way analysis of variance, followed by Tukey's post-hoc test for multiple comparisons. The log-rank test was used to compare group survival trends. *P* < 0.05 was considered to indicate a statistically significant difference.

## RESULTS

#### Network Pharmacology Analysis Predicted That XBJ May Regulate Fungal Infection

Effective in controlling systemic bacterial infection, XBJ has potentials in treating a series of diseases related to infections and tissue injuries (Wang et al., 2015; Xu et al., 2015; Chen et al., 2018; Hu et al., 2018; Tian et al., 2018). We conducted network pharmacology analysis to explore novel applications of XBJ. A library of 170 proteins/molecules related to fungal infection was built and top upstream regulators of these proteins were identified by IPA software (**Figure 1A** and **Supplementary Table 1**). Toll-like receptors (TLR2, TLR4), pro-inflammatory cytokines [tumor necrosis factor, interleukin 6 (IL-6), IL-1, and interferons], nuclear factor κB signaling, and HMGB1 were among top upstream regulators of the targets in fungal infection (**Figures 1B, C**). Interestingly, many of these molecules are known as Xuebijing targets (Jiang et al., 2013; Chen et al., 2018). These results indicated that XBJ may impact fungal infection. Pathway analysis revealed that HMGB1 signaling is among the top 4 pathways in fungal infection (**Figure 2A**). This echoes the reports that XBJ may target HMGB1 to attenuate organ injuries (Wang et al., 2007; Wang et al., 2015; Chen et al., 2016). In addition, multiple potential targets are related to inflammation and cell survival in the functional analysis (**Figure 2B**).

A further analysis predicted that 36 compounds derived from five herbs in XBJ regulate over 70 molecules related to fungal infection (**Figure 3A**). Indeed, many key upstream regulators among these molecules were XBJ targets (**Figures 3B, C**). IPA

pathway analysis foretold that these 36 compounds targeted HMGB1, IL-6, and signaling related to organ injuries which were among the top 20 pathways (**Figures 4A, B**). Our functional analysis predicted that XBJ targets play roles in cell activation, survival, and apoptosis (**Figures 4C, D**). Based on these results, we hypothesized that XBJ prevents fungal infection and decided to test this hypothesis in a well-established *C. albicans* sepsis model (Dominguez-Andres et al., 2017; Zhao et al., 2017).

#### Xuebijing Prevented C. albicans–Induced Septic Shock in a Murine Model

All infected mice died 8 days after systemic *C. albicans* infection. 6 ml/kg XBJ pre-treatment for 3 days rescued ~70% of *C. albicans* infected mice from acute death (**Figure 5A**), indicating that it may prevent systemic *C. albicans* infection to arrest the consequent acute death. To address the question of whether XBJ directly affects the growth of *C. albicans*, we treated *C. albicans* in culture with different dilutions of XBJ (from 1:100 to 1:1000). However, XBJ did not affect the growth of *C. albicans* and hyphal development (data not shown), indicating the mechanism is related to the defense system in hosts. The functional analysis by IPA predicted that XBJ targets signaling pathways involved in organ damage and cell death (**Figures 4C, D**). This is consistent with clinical observations (Shi et al., 2017; Song et al., 2018). Thus, we hypothesized that XBJ improves the survival of *C. albicans–*infected mice partially through protecting kidneys.

#### XBJ Prevented C. albicans Colonization in the Kidney

To determine whether *C. albicans* colonizes kidney after XBJ intervention, we conducted a histopathological analysis of mouse kidneys, and determined the fungal load in kidneys 4 days after *C. albicans* infection. While hyphae, neutrophil penetration, and tissue damages were detected in kidneys of the control group, few hyphae were detected in the XBJ treated group. Neutrophil penetration and tissue damage were also reduced in the XBJ treated group (**Figure 7C**), suggesting XBJ inhibited *C. albicans* colonization in kidneys. Consistently, the kidney fungal burden decreased threefold in XBJ treated mice in the

colony-forming assay (**Figure 5B**). No *C. albicans* was detected in blood and kidney tissues 3 weeks after the infection in XBJ treated mice (data not shown). Next, we determined the effect of XBJ on kidney epithelial cell survival in different conditions. XBJ treatment at 1:100 dilution did not affect the survival of 293T cells. However, it significantly reduced cell death from ~35% to 15% upon *C. albicans* infection (**Figure 5C**).

#### XBJ Up-Regulated the ER Stress Signaling Pathway During C. albicans Infection

To determine how XBJ protects kidney cells, 293T cells under different treatments were subjected to RNA-seq analysis, including cells treated with saline, cells treated with XBJ only, cells infected with *C. albicans* only, and cells infected with *C. albicans* in the presence of XBJ (1:100 dilution). While *C. albicans* infection inhibited the ER stress signaling, XBJ treatment restored the expression of ER stress signaling. XBJ up-regulated the ATF6B and GRP170 on the mRNA level (**Figures 6A, B**). This was further confirmed by Western blot of GRP78. XBJ restored GRP78 expression to a similar level as control groups (noninfected 293T cells and non-infected 293T cells treated with XBJ) (**Figure 6C**). Therefore, XBJ may maintain the expression of the key factors in the ER stress signaling pathway to improve kidney epithelial cell survival.

#### Key Ingredients in XBJ Rescued Mice From Septic Shock and Prevented the Colonization of C. albicans in the Kidney

Network pharmacology analysis suggested that four major compounds in XBJ may regulate most XBJ targets related to fungal infection (**Supplementary Figure 2**). To understand the mechanism of XBJ on improving the survival in *C. Albicans* sepsis, C0127, a formula comprised of the four major active compounds from XBJ, was used to treat mice before the systemic *C. albicans* infection. Similar to XBJ, pre-treatment with C0127 not only significantly improved the survival of *C. albicans* infected mice but also decreased fungal loads in kidneys (**Figures 7A, B**). Similarly, hyphae and neutrophils infiltration can hardly be detected 4 days after *C. albicans* infection following the C0127 pre-treatment (**Figure 7C**).

#### Key Ingredients in XBJ Up-Regulated GRP78 to Improve 293T Cell Survival Upon C. albicans Infection

Like XBJ, C0127 maintained GRP78 expression on the protein level in 293T cells upon the *C. albicans* insult (**Figures 8A, B**). In our preliminary study, HA15, a GRP78 specific inhibitor (Cerezo et al., 2016; Ruggiero et al., 2018), induced apoptosis in 293T cells

FIGURE 5 | XBJ pre-treatment inhibits the colonization of C. albicans in the kidney. (A) The survival curves of XBJ pre-treated and control mice after systemic C. albicans infection. XBJ (6 ml/kg) or 0.9% NaCl was administered by abdominal injections once/day for 3 days before systemic C. albicans infection to the treatment group or the control group. (B) The colony-forming assay to determine the fungal loads in the kidneys of control and XBJ treated mice. Kidney tissues from control and XBJ treated kidneys were harvested and subjected to C. albicans culture 4 days after the infection. Colonies were counted after 48 h of culture. (C) PI staining was used to determine the effect of XBJ on the survival of 293T cells 8 h after the C. albicans infection. \*P < 0.05.

in low-serum culture (data not shown). To determine whether GRP78 is required for the protective effect of XBJ and C0127, 293T cells were treated with HA15 upon *C. albicans* infection. It induced cell death in the presence of XBJ and C0127, indicating GRP78 is an important downstream effector of XBJ and C0127 for the survival of kidney cells (**Figure 8D**).

FIGURE 6 | XBJ up-regulates the ER stress signaling pathway during C. albicans infection. Six groups of 293T cells in different conditions were subjected to RNAseq analysis, including cells treated with XBJ only, infected with C. albicans, 293T cells infected with C. albicans in the presence of XBJ (1:100 dilution). (A, B) RNAseq results of ATF6B and GRP170, two genes in the ER stress pathway. (C) Western blot to determine the expression of GRP78 expression in different conditions in 293T cells. Data were representative of at least three independent experiments. \*P < 0.05.

