Project: The systems biology of network stress based on data generated from in vitro differentiated hepatocytes derived from patient-specific human iPS cells

Non-alcoholic fatty liver disease comprises a broad spectrum of disease states ranging from manageable stress as in simple steatosis (S) to excessive stress as in steatohepatitis (SH). In contrast to simple steatosis which has a good prognosis and may be considered a stressed state of normal liver, SH may ultimately lead to liver cirrhosis and hepatocellular carcinoma, which are both diseased states. A major unsolved problem is the marked difference in the individuals? ability to deal with the stress, e.g. as observed in terms of risk to develop steatohepatitis and to progress to cirrhosis. These differences in susceptibility to SH and its progression to cirrhosis have been attributed to a complex interplay of genetic and environmental factors. The complex interplay of pathways involved in the pathogenesis of steatohepatitis have hindered the discovery of susceptibility and modifier genes. The concept underpinning this project is that the various environmental exposures and genetic factors affect many different molecules in many different ways, but have very similar effects on network function. Hence, shifting focus from individual genes to the networks in which they are embedded, might well lead to a much clearer understanding. Accordingly, the proposed project will adopt a systems biology approach to develop a computational model for S and SH based on transcriptomics, proteomics and metabolomics data generated from patient-specific iPS cells (induced Pluripotent Stem cells) differentiated into hepatocytes and exposed to various environmental stimuli. To achieve this goal we will reprogram fibroblast cultures derived from skin biopsies taken from patients into iPS cells employing virus-mediated transduction of the transcription factors-OCT4, SOX2, KlF4 and c-MYC. The knowledge gained from these studies will be invaluable for the early means of identifying drugs that cause side effects in patients and most importantly for understanding the molecular (genes and associated signalling pathways) mechanisms underlying the etiology of simple steatosis and steatohepatitis.

Acronym livSYSiPS
Project Results
(after finalisation)
With an estimated prevalence of about 30% in western countries, non-alcoholic fatty liver disease (NAFLD) is a public health issue related to our sedentary life style and high fat diets. NAFLD is a metabolic syndrome of insulin resistance, obesity and glucose intolerance. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors interacting in an intracellular network. The concept underpinning the LivSYSiPS project is that the various environmental exposures and genetic factors affect many genes and associated pathways in distinct ways, but have very similar effects on network function. Hence, shifting the focus from a number of genes to the networks in which they are embedded might well lead to a much clearer understanding of the etiology of steatosis. To find our way in the multitude of possibly involved factors, we formulate hypothesis relating causes to effects and examine these by analyzing patient liver-biopsies, dermal fibroblasts and matching serum samples at the levels of the transcriptome, proteome and metabolome data with computational systems biology. We also developed two mathematical models, one concerning IGF pathway signaling and one addressing glutathione-mediated drug detoxification. The hypotheses address inadvertent cross-talk by insulin, inactivation of drug detoxification pathways, the robustness of ophtalmic acid and oxoproline as potential biomarkers of steatosis, the importance of methionine in drug detoxification robustness, and the relationship between mRNA levels and flux through the corresponding enzyme, all in the context of steatosis. Conclusion: We found different transcriptomic, proteomic and metabolomic profiles in patients with high-grade and low-grade steatosis pointing at a multi-factorial manifestation of the disease: differences in appetite-controlling hormone LEPTIN, relevant genes from fat metabolism (ACADSB), lipolysis (LIPA), IGF-axis and insulin-signalling (IGFBP2, IGFBP3, IGF1, INSIG1), the NASH marker KRT18 and branched chain amino acids (valine) differentially expressed in high- and low-grade steatosis. These all contribute to increasing insulin resistance. We further suggest that inappropriate insulin signaling, reduced diet-dependent detoxification, lack of correspondence between gene expression and pathway flux and requirements of model assisted biomarking, are all important in steatosis.
Network ERASysBio+
Call ERASysBio+-2008-01

Project partner

Number Name Role Country
1 Max Planck Institute for Molecular Genetics Coordinator Germany
2 Medical University of Graz Partner Austria
3 Charité Berlin Partner Germany
4 German Cancer Research Center Partner Germany
5 Manchester Interdisciplinary Biocentre (MIB) Partner United Kingdom
6 Magnetic Resonance Center (CERM) Partner Italy
7 Harvard University Medical School Partner United States