Identification of Key Molecular Networks and Regulatory Signatures Associated with Acute Resistance Exercise in Human Skeletal Muscle: An Integrated Bioinformatics Analysis

Background and Objectives: Acute resistance exercise induces rapid transcriptional remodeling in skeletal muscle and activates molecular programs involved in cellular stress responses, metabolic adaptation, tissue remodeling, and subsequent physiological adaptation. Characterization of the key genes and molecular interaction modules underlying these early responses may improve our understanding of the molecular mechanisms of exercise-induced skeletal muscle adaptation. This study aimed to identify differentially expressed genes (DEGs), central network hubs, highly interconnected molecular modules, and potential upstream regulatory mechanisms associated with the early skeletal muscle response to acute resistance exercise.Methods: The publicly available RNA-sequencing dataset GSE۱۰۷۹۳۴ was obtained from the NCBI Gene Expression Omnibus (GEO). Vastus lateralis skeletal muscle samples obtained from six healthy young men at baseline and ۴ h following acute resistance exercise were analyzed. Differential gene expression analysis was performed using GEO۲R, with Benjamini–Hochberg false discovery rate correction. Genes with an adjusted P value <۰.۰۵ and |log۲ fold change| ≥۱ were considered differentially expressed. The resulting DEGs were submitted to STRING to construct a protein–protein interaction (PPI) network, which was subsequently imported into Cytoscape. Hub genes were independently prioritized using Maximum Clique Centrality (MCC), Maximum Neighborhood Component (MNC), and Density of Maximum Neighborhood Component (DMNC) algorithms implemented in cytoHubba. Highly interconnected molecular modules were identified using the ClusterONE algorithm. For subsequent upstream regulatory investigation, promoter sequences of prioritized genes were analyzed for regulatory motifs, with identified motifs compared against known transcription factor-binding motifs using Tomtom and their potential functional associations evaluated using GOMO.Results: Differential expression analysis demonstrated substantial transcriptional alterations in skeletal muscle ۴ h following acute resistance exercise. GEO۲R identified ۳۴۷ genes with an adjusted P value <۰.۰۵ before application of the additional fold-change criterion. The resulting PPI network comprised ۲۴۲ nodes and ۱,۰۵۳ interactions. MCC prioritized MYC, CCL۲, HIF۱A, and ICAM۱, whereas MNC identified MYC, FOS, HIF۱A, and EGR۱ as the highest-ranked genes. DMNC independently prioritized CSF۱, ETS۲, NIFK, and WDR۴۳. Notably, MYC and HIF۱A were concurrently prioritized by both MCC and MNC, supporting their central positions within the exercise-responsive interaction network. ClusterONE detected ۳۱ candidate network modules, of which two were statistically significant. The first significant module contained ۱۳ nodes (density=۰.۶۲۸, quality=۰.۷۴۲, P=۵.۵۲۳×۱۰⁻⁵), whereas the second contained ۲۳ nodes (density=۰.۶۱۳, quality=۰.۳۷۹, P=۰.۰۱۹). These findings indicate the presence of distinct, highly interconnected molecular subnetworks within the early transcriptional response to resistance exercise.Conclusion: Acute resistance exercise induces marked early transcriptional and network-level alterations in human skeletal muscle. Integrated transcriptomic and PPI-network analyses highlighted MYC and HIF۱A as reproducibly high-ranking network hubs and revealed two statistically significant molecular modules potentially involved in the early adaptive response to resistance exercise. Further characterization of promoter regulatory motifs and their associated transcription factors may provide additional insight into the upstream mechanisms governing exercise-induced skeletal muscle adaptation.

کلیدواژه ها:

Resistance Exercise ، Skeletal Muscle ، Bioinformatics