## DISCUSSION

### Summary of the Results and Significance

Our network pharmacology analysis predicted that XBJ may impact fungal infection. XBJ rescued lethal *Candida* sepsis and improved kidney epithelial cell survival by maintaining the expression of GRP78. In addition, C0127, a combination of four key compounds in XBJ also prevented *C. albicans–*induced acute death in mice and the colonization of *C. albicans* in the kidney. It improved kidney epithelial cells' survival by up-regulating GRP78. These results revealed a novel mechanism of XBJ in preventing organ failure and cell death. GRP78 was identified as a potential novel target of XBJ in preventing *C. albicans* infection. Our results suggested that network pharmacology is beneficial to identify novel applications of XBJ and other Chinese medicine formulas.

### Mechanism of Kidney Failure in Candida Sepsis

During the bloodstream infection, *C. albicans* attaches to the host and then penetrates the host defense system to attack the target organs such as the kidney. It is characterized by the presence of hyphae and the damages to host cells (Brunke and Hube, 2013). The innate immune system plays an important role in controlling the progression of *Candida* sepsis. The damaged cells secrete pro-inflammation cytokines to recruit innate immune cells such as neutrophils to clear *C. albicans* (Wirnsberger et al., 2016; Dominguez-Andres et al., 2017).

Enhancing/activating the function of the innate immune system counteracts the progression of *Candida* sepsis by improving *C. albicans* clearance. Switching off a series of immune inhibitory regulators such as the E3 ubiquitin ligase CBLB, Sts, and Jnk1 significantly improved resistance to *C. albicans* in mice (Wirnsberger et al., 2016; Xiao et al., 2016; Zhao et al., 2017). The E3 ubiquitin ligase CBLB in the innate immune system negatively impacts the phagocytosis of neutrophils and macrophages (Wirnsberger et al., 2016; Xiao et al., 2016). The type I interferon-induced IL-15 production in the Ly6Chigh monocytes is also required for *C. albicans* clearance by innate immune cells (Dominguez-Andres et al., 2017). In contrast, compromising the function of the innate immune system accelerates *C. albicans–* induced kidney failure (Dominguez-Andres et al., 2017).

Other mechanisms that enhance host defense against *C. albicans* infection were not extensively studied. Both global and local inflammation contribute to *C. albicans–*induced kidney failure. Li et al. showed the systematic expression of microRNAs prevented *C. albicans* infection (Li et al., 2014b; Li et al., 2018). They found *C. albicans* induced acute kidney injury by attenuating miR-124 expression and up-regulating MIP-1 in the kidney (Li et al., 2018), indicating local inflammation in the kidney contributed to kidney failure. Administrating miR-124 mimic inhibited MIP-1 expression in the kidney and restored kidney functions (Li et al., 2018). It is still not clear how miR-124 and miR-204 regulate innate immune system. However, global inflammation in the absence of *C. albicans* may not be sufficient to induce kidney failure. *C. albicans* kills kidney epithelial cells directly, while XBJ partially rescued 293T cell survival upon *C. albicans* exposures (**Figure 6**). This is consistent with our previous result that XBJ improved RAW264.7 cells survival upon the insult of LPS (Lyu et al., 2018).

## XBJ and Organ Failure

XBJ has been approved to treat multiple organ dysfunction syndrome and sepsis in China for over a decade (Gao et al., 2015; Shi et al., 2017). A Meta-analysis by Song et al. demonstrated that combining XBJ with conventional intervention is superior to conventional intervention alone in treating MODS (Song et al., 2018). The benefits of XBJ to different organs (including lung, kidney, heart, and liver) were reported in a series of clinical studies (Fang and Wang, 2013; Wang et al., 2015; Zhang et al., 2016; Gao et al., 2018; Song et al., 2018) (Song et al., 2019). In a small-scale prospective, single-center, randomized doubleblinded trial, Gao et al. found XBJ significantly lowered IL-1β, IL-8, and C-reactive protein in blood and up-regulated IL-10 in blood and decreased adverse events in patients with lung injury (Gao et al., 2018). Zhang et al. found XBJ improved myocardial function in patients with septic myocardial injury, indicated by improving cardiac troponin I, N-terminal proB-type natriuretic peptide, and procalcitonin in blood samples of patients (Zhang et al., 2016).

The major causes of organ failure in sepsis are disseminated intravascular coagulation (DIC), dysfunction of circulation, and cytokine storm. Results from basic research confirmed clinical observations and provided clues to the working mechanism of XBJ in preventing and treating different types of organ injuries systemically. It improves microcirculation partially by inhibiting blood-clotting to prevents/treats organ injuries (Wang et al., 2015; Xu et al., 2015; Jin et al., 2018). This is reflected in reversing abnormalities of metabolic biomarkers in sepsis (Shi et al., 2017).

XBJ is also likely to prevent/reverse organ injuries by improving cell survival and regulating cell functions (Chen et al., 2013; Li et al., 2014a). Li et al. reported XBJ enhanced survival of hematopoietic stem cells and mono-nuclear cells upon radiation insults *in vitro* and *in vivo*. XBJ also regulated the secretory function of Kupffer cells in heat stroke rats (Chen et al., 2013).

At the molecular level, XBJ regulates multiple signaling pathways to combat organ injuries. It down-regulated TLR4 signaling and stimulated the expression of Toll-interacting protein to improve organ functions (Liu et al., 2014b; Liu et al., 2014c; He et al., 2018). XBJ reduced ROS production in rats to attenuate pulmonary injury (Chen et al., 2017). P38 MAPK might be a XBJ target in a lung injury model (Liu et al., 2014a). HMGB1, a nuclear protein related to organ injury, is a biomarker of organ injury and a potential therapeutic target of organ injury (Wang et al., 1999; Musumeci et al., 2014). A series of publications indicated XBJ attenuates HMGB1 expression in sepsis organ injuries (Li et al., 2007; Wang et al., 2007; Wang et al., 2015; Chen et al., 2016). However, no gain-of-function and loss-of-function study has been conducted *in vivo* to determine the influences of XBJ on these pathways.

XBJ was used to treat and prevent different types of kidney injuries in the clinic and pre-clinical studies. In a clinical study, XBJ improved clinical symptoms of sepsis-induced acute kidney injury (Yuxi et al., 2017). In addition, XBJ attenuated herbicide paraquat-induced acute kidney injury in rats (Xu et al., 2017). It also prevented paraquat-induced apoptosis in human kidney cell line HK-2 (Tian et al., 2018). However, mechanisms of these effects remain to be illustrated. In this study, we revealed that XBJ may regulate GRP78 to improve the survival of kidney epithelial cells upon *C. albicans* insult and the combination of four compounds in XBJ can maintain GRP78 expression. This mechanism may also apply to other organs and cell types.

#### The Advantages of Utilizing Different Methods to Understand the Working Mechanism of XBJ in Preventing Invasive C. albicans Infection

Network pharmacology predictions are based on our current knowledge. The experimental pharmacology has value to supplement network pharmacology to expand our knowledge. Our results showed that combining both methods provides advantages to advance medicine. RNA-seq, real-time PCR, and Western blot were used to identify novel signaling that impacts kidney failure during invasive *C. albicans* infection. Glucose-Regulated Protein 78 (GRP78, BiP) was identified by Western Blot in 293T cells. Results of real-time PCR confirmed the influence of XBJ on GRP78 at the transcription level. However, this result remains to be confirmed *in vivo.* ATF6B and GRP170, two ER stress-related proteins were influenced by XBJ in RNAseq. But we did not find their changes on protein level (data not shown). Whether XBJ influences the expression of GRP94, another important ER stress protein that shares a similar function with GRP78 (Zhu and Lee, 2015), is under investigation. Overall, combining multiple methods is superior to a single method approach in understanding the working mechanism of XBJ in preventing invasive *C. albicans* infection.

