Impact Factor 3.201 | CiteScore 3.22
More on impact ›

Editorial ARTICLE

Front. Physiol., 22 November 2019 | https://doi.org/10.3389/fphys.2019.01438

Editorial: Systems Biology and Bioinformatics in Gastroenterology and Hepatology

  • 1Emeritus Professor of Hepatology, Amsterdam University Medical Center, Amsterdam, Netherlands
  • 2Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
  • 3Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
  • 4Division of Molecular Hepatology, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany

How Systems Medicine Improves Our Understanding of Complex Gastroenterological Diseases

Traditional medical research gained tremendous improvements in detection and treatment of acute and chronic metabolic and inflammatory diseases as well as cancer. Especially the field of hepatology and gastroenterology has significantly benefitted from these advances. Indeed, the discovery of basic molecular and cellular disease mechanisms in the last 60 years led to the development of reliable diagnostic tests and effective therapies. For instance, the discovery of the hepatitis B and C viruses (HBV, HCV) led to powerful diagnostic tools, antiviral drugs, and an HBV vaccine (Szmuness et al., 1980, 1981; André, 1990; Lau and Wright, 1993). Indeed, mass vaccination in Taiwan led to a significant reduction of HBV prevalence and hepatocellular carcinoma incidence (Chang et al., 1997, 2016). Furthermore, the development of direct-acting antiviral drugs allows the eradication of HCV (Das and Pandya, 2018). These achievements occurred in a relatively short period of time. For example, HCV was discovered in 1989 and the first effective antiviral therapy for one genotype was developed only 20 years later (Boettler et al., 2019; Viganò et al., 2019; Zajac et al., 2019). Equally, for many monogenetic liver diseases reliable diagnostic tests exist (Lammert, 2016; Weber and Lammert, 2017). Although effective pharmacotherapy for some of these diseases exists (Wilson's disease), comparable treatments are not available for others (e.g., progressive familial intrahepatic cholestasis, PFIC). In this context, translation of gene knock-out mouse models emerged as powerful tool to understand the underlying disease processes (Liu, 2013) and may eventually lead to successful gene therapy.

In contrast to mono-factorial diseases caused by viruses or individual gene alterations, the situation is quite different in more complex multi-factorial diseases. For example non-alcoholic fatty liver disease (NAFLD) was first described by the pathologist Jurgen Ludwig in 1980 (Ludwig et al., 1980) but no effective therapy is currently available, 40 years later (Altinbas et al., 2015; Gottlieb et al., 2019). Despite all recent success in the development of treatments of HBV and HCV, targeting multi-factorial metabolic liver disease like NAFLD or the more serious non-alcoholic steatohepatitis (NASH) is still difficult. It remains unclear if targeting liver disease alone will pave the way to success or if broader approaches will ultimately lead to a decrease in patient's mortality and morbidity. Importantly, a plethora of reasons significantly complicates the systematic analysis of complex diseases:

1) Not single genes, but the identification of gene signatures would probably improve our understanding of NAFLD/NASH development and progression. This complex molecular behavior is usually affected by cellular processes, as well as paracellular communication networks (Gottlieb et al., 2019).

2) The dynamic and/or spatial organization of molecular and biochemical processes is controlled by overarching superior mechanisms (e.g., circadian rhythm) or by gender differences (Hashimoto and Tokushige, 2011; Pan and Fallon, 2014).

3) Cellular signaling pathways form dense interacting networks and it is difficult to predict how changes in one parameter/pathway affect other parameters/pathways under distinct conditions (Teufel et al., 2016; Bessone et al., 2019; Pierantonelli and Svegliati-Baroni, 2019).

4) Complex diseases such as NAFLD/NASH are multi-organ diseases affected by the brain, gut, the gut micro-flora, pancreas, subcutaneous, and abdominal fat (Konturek et al., 2011; Borrelli et al., 2018; Kolodziejczyk et al., 2019; Milosevic et al., 2019).

The multi-scale and multi-stage complexity of NAFLD and NASH, and the necessity to perform one or more liver biopsies to stage and monitor the disease, form a considerable obstacle in the development and application of targeted precision medicine. For instance, the anti-oxidant vitamin E is a recommended therapy in non-diabetic patients with biopsy-proven NASH (Sanyal et al., 2010; Chalasani et al., 2018). In real life only a minority of patients receives this drug (Ratziu et al., 2012). The FXR-agonist obeticholic acid also showed positive results in biopsy-proven non-cirrhotic patients with NASH (Neuschwander-Tetri et al., 2015). The mechanism of action of this drug in NAFLD is unclear and long-term effects and safety need to be assessed. Treatment of cirrhotic patients with this drug cannot be recommended. For successful wide-scale pharmacotherapy programs, non-invasive disease biomarkers are clearly needed.

