In response to exercise, protein kinases and signaling networks are rapidly engaged in skeletal muscle to maintain energy homeostasis. High-intensity interval training (HIIT) induces superior or similar health-promoting skeletal muscle and whole-body adaptations compared to prolonged, moderate-intensity continuous training (MICT). However, the exercise intensity-specific signaling pathways underlying HIIT versus MICT are unknown. Ten healthy male participants completed bouts of work- and duration-matched HIIT and MICT cycling in randomized crossover trials. Mass spectrometry-based phosphoproteomic analysis of human muscle biopsies mapped acute signaling responses to HIIT and MICT, identifying 14,931 phosphopeptides and 8,509 phosphosites. Bioinformatics uncovered >1,000 phosphosites significantly regulated by HIIT and/or MICT, including 92 and 348 respective HIIT-specific phosphosites after 5 and 10 min and >3,000 total phosphosites significantly correlated with plasma lactate. This first human muscle HIIT signaling network map has revealed rapid exercise intensity-specific regulation of kinases, substrates and pathways that may contribute to HIIT’s unique health-promoting effects.

The authors have declared no competing interest.

ACTRN12619000819123

This work was supported by Australian Catholic University (ACU) research funding awarded to N.J.H. N.J.H. and J.A.H.'s research is partially funded by the Australian Government through the Australian Research Council (ARC) Discovery Project grant DP200103542, 'Molecular networks underlying exercise-induced mitochondrial biogenesis in humans'. B.L.P. is funded by an Australian National Health and Medical Research Council (NHMRC) Emerging Leader Investigator Grant (APP2009642).

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Ethics committee of Australian Catholic University gave ethical approval for this work

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This article and its Supplemental Information include all datasets generated during this study. The MS phosphoproteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2022) partner repository with the dataset identifier PXD053295 (Reviewer login details - Username: reviewer_pxd053295ebi.ac.uk; Password: 1PKEot15EUSc). Further information and requests for materials and resources including raw data, code and unique materials collected and used in this study should be directed to and will be fulfilled by the corresponding author and lead contact, Nolan J. Hoffman (nolan.hoffmanacu.edu.au).

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