A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target

Eccles, Kristin M.; Karmaus, Agnes L.; Kleinstreuer, Nicole; Parham, Fred; Rider, Cynthia; Wambaugh, John F.; Messier, Kyle P.

doi:10.1016/j.scitotenv.2022.158905

Cited by 12 publications

(8 citation statements)

References 68 publications

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“…used spatialized exposure data to predict the potential for different geographic regions to be affected by chemicals. 47 While the focus of their methods was to characterize exposure to chemical mixtures based on potential molecular target perturbations (without consideration of specific SNPs, specific chemicals, or specific adverse outcomes), integrating some of their methods with our spatialized Pesticide–SNP–Disease linkages represents one approach to quantify the potential for the pesticides we identified to trigger the implicated pathways in disease progression.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population

Kosnik,

Antczak,

Fantke

2024

Environ Health Perspect

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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. https://doi.org/10.1289/EHP14108

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Section: Discussionmentioning

confidence: 99%

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population

Kosnik,

Antczak,

Fantke

2024

Environ Health Perspect

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“…To our knowledge, our approach using spatialized pesticide application data to identify SNPs that may be relevant to disease occurrence in different geographic regions has not been done before. Eccles et al 2023 used spatialized exposure data to predict the potential for different geographic regions to be affected by chemicals 33 . While the focus of their methods was to characterize exposure to chemical mixtures based on potential molecular target perturbations (without consideration of specific SNPs, specific chemicals, or specific adverse outcomes), integrating some of their methods with our spatialized Pesticide-SNP-Disease linkages represents one approach to quantify the potential for the pesticides we identified to trigger the implicated pathways in disease progression.…”

Section: Discussionmentioning

confidence: 99%

Genetic variability in pathways associates with pesticide-induced nervous system disease in the United States

Kosnik,

Antczak,

Fantke

2023

Preprint

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Nervous system disease development following pesticide exposure can vary in a population, but genetic susceptibility to chemicals is poorly characterized. We developed a framework to build Chemical – SNP (single nucleotide polymorphism) – Disease linkages via biological pathways. We integrated these linkages with spatialized pesticide application data for the United States from 1992 – 2018 and nervous system disease rates for 2018 to characterize genetic variability in pesticide-induced nervous system disease. We found that the number of SNPs implicated per pesticide in US states positively correlates with disease incidence and prevalence for Alzheimer’s disease, Parkinson disease, and multiple sclerosis. Further, only 2% of pesticide sets used together over time overlapped between high disease occurrence and low disease occurrence states, with more SNPs implicated in pathways in high disease occurrence states. This supports that pesticides contribute to nervous system disease, and we developed priority lists of SNPs, pesticides, and pathways for further study.

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“…Incorporating stochastic modeling approaches could help to predict a possible range of chemical effects in lieu of data representing reality. For example, geographic variability in effects from chemical exposures have been predicted for humans by using regional demographic data with TK models 89 and for earthworms by combining behavioral trait data with TKTD models and environmental data. 90 By following these approaches using variable data input representing a range of possible species TK traits and/or chemical exposures, potential changes in species' functional traits for specific geographic regions can be predicted.…”

Section: Environmentalmentioning

confidence: 99%

Harnessing Computational Methods to Characterize Chemical Impacts on Biodiversity

Kosnik,

Schuwirth,

Rico

2024

Environ. Sci. Technol. Lett.

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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.

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A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target

Cited by 12 publications

References 68 publications

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population

Genetic variability in pathways associates with pesticide-induced nervous system disease in the United States

Harnessing Computational Methods to Characterize Chemical Impacts on Biodiversity

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