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.

Most of the biological understanding of mechanisms underlying coronary artery disease (CAD) derives from studies of mouse models. The identification of multiple CAD loci and strong candidate genes in large human genome-wide association studies (GWAS) presented an opportunity to examine the relevance of mouse models for the human disease. We comprehensively reviewed the mouse literature, including 827 literature-derived genes, and compared it to human data. First, we observed striking concordance of risk factors for atherosclerosis in mice and humans. Second, there was highly significant overlap of mouse genes with human genes identified by GWAS. In particular, of the 46 genes with strong association signals in CAD-GWAS that were studied in mouse models all but one exhibited consistent effects on atherosclerosis-related phenotypes. Third, we compared 178 CAD-associated pathways derived from human GWAS with 263 from mouse studies and observed that over 50% were consistent between both species.

BackgroundComplex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies.ResultsWe developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package.ConclusionMergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3198-9) contains supplementary material, which is available to authorized users.

We report the genetic analysis of a "humanized" hyperlipidemic mouse model for progressive nonalcoholic steatohepatitis (NASH) and fibrosis. Mice carrying transgenes for human apolipoprotein E*3-Leiden and cholesteryl ester transfer protein and fed a "Western" diet were studied on the genetic backgrounds of over 100 inbred mouse strains. The mice developed hepatic inflammation and fibrosis that was highly dependent on genetic background, with vast differences in the degree of fibrosis. Histological analysis showed features characteristic of human NASH, including macrovesicular steatosis, hepatocellular ballooning, inflammatory foci, and pericellular collagen deposition. Time course experiments indicated that while hepatic triglyceride levels increased steadily on the diet, hepatic fibrosis occurred at about 12 weeks. We found that the genetic variation predisposing to NASH and fibrosis differs markedly from that predisposing to simple steatosis, consistent with a multistep model in which distinct genetic factors are involved. Moreover, genome-wide association identified distinct genetic loci contributing to steatosis and NASH. Finally, we used hepatic expression data from the mouse panel and from 68 bariatric surgery patients with normal liver, steatosis, or NASH to identify enriched biological pathways. Conclusion: The pathways showed substantial overlap between our mouse model and the human disease.

Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D.