Skeletal myocytes are metabolically active and susceptible to insulin resistance, thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network-context to integrate high-throughput data. We generated myocyte-specific RNA-seq data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the down-regulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D. SOURCE: Leif VäremoSystems biology Chalmers University of Technology

Pluto Bioinformatics

GSE63887: RNA-sequencing of healthy human skeletal myocytes