Environmental toxicant glyphosate induces cardiotoxicity: New insights from network toxicology, integrated machine learning, molecular modeling and multidimensional bioinformatics analysis

Glyphosate is one of the world's most widely used herbicide, yet the causal relationship between glyphosate and cardiotoxicity remains inadequate. To elucidate the cardiotoxic mechanisms of glyphosate, an integrative strategy was developed in this work, which combines computational toxicometry, predictive machine learning, and atomic-level molecular modeling. Initially, glyphosate's systematic toxicological profile was evaluated using ProTox-3.0 and ADMETlab 3.0. Candidate genes associated with glyphosate were obtained from 3 separate databases. Target genes associated with cardiotoxicity were integrated from 2 databases. Intersection analysis identified 113 core candidate genes associated with glyphosate and cardiotoxicity. Functional enrichment-related analyses were employed and revealed that the target genes were significantly involved in key biological processes such as ERBB3 signaling pathways. In order to refine the candidate genes to a more precise gene pool, net topology analyses were employed and identified 16 key feature genes. We subsequently integrated machine learning algorithms and validated the results using the GEO database, ultimately identifying 4 hub genes, AKT1, IL1B, BRCA1, and PTGS2. Finally, we applied molecular docking analysis and revealed strong binding affinities, which suggested direct interaction mechanisms in glyphosate-induced cardiotoxicity. In summary, our study establishes a comprehensive mechanistic framework for glyphosate-induced cardiotoxicity.