PuSH - Publikationsserver des Helmholtz Zentrums München: Quantitative proteomic characterization of cellular pathways associated with altered insulin sensitivity in skeletal muscle following high-fat diet feeding and exercise training.

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Kleinert, M. ; Parker, B.L.* ; Jensen, T.E.* ; Raun, S.H.* ; Pham, P. ; Han, X.* ; James, D.E.* ; Richter, E.A.* ; Sylow, L.*

Quantitative proteomic characterization of cellular pathways associated with altered insulin sensitivity in skeletal muscle following high-fat diet feeding and exercise training.

Sci. Rep. 8:10723 (2018)

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Regular exercise elicits advantageous metabolic adaptations in skeletal muscle, such as improved insulin sensitivity. However, the underpinning molecular mechanisms and the effect of diet on muscle exercise training benefits are unclear. We therefore characterized the skeletal muscle proteome following exercise training (ET) in mice fed chow or high-fat diet (HFD). ET increased exercise performance, lowered body-weight, decreased fat mass and improved muscle insulin action in chow-and HFD-fed mice. At the molecular level, ET regulated 170 muscle proteins in chow-fed mice, but only 29 proteins in HFD-fed mice. HFD per se altered 56 proteins, most of which were regulated in a similar direction by ET. To identify proteins that might have particular health-related bearing on skeletal muscle metabolism, we filtered for differentially regulated proteins in response to ET and HFD. This yielded 15 proteins, including the major urinary protein 1 (MUP1), which was the protein most decreased after HFD, but increased with ET. The ET-induced Mup1 expression was absent in mouse muscle lacking functional AMPK. MUP1 also potentiated insulin-stimulated GLUT4 translocation in cultured muscle cells. Collectively, we provide a resource of ET-regulated proteins in insulin-sensitive and insulin-resistant skeletal muscle. The identification of MUP1 as a diet-, ET-and AMPK-regulated skeletal muscle protein that improves insulin sensitivity in muscle cells demonstrates the usefulness of these data.

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