Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is a progressive disease of increasing public health concern. In western populations the disease has an estimated prevalence of 20%-40%, rising to 70%-90% in obese and type II diabetic individuals. Simplistically, NAFLD is the macroscopic accumulation of lipid in the liver, and is viewed as the hepatic manifestation of the metabolic syndrome. However, the molecular mechanisms mediating both the initial development of steatosis and its progression through non-alcoholic steatohepatitis to debilitating and potentially fatal fibrosis and cirrhosis are only partially understood. Despite increased research in this field, the development of non-invasive clinical diagnostic tools and the discovery of novel therapeutic targets has been frustratingly slow. We note that, to date, NAFLD research has been dominated by in vivo experiments in animal models and human clinical studies. Systems biology tools and novel computational simulation techniques allow the study of large-scale metabolic networks and the impact of their dysregulation on health. Here we review current systems biology tools and discuss the benefits to their application to the study of NAFLD. We propose that a systems approach utilising novel in silico modelling and simulation techniques is key to a more comprehensive, better targeted NAFLD research strategy. Such an approach will accelerate the progress of research and vital translation into clinic.

[1]  Denis Noble,et al.  Modelling the heart: insights, failures and progress. , 2002, BioEssays : news and reviews in molecular, cellular and developmental biology.

[2]  Nathan D. Price,et al.  Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE , 2012, BMC Systems Biology.

[3]  Nan Xiao,et al.  Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli , 2008, Bioinform..

[4]  S. Grundy,et al.  Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population. , 2005, American journal of physiology. Endocrinology and metabolism.

[5]  S. Koenig,et al.  Maintaining hepatocyte differentiation in vitro through co-culture with hepatic stellate cells , 2009, In Vitro Cellular & Developmental Biology - Animal.

[6]  Jing-Fei Huang,et al.  Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data , 2013, BioMed research international.

[7]  A. Henney,et al.  The virtual liver: a multidisciplinary, multilevel challenge for systems biology , 2012, Wiley interdisciplinary reviews. Systems biology and medicine.

[8]  Bernhard O. Palsson,et al.  A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1 , 2010, BMC Systems Biology.

[9]  Adam S. Hayward,et al.  Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME , 2013, Archives of Toxicology.

[10]  P. Spedini Tuberculosis presenting as immune thrombocytopenic purpura. , 2002, Haematologica.

[11]  I. Nookaew,et al.  Integration of clinical data with a genome-scale metabolic model of the human adipocyte , 2013, Molecular systems biology.

[12]  Aarash Bordbar,et al.  A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology , 2011, BMC Systems Biology.

[13]  G. Bedogni,et al.  Epidemiology of Non-Alcoholic Fatty Liver Disease , 2010, Digestive Diseases.

[14]  Matthias Hermes,et al.  Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration , 2010, Proceedings of the National Academy of Sciences.

[15]  Dawn C. Walker,et al.  The virtual cellça candidate co-ordinator for ‘middle-out’ modelling of biological systems , 2009 .

[16]  M. Bonin,et al.  Differential gene expression in periportal and perivenous mouse hepatocytes , 2006, The FEBS journal.

[17]  Hermann-Georg Holzhütter,et al.  CardioNet: A human metabolic network suited for the study of cardiomyocyte metabolism , 2012, BMC Systems Biology.

[18]  J Bernadette Moore,et al.  Non-alcoholic fatty liver disease: the hepatic consequence of obesity and the metabolic syndrome , 2010, Proceedings of the Nutrition Society.

[19]  E. Bonora,et al.  Prevalence of non-alcoholic fatty liver disease and its association with cardiovascular disease in patients with type 1 diabetes. , 2010, Journal of hepatology.

[20]  Philip E. Bourne,et al.  Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model , 2010, PLoS Comput. Biol..

[21]  D. Noble,et al.  Systems biology and the virtual physiological human , 2009, Molecular systems biology.

[22]  Markus J. Herrgård,et al.  Network-based prediction of human tissue-specific metabolism , 2008, Nature Biotechnology.

[23]  P. Bedossa,et al.  A systematic review of follow-up biopsies reveals disease progression in patients with non-alcoholic fatty liver. , 2013, Journal of hepatology.

[24]  Feng-Chi Chen,et al.  GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization , 2012, Bioinform..

