Computational biology and chemistry · 2026
Investigating pesticide-induced risk in high myopia-related retinal detachment: An integration of machine learning and molecular dynamics simulations.
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Niu Jiahao, Wang Runzhe, Lou Junyi, Bai Mengtian, Liu Shujing, Zhang Qiongfang, Liao Xuan
Аннотация
BACKGROUND: Retinal detachment (RD) progression is influenced by genetic susceptibility, calcium overload-induced neuronal damage and oxidative stress (involving organophosphate pesticides), with high myopia (HM) being its strongest associated risk factor. This study aimed to investigate RD pathogenesis and identify potential causative pesticides by integrating bioinformatics, Weighted gene co-expression network analysis (WGCNA), machine learning, network toxicology, molecular docking, and dynamics simulations.
METHODS: Hub genes were identified by intersecting differentially expressed genes from RD datasets and HM datasets with key modules derived from WGCNA on RD data, yielding 55 candidates. Eight machine learning algorithms were employed to build predictive models. Following exclusion of two models showing significant overfitting and high root mean square of residuals (RMSR), Shapley additive explanations (SHAP) analysis pinpointed the top 15 genes contributing to RD prediction. A competing endogenous RNA (ceRNA) network was constructed for these genes, and a novel reverse targeting network toxicology framework was developed to screen associated environmental toxicants.
RESULTS: Three pesticide candidates - fonofos, terbufos, and parathion were identified as exhibiting strong interactions with these core targets. Molecular docking and molecular dynamics simulations further suggests potential binding under the simulated conditions between these pesticides and the hub targets. Functional enrichment analysis implicated extracellular matrix-related pathways and the VEGF-HSPGs pathway in RD pathogenesis.
CONCLUSION: This study integrates multi-omics data and employs diverse methodological approaches to identify key target genes associated with the comorbidity of HM and RD, offering potential biomarkers for diagnosis and therapeutic interventions. Moreover, it innovatively applies a reverse toxicology framework to elucidate the possible mechanism by which organophosphorus pesticides (OPs) contribute to the pathogenesis of RD through the calcium overload/oxidative stress/inflammation axis.
METHODS: Hub genes were identified by intersecting differentially expressed genes from RD datasets and HM datasets with key modules derived from WGCNA on RD data, yielding 55 candidates. Eight machine learning algorithms were employed to build predictive models. Following exclusion of two models showing significant overfitting and high root mean square of residuals (RMSR), Shapley additive explanations (SHAP) analysis pinpointed the top 15 genes contributing to RD prediction. A competing endogenous RNA (ceRNA) network was constructed for these genes, and a novel reverse targeting network toxicology framework was developed to screen associated environmental toxicants.
RESULTS: Three pesticide candidates - fonofos, terbufos, and parathion were identified as exhibiting strong interactions with these core targets. Molecular docking and molecular dynamics simulations further suggests potential binding under the simulated conditions between these pesticides and the hub targets. Functional enrichment analysis implicated extracellular matrix-related pathways and the VEGF-HSPGs pathway in RD pathogenesis.
CONCLUSION: This study integrates multi-omics data and employs diverse methodological approaches to identify key target genes associated with the comorbidity of HM and RD, offering potential biomarkers for diagnosis and therapeutic interventions. Moreover, it innovatively applies a reverse toxicology framework to elucidate the possible mechanism by which organophosphorus pesticides (OPs) contribute to the pathogenesis of RD through the calcium overload/oxidative stress/inflammation axis.