Computational identification of candidate biomarkers and compounds associated with skeletal muscle insulin sensitivity improvement mediated by physical activity: an integrative multi-omics and computational chemistry study

BackgroundInsulin resistance (IR) underlies many chronic diseases. While physical activity improves skeletal muscle insulin sensitivity (IS), the underlying molecular mechanisms and targeted drug strategies remain elusive. This hypothesis-generating study integrates bioinformatics and computational chemistry using public datasets; all findings are computational predictions requiring experimental validation.MethodsUsing a skeletal muscle dataset (GSE24215, n = 10) of bed rest-induced IR and exercise-reversed IR, candidate genes were identified via WGCNA and differential expression analysis. Machine learning, GSEA, and regulatory network construction identified key targets and predicted mechanisms. To address the lack of targeted drugs, a deep learning-driven drug discovery and multi-scale binding validation strategy screened candidate compounds from natural and marine product databases.ResultsWNT4 was the only candidate gene repeatedly identified across both phases, displaying a bidirectional expression pattern of “upregulation after bed rest and downregulation after exercise retraining.” GSEA predicts WNT4 expression is positively correlated with energy metabolism pathways (e.g., oxidative phosphorylation) and is mediated by predicted transcriptional (centered on EGR1) and post-transcriptional (hsa-miR-1238-3p/lncRNAs) regulatory networks. From over 700,000 screened compounds, three top candidates were identified. Molecular dynamics, binding free energy calculations, and quantum chemical analysis preliminarily predicted their binding stability and electronic structure features.ConclusionThis study proposes that WNT4 may play a role in the dynamic regulation of skeletal muscle IS, presenting a functional hypothesis of “endogenous compensation–stage transition”. It establishes a computational drug discovery pipeline providing a candidate compound library for WNT4. Given the limited sample size (n = 10), all findings require further validation in independent cohorts and experiments.