Environmental toxicant glyphosate induces cardiotoxicity: New insights from network toxicology, integrated machine learning, molecular modeling and multidimensional bioinformatics analysis
Clinical Snapshot
PICO Framework
| P — Population | Human cardiac tissue/cells (computational model) |
| I — Intervention | Glyphosate exposure |
| C — Comparator | No explicit comparator (computational analysis) |
| O — Outcomes | Cardiotoxicity mechanisms, gene expression patterns, molecular binding affinities |
Bottom Line
This computational study suggests potential mechanisms for glyphosate-induced cardiotoxicity through four key genes (AKT1, IL1B, BRCA1, PTGS2) and ERBB3 signaling pathways. While the bioinformatics approach is comprehensive, the findings represent theoretical predictions requiring experimental validation. The study provides valuable hypotheses for future research but cannot inform clinical practice or regulatory decisions without supporting experimental evidence. Australian clinicians should await confirmatory studies before considering these findings in patient care, particularly given widespread agricultural glyphosate use. The work contributes to understanding potential molecular mechanisms but falls short of establishing causal relationships or clinical relevance.
Key Findings
P Value: Not specified
Effect Size: Strong binding affinities demonstrated through molecular docking
Primary Outcome: Identification of 4 hub genes (AKT1, IL1B, BRCA1, PTGS2) associated with glyphosate cardiotoxicity
Nnt Or Sensitivity: 113 core candidate genes narrowed to 16 key features, then 4 hub genes
Confidence Interval: Not provided
Clinical Application
Currently limited to research hypothesis generation Relevant to Australian agricultural exposure patterns; TGA regulates glyphosate use; findings may inform future risk assessment but require validation Requires experimental validation before clinical application
Abstract
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.
References
- 1.Sun, M., Wang, Y., Tong, R., Yan, R., & Pu, J. (2026). Environmental toxicant glyphosate induces cardiotoxicity: New insights from network toxicology, integrated machine learning, molecular modeling and multidimensional bioinformatics analysis. Medicine, Article 0000000000048974. https://doi.org/10.1097/MD.0000000000048974
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