### ER Stress Pathway in Cell Death and Organ Failure

GRP78 is a negative regulator of the unfolded protein response (UPR). Knocking down GRP78 triggered the UPR in un-stressed cells (Pyrko et al., 2007; Li et al., 2008). GRP78 represses apoptosis by inhibiting BIK and caspase-7 activation (Reddy et al., 2003; Fu et al., 2007). The function of GRP78 was not determined in a kidney-specific knockout animal model yet. However, its liver-specific knockdown induced liver injury, suggesting GRP78 plays a role in organ protection and may render protection for the kidney upon *C. albicans* insults (Ji et al., 2011; Chen et al., 2014; Zhu and Lee, 2015). Consistent with the literature, inhibiting the function of GRP78 increased the death of 293T cells (**Figure 8**). HA15, a specific GRP78 inhibitor, did not induce a dramatic increase of 293T cell death. This may due to the redundant function of GRP78 and GRP94 (Zhu and Lee, 2015). This hypothesis remains to be tested.

#### Network Pharmacology in Developing Novel Regimens to Prevent Fungal Infection

Network pharmacology may shed light on developing novel Chinese medicine (Lyu et al., 2017; Suo et al., 2017). In this study, it also provided hints for a potential application of XBJ. Our aim was to take the advantages of network pharmacology to reveal a novel mechanism of XBJ in preventing invasive fungal infection. ER stress signaling was not predicted as top signaling related to invasive fungal infection by our network pharmacology analysis. In addition, our literature mining did not retrieve strong evidence indicating an important role of ER stress signaling in invasive fungal infection. It emerged from our RNA-seq analysis using RNA extracted from 293T cells in the presence of *C. albicans* and XBJ treatments. The alteration of GRP78 was more pronounced on the protein level rather than the transcription level in *C. albicans–*infected 293T cells treated by XBJ. However, Western Blot confirmed XBJ and C0127 did sustain the expression of GRP78 (**Figures 6** and **8**). Hence, our new finding indicated network pharmacology analysis and experiments complemented each other in illustrating the mechanism of compound Chinese medicine in treating human diseases.

#### C0127 Prevents C. albicans–Induced Kidney Failure

Our network pharmacology analysis predicted that four compounds in XBJ regulated 2/3 of predicted XBJ targets in fungal infection (**Figure 3A** and **Supplementary Figure 2**). This prediction was confirmed in our *in vivo* study (**Figure 7**). C0127 not only prevented *Candida* sepsis but also prevented *C. albicans* colonization in the kidney. Paeoniflorin and hydroxysafflor yellow A, the top two high-concentration compounds in XBJ, may play major roles in preventing kidney failure upon systemic *C. albican*s infection. Paeoniflorin which claims the highest concentration in XBJ was isolated from Chishao (Liu et al., 2015; Han et al., 2017). Several groups reported that paeoniflorin attenuates ER stress in different tissues and organs (Chen et al., 2018a; Gu et al., 2016; Jiang et al., 2014; Zhu et al., 2018). Gu et al. indicated paeoniflorin exerted protection for MCAO rats by regulating ER stress. Zhu et al. reported that paeoniflorin attenuates ER stress in retinal pigment epithelial cells *via* triggering Ca(2+)/CaMKIIdependent activation of AMPK (Zhu et al., 2018). It is likely that paeoniflorin plays a major role in regulating the expression of GRP78 in kidney epithelial cells. However, this was not verified by our *in vitro* experiments. Paeoniflorin did not significantly enhance the expression of GRP78 individually (data not shown). Consistent with our results, Gu et al. found that a combination of *L. chuanxiong* and *Radix paeoniae*, two herbs in XBJ, attenuated

ER stress–dependent apoptotic signaling pathway in MCAO rats (Gu et al., 2016). Hydroxysafflor yellow A (HSYA) plays a role in preventing tissue injuries (Han et al., 2017). Bai et al. reported the protective effect of HSYA on acute kidney injury in an ischemia/ reperfusion (I/R) model. They found HSYA prevented I/R induced apoptosis in kidney epithelial cells *in vivo* and *in vitro*. HSYA may attenuate TLR4 signaling to prevent apoptosis in kidney epithelial cells (Bai et al., 2018). Increasing the stability of HSYA in XBJ may further enhance the protection to the kidney (Pu et al., 2017). In our experiment, XBJ or C0127 does not inhibit *C. albicans* growth *in vitro*. This is consistent with the observation of Canturk which showed 1mg/ml ferulic acid-induced necrosis in *C. albicans* and it synergistically enhanced the anti-fungal effect of caspofungin (Canturk, 2018). Thus, low conc. of ferulic acid is unlikely to influence the survival of *C. albicans* in systematic infection. The combination of four compounds does not have a synergistic inhibition on the growth of *C. albicans in vitro* either (data not shown).

## Future Directions

Our network pharmacology analysis predicted that XBJ regulates type I interferon and its upstream regulators such as interferon α, TLR2, and TLR4 in invasive fungal infection (**Figures 3B, C**). Thus, we hypothesized that XBJ may regulate type I interferon signaling in the innate immune system to enhance the clearance of *C. albicans* in invasive *C. albicans* infection. XBJ and C0127 may regulate innate immune system to enhance the survival of *Candida* infected mice while enhancing the survival of kidney epithelial cells. We also aim to determine how XBJ and C0127 regulate the innate immune cells and whether type I interferon signaling mediates their effects.

## CONCLUSIONS

In conclusion, XBJ may prevent systemic *C. albicans* infection in sepsis patients. Compounds with higher concentrations in XBJ played major roles in preventing *Candida* induced kidney failure. GRP78 is a novel target of XBJ and C0127 in kidney epithelial cells. Members of ER stress signaling might be novel therapeutic targets in organ protection and sepsis.

## ETHICS STATEMENT

This study was carried out in accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85-23, revised 1996, USA) and the recommendations in the Guidance for the Care and Use of Laboratory Animals issued by the Ministry of Science and Technology of China. All experiments were approved by the Institutional Animal Care and Use Committee of Nankai University and the Laboratory Animal Ethics Committee of the Tianjin University of Traditional Chinese Medicine (Tianjin, china) and were performed in accordance with its guidelines (Permit Number: TCM-LAE-20170017).

### AUTHOR CONTRIBUTIONS

QY, YF, and M-CL designed the study and developed the methodologies. TS, QY, YF, TR, X-TW, HZ and J-MG conducted research. QY, YF, TS, TR, HZ, X-TW, J-MG, GP, XG, YZ and M-C.L. analyzed the data and contributed critical reagents. QY, YF, YZ, TS, and M-CL wrote and revised the manuscript.

#### FUNDING

This project was supported by the National Science Foundation of China (Grant number: 81774018, 81973581, 81873037, 31670146, 81873961); Tianjin University of Traditional Chinese Medicine (Startup Grant for Y.F.); Nankai University (The Open Fund of Ministry of Education Key Laboratory of Molecular Microbiology and Technology);

### REFERENCES


Tianjin Municipal Education Commission (Grant number: TD13-5046).

