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.
- 1Department of Geriatrics, Hunan Provincial Hospital of Integrated Traditional Chinese and Western Medicine (The Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine), Changsha, China.
- 2Second Clinical Medical School, Zhejiang Chinese Medical University, Hangzhou, China.
- 3Graduate School, Hunan University of Chinese Medicine, Changsha, China.
Abstract
Background
Insulin 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.Methods
Using 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.Results
WNT4 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.Conclusion
This 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.
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BibTeX
@article{liu2026computational,
title = {{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.}},
author = {Hong Liu and Zicheng Liang and Jun Tan and Chunyun Yuan},
journal = {Frontiers in bioinformatics},
year = {2026},
month = oct,
doi = {10.3389/fbinf.2026.1885995},
url = {https://europepmc.org/article/MED/42908988},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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.
AU - Hong Liu
AU - Zicheng Liang
AU - Jun Tan
AU - Chunyun Yuan
PY - 2026
DA - 2026/10/07
JO - Frontiers in bioinformatics
DO - 10.3389/fbinf.2026.1885995
UR - https://europepmc.org/article/MED/42908988
AB - Background Insulin 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.Methods Using 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.Results WNT4 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.Conclusion This 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.
SN - 2673-7647
ER -