NAFLD represents a multi-scale disease, in which the entire metabolic program of liver hepatocytes (incl. structures at the molecular level and subcellular organelles), non-parenchymal cells (incl. cholangiocytes, endothelial cells, Kupffer cells, and hepatic stellate cells), and the blood stream (incl. the presence of immune cells and sub-cellular blood components) are critically involved in disease development and progression. In addition, there is dysfunctional temporal communication between organs such as liver, gut, brain, pancreas and fatty tissues. Indeed, NAFLD develops in 20–30 years, starting from “simple” steatosis and progresses to pronounced liver cirrhosis. These dynamic spatial and temporal changes significantly increase the level of complexity and further complicate biomarker and drug development. For instance, pharmacotherapy targeting steatosis may be more effective in early disease stages while drugs that act on inflammation and fibrosis are more suitable at later stages. Once advanced cirrhosis with profound architectural changes of the liver and portal hypertension is established, effective pharmacotherapy becomes even more difficult.

Are computational approaches and systems medicine the solution for complex diseases? Dynamic processes can be described mathematically with a set of differential equations. With a number of these equations, scientists can generate computational models, which can describe the time-resolved behavior of molecular reactions and cellular processes (Schliess et al., 2014; Meyer et al., 2017; Berndt et al., 2018; Hoehme et al., 2018; Lucarelli et al., 2018; Poloznikov et al., 2018; Kockerling et al., 2019). In this process, experimentalists provide quantitative and semi-quantitative data derived from in vitro and in vivo models to feed these mathematical constructs. Once a reliable and robust computational model is established, the model can be used for in silico research, an approach that has the potential to save laboratory animals and to protect people and patients before a drug is used or tested in a clinical setup.

This model-based gain of knowledge leads to a process of iteration and re-iteration between theoretical and experimental scientists until the mathematical model is a reliable proxy of the in vivo situation. Sometimes predictions cannot perfectly reflect the processes observed in living cells or organisms; however, these complications can also lead to new scientific knowledge. One example within one of the biggest systems biology consortia (LiSyM, see below) was the finding that ammonia detoxification was less affected by damaging the centri-zonal glutamine synthase-containing hepatocytes than predicted. These unexpected findings led to the discovery of a novel ammonia detoxification pathway (Schliess et al., 2014).

Since 15 years the German Ministry of Education and Research (BMBF) fosters systems biology and systems medicine by supporting the collaboration of multidisciplinary research groups, including biologists, clinical researchers, and mathematicians, working on liver physiology and liver diseases, including NAFLD. The research network HepatoSys was launched in 2004 to study the processes in liver cells with a systems biology approach. It was Europe's first funding measure in this field. The follow-up project the “Virtual Liver Network (VLN)” took the systems biology liver research to the next biological level. Drawing on the findings at cellular level, the network examined the processes for the whole organ. The initiative was the first systems biology network that focused on an entire organ. The current funding activity “Research Network Systems Medicine of the Liver—LiSyM” builds again on the results produced by HepatoSys and VLN. LiSyM aims to transfer the computational models into clinical application for use as diagnostic tools to assist doctors in choosing the most appropriate therapy. LiSyM and the proceeding initiatives have been successful over the years in developing computational models that help theoreticians and experimentalists to discover new aspects of signaling pathways and mechanisms to test the therapeutic potential of new molecules or biological agents in vivo as in silico (www.lisym.org).

However, opportunities to discuss the diverse facets of systems biology from data generation, utilization of mathematical models, and data integration among experts in the field remain rare. In this regard, we were happy that Frontiers in Physiology provided a platform for such urgently needed discussions and visibility beyond the German networks. The collection of articles in this special issue of Frontiers in Physiology provides examples of the current status of research in gastrointestinal diseases, including NAFLD, alcoholic hepatitis, viral hepatitis, liver fibrosis, and liver cancer, applying systems biology at the level of cells, zones, tissues, networks, and with regard to systemic consequences.

