bioRxivGenomicsPreprint
Inverse directions of association of higher physical activity and higher insulin resistance with human skeletal muscle cell type abundance and fiber type-level gene expression
Dan L. CiotlosiD, Sarah C. HanksiD, Arushi VarshneyiD, Michael R. ErdosiD, Nandini ManickamiD, Heather M. StringhamiD, Peter OrchardiD, Erin M. Hill‐Burns, Narisu NarisuiD, Lori L. BonnycastleiD, M. D. SweeneyiD, Jouko SaramiesiD, Markku LaaksoiD, Jaakko Tuomilehto, Timo A Lakka, Karen L. MohlkeiD, Michael BoehnkeiD, Francis S. CollinsiD, Heikki A. KoistineniD, Stephen C.J. ParkeriD, Laura J. ScottiD
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Abstract
To investigate the interplay between physical activity and cardiometabolic traits in human skeletal muscle, we characterized gene expression and chromatin accessibility across skeletal muscle cell types in 263 Finnish individuals from the FUSION Tissue Biopsy Study. We analyzed skeletal muscle single-nucleus RNA-seq data (168,309 nuclei, 23,849 genes), ATAC-seq data (242,069 nuclei, 927,588 peaks), and bulk RNA-seq data (22,309 genes). Lower insulin resistance (HOMA-IR) and higher total physical activity were both associated with higher proportions of Type 1 nuclei and lower proportions of Type 2x nuclei. We identified cell-type-level and tissue-level gene expression-trait and gene set-trait associations for cardiometabolic and physical activity traits, and a smaller proportion of cell-type-level chromatin accessibility-trait associations. Traits typically associated with better health--lower trait values of cardiometabolic traits (BMI, HOMA-IR, normal glucose tolerance vs. type 2 diabetes, 2-hour plasma glucose) and higher physical activity levels (total and vigorous)--were associated with higher expression of energy metabolism genes and lower expression of signaling pathway genes across muscle fiber types, total pseudobulk, and to some extent in bulk tissue. For HOMA-IR and physical activity, these directions of association remained when adjusting for both traits in the same model, indicating apparently independent associations in the same pathways.
- organism: human
- assay: scRNA-seq
- assay: ATAC-seq
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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