[25]  B. Palsson,et al.  Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions , 2010, Molecular systems biology.

[26]  W. Dietz,et al.  Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. , 2002, JAMA.

[27]  B. Palsson,et al.  Regulation of gene expression in flux balance models of metabolism. , 2001, Journal of theoretical biology.

[28]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010 .

[29]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[30]  M. Mann,et al.  Comparative Proteomic Phenotyping of Cell Lines and Primary Cells to Assess Preservation of Cell Type-specific Functions , 2009, Molecular & Cellular Proteomics.

[31]  Moore Jb,et al.  Proteomics and systems biology: current and future applications in the nutritional sciences. , 2011, Advances in nutrition.

[32]  Bronwen L. Aken,et al.  GENCODE: The reference human genome annotation for The ENCODE Project , 2012, Genome research.

[33]  Jason A. Papin,et al.  Reconstruction of cellular signalling networks and analysis of their properties , 2005, Nature Reviews Molecular Cell Biology.

[34]  S. Sookoian,et al.  Systems Biology Elucidates Common Pathogenic Mechanisms between Nonalcoholic and Alcoholic-Fatty Liver Disease , 2013, PloS one.

[35]  Igor Goryanin,et al.  Compartmentalization of the Edinburgh Human Metabolic Network , 2010, BMC Bioinformatics.

[36]  P. Karp,et al.  Computational prediction of human metabolic pathways from the complete human genome , 2004, Genome Biology.

[37]  G. Svegliati-Baroni,et al.  From the metabolic syndrome to NAFLD or vice versa? , 2010, Digestive and Liver Disease.

[38]  S. Dooley,et al.  Primary mouse hepatocytes for systems biology approaches: a standardized in vitro system for modelling of signal transduction pathways. , 2006, Systems biology.

[39]  I. Goryanin,et al.  Human metabolic network reconstruction and its impact on drug discovery and development. , 2008, Drug discovery today.

[40]  Luay Nakhleh,et al.  Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle , 2008, BMC Systems Biology.

[41]  Erwin P. Gianchandani,et al.  Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks , 2008, PLoS Comput. Biol..

[42]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..

[43]  Lake-Ee Quek,et al.  ON THE RECONSTRUCTION OF THE MUS MUSCULUS GENOME-SCALE METABOLIC NETWORK MODEL , 2008 .

[44]  C. Fierbinţeanu-Braticevici,et al.  Noninvasive investigations for non alcoholic fatty liver disease and liver fibrosis. , 2010, World journal of gastroenterology.

[45]  P. Newsome,et al.  Current therapeutic strategies in non‐alcoholic fatty liver disease , 2011, Diabetes, obesity & metabolism.

[46]  J. Nielsen,et al.  Nutritional systems biology: definitions and approaches. , 2009, Annual review of nutrition.

[47]  Ben van Ommen,et al.  Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology , 2009, PLoS Comput. Biol..

[48]  U. Nöthlings,et al.  Diagnosing Fatty Liver Disease: A Comparative Evaluation of Metabolic Markers, Phenotypes, Genotypes and Established Biomarkers , 2013, PloS one.

[49]  Andrzej M. Kierzek,et al.  SurreyFBA: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks , 2011, Bioinform..

[50]  Xiaoyan Zhu,et al.  Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts , 2009, PLoS Comput. Biol..

[51]  Hans V. Westerhoff,et al.  Optimization of stress response through the nuclear receptor-mediated cortisol signalling network , 2013, Nature Communications.

[52]  Jingfei Huang,et al.  Reconstruction and analysis of human heart-specific metabolic network based on transcriptome and proteome data. , 2011, Biochemical and biophysical research communications.

[53]  B. Palsson,et al.  Large-scale in silico modeling of metabolic interactions between cell types in the human brain , 2010, Nature Biotechnology.

[54]  Natapol Pornputtapong,et al.  Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT , 2012, PLoS Comput. Biol..

[55]  N. W. Davis,et al.  The complete genome sequence of Escherichia coli K-12. , 1997, Science.

[56]  E. Hoffman,et al.  Proteomics and Systems Biology in Exercise and Sport Sciences Research , 2007, Exercise and sport sciences reviews.

