Bioinformatic inference of the exercise-responsive control of p70 S6 kinase through RPS6KB1 expression

Abstract

Previous research suggests that the absolute levels of p70 S6 kinase (p70S6K) are a key determinant of the rate of skeletal muscle protein synthesis (MPS). p70S6K levels are in part determined by the transcriptional control of the gene encoding p70S6K, RPS6KB1, but the molecular mechanisms governing its expression are poorly understood. The purpose of this study was to infer the molecular regulatory network governing RPS6KB1 expression. We applied a novel bioinformatic network inference algorithm called CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) to infer the signaling network downstream of canonical exercise sensors controlling RPS6KB1-specific transcription factors (TFs) after acute aerobic (AE) or resistance exercise (RE). CARNIVAL integrates a prior knowledge network, TF and signaling pathway activities inferred from transcriptomic data, and perturbation targets to predict the network that best explains the data. The networks revealed intracellular sensors and hormone receptors controlling RPS6KB1-specific TFs. Both exercise types resulted in AMPK-mediated SNAI1 regulation, but HIF1A was distinctly controlled (AE: PHD1-3, FIH; RE: AMPK). AE controlled FOXA1 via insulin, TGF-{beta}, and myostatin signalling, while RE controlled CEBPA via MAP3Ks. Our study is the first to apply a comprehensive bioinformatic network inference algorithm to infer causal exercise-responsive signaling networks. The results of our analysis motivate experimentally testable hypotheses pertaining to the molecular control of RPS6KB1 transcription in human skeletal muscle in response to aerobic and resistance exercise.