ArticleFrontiers in pharmacology2026
Integrating multi-omics and deep learning to explore the active ingredients and molecular mechanisms of
Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Coronary heart disease (CHD) is a major cardiovascular disease. Objective: To clarify the chemical basis and potential anti-CHD mechanisms of CM-VO. Methods: GC-MS was used to identify CM-VO constituents and serum-related metabolites. A mouse CHD model was established to evaluate pharmacodynamic effects. Network analysis was used for full-component mechanism prediction, while transcriptomics was used to screen treatment-responsive genes. UniProt standardization, deep learning, molecular docking, and H9c2 cell experiments were further performed for target prediction and validation. Results: A total of 68 CM-VO constituents and 8 serum-related metabolites were identified. CM-VO improved myocardial injury, inflammation, oxidative stress, endothelial dysfunction, and lipid-related abnormalities in CHD mice. Network analysis predicted 39 bioactive compounds and 58 key targets, while transcriptomics identified 44 DEGs and 101 CHD-related enriched genes. After UniProt standardization, 19 human genes were analyzed, and GADD45A, MTHFS, and ALAS2 were prioritized as high-affinity targets. In H9c2 cells, CM-VO-containing serum reduced lipid accumulation and injury markers, enhanced antioxidant capacity, and regulated GADD45A, MTHFS, and ALAS2 expression. Conclusion: CM-VO may protect against CHD mainly by regulating inflammation, oxidative stress, endothelial function, and lipid metabolism. This study provides integrated evidence for the pharmacological basis and molecular mechanisms of CM-VO against CHD.
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