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Systems-level organization of non-alcoholic fatty liver disease progression network

Abstract

Non-Alcoholic Fatty Liver Disease (NAFLD) is a complex spectrum of diseases ranging from simple steatosis to Non-Alcoholic Steatohepatitis (NASH) with fibrosis, which can progress to cirrhosis and hepatocellular carcinoma. The pathogenesis of NAFLD is complex involving crosstalk between multiple organs, cell-types, environmental and genetic factors. Dysfunction of adipose tissue plays a central role in the NAFLD progression. Here, we analysed transcriptomics data obtained from the Visceral Adipose Tissue (VAT) of NAFLD patients to understand how the VAT metabolism is altered at the genome scale and co-regulated with other cellular process during the progression from obesity to NASH with fibrosis. For this purpose, we performed Weighted Gene Co-expression Network Analysis (WGCNA), a method that organizes the disease transcriptome into functional modules of cellular processes and pathways. Our analysis revealed the coordination of metabolic and inflammatory modules (termed “immunometabolism”) in the VAT of NAFLD patients. We found that genes of arachidonic acid, sphingolipid and glycosphingolipid metabolism were upregulated and co-expressed with genes of proinflammatory signalling pathways and hypoxia in NASH/NASH with fibrosis. We hypothesize that these metabolic alterations might play a role in sustaining VAT inflammation. Further, immunometabolism related genes were also co-expressed with genes involved in the Extracellular Matrix (ECM) degradation. Our analysis indicates that upregulation of both ECM degrading enzymes and their inhibitors (incoherent feedforward loop) potentially lead to the ECM deposition in the VAT of NASH with fibrosis patients.

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Publication details

The article was received on 05 Jan 2017, accepted on 13 Jul 2017 and first published on 14 Jul 2017


Article type: Paper
DOI: 10.1039/C7MB00013H
Citation: Mol. BioSyst., 2017, Accepted Manuscript
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    Systems-level organization of non-alcoholic fatty liver disease progression network

    K. Shubham, L. Vinay and P. K. Vinod, Mol. BioSyst., 2017, Accepted Manuscript , DOI: 10.1039/C7MB00013H

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