Environmental pollutant-induced cholinergic disruption: Advances and perspectives in mechanistic insights, target heterogeneity, and neurotoxic synergy

The cholinergic system serves as a central regulatory network for neurotransmission and behavioral control, and its complex signaling architecture renders it highly vulnerable to mixed environmental pollutants. In real-world scenarios, pollutants typically exist as complex mixtures whose synergistic or antagonistic interactions increase toxicity uncertainty, thereby challenging the traditional single-pollutant, single-target paradigm in mixture risk prediction. Integrating evidence across pollutant categories to uncover shared mechanistic principles is therefore essential for building a predictive assessment framework. Through quantitative interactome analysis of twenty pollutant categories, we identify two unifying principles of cholinergic disruption: mechanistic convergence and target heterogeneity. Pollutant effects converge on a limited set of shared pathways, primarily acetylcholinesterase (AChE) inhibition and acetylcholine receptor (AChR) modulation, while molecular targets differ in sensitivity, forming an AChE and nicotinic AChRs (nAChRs) core attack module. We further propose a dual-axis model of damage facilitation and regulatory remodeling, delineating the transition from acute target-specific perturbations to chronic regulatory dysfunction. The model establishes chemical structure as a key determinant through a three-tier cascade encompassing molecular initiating events, distributional behavior, and metabolic fate. This framework supports a tiered risk-assessment strategy that integrates qualitative, quantitative, and computational approaches. By linking molecular perturbations to ecological outcomes, we establish an exposure-target-outcome paradigm that captures multilayered pollutant impacts. Finally, we identify key bottlenecks in predictive modeling, cross-species extrapolation, and regulatory translation, and propose a translational roadmap integrating multi-omics biomarkers, advanced in vitro models, and artificial intelligence to advance environmental neurotoxicology toward a mechanism-driven predictive science.