Epidemiology meets toxicogenomics: Mining toxicologic evidence in support of an untargeted analysis of pesticides exposure and Parkinson's disease

Abstract

Background: Pesticides have been widely used in agriculture for more than half a century. However, with thousands currently in use, most have not been adequately assessed for influence Parkinson's disease (PD).

Objectives: Here we aimed to assess biologic plausibility of 70 pesticides implicated with PD through an agnostic pesticide-wide association study using a data mining approach linking toxicology and toxicogenomics databases.

Methods: We linked the 70 targeted pesticides to quantitative high-throughput screening assay findings from the Toxicology in the 21st Century (Tox21) program and pesticide-related genetic/disease information with the Comparative Toxicogenomics Database (CTD). We used the CTD to determine networks of genes each pesticide has been linked to and assess enrichment of relevant gene ontology (GO) annotations. With Tox21, we evaluated pesticide induced activity on a series of 43 nuclear receptor and stress response assays and two cytotoxicity assays.

Results: Overall, 59 % of the 70 pesticides had chemical-gene networks including at least one PD gene/gene product. In total, 41 % of the pesticides had chemical-gene networks enriched for ≥ 1 high-priority PD GO terms. For instance, 23 pesticides had chemical-gene networks enriched for response to oxidative stress, 21 for regulation of neuron death, and twelve for autophagy, including copper sulfate, endosulfan and chlorpyrifos. Of the pesticides tested against the Tox21 assays, 79 % showed activity on ≥ 1 assay and 11 were toxic to the two human cell lines. The set of PD-associated pesticides showed more activity than expected on assays testing for xenobiotic homeostasis, mitochondrial membrane permeability, and genotoxic stress.

Conclusions: Overall, cross-database queries allowed us to connect a targeted set of pesticides implicated in PD via epidemiology to specific biologic targets relevant to PD etiology. This knowledge can be used to help prioritize targets for future experimental studies and improve our understanding of the role of pesticides in PD etiology.

Keywords: Chlorpyrifos; Copper sulfate; Parkinson's disease; Pesticides; Tox21; Toxicogenomics.

Fig. 1.

A. Inference score and reference count from the CTD linking each pesticide to PD through the pesticide-gene networks. Three sets of pesticides are shows: pesticides selected as positive controls, the targeted pesticide set, and a set of negative control pesticides. B. The pesticide-gene network for chlorpyrifos from the CTD, limiting to PD genes/gene products.

Fig. 2.

A. The most common genes involved in pesticide interactions with the targeted set of pesticides based on the pesticide-gene interactions in the CTD. Figure shows the number of unique pesticides each gene has been reported to interact. B. The most common genes involved in pesticide interactions with the targeted set of pesticides based on the pesticide-gene interactions in the CTD, limiting to KEGG PD pathway genes. C. Heatmap indicating the reference count supporting the different pesticide-gene interactions for KEGG PD pathway genes. The reference counts are aggregated across all interaction types within a gene. Reference counts, therefore, may not be unique, if the same reference is indicated in the CTD for multiple unique interactions within the gene. References can be accessed through query of the CTD database online tool. We did not display-six negative controls in the heatmap as they were unrelated to all genes.

Fig. 3.

Map of the KEGG Parkinson disease pathway (hsa05012), indicating pesticide-gene interactions, with the color indicating the number of pesticides in the targeted pesticide set shown to interact with different genes/gene products in the pathways, based on the CTD.

Fig. 4.

A. Heatmap indicating pesticides with chemical-gene networks significantly enriched high-priority PD GO terms, based on the CTD. B. The network of all the autophagy genes that copper sulfate has been shown to interact with leading to significant enrichment. We did not display-six negative controls in the heatmap as they were unrelated to all pathways.

Fig. 5.

Heatmap indicating activity on the Tox21 assays due to the pesticides of interest. The # indicates variability between sources, the heatmap displays the mean activity across screens, however the individual activities across all sources are listed in Supplemental Table 8. Excludes activity from chemicals with suboptimal QC grade, excludes activity (antagonist-type calls) due to cytotoxicity, excludes activity due to auto-fluorescencent, excludes activity with no reporter gene activity readout support. # indicates results varied between source.

Fig. 6.

Overlap between the Tox21 and CTD, indicating if a pesticide showed activity on the target (Tox21) or interaction with the gene (CTD). If different Tox21 assays tested for activity on the same gene targets (e.g., 5 different assays with ESR1 gene target), the highest activity level is shown.

Fig. 7.

Summary profile for the targeted set of pesticides and PD, based on the PEWAS association, Tox21 cytotoxicity, CTD inference score, and Tox21 SR/NR panel ToxScore. The cytotoxicity outcomes (active, inconclusive) are aggregated across the intervals of exposure (6intervalsfrom0 to 40 h), so a higher count indicates more of the respective outcomes was measured at more intervals. For the Tox21 heatmaps, a box color is not shown (e.g., transparent) for pesticides that were not assessed or had low purity data that was excluded.