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Hepatic expression profiling identifies steatosis-independent and steatosis-driven advanced fibrosis genes
Divya Ramnath, … , Elizabeth E. Powell, Matthew J. Sweet
Divya Ramnath, … , Elizabeth E. Powell, Matthew J. Sweet
Published July 25, 2018
Citation Information: JCI Insight. 2018;3(14):e120274. https://doi.org/10.1172/jci.insight.120274.
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Research Article Hepatology Inflammation

Hepatic expression profiling identifies steatosis-independent and steatosis-driven advanced fibrosis genes

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Abstract

Chronic liver disease (CLD) is associated with tissue-destructive fibrosis. Considering that common mechanisms drive fibrosis across etiologies, and that steatosis is an important cofactor for pathology, we performed RNA sequencing on liver biopsies of patients with different fibrosis stages, resulting from infection with hepatitis C virus (HCV) (with or without steatosis) or fatty liver disease. In combination with enhanced liver fibrosis score correlation analysis, we reveal a common set of genes associated with advanced fibrosis, as exemplified by those encoding the transcription factor ETS-homologous factor (EHF) and the extracellular matrix protein versican (VCAN). We identified 17 fibrosis-associated genes as candidate EHF targets and demonstrated that EHF regulates multiple fibrosis-associated genes, including VCAN, in hepatic stellate cells. Serum VCAN levels were also elevated in advanced fibrosis patients. Comparing biopsies from patients with HCV with or without steatosis, we identified a steatosis-enriched gene set associated with advanced fibrosis, validating follistatin-like protein 1 (FSTL1) as an exemplar of this profile. In patients with advanced fibrosis, serum FSTL1 levels were elevated in those with steatosis (versus those without). Liver Fstl1 mRNA levels were also elevated in murine CLD models. We thus reveal a common gene signature for CLD-associated liver fibrosis and potential biomarkers and/or targets for steatosis-associated liver fibrosis.

Authors

Divya Ramnath, Katharine M. Irvine, Samuel W. Lukowski, Leigh U. Horsfall, Zhixuan Loh, Andrew D. Clouston, Preya J. Patel, Kevin J. Fagan, Abishek Iyer, Guy Lampe, Jennifer L. Stow, Kate Schroder, David P. Fairlie, Joseph E. Powell, Elizabeth E. Powell, Matthew J. Sweet

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Figure 1

An overview of DEGs in early- versus late-stage liver fibrosis.

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An overview of DEGs in early- versus late-stage liver fibrosis.
(A) Prin...
(A) Principal component analysis of the corrected counts for the filtered genes (9,624 genes), highlighting the components that contribute to maximum variability (n = 69 patients; circles, early stages; triangles, advanced stages). (B) Volcano plot illustrating genes, from the filtered data set, that are significantly differentially expressed between early and advanced stages of liver fibrosis, as identified by RNA sequencing (n = 69 patients; EdgeR-generated FWER value and log2 fold change). (C) A heatmap showing the expression patterns of the 168 DEGs after hierarchical clustering using the complete linkage method (n = 69 patients). (D) KEGG pathway analysis of the DEGs between early- versus late-stage fibrosis (n = 69 patients; 1,595 FDR-corrected list). Circle size correlates with number of genes, and circle color indicates statistical significance.

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ISSN 2379-3708

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