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<front>
<?covid-19-tdm?>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Pharmacol.</journal-id>
<journal-title>Frontiers in Pharmacology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Pharmacol.</abbrev-journal-title>
<issn pub-type="epub">1663-9812</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">631206</article-id>
<article-id pub-id-type="doi">10.3389/fphar.2021.631206</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Pharmacology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Glycyrrhizic Acid for COVID-19: Findings of Targeting Pivotal Inflammatory Pathways Triggered by SARS-CoV-2</article-title>
<alt-title alt-title-type="left-running-head">Zheng et&#x20;al.</alt-title>
<alt-title alt-title-type="right-running-head">Glycyrrhizic Acid for COVID-19</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Zheng</surname>
<given-names>Wenjiang</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="fn" rid="fn1">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1260640/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Huang</surname>
<given-names>Xiufang</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="fn" rid="fn1">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/921977/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lai</surname>
<given-names>Yanni</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="fn" rid="fn1">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1151093/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Liu</surname>
<given-names>Xiaohong</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1088347/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Jiang</surname>
<given-names>Yong</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1146850/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhan</surname>
<given-names>Shaofeng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1260660/overview"/>
</contrib>
</contrib-group>
<aff id="aff1">
<label>
<sup>1</sup>
</label>The First Clinical Medical School, Guangzhou University of Chinese Medicine, <addr-line>Guangzhou</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<label>
<sup>2</sup>
</label>The First Affiliated Hospital, Guangzhou University of Chinese Medicine, <addr-line>Guangzhou</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<label>
<sup>3</sup>
</label>Lingnan Medical Research Center, Guangzhou University of Chinese Medicine, <addr-line>Guangzhou</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<label>
<sup>4</sup>
</label>Guangzhou University of Chinese Medicine, <addr-line>Guangzhou</addr-line>, <country>China</country>
</aff>
<aff id="aff5">
<label>
<sup>5</sup>
</label>Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, <addr-line>Shenzhen</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/572115/overview">Ilaria Peluso</ext-link>, Council for Agricultural and Economics Research (CREA), Italy</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/542293/overview">Simone Carradori</ext-link>, University &#x201c;G. d&#x2019;Annunzio&#x201d; of Chieti-Pescara, Italy</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/369244/overview">Kuldeep Dhama</ext-link>, Indian Veterinary Research Institute, India</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1017990/overview">He Huang</ext-link>, Zhejiang University, China</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Xiaohong Liu, <email>drlxh@foxmail.com</email>; Yong Jiang, <email>jiangyongszzxy@163.com</email>; Shaofeng Zhan, <email>zsfstone@163.com</email>
</corresp>
<fn id="fn1" fn-type="equal">
<label>
<sup>&#x2020;</sup>
</label>
<p>These authors share first authorship</p>
</fn>
<fn fn-type="other">
<p>This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>09</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="collection">
<year>2021</year>
</pub-date>
<volume>12</volume>
<elocation-id>631206</elocation-id>
<history>
<date date-type="received">
<day>19</day>
<month>11</month>
<year>2020</year>
</date>
<date date-type="accepted">
<day>04</day>
<month>02</month>
<year>2021</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2021 Zheng, Huang, Lai, Liu, Jiang and Zhan.</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>Zheng, Huang, Lai, Liu, Jiang and Zhan</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>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&#x20;terms.</p>
</license>
</permissions>
<abstract>
<p>
<bold>Background:</bold> Coronavirus disease 2019 (COVID-19) is now a worldwide public health crisis. The causative pathogen is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Novel therapeutic agents are desperately needed. Because of the frequent mutations in the virus and its ability to cause cytokine storms, targeting the viral proteins has some drawbacks. Targeting cellular factors or pivotal inflammatory pathways triggered by SARS-CoV-2 may produce a broader range of therapies. Glycyrrhizic acid (GA) might be beneficial against SARS-CoV-2 because of its anti-inflammatory and antiviral characteristics and possible ability to regulate crucial host factors. However, the mechanism underlying how GA regulates host factors remains to be determined.</p>
<p>
<bold>Methods:</bold> In our report, we conducted a bioinformatics analysis to identify possible GA targets, biological functions, protein-protein interactions, transcription-factor-gene interactions, transcription-factor-miRNA coregulatory networks, and the signaling pathways of GA against COVID-19.</p>
<p>
<bold>Results:</bold> Protein-protein interactions and network analysis showed that ICAM1, MMP9, TLR2, and SOCS3 had higher degree values, which may be key targets of GA for COVID-19. GO analysis indicated that the response to reactive oxygen species was significantly enriched. Pathway enrichment analysis showed that the IL-17, IL-6, TNF-&#x3b1;, IFN signals, complement system, and growth factor receptor signaling are the main pathways. The interactions of TF genes and miRNA with common targets and the activity of TFs were also recognized.</p>
