Exploring pesticide risk in autism via integrative machine learning and network toxicology

Abstract

Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition influenced by both genetic and environmental factors, including pesticide exposure. This study aims to investigate the pathogenic mechanisms of ASD and identify potential causative pesticides by integrating bioinformatics, machine learning, network toxicology, and molecular docking approaches. A total of 156 differentially expressed genes (128 upregulated and 28 downregulated) were identified from ASD-related transcriptomic datasets. Using the LASSO algorithm, 23 key targets were initially selected. Each combination of 1-23 targets was used to construct predictive models using eight different machine learning algorithms. The Stochastic Gradient Descent (SGD) model demonstrated the best predictive performance for 20 features, which were defined as hub targets. These targets were subsequently used in a network toxicology framework to screen for associated environmental toxicants. Three pesticide candidates-epoxiconazole, flusilazole, and DEET-were identified as strongly interacting with these core targets. Molecular docking analysis further validated stable binding affinities between these pesticides and the hub targets. Functional enrichment analysis revealed significant involvement of glycosylation-related pathways, including mucin-type O-glycan biosynthesis, implicating potential mechanisms in ASD pathogenesis. Collectively, our findings highlight novel biomolecular links between pesticide exposure and ASD risk, and propose a set of candidate biomarkers and toxicants for further experimental validation and regulatory consideration.

Keywords: ASD; Machine learning; Molecular docking; Network toxicology; Pesticide exposures.