Weighted Gene Co-expression Network Analysis of Metal Mixtures in Drinking Water and Their Effects on Zebrafish Behaviour

Mixtures of chemical contaminants can pose significant health risks to humans and wildlife, even at levels deemed safe for individuals. Zebrafish (Danio rerio) offer a high-throughput exposure model with the complexity of a vertebrate organism, making them well-suited for evaluating mixture toxicity. However, substantial gaps exist in statistical methods for assessing the associations between chemical mixtures and phenotypic end-points in toxicity testing. Here, a Weighted Gene Co-expression Network Analysis (WGCNA) was developed to address this challenge, focusing on a larval behavioural assay and leveraging data from 92 well-water samples from Maine and New Hampshire, USA. Our study aims to implement mixture-relevant statistical approaches to elucidate the relationships be-tween chemicals in drinking water and the behavioural responses observed in zebrafish. A WGCNA was employed to uncover gene expression patterns that mediate behavioural effects induced by these chemical mixtures. Individual chemicals within these mixture models exhibit both positive and negative partial effects. For instance, within the turquoise module, Cadmium (Cd) is positively weighted, while Copper (Cu), Nickel (Ni), and Lead (Pb) are negatively associated with zebrafish behaviour. Similarly, within the grey module, Arsenic (As), Uranium (U) and Chromium (Cr) show positive effects, whereas Selenium (Se) and Antimony (Sb) exhibit negative effects. These findings underscore the importance of evaluating overall mixture exposure effects and highlight the critical need to consider complex interactions within chemical mixtures in environmental toxicology.