<p>
<bold>Conclusions:</bold> GA may inhibit COVID-19 through its anti-oxidant, anti-viral, and anti-inflammatory effects, and its ability to activate the immune system, and targeted therapy for those pathways is a predominant strategy to inhibit the cytokine storms triggered by SARS-CoV-2 infection.</p>
</abstract>
<kwd-group>
<kwd>coronavirus disease 2019</kwd>
<kwd>SARS-CoV-2</kwd>
<kwd>glycyrrhizic acid</kwd>
<kwd>network pharmacology</kwd>
<kwd>inflammatory pathways</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), is the greatest public health crisis nowadays. According to the World Health Organization (<ext-link ext-link-type="uri" xlink:href="https://covid19.who.int/">https://covid19.who.int/</ext-link>), as of January 21, 2020, there were 95,612,831 confirmed cases worldwide and 2,066,176 confirmed deaths. Novel therapeutic agents and vaccines are desperately needed to address the COVID-19 crisis. Many studies conducted in clinical settings have shown that traditional Chinese medicine plays an essential role in the treatment of COVID-19. Seen through the lens of conventional Western medicine, it has offered new medical solutions to control the disease (<xref ref-type="bibr" rid="B22">Huang et&#x20;al., 2020</xref>). Many chemical components of Chinese medicine have been confirmed to provide specific health effects, including anti-inflammatory and antiviral activity <italic>in&#x20;vitro</italic> and <italic>in vivo</italic> (<xref ref-type="bibr" rid="B12">Chu et&#x20;al., 2020</xref>). Glycyrrhizic acid (GA) might be beneficial against SARS-CoV-2 for its anti-inflammatory and antiviral characteristics (<xref ref-type="bibr" rid="B53">Siddiqui et&#x20;al., 2020</xref>). GA, a triterpene saponin, is considered the principal component in <italic>Glycyrrhizae Radix et Rhizoma</italic> with various biological functions and pharmacological effects, including a wide spectrum of anti-inflammatory activity, antiviral activity and immunomodulatory effects. Several of the biochemical, molecular, and cellular mechanisms underlying the action of GA has also been explored in experimental studies. GA has been shown to abolish the expression of ICAM-1 upregulation, reduce the amounts of TNF-&#x3b1; and IL-1&#x3b2; in the plasma, reduce STAT-3 activation, and suppress NF-&#x3ba;B activation in inflammation (<xref ref-type="bibr" rid="B17">Di Paola et&#x20;al., 2009</xref>).</p>
<p>Particularly, one study in <italic>Nature</italic> indicated that GA inhibits viral growth and inactivates viral particles (<xref ref-type="bibr" rid="B46">Pompei et&#x20;al., 1979</xref>), in addition to inhibiting virus replication. A study in <italic>Lancet</italic> revealed that GA was the most active in inhibiting replication of the SARS-associated virus especially the adsorption and penetration of viruses early in the replication cycle (<xref ref-type="bibr" rid="B13">Cinatl et&#x20;al., 2003</xref>). A study in <italic>Phytomedicine</italic> has shown that GA exerted inhibitory activity against the spike protein of SARS-CoV-2 (<xref ref-type="bibr" rid="B64">Yu et&#x20;al., 2020</xref>). Underlying mechanisms of GA against COVID-19 has also been explored in several bioinformatic studies (<xref ref-type="bibr" rid="B9">Chen et&#x20;al., 2020a</xref>; <xref ref-type="bibr" rid="B32">Li R. et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B45">Patil et&#x20;al., 2021</xref>). A systematic review of five retrospective cohort studies has indicated that GA could be an optional therapeutic strategy for SARS-CoV-2 infections (<xref ref-type="bibr" rid="B30">Li H. et&#x20;al., 2020</xref>). Importantly, there is one on-going pilot, non-randomized clinical study with GA as complementary medicine in patients with COVID-19 (NCT04487964).</p>
<p>GA induces cholesterol-dependent disintegration of lipid rafts at the membrane level, which is important for hindering the entry of coronaviruses into cells. At the intracellular and circulating levels, GA can capture the high mobility group box 1 protein (HMGB1), thereby blocking the alarm function itself (<xref ref-type="bibr" rid="B3">Bailly and Vergoten, 2020</xref>). Researchers performed genome-wide CRISPR screens with of SARS-CoV-2, and they demonstrated that HMGB1 regulated ACE2 transcription, which is indispensable for the entry of SARS-CoV-2 (<xref ref-type="bibr" rid="B62">Wei et&#x20;al., 2020</xref>). In terms of clinical evidence, it has been reported that elevated serum HMGB1 on admission is associated with poor clinical prognosis of COVID-19 patients (<xref ref-type="bibr" rid="B10">Chen et&#x20;al., 2020b</xref>). Extracellular HMGB1 may be an attractive potential therapeutic target for severe lung inflammation such as COVID-19 (<xref ref-type="bibr" rid="B2">Andersson et&#x20;al., 2020</xref>). In COVID-19 patients, the host response failed to launch a strong IFN response while a waning immune response would enable sustained viral replication and further simultaneously induced high levels of chemokines (<xref ref-type="bibr" rid="B6">Blanco-Melo et&#x20;al., 2020</xref>). And this is closely related to the severity of SARS-CoV-2 infection. Excitingly, GA contributes to the stimulation of IFN-gamma production by T&#x20;cells and exerted a protective effect on mice exposed to a lethal dose of influenza virus as has been previously shown <italic>in vivo</italic> study (<xref ref-type="bibr" rid="B57">Utsunomiya et&#x20;al., 1997</xref>).</p>
