Time trajectories in the transcriptomic response to exercise - a meta-analysis

Exercise training prevents multiple diseases, yet the molecular mechanisms that drive exercise adaptation are incompletely understood. To address this, we create a computational framework comprising data from skeletal muscle or blood from 43 studies, including 739 individuals before and after exercise or training. Using linear mixed effects meta-regression, we detect specific time patterns and regulatory modulators of the exercise response. Acute and long-term responses are transcriptionally distinct and we identify SMAD3 as a central regulator of the exercise response. Exercise induces a more pronounced inflammatory response in skeletal muscle of older individuals and our models reveal multiple sex-associated responses. We validate seven of our top genes in a separate human cohort. In this work, we provide a powerful resource (www.extrameta.org) that expands the transcriptional landscape of exercise adaptation by extending previously known responses and their regulatory networks, and identifying novel modality-, time-, age-, and sex-associated changes. Regular exercise promotes overall health and prevents non-communicable diseases, but the adaptation mechanisms are unclear. Here, the authors perform a meta-analysis to reveal time-specific patterns of the acute and long-term exercise response in human skeletal muscle, and identify sex- and age-specific changes.

Automated summary

This study explores the molecular mechanisms driving exercise adaptation by conducting a meta-analysis on human skeletal muscle, revealing time-specific patterns of acute and long-term exercise responses, as well as identifying sex- and age-specific changes.