The issue comprises 20 articles from more than 170 authors. From June 2017, where the first article was accepted, until July 30 2019, the manuscripts of the issue have nearly 46,000 views. In more detail, 1 review and 19 original articles are included with nearly half of it (9 in total) from participants of the BMBF-funded networks VLN and LiSyM.

Twelve contributions are related to liver, 3 to colon and gastrointestinal tract and 1 to pancreas, again highlighting the predominant role of the German network in this new scientific field. Experimental data of the contributions include gene expression arrays (4), metabolomics data (6), proteomics data (3), imaging (2), and signal transduction pathways (6). The modeling type of the manuscripts include high throughput data and bioinformatics in 10 cases and mathematical modeling in 9 contributions. We believe that this initiative successfully provided a platform for researchers and clinicians who are interested in systems medicine with focus on gastroenterology and hepatology.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

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.

References

Altinbas, A., Sowa, J. P., Hasenberg, T., and Canbay, A. (2015). The diagnosis and treatment of non-alcoholic fatty liver disease. Minerva Gastroenterol. Dietol. 61, 159–169.

PubMed Abstract | Google Scholar

André, F. E. (1990). Overview of a 5-year clinical experience with a yeast-derived hepatitis B vaccine. Vaccine 8(Suppl.), S74–S78; discussion: S79–S80. doi: 10.1016/0264-410X(90)90222-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Berndt, N., Bulik, S., Wallach, I., Wünsch, T., König, M., Stockmann, M., et al. (2018). HEPATOKIN1 is a biochemistry-based model of liver metabolism for applications in medicine and pharmacology. Nat. Commun. 9:2386. doi: 10.1038/s41467-018-04720-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Bessone, F., Razori, M. V., and Roma, M. G. (2019). Molecular pathways of nonalcoholic fatty liver disease development and progression. Cell. Mol. Life Sci. 76, 99–128. doi: 10.1007/s00018-018-2947-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Boettler, T., Lohmann, V., and Bartenschlager, R. (2019). [Hepatitis C: from individual cure to worldwide elimination?]. Dtsch. Med. Wochenschr. 144, 535–542. doi: 10.1055/a-0837-2424

PubMed Abstract | CrossRef Full Text | Google Scholar

Borrelli, A., Bonelli, P., Tuccillo, F. M., Goldfine, I. D., Evans, J. L., Buonaguro, F. M., et al. (2018). Role of gut microbiota and oxidative stress in the progression of non-alcoholic fatty liver disease to hepatocarcinoma: current and innovative therapeutic approaches. Redox Biol. 15, 467–479. doi: 10.1016/j.redox.2018.01.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Chalasani, N., Younossi, Z., Lavine, J. E., Charlton, M., Cusi, K., Rinella, M., et al. (2018). The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the AmericanAssociation for the Study of Liver Diseases. Hepatology 67, 328–357. doi: 10.1002/hep.29367

PubMed Abstract | CrossRef Full Text | Google Scholar

Chang, M. H., Chen, C. J., Lai, M. S., Hsu, H. M., Wu, T. C., Kong, M. S., et al. (1997). Universal hepatitis B vaccination in Taiwan and the incidence of hepatocellular carcinoma in children. Taiwan childhood hepatoma study group. N. Engl. J. Med. 336, 1855–1859. doi: 10.1056/NEJM199706263362602

PubMed Abstract | CrossRef Full Text | Google Scholar

Chang, M. H., You, S. L., Chen, C. J., Liu, C. J., Lai, M. W., Wu, T. C., et al. (2016). Long-term effects of hepatitis B immunization of infants in preventing liver cancer. Gastroenterology 151, 472–480.e471. doi: 10.1053/j.gastro.2016.05.048

PubMed Abstract | CrossRef Full Text | Google Scholar

Das, D., and Pandya, M. (2018). Recent advancement of direct-acting antiviral agents (DAAs) in hepatitis C therapy. Mini Rev. Med. Chem. 18, 584–596. doi: 10.2174/1389557517666170913111930

PubMed Abstract | CrossRef Full Text | Google Scholar

Gottlieb, A., Mosthael, W., Sowa, J. P., and Canbay, A. (2019). Nonalcoholic-fatty-liver-disease and nonalcoholic steatohepatitis: successful development of pharmacological treatment will depend on translational research. Digestion 100, 79–85. doi: 10.1159/000493259