<p>Identifying host genes that are critical to SARS-CoV-2 infection may identify new therapeutic targets and help us understand the pathogenesis of COVID-19. The superiority of targeting intracellular molecules rather than viral proteins is that mutations in the viral genome are unlikely to counteract their effects. However, the mechanism by which genes other than HMGB1 regulate viral infection remains to be verified. Many effective drugs work by modulating multiple proteins rather than a single target. Network pharmacology integrates a variety of modern publicly available database resources and collects a full range of biomedical information such as drugs, compounds, genes, and disease targets to build a multilevel complex network of drug-target-disease interactions, to reveal the mechanism of natural compounds from Chinese Medicine that have multiple components and multiple targets in the human body. In this way, network pharmacology is becoming the next paradigm for drug discovery, especially in the field of Chinese medicine (<xref ref-type="bibr" rid="B20">Hopkins, 2008</xref>). Based on this network pharmacology, we here to explore possible mechanism by which GA affects for COVID-19 by targeting pivotal inflammatory pathways of the host triggered by SARS-CoV-2.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>Materials and Methods</title>
<sec id="s2-1">
<title>GA Targets and COVID-19-Related Genes</title>
<p>We collected potential targets of GA in various databases, including SwissTargetPrediction database (STP, version: 2019, <ext-link ext-link-type="uri" xlink:href="http://www.swisstargetprediction.ch/">http://www.swisstargetprediction.ch</ext-link>) (<xref ref-type="bibr" rid="B15">Daina et&#x20;al., 2019</xref>), STITCH (version: 5.0, <ext-link ext-link-type="uri" xlink:href="http://stitch.embl.de/">http://stitch.embl.de/</ext-link>) (<xref ref-type="bibr" rid="B54">Szklarczyk et&#x20;al., 2016</xref>), PharmMapper (version: 2017, <ext-link ext-link-type="uri" xlink:href="http://www.lilab-ecust.cn/pharmmapper/">http://www.lilab-ecust.cn/pharmmapper/</ext-link>) (<xref ref-type="bibr" rid="B60">Wang et&#x20;al., 2017</xref>), and GEO Dataset (GSE85871 and GSE67504) (<xref ref-type="bibr" rid="B21">Hsiang et&#x20;al., 2015</xref>; <xref ref-type="bibr" rid="B36">Lv et&#x20;al., 2017</xref>), those differentially expressed genes (DEGs) from gene expression profiles in traditional Chinese medicine components (GA) were regarded as potential effector targets as well. DEGs for GSE85871 and GSE67504 datasets were analyzed by GEO2R (<ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/geo/geo2r/">https://www.ncbi.nlm.nih.gov/geo/geo2r/</ext-link>). In GEO2R, the Benjamini and Hochberg false discovery rate (FDR) method is selected by default and adj-<italic>p</italic>-value significance level cut-off is&#x20;0.05.</p>
<p>COVID-19-related targets were gathered from DEGs by analyzing available transcriptomic RNA-seq COVID-19 data (GSE147507, <ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507">https://www.ncbi.nlm.nih.gov/geo</ext-link>) (<xref ref-type="bibr" rid="B6">Blanco-Melo et&#x20;al., 2020</xref>), it contains three infected primary lung epithelial samples and three uninfected samples. Screening criteria for DEGs were FDR &#x3c; 0.05 and log2FC (absolute) &#x2009;&#x2265;1, and genes that met the inclusion criteria will be used in further analysis (<xref ref-type="bibr" rid="B23">Islam et&#x20;al., 2020</xref>). The common gene identification between GA targets and COVID-19-related targets were obtained using the online tool (<ext-link ext-link-type="uri" xlink:href="http://bioinformatics.psb.ugent.be/webtools/Venn/">http://bioinformatics.psb.ugent.be/webtools/Venn/</ext-link>) and FunRich (<ext-link ext-link-type="uri" xlink:href="http://www.funrich.org/">http://www.funrich.org/</ext-link>) (<xref ref-type="bibr" rid="B44">Pathan et&#x20;al., 2015</xref>).</p>
</sec>
<sec id="s2-2">
<title>Protein-Protein Interaction Analysis and Network Construction</title>
<p>Common genes were inserted in String database (version 11.0, <ext-link ext-link-type="uri" xlink:href="https://string-db.org/">https://string-db.org/</ext-link>) (<xref ref-type="bibr" rid="B55">Szklarczyk et&#x20;al., 2019</xref>) for generating PPI network. Minimum required interaction score was medium confidence (0.4). The PPI results were analyzed and visualized through Cytoscape (version: 3.8.1, <ext-link ext-link-type="uri" xlink:href="https://cytoscape.org/">https://cytoscape.org/</ext-link>) (<xref ref-type="bibr" rid="B52">Shannon et&#x20;al., 2003</xref>). The cytoHubba software (<ext-link ext-link-type="uri" xlink:href="http://apps.cytoscape.org/apps/cytohubba">http://apps.cytoscape.org/apps/cytohubba</ext-link>) (<xref ref-type="bibr" rid="B11">Chin et&#x20;al., 2014</xref>) and CentiScaPe software (version 2.2, <ext-link ext-link-type="uri" xlink:href="http://apps.cytoscape.org/apps/centiscape">http://apps.cytoscape.org/apps/centiscape</ext-link>) (<xref ref-type="bibr" rid="B51">Scardoni et&#x20;al., 2009</xref>) plugins in Cytoscape were used to performed network topology analysis.</p>
</sec>
<sec id="s2-3">
<title>Gene Ontology and Pathway Enrichment Analysis</title>
<p>We used the ClueGO (version 2.5.4) (<xref ref-type="bibr" rid="B39">Mlecnik et&#x20;al., 2019</xref>) and CluePedia (version 1.5.4) (<xref ref-type="bibr" rid="B4">Bindea et&#x20;al., 2013</xref>) to perform Wiki and KEGG pathway enrichment analysis of GA targets. We chose two-sided hypergeometric test and bonferroni step down for <italic>p</italic> value correction (<italic>p</italic>&#x20;&#x3c; 0.05). GO terms and all the pathways enrichment including COVID-19 related gene sets and BioCarta databases were also obtained through Enrichr (<ext-link ext-link-type="uri" xlink:href="https://maayanlab.cloud/Enrichr/">https://maayanlab.cloud/Enrichr/</ext-link>) (<xref ref-type="bibr" rid="B28">Kuleshov et&#x20;al., 2016</xref>) for the common genes between GA and COVID-19. Enrichr performed Fisher exact test for random gene sets and adj.<italic>p</italic>.value &#x3c; 0.05 were regarded as statistically significant.</p>