### ACKNOWLEDGMENTS

We thank our colleagues for their supports. We thank Zhengcan Zhou, Dr. Shuang He, Ming Lyu, Yu-Le Wang, Guang-Xu Xiao, and Xin-Yan Liu for their technical supports and constructive discussions. We thank Dr. John Orgh for editing our manuscript.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2019.01416/ full#supplementary-material


injury by UPLC-ESI-MS/MS after injection of Xuebijing. *BioMed. Chromatogr.* 28, 1090–1095. doi: 10.1002/bmc.3124


transforming growth factor beta signaling and enhance cell growth. *Mol. Cell Biol.* 28, 666–677. doi: 10.1128/MCB.01716-07


**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2020 Shang, Yu, Ren, Wang, Zhu, Gao, Pan, Gao, Zhu, Feng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# An Integrated Pharmacology-Based Analysis for Antidepressant Mechanism of Chinese Herbal Formula Xiao-Yao-San

Naijun Yuan1†, Lian Gong1†, Kairui Tang<sup>1</sup> , Liangliang He1,2, Wenzhi Hao<sup>1</sup> , Xiaojuan Li <sup>1</sup> , Qingyu Ma<sup>1</sup> and Jiaxu Chen<sup>1</sup> \*

*<sup>1</sup> Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China, <sup>2</sup> College of Pharmacy, Jinan University, Guangzhou, China*

#### Edited by:

*Yuanjia Hu, University of Macau, China*

#### Reviewed by:

*Rongbiao Pi, Sun Yat-sen University, China Bo Yang, Zhejiang University, China Yi Wang, Zhejiang University, China*

> \*Correspondence: *Jiaxu Chen chenjiaxu@hotmail.com*

*†These authors share first authorship*

#### Specialty section:

*This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology*

Received: *10 December 2018* Accepted: *27 February 2020* Published: *18 March 2020*

#### Citation:

*Yuan N, Gong L, Tang K, He L, Hao W, Li X, Ma Q and Chen J (2020) An Integrated Pharmacology-Based Analysis for Antidepressant Mechanism of Chinese Herbal Formula Xiao-Yao-San. Front. Pharmacol. 11:284. doi: 10.3389/fphar.2020.00284* Clinical studies and basic science experiments have widely demonstrated the antidepressant and anxiolytic effects of the herbal formula Xiao-Yao-San (XYS). However, the system mechanism of these effects has not been fully characterized. The present study conducted a comprehensive network pharmacological analysis of XYS and sorted all pharmacologically active components (149) through the TCMSP webserver. Then, all potential molecular targets (449) were predicted, of which there were 99 genes clearly related to depression. To further investigate the mechanism of antidepressant effects of XYS, a compound-depression targets (C-DTs) network was constructed, and Gene Ontology (GO) functional and KEGG pathway enrichment analyses were performed for the 99 targets. Enrichment results revealed that XYS could regulate multiple aspects of depression through these targets, related to metabolism, neuroendocrine function, and neuroimmunity. Prediction and analysis of protein–protein interactions resulted in selection of three hub genes (AKT1, TP53, and VEGFA). In addition, a total of seven ingredients from XYS could act on these hub genes and they were identified through ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS), including paeoniflorin, quercetin, luteolin, acacetin, aloe-emodin, Glyasperin C, kaempferol. Hereafter, we investigated the effects of paeoniflorin and its predicted target, the results suggest that it can reverse the neurotoxicity produced by CORT and could be a neuroprotective effect by promoting the phosphorylation of Akt. Overall, our research revealed the complicated antidepressant mechanism of XYS, and also provided a rational strategy for revealing the complex composition and function of Chinese herbal formula.

Keywords: network pharmacology, Xiao-Yao-San, depression, Chinese herb formula, integration analysis methods

## INTRODUCTION

Depression is a psychological disorder with widespread prevalence, affecting more than 350 million individuals worldwide, and the prevalence is gradually increasing (Kuo et al., 2015). Because of high morbidity and mortality, depression has received extensive attention. Currently, the main treatment strategy for depression is pharmacotherapy, but antidepressants typically do not completely alleviate depressive symptoms and may lead to drug addiction and adverse side effects for patients (Tom et al., 2014; Solem et al., 2017).

Development of medicines that have antidepressant effects and produce less side effects than current antidepressants is a priority. Therefore, complementary and alternative medicine (CAM) and traditional Chinese medicine (TCM) have received attention as potential strategies for treatment and prevention of depression. Xiao-Yao-San (XYS) has been used in TCM clinics for more than a century to treat various diseases with characteristic features of liver stagnation and spleen deficiency syndrome (LSSDS) (Meng et al., 2013). There are eight Chinese herbs in XYS: Angelica sinensis (Oliver) Diels (AS, family: Apiaceae); Paeonia lactiflora Pallas (PN, family: Paeoniaceae); Bupleurum Chinense De Candolle (BR, family: Apiaceae); Atractylodes macrocephala Koidzumi (AMR, family: Compositae); Poria cocos (Schweinitz) Wolf (PR, family: Polyporaceae); Mentha canadensis Linnaeus (MH; family: Lamiaceae); Glycyrrhiza uralensis Fischer (GRH; family: Leguminosea); Zingiber officinale Roscoe (ZRR, family: Zingiberaceae).

XYS has been widely prescribed as a safe and effective treatment or adjuvant therapy for depression, because psychological stress syndromes primarily are classified as LSSDS in TCM theory. Clinical studies and basic science experiments have demonstrated the antidepressant and anxiolytic effects of XYS (Dai et al., 2010; Chen and Hou, 2017), but the associated mechanisms have not been characterized.

Systems pharmacology is an emerging field that integrates bioinformatics and experimental methods to advance drug discovery and provides a method for clarification of mechanisms of action of Chinese herbs (Berger and Iyengar, 2009). A drug (active compounds)-targets-diseases (clinical symptoms) network can be constructed and analyzed using a holistic view though this method (Kim et al., 2019; Zhou et al., 2019). It can determine the enrichment of targets, and elucidate complex effects and pharmacological mechanisms of Chinese herbs (Kloft et al., 2016). The present study aimed to identify the bioactive components and mechanisms of action of the TCM formula XYS in the treatment of depression using system network pharmacological analysis.

### MATERIALS AND METHODS

#### Data Preparation

#### Construction of XYS Chemical Constituent Databases

All chemical constituents of each of the herbs in XYS were obtained from the TCMSP data server (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, http://lsp.nwu.edu.cn/tcmsp.php), which is a unique systems-level pharmacology server for TCM (Ru et al., 2014). TCMSP can also provide pharmacology-related properties and predict targets and related diseases for each active ingredient based on critically evaluated pharmacological and clinical knowledge. This ground-breaking data server highlights the role that systems pharmacology can play in the traditional Chinese medicine discipline. Moreover, ADME properties data were also derived from TCMSP, comprising molecular weight (MW), octanol-water coefficient (ALogP), number of possible hydrogen-bond donors (nHdon), and number of hydrogen-bond acceptors (nHacc).

#### Evaluation the Drug-Likeness

Potential drugs in XYS were mainly identified by integrating oral bioavailability (OB) and drug-likeness (DL) properties. Comprehensive analysis of bioavailability and structural descriptors for predicting OB values for each compound were previously determined in-silico (Xue et al., 2012). [OB%]. Data server-dependent DL models were used to determine solubility and chemical stability based on the Tanimoto coefficient, using the following formula: T (A, B) = (A×B)/(|A|<sup>2</sup> + |B|<sup>2</sup> – A×B) (Tao et al., 2013). Then, according to recommendations from TCMSP, we selected screening criteria of OB ≥ 30% and DL ≥ 0.18 for determination of possible bioactive components.

