UC San Diego

Enhanced coverage and sensitivity of next-generation omic platforms has allowed the characterization of gene, metabolite and protein responses in highly metabolic tissues, such as, skeletal muscle. A limitation, however, is the capability to determine interaction between dynamic biological networks. To address this limitation, we applied Weighted Analyte Correlation Network Analysis (WACNA) to RNA-seq and metabolomic datasets to identify correlated subnetworks of transcripts and metabolites in response to a high-fat diet (HFD)-induced obesity and/or exercise. HFD altered skeletal muscle lipid profiles and up-regulated genes involved in lipid catabolism, while decreasing 241 exercise-responsive genes related to skeletal muscle plasticity. WACNA identified the interplay between transcript and metabolite subnetworks linked to lipid metabolism, inflammation and glycerophospholipid metabolism that were associated with IL6, AMPK and PPAR signal pathways. Collectively, this novel experimental approach provides an integrative resource to study transcriptional and metabolic networks in skeletal muscle in the context of health and disease.