Dataset Information

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

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.

SUBMITTER: Ramnath D

PROVIDER: S-EPMC6124454 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Ramnath Divya D Irvine Katharine M KM Lukowski Samuel W SW Horsfall Leigh U LU Loh Zhixuan Z Clouston Andrew D AD Patel Preya J PJ Fagan Kevin J KJ Iyer Abishek A Lampe Guy G Stow Jennifer L JL Schroder Kate K Fairlie David P DP Powell Joseph E JE Powell Elizabeth E EE Sweet Matthew J MJ

JCI insight 20180726 14

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 ge ...[more]

PMID: 30046009

Similar Datasets

Project description:IntroductionPrader-Willi syndrome (PWS) is a rare genetic disorder characterized by loss of expression of paternal chromosome 15q11.2-q13 genes. Individuals with PWS exhibit unique physical, endocrine, and metabolic traits associated with severe obesity. Identifying liver steatosis in PWS is challenging, despite its lower prevalence compared to non-syndromic obesity. Reliable biomarkers are crucial for the early detection and management of this condition associated with the complex metabolic profile and cardiovascular risks in PWS.MethodsCirculating proteome profiling was conducted in 29 individuals with PWS (15 with steatosis, 14 without) using the Olink Target 96 metabolism and cardiometabolic panels. Correlation analysis was performed to identify the association between protein biomarkes and clinical variables, while the gene enrichment analysis was conducted to identify pathways linked to deregulated proteins. Receiver operating characteristic (ROC) curves assessed the discriminatory power of circulating protein while a logistic regression model evaluated the potential of a combination of protein biomarkers.ResultsCDH2, CTSO, QDPR, CANT1, ALDH1A1, TYMP, ADGRE, KYAT1, MCFD, SEMA3F, THOP1, TXND5, SSC4D, FBP1, and CES1 exhibited a significant differential expression in liver steatosis, with a progressive increase from grade 1 to grade 3. FBP1, CES1, and QDPR showed predominant liver expression. The logistic regression model, -34.19 + 0.85 * QDPR*QDPR + 0.75 * CANT1*TYMP - 0.46 * THOP1*ALDH1A, achieved an AUC of 0.93 (95% CI: 0.63-0.99), with a sensitivity of 93% and specificity of 80% for detecting steatosis in individuals with PWS. These biomarkers showed strong correlations among themselves and were involved in an interconnected network of 62 nodes, related to seven metabolic pathways. They were also significantly associated with cholesterol, LDL, triglycerides, transaminases, HbA1c, FLI, APRI, and HOMA, and showed a negative correlation with HDL levels.ConclusionThe biomarkers identified in this study offer the potential for improved patient stratification and personalized therapeutic protocols.

Project description:Background and aimsPrimary sclerosing cholangitis (PSC) is a heterogeneous cholangiopathy characterized by progressive biliary fibrosis. RNA sequencing of liver tissue from patients with PSC (n = 74) enrolled in a 96-week clinical trial was performed to identify associations between biological pathways that were independent of fibrosis and clinical events.Approach and resultsThe effect of fibrosis was subtracted from gene expression using a computational approach. The fibrosis-adjusted gene expression patterns were associated with time to first PSC-related clinical event (e.g., cholangitis, hepatic decompensation), and differential expression based on risk groups and Ingenuity Pathway Analysis were performed. Baseline demographic data were representative of PSC: median age 48 years, 71% male, 49% with inflammatory bowel disease, and 44% with bridging fibrosis or cirrhosis. The first principle component (PC1) of RNA-sequencing data accounted for 18% of variance and correlated with fibrosis stage (ρ = -0.80; P < 0.001). After removing the effect of fibrosis-related genes, the first principle component was not associated with fibrosis (ρ = -0.19; P = 0.11), and a semisupervised clustering approach identified two distinct patient clusters with differential risk of time to first PSC-related event (P < 0.0001). The two groups had similar fibrosis stage, hepatic collagen content, and α-smooth muscle actin expression by morphometry, Enhanced Liver Fibrosis score, and serum liver biochemistry, bile acids, and IL-8 (all P > 0.05). The top pathways identified by Ingenuity Pathway Analysis were eukaryotic translation inhibition factor 2 (eIF2) signaling and regulation of eIF4/p70S6K signaling. Genes involved in the unfolded protein response, activating transcription factor 6 (ATF6) and eIF2, were differentially expressed between the PSC clusters (down-regulated in the high-risk group by log-fold changes of -0.18 [P = 0.02] and -0.16 [P = 0.02], respectively). Clinical events were enriched in the high-risk versus low-risk group (38% [12/32] vs. 2.4% [1/42], P < 0.0001).ConclusionsRemoving the contribution of fibrosis-related pathways uncovered alterations in the unfolded protein response, which were associated with liver-related complications in PSC.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets