Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

Abstract

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

Systems toxicology of chemically induced liver and kidney injuries: histopathology‐associated gene co‐expression modules

“…Most of the false‐positive conditions are related to liver fibrosis. In our previous study, a liver fibrosis module was generated from a combination of differentially expressed genes, co‐expression clustering, pathway enrichment analyzes and protein–protein interaction networks using the DrugMatrix database (AbdulHameed et al ., ). Seventeen conditions, either with observed fibrosis in DrugMatrix or known to cause fibrosis, clustered together based on the activation of the previous DrugMatrix‐based fibrosis module (AbdulHameed et al ., ).…”

“…In our previous study, a liver fibrosis module was generated from a combination of differentially expressed genes, co‐expression clustering, pathway enrichment analyzes and protein–protein interaction networks using the DrugMatrix database (AbdulHameed et al ., ). Seventeen conditions, either with observed fibrosis in DrugMatrix or known to cause fibrosis, clustered together based on the activation of the previous DrugMatrix‐based fibrosis module (AbdulHameed et al ., ). Of the 11 false positive conditions of LM7 in DrugMatrix, 10 conditions were part of the previously identified cluster of 17 conditions associated with liver fibrosis (AbdulHameed et al ., ).…”

“…In our previous work, we were able to conceptually connect molecular toxicity pathways to co‐expressed gene modules and link these pathways to specific injuries in the liver with the objective of identifying sensitive, specific and non‐invasive biomarkers for diseases and injuries (AbdulHameed et al ., ; Tawa et al ., ). We found that modules generated from the Iterative Signature Algorithm (ISA) performed satisfactorily in terms of generating modules that were specifically activated in response to chemical injuries (Tawa et al ., ).…”

Lipocalin 2 in the pathogenesis of fatty liver disease and nonalcoholic steatohepatitis

“…LCN2 serum levels increased significantly within 6 h LCN2 is a marker for the early phase of radiation-induced liver damage [61] Rat Toxic liver injury Data taken from the toxicogenomics database DrugMatrix LCN2 is correlated to liver fibrogenesis LCN2 is a marker of hepatic fibrogenesis in rats induced by different toxic treatment [62] Hepatocyte Hepatocytes are the major cell type responsible for LCN2 production after bacterial infection or partial hepatectomy. Hepatocyte-derived LCN2 plays an important role in inhibiting bacterial infection and promoting liver regeneration [63] Mouse Partial hepatectomy Mice were subjected to partial hepatectomy LCN2 expression is dramatically induced in livers and sera of mice after partial hepatectomy, whereas liver LCN2-receptor expression was decreased LCN2, although massively induced in mice after partial hepatectomy, is not relevant in murine hepatic regeneration [64] Mouse Acetaminophen intoxication, Human Hepatitis C-induced fibrosis/cirrhosis 18 patients with mild and 24 patients with severe fibrosis/cirrhosis Urine LCN2 levels (normalized to urine creatinine) were higher in patients with severe fibrosis/ cirrhosis showing a positive correlation with urine MMP-9/ MMP-2 activity ratios Urinary LCN2 is a marker of hepatic fibrosis that correlates to urine MMP-9 activity [68] Human Acute-on-chronic liver failure, acute liver failure, chronic hepatic failure Very small cohort of patients with acute-on-chronic liver failure, acute liver failure (n = 8) and chronic hepatic failure (n = 14)…”

A simple in silico strategy identifies candidate biomarkers for the diagnosis of liver fibrosis in morbidly obese subjects

“…This software allows analysing the biological information about protein‐protein interactions (PPI) stored in different molecular databases, such as IntAct, MINT, UniProt, etc. Thus, in silico analysis of biological networks represents an alternative option to elucidate novel biomarkers, as previously described by Page and AbdulHameed …”