#### Known Antidepressant Drug/Drug-Like Compounds Database Construction

#### **Known antidepressant drug/drug-like compounds from the cMap database**

Gene profiling of depression was performed using GSE12654 from GEO database, which contains frozen brain tissues from 11 depressed individuals and brain tissues from 15 nondepressed individuals (Iwamoto et al., 2004). Differentially expressed genes (DEGs) with the criteria |logFC|>1.2 and P < 0.05 were determined by "limma" package in R studio software. Moreover, the connectivity map (cMap) database is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules that uses simple pattern-matching algorithms allowing for elucidation of functional connections between drugs, genes, and diseases through transitory common gene expression changes (Fang et al., 2003). After determining dysregulated genes, we converted the associated gene symbols to Affymetrix probe IDs, as the cMap database is based on these identifiers. P <0.05 was used as the threshold for statistical significance.

**Abbreviations:** CAM, Complementary and alternative medicine; TCM, Traditional Chinese Medicine; XYS, Xiao-Yao-San; LSSDS, Liver stagnation and spleen deficiency syndrome; AS, Angelica sinensis (Oliver) Diels; PN, Paeonia lactiflora Pallas; BR, Bupleurum Chinense De Candolle; AMR, Atractylodes macrocephala Koidzumi; PR, Poria cocos (Schweinitz) Wolf; MH, Mentha canadensis Linnaeus; GRH, Glycyrrhiza uralensis Fischer; ZRR, Zingiber officinale Roscoe; OB, oral bioavailability; DL, drug-likeness; DEGs, differentially expressed genes; PCA, principal component analysis; GO, Gene Ontology; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; MW, molecular weight; ALogP, the value of partition coefficient between octanol and water; nHdon, the number of possible hydrogen-bond donors; nHacc, and the number of hydrogen-bond acceptors; BDNF, brainderived neurotrophic factor; UPLC–Q/TOF-MS, ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry; CORT, corticosterone; PF, paeoniflorin.

#### **Known antidepressant drug/drug-like compounds from DrugBank database**

DrugBank (http://www.drugbank.ca/) is a unique online database used as a bioinformatics and cheminformatics resource that compiles extensive drug-related information (Wishart et al., 2017). Therefore, DrugBank was also searched for antidepressant drugs or drug-like compounds which were approved by the United States Food and Drug Administration (FDA) for clinical trials.

#### Principal Component Analysis and Correlation Analysis

To evaluate the chemical distribution of antidepressant-related compounds in XYS, we used principal component analysis (PCA) for physicochemical-related parameters (MW, ALogP, nHDon, and nHAcc) though the prcomp function in R studio software. Therefore, known antidepressant drug/drug-like compounds from cMap, DrugBank, and active ingredients from XYS were also analyzed by PCA. Furthermore, the correlation of ADME properties of these compounds was also analyzed. Pearson r coefficients were calculated to evaluate the correlation between the three groups.

#### **Target prediction**

Corresponding protein targets of each active ingredient in XYS were predicted using several databases. Predicted targets from TCMSP were extracted and converted to target names using UniProtKB (http://www.uniprot.org) (Emmanuel et al., 2016). Then, PubChem, STITCH (Kuhn et al., 2012), and PharmMapper were used to obtain potential targets. The prediction results from the PharmMapper server (http://lilab.ecust.edu.cn/pharmmapper/) were based on the pharmacophore model, and this server is a large, in-house collection of pharmacophore databases extracted from all targets in TargetBank, DrugBank, BindingDB, and PDTD (Wang et al., 2017). Target information was set to Homo sapiens and aggregated for further analysis.

#### Network Construction

#### Construction of an Active Compound-Targets Network for XYS

To clarify the relationship between active compounds in XYS (C) and depression-related targets, depression-related genes were retrieved from the OMIM database (Ada et al., 2005), DisGeNET database (Piñero et al., 2015), TTD database (Zhu et al., 2010), and GSE12654 gene chip. Intersections of disease (depression) related genes (DGs) with the above predicted targets were determined to create an XYS antidepressant C-DTs network. This network was constructed and visualized using Cytoscape 3.4.0 software.

#### Comprehensive Analysis

#### **Functional enrichment analysis of XYS related targets**

To analyse targets groups of active compounds in XYS, we used enrichment analysis, as this method can effectively increase the reliability of the identification of biological phenomena, resulting in meaningful annotation information. (i) GO annotation: In this study, "clusterProfiler" package in R studio was used for GO (Gene Ontology) enrichment analysis. "ClusterProfiler" is based on multiple resources, and also serves as a userfriendly enrichment tool with integrated gene cluster analysis (Yu et al., 2012). This R package provides for GO gene annotation enrichment analysis, including cellular component (CC), molecular function (MF), and biological process (BP) analyses. P < 0.05 were considered statistically significant. (ii) KEGG pathway analysis: To explore potential biological mechanisms of predicted targets, the "clusterProfiler" R package was used to annotate Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of targets genes potentially regulated by active compounds contained in XYS. In addition, significantly enriched KEGG pathways should contain at least three genes, and have P < 0.05.

#### **Interactions between targets in the C-DTs network**

Protein–protein interaction data analysis is an important mechanism to characterize the molecular basis of disease. Interactions between each target in the C-DTs network which could be regulated by XYS were searched using the STRING website, which is a comprehensive prediction server (Szklarczyk et al., 2017). Interaction scores should be more than 0.9 to indicate appropriate confidence in identified protein interactions. Using this tool, it is feasible to discover targets related to active components of XYS based on protein network.

#### Key Active Compounds Screening and Evaluation **Key active compounds screening**

To evaluate the antidepressant role of each active compound in XYS, hub DGs were identified using four different analysis methods in "cytoHubba" of cytoscape 3.4.0 software (Chin et al., 2014). The potential key active compounds in XYS for treatment of depression were incorporated into a Sankey diagram to reveal relationships between "hub genes - active compounds."

#### **UPLC–Q/TOF-MS analysis**

The test samples of XYS according to the prescription proportion of 'Chinese Pharmacopeia' (AS: PN: BR: AMR: PR: ZRR: GRH: MH = 5: 5: 5: 5: 5: 5: 4: 1) were provided by Jiuzhitang Co., Ltd. (Commission, 2015). And the fine powder was carried out by crushing and mixing all herbs. Next, we accurately weighted 1 g of above powder and added 50% methanol to dissolve, then using ultrasonic treatment at room temperature for 30 min. After centrifuging at 13,000 rpm for 10 min, we injected in supernatant (2 µL) for further UPLC-Q/TOF-MS analysis.

The information of equipment used in the UPLC-Q/TOF-MS analysis was detailed described in our previous research (He et al., 2018). The universal mixed mobile phase composed of water (solvent A) and acetonitrile (solvent B), both of which contain 0.1% formic acid (v/v). Gradient elution system used a flow rate of 0.4 mL/min, and adopted as follow: 5% B, 0–0.5 min; 5– 80% B, 0.5–10 min; 80–100% B, 10–12 min; 100% B, 12–13 min; 100–5% B, 13–14 min; 5% B, 14–15 min. The injection volume was set as 2 µL and under 280 nm wavelength to detect. Mass spectral data were collected in Centroided. And the purity of all reference standards was above 98% that analyzed by HPLC (High performance liquid chromatography).

#### Experiments and Molecular Evaluation

#### **Cell culture**

Differentiated PC12 cells were employed in the experiments. RPIM 1640 basic medium supplemented with 5% fetal bovine serum (Gibco, Rockville, MD, USA), 10% Horse serum (Gibco, Rockville, MD, USA), and 1% antibiotic mixture comprising penicillin and streptomycin (Gibco, Rockville, MD, USA). Cells were under the 5% CO<sup>2</sup> in a humidified incubator (Thermo, USA) at 37◦C. NGF (nerve growth factor, 50 ng/ml, 48 h) was used for PC12 differentiation.