</sec>
<sec id="s2-4">
<title>Analysis of TF (Transcription factor)-Gene Interactions and TF-miRNA Co-regulatory Network</title>
<p>NetworkAnalyst tool (version 3.0, <ext-link ext-link-type="uri" xlink:href="https://www.networkanalyst.ca/">https://www.networkanalyst.ca/</ext-link>) (<xref ref-type="bibr" rid="B66">Zhou et&#x20;al., 2019</xref>) was used to evaluate the TF-gene interplay with common genes between GA and COVID-19 related targets. The basic data for the TF-gene interplay network was acquired from the ENCODE database (<ext-link ext-link-type="uri" xlink:href="https://www.encodeproject.org/">https://www.encodeproject.org/</ext-link>). Only peak intensity signal &#x3c;500 and the predicted regulatory potential score &#x3c;1 were used (by using BETA Minus algorithm) (<xref ref-type="bibr" rid="B59">Wang et&#x20;al., 2013</xref>). Regarding TF-miRNA co-regulation network, the interplays for TF-miRNA co-regulation were collected from the RegNetwork repository database (version 2019, <ext-link ext-link-type="uri" xlink:href="http://www.regnetworkweb.org/">http://www.regnetworkweb.org/</ext-link>) (<xref ref-type="bibr" rid="B33">Liu et&#x20;al., 2015</xref>) and we visualized it by using NetworkAnalyst. The current network will help to disclose miRNAs and TFs that regulated the shared genes at the post-transcriptional and transcriptional levels.</p>
</sec>
<sec id="s2-5">
<title>Inferring Upstream Pathway Activity</title>
<p>Extracting the activity of signal transduction pathways from transcriptome data is important for inferring the mechanism origin of abnormal transcriptome regulation. So we used SPEED2 (<ext-link ext-link-type="uri" xlink:href="https://speed2.sys-bio.net/">https://speed2.sys-bio.net/</ext-link>) (<xref ref-type="bibr" rid="B50">Rydenfelt et&#x20;al., 2020</xref>) to infer upstream pathway activity from common genes between GA and COVID-19. When providing a list of genes, the Web server allows inferences about signaling pathways that may cause these genes to be dysregulated. We tested if average gene rank was shifted away from 0 using the Bates distribution on [&#x2212;1,&#x20;1].</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec id="s3-1">
<title>Target Identification of GA</title>
<p>100 potential targets were predicted from SWISS, 50 from STITCH, and 283 from PharmMapper. 364 DEGs were identified from GSE85871, and 389 DEGs from GSE67504 (<xref ref-type="sec" rid="s10">Supplementary Table S1</xref>). Therefore, 1,125 unique potential targets of GA were scratched together after the removal of duplications. The GA targets of each dataset were shown in <xref ref-type="fig" rid="F1">Figure&#x20;1</xref>.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>
<bold>(A)</bold> Venn diagram based on Draw Venn Diagram Tool, showing the overlapping relationship of GA potential targets from five datasets. <bold>(B)</bold> Venn diagram based on FunRich Tool, showing the constituent ratio of GA potential targets from five datasets.</p>
</caption>
<graphic xlink:href="fphar-12-631206-g001.tif"/>
</fig>
</sec>
<sec id="s3-2">
<title>Pathway Enrichment Analysis of GA Targets</title>
<p>KEGG pathway enrichment analysis showed that GA targets involved in 37 pathways. Those targets were meaningfully enriched in multiple pathways such as RAS, MAPK, PI3K-Akt, VEGF, IL-17, and T&#x20;cell receptor signaling pathways (<xref ref-type="fig" rid="F2">Figure&#x20;2A</xref> and <xref ref-type="sec" rid="s10">Supplementary Table S2</xref>). The 49 enriched Wiki pathways were involved in estrogen receptor, MAPK, TNF related weak inducer of apoptosis, IL-7, IL-6, IL-3, B&#x20;Cell receptor signaling pathway, signaling of hepatocyte growth factor receptor, and PI3K-Akt-mTOR-signaling pathways (<xref ref-type="fig" rid="F2">Figure&#x20;2B</xref>, <xref ref-type="sec" rid="s10">Supplementary Table&#x20;S3</xref>).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>
<bold>(A)</bold> KEGG pathways enrichment analysis of GA targets (<italic>p</italic> values &#x3c; 0.05) <bold>(B)</bold> Wiki pathways enrichment analysis of GA targets (<italic>p</italic> values &#x3c; 0.05).</p>
</caption>
<graphic xlink:href="fphar-12-631206-g002.tif"/>
</fig>
</sec>
<sec id="s3-3">
<title>Protein-Protein Interaction and Network Analysis</title>
<p>We screened the GSE147507 dataset to identify 177 COVID-19-associated genes. Compared with GA target, venn diagrams revealed 14 common targets against COVID-19 (<xref ref-type="fig" rid="F3">Figure&#x20;3A</xref>). The shared targets were loaded in String and the documents generated by its analysis were re-introduced into Cytoscape for further network topology analysis and visual representation (<xref ref-type="fig" rid="F3">Figure&#x20;3B</xref>, <xref ref-type="sec" rid="s10">Supplementary Table S4</xref>). Among those common targets, ICAM1, MMP9,TLR2, and SOCS3 had higher degree values (<xref ref-type="fig" rid="F4">Figure&#x20;4</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>
<bold>(A)</bold> Fourteen shared targets were found common from the 117 DEGs of COVID-19 and 1,125 targets of GA. <bold>(B)</bold> Protein-protein interactions network and network topology analysis. Deeper colors represent higher degree values.</p>
</caption>
<graphic xlink:href="fphar-12-631206-g003.tif"/>
</fig>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Topological result of GA targets on COVID-19. <bold>(A)</bold> The ordinate indicates the degree, and the abscissa indicates the degree of betweenness <bold>(B)</bold> Network topology analysis parameter of ICAM1. <bold>(C)</bold> Network topology analysis information of MMP9.</p>
</caption>
<graphic xlink:href="fphar-12-631206-g004.tif"/>
