Exercise-responsive signaling network dynamics in skeletal muscle cells

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Exercise training develops fitness via molecular and cellular-level adaptations in skeletal muscle. Protein-based signaling systems operate within muscle to sense and coordinate the adaptations to the stressors of exercise. Two examples of exercise-responsive protein signaling pathways include AMP-activated protein kinase (AMPK) and hypoxia inducible factor-1 (HIF1). AMPK is activated by lowered cellular energy levels, while HIF1 is activated by decreased oxygen levels. The types and degree of adaptations promoted by exercise depend on its amount and intensity, which define its overall "dose". How these pathways promote specific physiological adaptations to different doses of exercise remains unanswered. We hypothesize that the signaling pathways encode exercise duration and intensity in their dynamics, which refers to how the activities of the signaling pathways vary in magnitude over time. Our long-term goal is to develop, validate, and study predictive models of exercise-responsive protein signaling dynamics that govern the molecular and cellular adaptations to exercise training. In the next five years, we will pursue three short-term objectives: 1) Evaluate the determinants and function of AMPK signaling dynamics, 2) Characterize HIF1 signaling dynamics in skeletal muscle cells, and 3) Characterize the structure of the exercise-responsive signaling network. We will apply computational modeling and experiments using our newly developed "in vitro exercise model", in which we seek to mimic exercise using cultured cells. These methods facilitate the study of signaling dynamics by enabling us to distinguish the three following factors that shape dynamics: 1) the time profiles of the biochemical and biophysical stimuli that act on the pathways (the stimulus dynamics), 2) the biochemical reaction kinetics of the pathway, and 3) modulation by crosstalk with other signaling pathways. The results of our research will advance basic understanding of exercise physiology by providing insights into how exercise-responsive protein signaling encodes exercise intensity and duration. The results could shift the entrenched conceptualization of signaling as "on-off switches" to a system whose dynamics encode information. Our findings will be of interest to exercise biologists, cell biologists, and systems biologists. Our innovative in vitro exercise model will be of interest to biotechnology companies. The mathematical models we develop will be made publicly available for use by scientists and exercise professionals. As the models improve over time, we foresee their implementation in software used by exercise professionals to design optimized exercise training programs that elicit targeted physiological outcomes. Collectively, the concepts and tools developed will help close the research-practice gap that exists in exercise physiology.