#### **Cell viability assay**

The effect of paeoniflorin reverse neurotoxicity from corticosterone, cell viability was determined by the MTT assay (Fotakis and Timbrell, 2006). Briefly, PC12 cells were seeded into 96-well plate with the concentration of 5 × 10<sup>3</sup> /100 µl, then incubated the cells under the conventional culture condition overnight. After 24 h treatment that with the mixture of 200µM CORT plus different concentrations of paeoniflorin (10µM, 20µM, 40µM), 10 µl MTT (0.5 mg/ml) was added to each well, followed by 4 h of incubation at 37◦C. Then carefully remove the supernatant, and 100 µl DMSO was added to each well to dissolve the formazan crystal. The absorbance was recorded at 570 nm by microtiter plate reader. Cell viability was calculated as a percentage of the untreated or vehicle-treated control and averaged from three independent experiments.

#### **Western blot analysis**

Total protein was extracted from the PC-12 cells in different groups. Briefly, the collected were immediately lysed in RIPA lysis buffer containing PMSF and phosphatase protein inhibitor (Beyotime Biotechnology, Jiangsu, China). The protein lysates separated by 10% SDS-PAGE gel were electrophoretically transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA, USA). The following specific primary antibodies were used for incubate membranes overnight at 4◦C: anti-GAPDH (Beijing BioDee Biotechnology Co., Ltd.), anti-AKT (Cell Signaling Technology), anti-Phospho-AKT (Cell Signaling Technology). After probed with a 1:5000 dilution of horseradish peroxidase (HRP) conjugated secondary antibody (Beijing BioDee Biotechnology Co., Ltd.), the labeled proteins were detected by using enhanced chemiluminescence (ECL) detection.

#### **Molecular docking evaluation**

Furthermore, AutoDock 4.0 was used to evaluate the hub genes with active compounds, which is widely used for the study of bound conformation and binding free energy of ligand to the protein (Forli et al., 2016).

#### Statistical Analysis

Data are presented as means ± standard deviation. The standard two-tailed student's t-test was used for analysis and P < 0.05 were considered significant.

### RESULTS

#### Bioactive Compounds in Xiaoyao San

Bioactive ingredients in XYS and corresponding ADME information were extracted from the TCMSP data server by filtering by drug-like threshold (OB > 30% and DL > 0.18). Several of the herbal medicines in XYS contain the same active ingredients, such as quercetin, a natural flavonoid, which is present in BR and in GRH. In addition, stigmasterol is present in AS, RB and ZRR. In total, 149 active compounds were identified in XYS, including 12 in PN, 2 in AS, 18 in BR, 15 in PR, 7 in AMR, 91 in GRH, 5 in ZRR, and 10 in MH. Detailed information regarding these molecules is summarized in **Table S1**.

To determine the molecular diversity of the constituents of each herbal medicine, the constituents were evaluated based on four significant physicochemical properties (**Figures 1A–D**, **Table 1**): MW, ALogP, nHdon, and nHacc. (i) MW index indicated that PR had the highest average MW of bioactive components (467.78 ± 37.66), and RG (348.41 ± 63.69) had the lowest average MW of active components. (ii) Bioactive components in AS had the highest average ALogP value (7.86 ± 0.31), and MHH constituents had the lowest average ALogP value (2.32 ± 2.27). (iii) HM had the highest average number of nHdon (3.60 ± 2.01), and RAM (0.43 ± 0.53) had the lowest average number of nHdon. (iv) The herbal constituents with the highest and lowest average number of hydrogen-bond acceptors were MHH and AS, respectively.

#### Identified Antidepressant Drugs and PCA

We retrieved the publicly available microarray dataset GSE12654 from GEO, which included frozen brain tissues of 11 humans with depression and 15 individuals without depression (Iwamoto et al., 2004). After applying thresholds log|FC| >1.2 and P < 0.05 using "limma" in R studio, a total of 29 significantly differentially expressed genes between depressed and normal patients were identified. Fourteen genes were down-regulated, and 15 genes were up-regulated (**Table S2**).

Furthermore, to evaluate the relationship between ingredients form XYS and other known-compounds, we first identified the known-compounds which contain anti-depression function based on the DEGs results from cMap data server. Finally, 12 significant compounds with adequate relative connection mean scores (absolute value >0.5) were identified. Of the twelve compounds, 2 had high positive connection scores, and the other ten compounds possess negative connectivity scores, indicating they can act with these dysregulated genes in depression. **Figure 2A** and **Table 2** summarize these results. In addition, a search of the DrugBank database resulted in identification of 64 FDA-approved compounds for treatment of depression (**Table 2**).

PCA was performed to characterize the physicochemical parameters (MW, ALogP, nHDon, and nHAcc) of these antidepressant drugs/compounds. PC1 and PC2, as shown in **Figure 2B** and **Table S3**, showed a distribution of different color nodes that flocked together, indicating extensive overlap among the three groups of ingredients in XYS, and the antidepressant compounds determined by cMap and Drugbank. Furthermore,


the significant positive correlation between these compounds of three groups (**Figure 2C, Table S3**). In ADME properties, Pearson r value is 0.3404 (P < 0.0001) for MW, AlogP (r = 01969, P = 0.003), Hdon (r = 0.3018, P < 0.0001), Hacc (r = 0.351, P < 0.0001). These results indicated that bioactive compounds from XYS are highly correlated with known antidepressant compounds in molecular properties, and could as potential drug therapies for depression treatment.

#### Targets of Xiaoyao San

An in silico target screening approach was performed to identify the putative targets of each bioactive component of XYS. Predictive tools were used, including the TCMSP server, STITCH, and PharmMapper data servers. In total, 446 targets were identified for 149 bioactive compounds (9 compounds did not receive predictions), and many compounds were predicted to target the same proteins, such as Monoamine Oxidase B (gene symbol: MAOB). Depression-related putative targets were obtained from Therapeutic Target, DrugBank, OMIM, and DisGeNET databases, and DEGs resulting from analysis of GSE12654. Finally, 121 bioactive compounds in XYS which associated with 99 depression-related targets were identified. Multiple therapeutic targets were mediated by active ingredients of XYS, such as IL2, IL4, IL6, IL10, and STAT3, which are involved in immune and inflammatory responses closely associated with depression. For example, STAT3 (Signal transducer and activator of transcription 3), a component of the JAK/STAT signaling pathway, influences the fate and function of brain cells.

#### Construction of a Compound-Depression Target Network (C-DT)

A C-DT network was constructed (**Figure 3**) for further biological analysis. This network contained information regarding complex relationships between all bioactive compounds in XYS and their depression-related targets. There were 220 nodes in the C-DT network composed of 121 active ingredients and 99 depression targets, connected by 1583 interactions, the details were listed in **Table S4**. As shown in **Figure 3**, node size indicated the number of connections

calculated using "NetworkAnalyzer," an analytical tool in cytoscape 3.7.0.

#### Enrichment Analysis of the C-DT Network GO and KEGG Pathway Analysis

To investigate biological functions associated with this interaction network relative to treatment of depression, we performed GO and KEGG pathway enrichment analyses using the "clusterprofile" R package. Three GO categories were analyzed, which included CC, MF, and BP. The top 15 GO enrichment results for each category are listed in **Table 3**. Direct outcomes are shown in **Figure 4A**. These targets were significantly involved in formation of neuronal receptor and cell, which are directly related to generation of brain cells and tissue. Molecular functions involved activity and binding of various molecules, neurotransmitter receptors, cytokines, transmembrane receptor protein kinases, and nuclear receptor, as well as cytokine receptor binding, phoshphatase, and steroid. Moreover, combined BP results indicated that XYS may regulate depression-related biological processes, such as "regulation of cell migration," "reactive oxygen species metabolic process," and "aging" to exert antidepressant effects.

KEGG signaling pathway results are shown in **Figure 4** and **Table 4**. The top enriched terms were related to several critical pathways associated with disease, such as "PI3K-Akt signaling pathway," "MAPK signaling pathway," and "neuroactive ligandreceptor interaction" (**Figure 4B** and **Figure S1**). These findings demonstrated complex pathological features of depression and the potential of XYS as an antidepressant.

