Genetic predisposition and environmental factors contribute to an individual's susceptibility to develop hepatosteatosis. In a systematic, comparative survey we focused on genotype-dependent and -independent adaptations early in the pathogenesis of hepatosteatosis by characterizing C3HeB/FeJ, C57BL/6NTac, C57BL/6J, and 129P2/OlaHsd mice after 7, 14, or 21 days high-fat-diet exposure. Strain-specific metabolic responses during diet challenge and liver transcript signatures in mild hepatosteatosis outline the suitability of particular strains for investigating the relationship between hepatocellular lipid content and inflammation, glucose homeostasis, insulin action, or organelle physiology. Genetic background-independent transcriptional adaptations in liver paralleling hepatosteatosis suggest an early increase in the organ's vulnerability to oxidative stress damage what could advance hepatosteatosis to steatohepatitis. "Universal" adaptations in transcript signatures and transcription factor regulation in liver link insulin resistance, type 2 diabetes mellitus, cancer, and thyroid hormone metabolism with hepatosteatosis, hence, facilitating the search for novel molecular mechanisms potentially implicated in the pathogenesis of human non-alcoholic-fatty-liver-disease.

Metformin is the first-line oral medication to increase insulin sensitivity in patients with type 2 diabetes (T2D). Our aim was to investigate the pleiotropic effect of metformin using a nontargeted metabolomics approach. We analyzed 353 metabolites in fasting serum samples of the population-based human KORA (Cooperative Health Research in the Region of Augsburg) follow-up survey 4 cohort. To compare T2D patients treated with metformin (mt-T2D, n = 74) and those without antidiabetes medication (ndt-T2D, n = 115), we used multivariable linear regression models in a cross-sectional study. We applied a generalized estimating equation to confirm the initial findings in longitudinal samples of 683 KORA participants. In a translational approach, we used murine plasma, liver, skeletal muscle, and epididymal adipose tissue samples from metformin-treated db/db mice to further corroborate our findings from the human study. We identified two metabolites significantly (P < 1.42E-04) associated with metformin treatment. Citrulline showed lower relative concentrations and an unknown metabolite X-21365 showed higher relative concentrations in human serum when comparing mt-T2D with ndt-T2D. Citrulline was confirmed to be significantly (P < 2.96E-04) decreased at 7-year follow-up in patients who started metformin treatment. In mice, we validated significantly (P < 4.52E-07) lower citrulline values in plasma, skeletal muscle, and adipose tissue of metformin-treated animals but not in their liver. The lowered values of citrulline we observed by using a nontargeted approach most likely resulted from the pleiotropic effect of metformin on the interlocked urea and nitric oxide cycle. The translational data derived from multiple murine tissues corroborated and complemented the findings from the human cohort.

ObjectiveExcess lipid intake has been implicated in the pathophysiology of hepatosteatosis and hepatic insulin resistance. Lipids constitute approximately 50% of the cell membrane mass, define membrane properties, and create microenvironments for membrane-proteins. In this study we aimed to resolve temporal alterations in membrane metabolite and protein signatures during high-fat diet (HF)-mediated development of hepatic insulin resistance.MethodsWe induced hepatosteatosis by feeding C3HeB/FeJ male mice an HF enriched with long-chain polyunsaturated C18:2n6 fatty acids for 7, 14, or 21 days. Longitudinal changes in hepatic insulin sensitivity were assessed via the euglycemic-hyperinsulinemic clamp, in membrane lipids via t-metabolomics- and membrane proteins via quantitative proteomics-analyses, and in hepatocyte morphology via electron microscopy. Data were compared to those of age- and litter-matched controls maintained on a low-fat diet.ResultsExcess long-chain polyunsaturated C18:2n6 intake for 7 days did not compromise hepatic insulin sensitivity, however, induced hepatosteatosis and modified major membrane lipid constituent signatures in liver, e.g. increased total unsaturated, long-chain fatty acid-containing acyl-carnitine or membrane-associated diacylglycerol moieties and decreased total short-chain acyl-carnitines, glycerophosphocholines, lysophosphatidylcholines, or sphingolipids. Hepatic insulin sensitivity tended to decrease within 14 days HF-exposure. Overt hepatic insulin resistance developed until day 21 of HF-intervention and was accompanied by morphological mitochondrial abnormalities and indications for oxidative stress in liver. HF-feeding progressively decreased the abundance of protein-components of all mitochondrial respiratory chain complexes, inner and outer mitochondrial membrane substrate transporters independent from the hepatocellular mitochondrial volume in liver.ConclusionsWe assume HF-induced modifications in membrane lipid- and protein-signatures prior to and during changes in hepatic insulin action in liver alter membrane properties – in particular those of mitochondria which are highly abundant in hepatocytes. In turn, a progressive decrease in the abundance of mitochondrial membrane proteins throughout HF-exposure likely impacts on mitochondrial energy metabolism, substrate exchange across mitochondrial membranes, contributes to oxidative stress, mitochondrial damage, and the development of insulin resistance in liver.

Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η2 (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.