Integration of Multi-omics Data from Mouse Diversity Panel Highlights Mitochondrial Dysfunction in Non-alcoholic Fatty Liver Disease

Abstract

The etiology of non-alcoholic fatty liver disease (NAFLD), the most common form of chronic liver disease, is poorly understood. To understand the causal mechanisms underlying NAFLD, we conducted a multi-omics, multi-tissue integrative study using the Hybrid Mouse Diversity Panel, consisting of ∼100 strains of mice with various degrees of NAFLD. We identified both tissue-specific biological processes and processes that were shared between adipose and liver tissues. We then used gene network modeling to predict candidate regulatory genes of these NAFLD processes, including Fasn, Thrsp, Pklr, and Chchd6. In vivo knockdown experiments of the candidate genes improved both steatosis and insulin resistance. Further in vitro testing demonstrated that downregulation of both Pklr and Chchd6 lowered mitochondrial respiration and led to a shift toward glycolytic metabolism, thus highlighting mitochondria dysfunction as a key mechanistic driver of NAFLD.

Tissue-specific pathways and networks underlying sexual dimorphism in non-alcoholic fatty liver disease

“…We aimed to understand the sexually dimorphic mechanisms underlying NAFLD using an integrative genomics approach, Mergeomics [ 19 , 20 ]. In our recent study [ 18 ], we used this pipeline to integrate the multi-omics data from the male mice of the hybrid mouse diversity panel (HMDP) [ 17 , 21 ] to identify causal NAFLD gene networks and predict key regulator (driver) genes in these networks. Our subsequent in vivo and ex vivo experiments supported the reliability of our multi-omics modeling approach [ 18 ].…”

“…In our recent study [ 18 ], we used this pipeline to integrate the multi-omics data from the male mice of the hybrid mouse diversity panel (HMDP) [ 17 , 21 ] to identify causal NAFLD gene networks and predict key regulator (driver) genes in these networks. Our subsequent in vivo and ex vivo experiments supported the reliability of our multi-omics modeling approach [ 18 ]. In our current study, we predicted the NAFLD processes and their key driver genes using the female HMDP mice [ 17 , 21 ] and compared these findings with those from the male-focused study [ 18 ].…”

“…To fully leverage the multi-omic datasets from HMDP mice and incorporate disease-associated molecular signals with strong, moderate, and subtle effects, we have recently deployed an integrative approach to identify potential causal pathways, gene networks, and key regulators of NAFLD in males in a tissue-specific fashion, followed by experimental validation of the novel predictions [ 18 ]. In the current study, we apply this validated approach to compare the NAFLD biology between sexes to uncover the shared, male-specific, and female-specific genes, processes, and networks potentially driving NAFLD pathogenesis, thereby providing more comprehensive tissue-specific insight into the differential prevalence and manifestations of NAFLD between sexes.…”

Integrated omics: tools, advances and future approaches

“…Their findings demonstrate interconnectivity in age-related diseases and that use of integrated omics can reveal novel molecular networks relevant to complex phenotypes. Krishnan et al (2018) used adipose and liver tissue gene expression analysis by microarray, bioenergetics measurements in cell lines and mitochondria followed by GWAS and eQTL analyses to integrate various omics datasets via an advanced multiscale embedded gene coexpression network analysis (Song & Zhang 2015) that was preferred over WGCNA analyses for identification of networks. Clearly, the authors concluded that network modeling from a large dataset and in vitro approaches helped predict key driver genes regulating non-alcoholic fatty liver disease.…”

Mouse Systems Genetics as a Prelude to Precision Medicine

“…A systems genetics approach, using expression data from mouse brain, identified CSF1R as a potential target for epilepsy and suggested CSF1R blockade as a novel therapeutic strategy [101]. Other studies applied gene network modeling algorithms to identify the potential regulators in complex diseases, for example cardiomyopathy [102], hepatic steatosis [103], as well as coronary artery disease [104].…”