Department Environmental Toxicology

BACKGROUND: Genetic susceptibility to chemicals is incompletely characterized. However, nervous system disease development following pesticide exposure can vary in a population, implying some individuals may have higher genetic susceptibility to pesticide-induced nervous system disease. OBJECTIVES: We aimed to build a computational approach to characterize single-nucleotide polymorphisms (SNPs) implicated in chemically induced adverse outcomes and used this framework to assess the link between differential population susceptibility to pesticides and human nervous system disease. METHODS: We integrated publicly available datasets of Chemical-Gene, Gene-Pathway, and SNP-Disease associations to build Chemical-Pathway-Gene-SNP-Disease linkages for humans. As a case study, we integrated these linkages with spatialized pesticide application data for the US from 1992 to 2018 and spatialized nervous system disease rates for 2018. Through this, we characterized SNPs that may be important in states with high disease occurrence based on the pesticides used there. RESULTS: We found that the number of SNP hits per pesticide in US states positively correlated with disease incidence and prevalence for Alzheimer's disease, Parkinson disease, and multiple sclerosis. We performed frequent itemset mining to differentiate pesticides used over time in states with high and low disease occurrence and found that only 19% of pesticide sets overlapped between 10 states with high disease occurrence and 10 states with low disease occurrence rates, and more SNPs were implicated in pathways in high disease occurrence states. Through a cross-validation of subsets of five high and low disease occurrence states, we characterized SNPs, genes, pathways, and pesticides more frequently implicated in high disease occurrence states. DISCUSSION: Our findings support that pesticides contribute to nervous system disease, and we developed priority lists of SNPs, pesticides, and pathways for further study. This data-driven approach can be adapted to other chemicals, diseases, and locations to characterize differential population susceptibility to chemical exposures.

Chemical pollution can threaten biodiversity at different levels, from genetically diverse populations (genetic diversity) to different species (species diversity) and ecosystem traits/interactions (functional diversity). Most assessments of chemical impacts on different biodiversity levels depend on wet lab and field experiments, including sequencing large numbers of organisms, environmental DNA approaches, single chemical-species-outcome toxicity tests, and trait-based methods. However, it is impossible to assess all chemicals, species, populations, and ecosystems using these methods. Therefore, we advocate that computational methods are necessary to characterize, quantify, and predict chemical impacts on biodiversity. We briefly introduce the current state of research into chemical impacts on genetic diversity, species diversity, and functional diversity and describe new opportunities for computational methods like data integration, machine learning, cross-species/cross-ecosystem extrapolation, adverse outcome pathways, and Bayesian methods to support research in these three areas. By harnessing data and methods currently at our disposal and preparing methods to take advantage of continuously emerging data sets, computational approaches can be paired with environmental monitoring so different levels of biological organization can serve as consecutive warning signs for chemical impacts on biodiversity. This will enable effective ecosystem protection measures to be better developed and implemented to prevent biodiversity loss from chemical pollution.