</fig>
</sec>
<sec id="s3-4">
<title>Gene Annotation of Common Targets</title>
<p>We identified 14 shared targets of GA related to COVID-19 (MMP13, SOD2, PPARGC1A, EPSTI1, SOCS3, PDK4, MAOB, SERPINB4, ICAM1, DNAH9, CFB, TLR2, TYMP, MMP9). Enrichr tool was used to perform the analysis of gene set enrichment including GO terms, pathways and COVID-19 related gene sets. The GO enrichment study illustrated the three parts (biological process, molecular functions, and cellular component) of the top 10 GO terms. The pathway enrichment study illustrated the KEGG, WikiPathways, Reactome, and BioCarta of the top 10 pathway terms (<xref ref-type="fig" rid="F5">Figure&#x20;5</xref> and <xref ref-type="sec" rid="s10">Supplementary Table&#x20;S5</xref>).</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Pathway enrichment analysis from KEGG, WikiPathways, Reactome and BioCarta databases. GO terms enrichment analysis including biological process, molecular function and cellular component aspects. All the results along with COVID-19 related gene sets were identified through the <italic>p</italic>&#x20;value.</p>
</caption>
<graphic xlink:href="fphar-12-631206-g005.tif"/>
</fig>
</sec>
<sec id="s3-5">
<title>TF-Gene Interplays</title>
<p>TF-gene interplays were constructed by NetworkAnalyst tool. The selected targets of the previous analysis were matched to the corresponding molecular interaction database. Subnetworks with at least three nodes were listed and visualized below (<xref ref-type="fig" rid="F6">Figure&#x20;6</xref>). The network consisted of 189 nodes and 325 edges. Targets in the T-F network were associated with regulating immunity and anti-inflammatory effect. ICAM1 was regulated by 69&#xa0;TF genes and MMP9 was regulated by 22 TF&#x20;genes.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Network for transcription factor gene interplay with shared targets.</p>
</caption>
<graphic xlink:href="fphar-12-631206-g006.tif"/>
</fig>
</sec>
<sec id="s3-6">
<title>TF-miRNA Co-regulation Network</title>
<p>TF-miRNA co-regulation network was performed by NetworkAnalyst tool. Subnetworks with at least three nodes were shown below (<xref ref-type="fig" rid="F7">Figure&#x20;7</xref>). The network consisted of 270 nodes and 343 edges. This interplay may be responsible for regulating the expression of the above shared targets. The degree of MMP9, ICAM1 and SOCS3 were 35, 31 and 29, respectively.</p>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption>
<p>The network presents the transcription factor-miRNA coregulatory network. The nodes in yellow are the shared targets, a purple node displays miRNA and other nodes in red color represent transcription factor -genes.</p>
</caption>
<graphic xlink:href="fphar-12-631206-g007.tif"/>
</fig>
</sec>
<sec id="s3-7">
<title>Upstream Pathway Activity</title>
<p>SPEED2 offered an easy method to score signaling activity for sets of dysregulated genes gained from transcriptome analysis. To infer the upstream pathway activity from common targets, we evaluated the consistently up and down-regulated genes after pathway perturbation, and then we scored the genes. The ranked lists were determined by <italic>p</italic>-value, the more significant genes are up-regulated, the higher the ranking, the most significantly down-regulated genes matched to the bottom of the list. Colors showed FDR-adjusted <italic>p</italic>-values (<xref ref-type="fig" rid="F8">Figure&#x20;8</xref> and <xref ref-type="sec" rid="s10">Supplementary Table&#x20;S6</xref>).</p>
<fig id="F8" position="float">
<label>FIGURE 8</label>
<caption>
<p>Pathway activity ranking.</p>
</caption>
<graphic xlink:href="fphar-12-631206-g008.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>The results of pathway enrichment analysis showed that the KEGG and WIKI pathways of GA targets were mainly involved in multiple inflammation- and immune-related signaling pathways, including PI3K- AKT, IL-17, and MAPK signaling pathways, as well as complement and coagulation cascades and inflammatory mediator regulation. Activation of the coagulation pathway during the immune response to viral infection leads to the overproduction of pro-inflammatory cytokines, which leads to multiple organ damage. The interplay between inflammation and coagulation may contribute to COVID-19 cytokine storm (<xref ref-type="bibr" rid="B24">Jose and Manuel, 2020</xref>). The sustained inflammatory response caused by SARS-CoV-2 can cause cytokine storms that lead to activation of coagulation and complement cascades and this may trigger an acute respiratory distress syndrome (ARDS) (<xref ref-type="bibr" rid="B61">Wang et&#x20;al., 2020</xref>). Research revealed that GA is a selective inhibitor of thrombin, thus it is possible that changes in this pathway impact SARS-CoV-2 infection process, as has been previously shown for antithrombotic effect (<xref ref-type="bibr" rid="B38">Mendes-Silva et&#x20;al., 2003</xref>).</p>
<p>GA is a triterpene saponin with various pharmacological effects. It has been found to inhibit viral replication of SARS-CoV <italic>in&#x20;vitro</italic>, GA has been found to inhibit adsorption and penetration of the early stages of the viral replication (<xref ref-type="bibr" rid="B13">Cinatl et&#x20;al., 2003</xref>). Regarding the mechanism underlying the action, nitrous oxide may be responsible for inhibiting viral replication. Other studies have shown that inhibiting the TLR-4/NF-&#x3ba;B signaling pathway may be a mechanism of GA action, in which proinflammatory cytokines are inhibited during the initial phase of the inflammatory response (<xref ref-type="bibr" rid="B29">Lee et&#x20;al., 2019</xref>). GA inhibited TLR2, which inactivated TLR signaling pathway to reduce lipopolysaccharide-induced acute lung injury (<xref ref-type="bibr" rid="B27">Kong et&#x20;al., 