#### PPI Network Analysis and Screening of Key Active Ingredients

To investigate the characteristics of protein targets in the C-DT network, protein–protein interactions were analyzed using the STRING online database, and the related PPI network was constructed and visualized using cytoscape software. Using confidence of 0.9 as the threshold, we found 90 nodes and 288 interactions in the PPI network. In addition, we used four different analysis methods (Stress, Closeness, MNC and EcCentricity) using the "cytoHubba" plugin to identify the top 10 hub nodes in the PPI network. Detailed results are shown in **Figures 5A–D**, and the top hub genes based on these four indices were summarized in a Venn diagram (**Figure 5E**, **Table S5**). Thus, 3 core genes were identified as hubs: AKT1, TP53, and VEGFA. Furthermore, based on GO and KEGG analyses, these three core genes were enriched in multiple biological processes and signaling pathways. For instance, AKT1 is involved in myelination of the peripheral nervous system, peripheral nervous system axon ensheathment, and the neurotrophin signaling pathway, all of which are closely related to depression (Barros et al., 2009; Musashe et al., 2016; Liu et al., 2018). Using these core genes, a Sankey diagram (**Figure 6A**) was used to facilitate shown of bioactive compounds that can act on multiple targets. Nine ingredients of XYS and their hub targets were identified,


*(Continued)*



including quercetin, luteolin, acacetin, aloe-emodin, gadelaidic acid, Glyasperin C, kaempferol, naringenin, paeoniflorin.

#### Identification of the Chemical Characterization of XYS

As shown in **Figure 6B**, the chemical base peak ion (BPI) chromatogram of XYS based on the positive and negative ion modes of UPLC-Q-TOF/MS. According to the above analysis, nine key compounds of XYS were further examined. All of the key compounds from XYS, seven ingredients (paeoniflorin, quercetin, luteolin, acacetin, aloe-emodin, Glyasperin C, kaempferol) were successful identified by UPLC-Q-TOF/MS analysis, the details were listed in **Table 5**. Among them, we found major compounds are flavonoids, only paeoniflorin as monoterpene glycoside, and most importantly it is a principal ingredient of Paeonia lactiflora Pallas (**Figure 7A**). Thus, we next performed cellular model and investigated the neuron protected effect of paeoniflorin in vitro.

#### Evaluations

To determine the protected effect of Paeoniflorin from CORT neurotoxicity, the cell viability assay was examined. The detailed MTT assay results as shown in **Figure 7B**, that 200µM CORT reduced the cell viability of PC12 cells, whereas treatment 20 and 40µM paeoniflorin were very significant able to moderate the cell viability (P < 0.01). Western blot analysis results showed that compared with the control cells, the expression of p-Akt in CORT stimulated cells was significantly decreased (P < 0.01). However, the down-regulated expression of p-Akt was significantly reversed by paeoniflorin treatment (**Figures 7C,D**).

Furthermore, quercetin and luteolin could act on all hub genes, so we further validated their molecular docking to detect and calculate the binding energy between small molecules and proteins. The best bound conformation site of quercetin and luteolin binding to proteins were shown in **Figure 8**, respectively. The binding energy between quercetin with AKT1 is −17.6341 kcal/mol, and luteolin with AKT1 is −12.7977 kcal/mol, that initiated both two compounds had strong binding activity with AKT1. Similarly, these two compounds contain strong binding activity with other hub targets, the binding energy between quercetin with VEGFA and TP53 (−67.4121 kcal/mol), luteolin with VEGFA and TP53 (−53.3754 kcal/mol).

### DISCUSSION

Traditional Chinese medicine (TCM) is being more frequently chosen by patients to treat depressive and anxiety disorders. A meta-analysis reported that use of XYS as an adjuvant therapy with antidepressant drugs in 1,837 patients was more effective in improving symptoms than antidepressants alone (Zhang et al., 2011). Furthermore, previous studies indicated that XYS treatment of depression and anxiety involves several biological aspects. For instance, some psychological disorders have been associated with increased cytokine levels resulting from immune activation, known as the "cytokine hypothesis" (Sharma, 2016). Inflammatory cytokines may act as modulators of 5-HT (serotonin) receptors, and upregulate indoleamine 2,3 dioxygenase (IDO), resulting in anxiety-like disorders (Myint and Kim, 2003). The antidepressant-like effects of XYS were associated with downregulation of peripheral IL-1β, IFNγ, TNF-α, and IL-6, and increased synthesis of tryptophan hydroxylase (TPH) and IDO, which play a role in promoting 5-HT synthesis (Jiao et al., 2019). XYS significantly inhibited TNF-α release in serum and the hippocampus through activation of the TNF-α/JAK2-STAT3 pathway, as shown in our preliminary study (Li et al., 2017). Moreover, the HPA axis and brain-derived neurotrophic factor (BDNF) have also been widely studied in the context of pathogenesis and treatment of depressive disorders (Jiang et al., 2016). Previous studies have demonstrated that XYS could reverse chronic stress-induced anxiety-like symptoms by regulating the apelin-APJ system in the hypothalamus and activity of the HPA axis (Yan et al., 2018).

Many studies have demonstrated the antidepressant effects of XYS, which involves multiple pathways associated with depression. Therefore, the complex bioactive ingredients and targets with potential antidepressant activities require clarification. Previous studies using High Performance Liquid Chromatography coupled with LTQ Orbitrap Mass Spectrometry (HPLC-LTQ-Orbitrap-MS) identified paeoniflorin, liquiritin, glycyrrhizic acid, ferulic acid, saikosaponins A and C, curcumin, and poria cocos alcohol were eight active compounds identified in XYS (Li et al., 2015). Yuzhi Zhou and his colleagues identified 4 components: Z-ligustilide, palmitic acid, atractylenolide I, and atractylenolide II in XYS using gas chromatography– mass spectrometry (GC-MS) methods (Zhou et al., 2012). In addition, network pharmacology methods have been used to predict and analyse 13 components of XYS (Gao et al., 2015). However, no comprehensive analysis of all bioactive compounds in XYS had been previously reported. Therefore, the present study adopted a comprehensive systematic network pharmacology approach for evaluation of XYS.

In this study, we identified 149 compounds in active fractions of XYS. PCA analysis and correlation analysis was used to compare these compounds with antidepressants approved by the FDA and small molecule drugs predicted by the cMap database. This was the first comparative study of drug properties of TCM formula compounds using Drugbank and a gene chip combined with cMap. Our results indicated that these 149 bioactive compounds in XYS possessed potential antidepressant properties. Therefore, we constructed a compound-depression targets (C-DTs) network, and further enriched this network using GO KEGG pathways analyses of 99 depression-related targets. CC enrichment results indicated that a considerable number of genes were involved in formation of nerves and synapses. Synapses are the fundamental structures for information transmission between neurons, and they adapt to stimuli by continuously modifying neural connections and neurological circuits. Therefore, synaptic plasticity is the main manifestation of neuroplasticity, and depression and other psychological disorders are typically associated with decreased synaptic plasticity in the hippocampus (Wainwright and Galea, 2013). Molecular functions associated with components of XYS involved activity and binding of various molecules, such as neurotransmitter receptors, cytokines, transmembrane receptor protein kinases, nuclear receptors, cytokine receptor binding, steroids, and catecholamines. Moreover, a large number of genes were significantly enriched as BP terms in regulation of aging, reactive oxygen species metabolic processes, and inflammatory TABLE 3 | GO enrichment analysis.


response, which are associated with pathogenesis of depression. Combined BP results indicated that XYS may act via generation of nerves and synapses, alteration of neurotransmitter receptor function, and regulation of cytokines, resulting in antidepressant effects. The PI3K/Akt signaling pathway was enriched to the greatest degree, resulting in association of 20 genes. It has been wildly regarded to modulate antidepressant-like functions and contributes to synaptic plasticity and neurotransmission formation (Ludka et al., 2016). Furthermore, several metabolic pathways directly related to the nervous system such as "neuroactive ligand-receptor interaction," "GABAergic synapse," and "neurotrophin signaling pathway" were also enriched. Analysis of the PPI network identified hub nodes which were most closely related to other proteins: AKT1, TP53, VEGFA. These proteins were consistent with our enrichment analysis, and have been previously reported to be related to neurotrophic factors and depression.

