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骨骼肌细胞信号传导的网络模型预测了对耐力和抗阻训练的不同反应。

Network model of skeletal muscle cell signalling predicts differential responses to endurance and resistance exercise training.

机构信息

Department of Bioengineering, University of California San, Diego, La Jolla, California, USA.

Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA.

出版信息

Exp Physiol. 2024 Jun;109(6):939-955. doi: 10.1113/EP091712. Epub 2024 Apr 21.

DOI:10.1113/EP091712
PMID:38643471
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11140181/
Abstract

Exercise-induced muscle adaptations vary based on exercise modality and intensity. We constructed a signalling network model from 87 published studies of human or rodent skeletal muscle cell responses to endurance or resistance exercise in vivo or simulated exercise in vitro. The network comprises 259 signalling interactions between 120 nodes, representing eight membrane receptors and eight canonical signalling pathways regulating 14 transcriptional regulators, 28 target genes and 12 exercise-induced phenotypes. Using this network, we formulated a logic-based ordinary differential equation model predicting time-dependent molecular and phenotypic alterations following acute endurance and resistance exercises. Compared with nine independent studies, the model accurately predicted 18/21 (85%) acute responses to resistance exercise and 12/16 (75%) acute responses to endurance exercise. Detailed sensitivity analysis of differential phenotypic responses to resistance and endurance training showed that, in the model, exercise regulates cell growth and protein synthesis primarily by signalling via mechanistic target of rapamycin, which is activated by Akt and inhibited in endurance exercise by AMP-activated protein kinase. Endurance exercise preferentially activates inflammation via reactive oxygen species and nuclear factor κB signalling. Furthermore, the expected preferential activation of mitochondrial biogenesis by endurance exercise was counterbalanced in the model by protein kinase C in response to resistance training. This model provides a new tool for investigating cross-talk between skeletal muscle signalling pathways activated by endurance and resistance exercise, and the mechanisms of interactions such as the interference effects of endurance training on resistance exercise outcomes.

摘要

运动引起的肌肉适应性变化取决于运动方式和强度。我们从 87 项已发表的关于人类或啮齿动物骨骼肌细胞对体内耐力或抗阻运动或体外模拟运动反应的研究中构建了一个信号网络模型。该网络由 259 个信号相互作用组成,涉及 120 个节点,代表 8 个膜受体和 8 个调节 14 个转录调节剂、28 个靶基因和 12 个运动诱导表型的经典信号通路。使用该网络,我们制定了一个基于逻辑的常微分方程模型,预测急性耐力和抗阻运动后随时间变化的分子和表型改变。与 9 项独立研究相比,该模型准确预测了 18/21(85%)项抗阻运动的急性反应和 12/16(75%)项耐力运动的急性反应。对抗阻和耐力训练的差异表型反应的详细敏感性分析表明,在该模型中,运动主要通过 Akt 激活的雷帕霉素靶蛋白(mTOR)信号转导来调节细胞生长和蛋白质合成,而在耐力运动中被 AMP 激活的蛋白激酶(AMPK)抑制。耐力运动通过活性氧和核因子κB 信号优先激活炎症。此外,在该模型中,蛋白激酶 C 对抗阻训练的反应抵消了耐力运动对线粒体生物发生的预期优先激活,这为研究耐力和抗阻运动激活的骨骼肌信号通路之间的串扰以及相互作用的机制(如耐力训练对抗阻运动结果的干扰效应)提供了一个新的工具。

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