[57]  S. Allender,et al.  The burden of overweight and obesity‐related ill health in the UK , 2007, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[58]  C. Anania,et al.  Pediatric nonalcoholic fatty liver disease, metabolic syndrome and cardiovascular risk. , 2011, World journal of gastroenterology.

[59]  Frédéric Y. Bois,et al.  GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models , 2009, Bioinform..

[60]  O. Demin,et al.  The Edinburgh human metabolic network reconstruction and its functional analysis , 2007, Molecular systems biology.

[61]  Monika Heiner,et al.  Snoopy - a unifying Petri net framework to investigate biomolecular networks , 2010, Bioinform..

[62]  R. Maronpot,et al.  New Insights into Functional Aspects of Liver Morphology , 2005, Toxicologic pathology.

[63]  Andrzej M. Kierzek,et al.  QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells , 2013, Bioinform..

[64]  Igor Goryanin,et al.  The reconstruction and analysis of tissue specific human metabolic networks. , 2012, Molecular bioSystems.

[65]  Barbara M. Bakker,et al.  Emergence of the silicon human and network targeting drugs. , 2012, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[66]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[67]  F. Al-Kassimi,et al.  Tuberculosis presenting as immune thrombocytopenic purpura. , 1995, Acta haematologica.

[68]  Aarash Bordbar,et al.  iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states , 2011, BMC Systems Biology.

[69]  Douglas B Kell,et al.  Implications of the dominant role of transporters in drug uptake by cells. , 2009, Current topics in medicinal chemistry.

[70]  Michael J. Keiser,et al.  Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets , 2012, Nature.

[71]  Lake-Ee Quek,et al.  On the reconstruction of the Mus musculus genome-scale metabolic network model. , 2008, Genome informatics. International Conference on Genome Informatics.

[72]  Mudita Singhal,et al.  COPASI - a COmplex PAthway SImulator , 2006, Bioinform..

[73]  Dong-Yup Lee,et al.  Genome-scale modeling and in silico analysis of mouse cell metabolic network. , 2009, Molecular bioSystems.

[74]  Ronan M. T. Fleming,et al.  A community-driven global reconstruction of human metabolism , 2013, Nature Biotechnology.

[75]  R. Guo,et al.  Therapeutic approaches to non-alcoholic fatty liver disease: past achievements and future challenges. , 2013, Hepatobiliary & pancreatic diseases international : HBPD INT.

[76]  D. Cassio,et al.  Which in vitro models could be best used to study hepatocyte polarity? , 2008, Biology of the cell.

[77]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[78]  F. Oesch,et al.  New Hepatocyte In Vitro Systems for Drug Metabolism: Metabolic Capacity and Recommendations for Application in Basic Research and Drug Development, Standard Operation Procedures , 2003, Drug metabolism reviews.

[79]  Y. Soejima,et al.  Animal models of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. , 2012, World journal of gastroenterology.

[80]  E. Ruppin,et al.  Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism , 2010, Molecular systems biology.

[81]  Luay Nakhleh,et al.  The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks , 2008, PLoS Comput. Biol..

[82]  Y. Qi,et al.  A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes , 2012, British Journal of Cancer.

[83]  C. Gille,et al.  HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology , 2010, Molecular systems biology.

[84]  International Human Genome Sequencing Consortium Finishing the euchromatic sequence of the human genome , 2004 .

[85]  V. Ozkocaman,et al.  Tuberculosis presenting as immune thrombocytopenic purpura , 2004, Annals of Clinical Microbiology and Antimicrobials.

[86]  J Bernadette Moore,et al.  Proteomics and systems biology: current and future applications in the nutritional sciences. , 2011, Advances in nutrition.

[87]  Jian Wang,et al.  In Silico Elucidation of the Molecular Mechanism Defining the Adverse Effect of Selective Estrogen Receptor Modulators , 2007, PLoS Comput. Biol..

[88]  C. Selden,et al.  Three‐dimensional in Vitro Cell Culture Leads to a Marked Upregulation of Cell Function in Human Hepatocyte Cell Lines‐an Important Tool for the Development of a Bioartificial Liver Machine , 1999, Annals of the New York Academy of Sciences.

[89]  Jonathan C. Cohen,et al.  Human Fatty Liver Disease: Old Questions and New Insights , 2011, Science.