PubMed Abstract | CrossRef Full Text | Google Scholar

Hashimoto, E., and Tokushige, K. (2011). Prevalence, gender, ethnic variations, and prognosis of NASH. J. Gastroenterol. 46(Suppl. 1), 63–69. doi: 10.1007/s00535-010-0311-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Hoehme, S., Bertaux, F., Weens, W., Grasl-Kraupp, B., Hengstler, J. G., and Drasdo, D. (2018). Model prediction and validation of an order mechanism controlling the spatiotemporal phenotype of early hepatocellular carcinoma. Bull. Math. Biol. 80, 1134–1171. doi: 10.1007/s11538-017-0375-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Kockerling, D., Nathwani, R., Forlano, R., Manousou, P., Mullish, B. H., and Dhar, A. (2019). Current and future pharmacological therapies for managing cirrhosis and its complications. World J. Gastroenterol. 25, 888–908. doi: 10.3748/wjg.v25.i8.888

PubMed Abstract | CrossRef Full Text | Google Scholar

Kolodziejczyk, A. A., Zheng, D., Shibolet, O., and Elinav, E. (2019). The role of the microbiome in NAFLD and NASH. EMBO Mol. Med. 11:e9302. doi: 10.15252/emmm.201809302

PubMed Abstract | CrossRef Full Text | Google Scholar

Konturek, P. C., Brzozowski, T., and Konturek, S. J. (2011). Gut clock: implication of circadian rhythms in the gastrointestinal tract. J. Physiol. Pharmacol. 62, 139–150.

PubMed Abstract | Google Scholar

Lammert, F. (2016). Genetics in common liver diseases: from pathophysiology to precise treatment. Dig. Dis. 34, 391–395. doi: 10.1159/000444554

PubMed Abstract | CrossRef Full Text | Google Scholar

Lau, J. Y., and Wright, T. L. (1993). Molecular virology and pathogenesis of hepatitis B. Lancet 342, 1335–1340. doi: 10.1016/0140-6736(93)92249-S

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Y. (2013). Animal models of chronic liver diseases. Am. J. Physiol. Gastrointest. Liver Physiol. 304, G449–G468. doi: 10.1152/ajpgi.00199.2012

PubMed Abstract | CrossRef Full Text | Google Scholar

Lucarelli, P., Schilling, M., Kreutz, C., Vlasov, A., Boehm, M. E., Iwamoto, N., et al. (2018). Resolving the combinatorial complexity of smad protein complex formation and its link to gene expression. Cell Syst. 6, 75–89.e11. doi: 10.1016/j.cels.2017.11.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Ludwig, J., Viggiano, T. R., McGill, D. B., and Oh, B. J. (1980). Nonalcoholic steatohepatitis: Mayo Clinic experiences with a hitherto unnamed disease. Mayo Clin. Proc. 55, 434–438.

PubMed Abstract | Google Scholar

Meyer, K., Ostrenko, O., Bourantas, G., Morales-Navarrete, H., Porat-Shliom, N., Segovia-Miranda, F., et al. (2017). A predictive 3D Multi-scale model of biliary fluid dynamics in the liver lobule. Cell Syst. 4, 277–290.e279. doi: 10.1016/j.cels.2017.02.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Milosevic, I., Vujovic, A., Barac, A., Djelic, M., Korac, M., Radovanovic Spurnic, A., et al. (2019). Gut-liver axis, gut microbiota, and its modulation in the management of liver diseases: a review of the literature. Int. J. Mol. Sci. 20:E395. doi: 10.3390/ijms20020395

PubMed Abstract | CrossRef Full Text | Google Scholar

Neuschwander-Tetri, B. A., Loomba, R., Sanyal, A. J., Lavine, J. E., Van Natta, M. L., Abdelmalek, M. F., et al. (2015). Farnesoid X nuclear receptor ligand obeticholic acid for non-cirrhotic, non-alcoholic steatohepatitis (FLINT): a multicentre, randomised, placebo-controlled trial. Lancet 385, 956–965. doi: 10.1016/S0140-6736(14)61933-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Pan, J. J., and Fallon, M. B. (2014). Gender and racial differences in nonalcoholic fatty liver disease. World J. Hepatol. 6, 274–283. doi: 10.4254/wjh.v6.i5.274

