Exercise-responsive signaling network dynamics in skeletal muscle cells

运动训练通过骨骼肌分子和细胞水平的适应来发展健康。基于蛋白质的信号系统在肌肉中运作，感知和协调对运动压力源的适应。运动反应蛋白信号通路的两个例子包括amp活化蛋白激酶（AMPK）和缺氧诱导因子-1 （HIF1）。AMPK通过降低细胞能量水平激活，而HIF1通过降低氧水平激活。运动促进适应的类型和程度取决于它的量和强度，这决定了它的总“剂量”。这些途径如何促进特定的生理适应不同剂量的运动仍然没有答案。我们假设信号通路在其动态中编码运动持续时间和强度，这是指信号通路的活动如何随着时间的推移而变化。我们的长期目标是开发、验证和研究运动反应蛋白信号动力学的预测模型，这些模型控制着分子和细胞对运动训练的适应。在接下来的五年里，我们将追求三个短期目标：1)评估AMPK信号动力学的决定因素和功能，2)表征骨骼肌细胞中HIF1信号动力学，以及3)表征运动反应信号网络的结构。我们将使用我们新开发的“体外运动模型”应用计算建模和实验，其中我们试图用培养细胞模拟运动。这些方法有助于信号动力学的研究，使我们能够区分以下三个形成动力学的因素：1)作用于途径的生化和生物物理刺激的时间概况（刺激动力学），2)途径的生化反应动力学，以及3)与其他信号通路的串扰调制。我们的研究结果将通过提供对运动反应蛋白信号如何编码运动强度和持续时间的见解，促进对运动生理学的基本理解。研究结果可能会将信号作为“开关”的根深蒂固的概念转变为一个动态编码信息的系统。我们的发现将引起运动生物学家、细胞生物学家和系统生物学家的兴趣。我们创新的体外锻炼模型将引起生物技术公司的兴趣。我们开发的数学模型将公开供科学家和运动专业人士使用。随着模型的不断完善，我们预计这些模型将在软件中实现，供运动专业人士使用，以设计优化的运动训练计划，从而产生有针对性的生理结果。总的来说，开发的概念和工具将有助于缩小运动生理学中存在的研究与实践之间的差距。

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.