2019</xref>). One study indicated that GA inhibits the increase in inflammatory factors in ALI by regulating the PI3K/AKT/mTOR pathway (<xref ref-type="bibr" rid="B47">Qu et&#x20;al., 2019</xref>). GA regulated the expression of inflammatory cytokines and CXCL-2 by blocking the IL-17/STAT3 pathway, thereby inhibiting neutrophil airway inflammation (<xref ref-type="bibr" rid="B25">Kim et&#x20;al., 2020</xref>). GA alleviated the inflammation by reducing neutrophil and macrophage infiltration in lung tissue and the multiple cytokines including HMGB1, TNF-&#x3b1;, IL-1&#x3b2;, IL-6, and IL-8 in serum and lung tissue (<xref ref-type="bibr" rid="B63">Yao and Sun, 2019</xref>). Because reactive oxygen species (ROS) play a key role in the inflammatory response, antioxidants may also be useful against cytokine storms caused by infections (<xref ref-type="bibr" rid="B34">Liu et&#x20;al., 2016</xref>), and GA has a pronounced ROS quenching activity, blocking the cascade of incidents including MAPK pathway and cell death (<xref ref-type="bibr" rid="B18">Farrukh et&#x20;al., 2015</xref>). Furthermore, GA itself has the properties of anti-inflammatory via TLR4 antagonism so as to compensate for the reduced protection of ACE2 downregulation (<xref ref-type="bibr" rid="B41">Murck, 2020</xref>). These mutually dependent results indicated that GA may have therapeutic benefits for COVID-19 by targeting several core inflammatory pathways of the host triggered by SARS-CoV-2, as has been previously shown for anti-inflammation effect in ARDS and&#x20;ALI.</p>
<p>Regarding the key targets of GA for COVID-19. Compared with the analysis of SARS-CoV-2 affected lung tissue samples, we obtained 14 common targets of GA against COVID-19 (MMP13, SOD2, PPARGC1A, EPSTI1, SOCS3, PDK4, MAOB, SERPINB4, ICAM1, DNAH9, CFB, TLR2, TYMP, MMP9), those overlapping targets of GA-related targets and SARS-CoV-2 DEGs were considered as potential targets, the results of protein-protein interaction and network analysis showed that ICAM1, MMP9, TLR2, and SOCS3 had higher degree values, which may be core targets of GA for COVID-19 treatment. Then we picked those 14 targets into Enrichr tool to perform gene annotation.</p>
<p>A basic element in our study was harnessing common targets between GA and COVID-19 to develop a more extended therapeutic hypothesis. Our study suggests that ICAM1, MMP9, TLR2, and SOCS3 are a promising intervention target: SARS-CoV-2 infection aggravates the endothelin induction in multiple organs of COVID-19 patients via viral involvement in the host inflammatory and immune response, and the presence of elements within endothelial cells and the gathering of inflammatory cells (<xref ref-type="bibr" rid="B58">Varga et&#x20;al., 2020</xref>). The results of clinical research suggested that in severe COVID-19 patients, intercellular adhesion molecule 1 (ICAM-1), also known as cluster of differentiation 54 (CD54), was significantly elevated, but it was reduced during the recovery period. The increased expression of endothelial cell adhesion molecules like ICAM1 is related to the severity of COVID-19 and may lead to coagulation dysfunction (<xref ref-type="bibr" rid="B56">Tong et&#x20;al., 2020</xref>). Activated endothelial cells contribute to the expression of ICAM-1, which may attract leukocytes in addition to transmitting intracellular signals, resulting in a sustained pro-inflammatory state. The continuing inflammatory signals of these adhesion molecules can also cause thrombotic events (<xref ref-type="bibr" rid="B42">Nagashima et&#x20;al., 2020</xref>).</p>
<p>The hub node MMP9 is an immunogenic protease related to cell-matrix degradation. It was initially thought that the main function of matrix metalloproteinases (MMPs) was to degrade various components of the extracellular matrix (ECM), but recent studies have shown that they were important regulators of signal transduction networks in extracellular tissues (<xref ref-type="bibr" rid="B35">Loffek et&#x20;al., 2011</xref>). Macrophages produce many inflammation-associated molecules, released by MMPs, such as adhesion molecules and cytokines (<xref ref-type="bibr" rid="B31">Li K. et&#x20;al., 2020</xref>). MMP9 producing mononuclear cells enable T&#x20;cells to invade the vascular wall and trigger vasculitis. The production of proinflammatory cytokines and vascular inflammation was mediated through TLRs, NLRs and other pattern recognition receptors. Particularly, viral particles of SARS-CoV-2 infection may contribute to TLRs activation directly or indirectly (<xref ref-type="bibr" rid="B5">Biswas and Khan, 2020</xref>). SARS-CoV-2-mediated TLRs activation was blamed for the later activation of the extrinsic coagulation pathway, which ultimately resulted in thrombosis.</p>
<p>In GO BP analysis, response to reactive oxygen species (ROS) was significantly enriched. Interestingly, it has been demonstrated that GA exerts an anti-inflammatory effect by reducing ROS production in neutrophils (<xref ref-type="bibr" rid="B1">Akamatsu et&#x20;al., 1991</xref>). The current cellular components analysis of GO enrichment displayed the connection of MMP9 with function of lumens releasing tertiary granules. We found that the MMP9 was related to serine protease activities in molecular function aspect. Those results were consistent with previous researches (<xref ref-type="bibr" rid="B7">Cassatella et&#x20;al., 2019</xref>). TLR-2, TLR-3, and TLR-4 activation by SARS-CoV-2 triggered the release procedure of IL-1&#x3b2;. The binding of it to TLRs contributed to the release of pro-IL-1&#x3b2;, the activation of inflammatory, and the production of mature IL-1&#x3b2;. What&#x2018;s more, plasmin activated MMP9 in monocytes and macrophages level, all of these further responds with TLR9 signaling to induce the generation of TNF that caused the expansion of cytokine storm (<xref ref-type="bibr" rid="B19">Gomez-Salinero and Rafii, 2017</xref>).</p>