#### TABLE 4 | KEGG pathway.


Bioactive ingredients in XYS were classified in the following categories: volatile oils, alkaloids, flavonoids, saponins, and polysaccharides. Among these, polysaccharides can regulate intestinal flora to moderate depression though the brain-gutmicrobiota axis (Cryan and Dinan, 2012; Schwalm et al., 2016), and flavonoids can cross the blood-brain barrier, directly acting on brain targets (Youdim et al., 2003). We concluded that XYS exerts its effects in many ways through the actions of many compounds and may confer advantages over the compounds by reducing drug resistance.

After identified hub targets, 9 ingredients of XYS could interact with them, and 7 of them were identified by UPLC- Q/TOF-MS analysis. As a result, we found that 3 hub genes predicted by the PPI network could be regulated by quercetin and luteolin, which are both flavonoids. Nevertheless, most of the reports of flavonoids are limited to in vitro experiments, and these flavonoids are common constituents in a variety of herbs. It is considered important to clarify their specific role in the treatment (Heinricha et al., 2020). Actually, many studies have verified their neuroprotective effects in vitro and in vivo. For instance, the genes regulated by luteolin could act as receptors in the central nervous system, resulting in antidepressant effects (Sasaki et al., 2013; Akinrinde and Adebiyi, 2019). Quercetin, a flavonoid, is a component of Bupleurum Chinense Dc. This flavonoid confers antioxidant activity and antiinflammatory effects in the treatment of neurological diseases. A previous study reported that quercetin relieved anxiety and depression of chronic stress rats by protecting neurons from oxidation and inflammation (Mehta et al., 2017). In LPSinduced neuroinflammation of mice, quercetin still exhibited a function of eliminating inflammation through significantly reduced LPS-induced proliferation of astrocytes in the brain and decreased the expression of inflammatory factors (Khan et al., 2018). Additionally, researchers found that quercetin could inhibit neuronal apoptosis to preventing anxiety-like behaviors in vivo via regulating the Akt1 and ASK1/JNK3/caspase-3 expression (Pei et al., 2016). Besides, in a previous network pharmacology study, quercetin was found also as a high contribution compound of Huangqi and Huanglian for treating diabetes, participated in polypharmacological and synergistic mechanisms (Yue et al., 2017).

However, among these 7 ingredients, only paeoniflorin, a monoterpene glycoside compound, as main component of herb (Paeonia lactiflora Pallas) from XYS. And it is one of the quality identification standards for XYS in the Chinese Pharmacopeia. Thus, it should be a representative compound from this formula, so that the current research further analyzed its neuroprotective effect and validated its potential roles with predicted target Akt. Previous reports by several researchers had provided clues supporting the role for paeoniflorin in the protection of neuron cells and the therapeutic effects in neurological diseases (Mao et al., 2012; Cong et al., 2019). Gu et al. had demonstrated that paeoniflorin via upregulating the p-Akt expression and Bcl-2/Bax ratio to reduce neuron death in Alzheimer's disease mice (Gu et al., 2016). Recent evidence suggests that CUMS rats had improved depressive-like behavior after paeoniflorin treatment, it could be regulated by acting on the ERK-CREB signaling pathway (Zhong et al., 2018). Consist with previous findings, we found paeoniflorin can reverse the neurotoxicity produced through CORT and promote the phosphorylation of Akt. On the one hand, from the perspective of network pharmacology, the representative importance of paeoniflorin in XYS has been clarified for the first time. On the other hand, our results indicated that paeoniflorin as the XYS's representative compound which could modulate the several signaling upstream target AKT's expression, that validate the current network pharmacology prediction.

## LIMITATIONS AND CONCLUSIONS

Current research based on network pharmacology which could provide a novel and systematic analysis way for the research of Chinese herbal formula. However, there are still some limitations in our article. Firstly, as we all know, components of Chinese herbal are complicated, it's doubtful whether there are synergistic or side effects caused by the interaction between compounds that is hard to clarified in the prediction and composition identification of this study. Secondly, although CORT is one of the most common methods to simulate depression in vitro, the pathogenesis of depression is complicated, which means that it is difficult to simulate whole pathogenic factors in vitro. Moreover, western blots suggested that paeoniflorin could regulate the phosphorylation of Akt, but it is only in protein expression level. The direct or indirect regulation between them would need to be further explored. Last but not least, hub genes were selected by PPI network in our study that they are usually as upstream molecules or central molecules in those important signaling pathways. However, there still exist multiple downstream proteins in pathogenesis of depression. And thus, it is important to discover and identify more compounds that interact with these downstream proteins through future works.

Overall, this study systematically summarized key active compounds in XYS and comprehensively analyzed potential mechanisms of its action and pathways. We also evaluated

paeoniflorin. (B) The chemical base peak intensity (BPI) chromatogram of key compounds characterization of XYS in positive ion mode and negative ion mode determined by UPLC-Q-TOF/MS. Identification No.:1. Paeoniflorin; 2. Kaempferol; 3. Quercetin; 4. Aloe emodin; 5. Luteolin; 6. Glyasperin C; 7. Acacetin.


TABLE 5 | Chemical composition of key ingredients from XYS identified by UPLC-Q/TOF-MS.

\**Accurately identified with reference standards.*

for representative compound paeoniflorin in vitro. The current findings provide a basis and new insights for XYS in the treatment of depression. Certainly, we will explore widely experiments of pharmaceutical ingredients to better understand the effects of XYS, such as component analyses, identification of drugs, and metabolic studies in animal models.

with luteolin. (C) Docking site between VEGFA with quercetin. (D) Docking site between VEGFA with luteolin. (E) Docking site between TP53 with quercetin. (F) Docking site between TP53 with luteolin.

### DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc. cgi?acc=GSE12654.

### AUTHOR CONTRIBUTIONS

NY and JC main contributed research conception and design. NY, KT, LH, WH, XL, and QM collected and processed data. NY, LG, and KT wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the final submitted version. JC takes primary responsibility for communication with the journal and editorial office during the submission process, throughout peer review, and during publication.

#### FUNDING

This work was financially supported by the National Natural Science Foundation of China (Nos. 81630104, 81803998, 81973748).

### REFERENCES


### ACKNOWLEDGMENTS

The authors gratefully appreciate the reviewers and editors for their constructive suggestions and kind help. And we sincerely appreciate Dr. Huang Chuiguo for his warm help.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2020.00284/full#supplementary-material

Figure S1 | Distribution of partial targets of XYS on the Neuroactive ligand-receptor interaction signaling pathway. The red nodes are potential targets. The green nodes are relevant targets in the pathway.

Table S1 | Bioactive ingredients of XYS.

Table S2 | The DEmRNAs of GSE12654.

Table S3 | (A) PCA analysis for physicochemical properties of potential antidepressant molecules; (B) Correlation analysis for properties between each molecular group.

Table S4 | C-DTs network.

Table S5 | Compounds acting on hub targets.

knowledgebase: how to use the entry view. Methods Mol. Biol. 1374, 23–54. doi: 10.1007/978-1-4939-3167-5\_2


**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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