The lack of high-throughput methods to analyze the adipose tissue protein composition limits our understanding of the protein networks responsible for age and diet related metabolic response. We have developed an approach using multiple-dimension liquid chromatography tandem mass spectrometry and extended multiplexing (24 biological samples) with tandem mass tags (TMT) labeling to analyze proteomes of epididymal adipose tissues isolated from mice fed either low or high fat diet for a short or a long-term, and from mice that aged on low high fat diets. The peripheral metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet and advancing age, with long-term high fat diet exposure being the worst. In response to short-term high fat diet, 43 proteins representing lipid metabolism ( AACS, ACOX1, ACLY) and red-ox pathways ( CPD2, CYP2E, SOD3) were significantly altered (FDR < 10%). Long-term high fat diet significantly altered 55 proteins associated with immune response ( IGTB2, IFIT3, LGALS1) and rennin angiotensin system ( ENPEP, CMA1, CPA3, ANPEP). Age-related changes on low fat diet significantly altered only 18 proteins representing mainly urea cycle ( OTC, ARG1, CPS1), and amino acid biosynthesis ( GMT, AKR1C6). Surprisingly, high fat diet driven age-related changes culminated with alterations in 155 proteins involving primarily the urea cycle ( ARG1, CPS1), immune response/complement activation ( C3, C4b, C8, C9, CFB, CFH, FGA), extracellular remodeling ( EFEMP1, FBN1, FBN2, LTBP4, FERMT2, ECM1, EMILIN2, ITIH3) and apoptosis ( YAP1, HIP1, NDRG1, PRKCD, MUL1) pathways. Using our adipose tissue tailored approach we have identified both age-related and high fat diet specific proteomic signatures highlighting a pronounced involvement of arginine metabolism in response to advancing age, and branched chain amino acid metabolism in early response to high fat feeding. Data are available via ProteomeXchange with identifier PXD005953.

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.

Objective Genome-wide association studies (GWAS) have so far identified 159 significant and suggestive loci for coronary artery disease (CAD). We now report comprehensive bioinformatics analyses of sequence variation in these loci to predict candidate causal genes. Approach and Results All annotated genes in the loci were evaluated with respect to protein coding SNPs and gene expression parameters. The latter included expression quantitative trait loci, tissue specificity, and miRNA binding. High priority candidate genes were further identified based on literature searches and our experimental data. We conclude that the great majority of causal variations affecting CAD risk occur in non-coding regions, with 41 % affecting gene expression robustly versus 6% leading to amino acid changes. Many of these genes differed from the traditionally annotated genes, which was usually based on proximity to the lead SNP. Indeed, we obtained evidence that genetic variants at CAD loci affect 98 genes which had not been linked to CAD previously. Conclusions Our results substantially revise the list of likely candidates for CAD and suggest that GWAS efforts in other diseases may benefit from similar bioinformatics analyses.