<p>Regarding the pathway enrichment analysis, the present study shows that the IL-17, IL-6, TNF-&#x3b1;, and IFN signals, complement system, and growth factor receptor signaling were major pathways by which GA targets COVID-19. In ARDS, by stimulating the production of pro-inflammatory mediators, IL-17 increased the damage to lung parenchyma through the recruitment of maladaptive neutrophils (<xref ref-type="bibr" rid="B40">Muir et&#x20;al., 2016</xref>). What&#x2019;s more, the addition of exogenous IL-17 further aggravated the production of TNF, IL-1&#x3b2;, IL-6, and CXCL2 induced by LPS. These analyses showed that IL-17 can be used not only as a biomarker of disease severity but also as a potential therapeutic target to reduce SARS-CoV-2, particularly the damage to the lungs (<xref ref-type="bibr" rid="B43">Pacha et&#x20;al., 2020</xref>). IL-6 levels are associated with respiratory failure and poor prognosis. Most studies hold the view of blocking IL-6 and other signals and cytokines in the early period was beneficial for specific and adverse immune responses in COVID-19 patients (<xref ref-type="bibr" rid="B14">Copaescu et&#x20;al., 2020</xref>). The complement system plays an important role in the innate immune response to all viruses including SARS-CoV-2, which is also an attractive therapeutic target in COVID-19 by decreasing the severity and end-organ outcomes of this kind of response (<xref ref-type="bibr" rid="B49">Risitano et&#x20;al., 2020</xref>). The formation of IFNs is a universal host response after viral infection. IFN signals are the primary and front line signals, which launch a fast antiviral response in host cells (<xref ref-type="bibr" rid="B8">Catanzaro et&#x20;al., 2020</xref>). Well known that TNF-&#x3b1;, performing as a megaphone of inflammation, is crucial in nearly all kinds of acute inflammatory responses, including COVID-19. TNF-&#x3b1; serum levels in SARS-CoV-2 infection served as independent and sensitive predictors of disorders severity and mortality (<xref ref-type="bibr" rid="B16">Del Valle et&#x20;al., 2020</xref>). Inhibiting the growth factor receptor signaling pathway may prevent SARS-CoV-2 replication, which suggested practical strategies for COVID-19 treatment (<xref ref-type="bibr" rid="B26">Klann et&#x20;al., 2020</xref>). From those KEGG and WIKI pathway terms, results showed that targets of GA were significantly enriched in these pathways. Therefore, in the future clinical management of severe COVID-19 cases, the regulations of the mechanism behind the inflammation immune dysregulation of GA against SARS-CoV-2 might give us therapy clues for preventing the transition from mild to severe stages (<xref ref-type="bibr" rid="B30">Li H. et&#x20;al., 2020</xref>).</p>
<p>TF gene interplay was gained with the 14 shared targets. The TF genes served as regulators in accordance with genetic expressions. In this study, we found that ICAM1, MMP9, and TLR2 showed a high interplay degree with other TF genes. The degree of ICAM1 in this network is 69. Among the regulators in the TF gene interaction network, STAT3 and MMP9 together with TLR2 have significant interaction. STAT3 transcriptionally induces suppressor both SOCS1 and SOCS3, which later inhibit JAKs activity, and the viral components of SARS-CoV-2 induce compensatory hyperactivation of STAT3, which may lead to thrombosis, so an abnormal STAT signaling pathway is considerable in SARS-CoV-2 infection (<xref ref-type="bibr" rid="B37">Matsuyama et&#x20;al., 2020</xref>). This pathological phenomenon toward STAT3 may contribute to the clinical manifestations most common with COVID-19 patients in the hospital, such as coagulopathy, proinflammatory status, profibrotic condition, and T&#x20;cell lymphopenia. Based on the shared targets, we generated the TF-miRNA co-regulatory network that involved the interaction of miRNAs and TF genes. We found that PPARGC1A had the highest degree. The protein of this gene is a transcriptional co-activator that can regulate genes related to energy metabolism, so that the protein interacts with a variety of transcription factors, and may also participate in the control of blood pressure and the homeostasis of cellular cholesterol. The regulation of TF genes is finished by binding with targeted genes and miRNAs, they are able to regulate gene expression through mRNA degradation (<xref ref-type="bibr" rid="B65">Zhang et&#x20;al., 2015</xref>). At last, we scored signaling activity for dysregulated genes obtained from the above network analysis. We found the majority of common targets between GA and COVID-19 to be upregulated in the IL-1, VEGF, JAK-STAT, TNF&#x3b1;, and TLR pathway families. This was consistent with our other research conclusions.</p>
<p>Taken together, our integrative study identified essential targets of GA in COVID-19. The discovery of vital signal pathways that reformed during SARS-CoV-2 infection may help reveal the most meaningful molecular protein interaction that mediates viral infections (<xref ref-type="bibr" rid="B8">Catanzaro et&#x20;al., 2020</xref>). Currently, a limited number of vaccines have been approved and the process of vaccination is going or being planned in few countries (<xref ref-type="bibr" rid="B48">Rabaan et&#x20;al., 2020</xref>). Potential effective therapeutic agents are desperately needed in future clinical practice. Targeting pathways triggered by SARS-CoV-2 infection is an appropriate strategy for the treatment of COVID-19. It also inhibits cytopathic effects. Fortunately, GA may exert a therapeutic function in COVID-19 by suppressing SARS-CoV-2 infection through its combination of antioxidative, antiviral, and anti-inflammatory effects, especially activation of the immune system.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>Conclusion</title>