PubMed Abstract | CrossRef Full Text | Google Scholar

Pierantonelli, I., and Svegliati-Baroni, G. (2019). Nonalcoholic fatty liver disease: basic pathogenetic mechanisms in the progression from NAFLD to NASH. Transplantation 103, e1–e13. doi: 10.1097/TP.0000000000002480

PubMed Abstract | CrossRef Full Text | Google Scholar

Poloznikov, A., Gazaryan, I., Shkurnikov, M., Nikulin, S., Drapkina, O., Baranova, A., et al. (2018). In vitro and in silico liver models: current trends, challenges and opportunities. ALTEX 35, 397–412. doi: 10.14573/altex.1803221

PubMed Abstract | CrossRef Full Text | Google Scholar

Ratziu, V., Cadranel, J. F., Serfaty, L., Denis, J., Renou, C., Delassalle, P., et al. (2012). A survey of patterns of practice and perception of NAFLD in a large sample of practicing gastroenterologists in France. J. Hepatol. 57, 376–383. doi: 10.1016/j.jhep.2012.03.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Sanyal, A. J., Chalasani, N., Kowdley, K. V., McCullough, A., Diehl, A. M., Bass, N. M., et al. (2010). Pioglitazone, vitamin E, or placebo for nonalcoholic steatohepatitis. N. Engl. J. Med. 362, 1675–1685. doi: 10.1056/NEJMoa0907929

CrossRef Full Text | Google Scholar

Schliess, F., Hoehme, S., Henkel, S. G., Ghallab, A., Driesch, D., Böttger, J., et al. (2014). Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration. Hepatology 60, 2040–2051. doi: 10.1002/hep.27136

PubMed Abstract | CrossRef Full Text | Google Scholar

Szmuness, W., Stevens, C. E., Harley, E. J., Zang, E. A., Oleszko, W. R., William, D. C., et al. (1980). Hepatitis B vaccine: demonstration of efficacy in a controlled clinical trial in a high-risk population in the United States. N. Engl. J. Med. 303, 833–841. doi: 10.1056/NEJM198010093031501

PubMed Abstract | CrossRef Full Text | Google Scholar

Szmuness, W., Stevens, C. E., Zang, E. A., Harley, E. J., and Kellner, A. (1981). A controlled clinical trial of the efficacy of the hepatitis B vaccine (Heptavax B): a final report. Hepatology 1, 377–385. doi: 10.1002/hep.1840010502

PubMed Abstract | CrossRef Full Text | Google Scholar

Teufel, A., Itzel, T., Erhart, W., Brosch, M., Wang, X. Y., Kim, Y. O., et al. (2016). Comparison of gene expression patterns between mouse models of nonalcoholic fatty liver disease and liver tissues from patients. Gastroenterology 151, 513–525.e510. doi: 10.1053/j.gastro.2016.05.051

PubMed Abstract | CrossRef Full Text | Google Scholar

Viganò, M., Andreoni, M., Perno, C. F., Craxì, A., Aghemo, A., Alberti, A., et al. (2019). Real life experiences in HCV management in 2018. Expert Rev. Anti Infect. Ther. 17, 117–128. doi: 10.1080/14787210.2019.1563755

PubMed Abstract | CrossRef Full Text | Google Scholar

Weber, S. N., and Lammert, F. (2017). Genetics in liver diseases: from diagnostics to precise therapy. Clin. Liver Dis. 9, 1–4. doi: 10.1002/cld.605

PubMed Abstract | CrossRef Full Text | Google Scholar

Zajac, M., Muszalska, I., Sobczak, A., Dadej, A., Tomczak, S., and Jelinska, A. (2019). Hepatitis C - new drugs and treatment prospects. Eur. J. Med. Chem. 165:225–249. doi: 10.1016/j.ejmech.2019.01.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: gastroenterology, hepatology, systems biology, systems medicine, mathematical modeling

Citation: Jansen PLM, Breuhahn K, Teufel A and Dooley S (2019) Editorial: Systems Biology and Bioinformatics in Gastroenterology and Hepatology. Front. Physiol. 10:1438. doi: 10.3389/fphys.2019.01438

Received: 04 October 2019; Accepted: 07 November 2019;
Published: 22 November 2019.

Edited and reviewed by: Stephen J. Pandol, Cedars-Sinai Medical Center, United States

Copyright © 2019 Jansen, Breuhahn, Teufel and Dooley. 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.

*Correspondence: Steven Dooley, steven.dooley@medma.uni-heidelberg.de