<p>We used network pharmacology and bioinformatic analysis to determine the pharmacological mechanism by which GA affects COVID-19. GA may be a suitable molecular drug for ameliorating excessive inflammation triggered by SARS-CoV-2 through inhibition of the IL-17, IL-6, and TNF-&#x3b1; signaling pathways. Although solid proof of GA application on COVID-19 remains unclear, the anti-inflammatory and antiviral effects of GA have been proven in SARS-CoV related and other numerous studies. We found that targeting critical inflammatory pathways of SARS-CoV-2 infection may provide a suitable strategy to tackle the cytokine storms produced by COVID-19. The natural product GA is likely to be beneficial for the patients. However, the study of mechanisms should be based on the confirmed effect of GA on COVID-19 and SARS-CoV-2. These results need immediate further strategies like further experimental validation. This will contribute a lot for GA to be a successful molecular drug against the COVID-19 pandemic.</p>
</sec>
</body>
<back>
<sec id="s6">
<title>Data Availability Statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s10">Supplementary Material</xref>, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>WZ conceived and designed the work; XH and YL collected the data and performed the analysis; WZ wrote the original draft; XL, YJ and SZ reviewed and revised the manuscript. All authors approved and agreed to be responsible for all aspects of the&#x20;work.</p>
</sec>
<sec id="s8">
<title>Funding</title>
<p>This research was funded by grants from the the Natural Science Foundation of Guangdong Province (Grant No.2017A030310129), the Key-Area Research and Development Program of Guangdong Province (Grant No. 2020B1111100002), the National Natural Science Foundation of China (Grant No. 81973814 and No.81904132), the Natural Science Foundation of Guangdong Province (Grant No.2020A1515010589), the LX Famous Traditional Chinese Medicine Inheritance Studio from the Traditional Chinese Medicine Bureau of Guangdong Province (Grant No. 201805), Construction Project of the respiratory department National Clinical Medical Research Center (Grant No. 2110200309), 2018 Guangzhou University of Chinese Medicine National University Student Innovation and Entrepreneurship Training Project (Grant No. 201810572038), and 2020 National College Student Innovation and Entrepreneurship Training Project of Guangzhou University of Chinese Medicine (Grant No. 202010572001), the Student Learning Team Incubation Project of Innovation Academy from The First Affiliated Hospital of Guangzhou University of Chinese Medicine (Grant No. 2018XXTD003), and the Technology Research of COVID-19 Treatment and Prevention and Special Project of Traditional Chinese Medicine Application-Research on the platform construction for the prevention and treatment of viral infectious diseases with traditional Chinese medicine (Grant No. 2020KJCX-KTYJ-130).</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of Interest</title>
<p>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.</p>
</sec>
<ack>
<p>We thank the following outstanding researchers for their generous and selfless disclosure of their research data on Chinese medicine components and SARS-CoV-2 infection. Lv C, Wu X, Wang X, et&#x20;al. (GA-related targets, GSE85871, PMID 28336967). Hsiang CY, Lin LJ, Kao ST, et&#x20;al. (GA-related targets, GSE67504, PMID 26141764). Islam T, Rahman MR, Aydin B, et&#x20;al. (COVID-19 related genes, GSE147507, PMID 32971089). Blanco-Melo D, Nilsson-Payant BE, Liu WC, et&#x20;al. (COVID-19 related genes, GSE147507, PMID 32416070). We thank LetPub (<ext-link ext-link-type="uri" xlink:href="http://www.letpub.com">www.letpub.com</ext-link>) for its linguistic assistance during the preparation of this manuscript. We thank the SCI Writing Program at GZUCM 2020 for its valuable assistance. We thank the English courses for Ph. D. candidates of SCI paper writing of Guangzhou University of Chinese Medicine in 2020 (We thank the English teachers Shuaishuai Liu and Guoqi Shi). We thank our team leader professor Xiaohong Liu.</p>
</ack>
<sec id="s10">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fphar.2021.631206/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fphar.2021.631206/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table1.xls" id="SM1" mimetype="application/xls" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>SUPPLEMENTARY TABLE 1</label>
<caption>
<p>GA targets from five databases.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table2.xls" id="SM2" mimetype="application/xls" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>SUPPLEMENTARY TABLE 2</label>
<caption>
<p>KEGG pathways enrichment result of GA targets.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table5.xls" id="SM3" mimetype="application/xls" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>SUPPLEMENTARY TABLE 3</label>
<caption>
<p>WikiPathways enrichment result of GA targets.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table3.xls" id="SM4" mimetype="application/xls" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>SUPPLEMENTARY TABLE 4</label>
<caption>
<p>Protein-protein interaction analysis network of common targets.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table6.xlsx" id="SM5" mimetype="application/xlsx" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>SUPPLEMENTARY TABLE 5</label>
<caption>
<p>Gene annotation of common targets.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table4.xlsx" id="SM6" mimetype="application/xlsx" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>SUPPLEMENTARY TABLE 6</label>
<caption>
<p>Pathway activity ranking.</p>
</caption>
</supplementary-material>

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