Population-based dose–response analysis of liver transcriptional response to trichloroethylene in mouse

Studies of gene expression are common in toxicology and provide important clues to mechanistic understanding of adverse e?ects of chemicals. Most prior studies have been performed in a single strain or cell line; however, gene expression is heav-ily in?uenced by the genetic background, and these genotype-expression di?erences may be key drivers of inter-individual variation in response to chemical toxicity. In this study, we hypothesized that the genetically diverse Collaborative Cross mouse population can be used to gain insight and suggest mechanistic hypotheses for the dose- and genetic background-dependent e?ects of chemical exposure. This hypothesis was tested using a model liver toxicant trichloroethylene (TCE). Liver transcriptional responses to TCE exposure were evaluated 24?h after dosing. Transcriptomic dose–responses were examined for both TCE and its major oxidative metabolite trichloroacetic acid (TCA). As expected, peroxisome- and fatty acid metabolism-related pathways were among the most dose–responsive enriched pathways in all strains. However, nearly half of the TCE-induced liver transcriptional perturbation was strain-dependent, with abundant evidence of strain/dose interaction, including in the peroxisomal signaling-associated pathways. These e?ects were highly concordant between the administered TCE dose and liver levels of TCA. Dose–response analysis of gene expression at the pathway level yielded points of departure similar to those derived from the traditional toxicology studies for both non-cancer and cancer e?ects. Mapping of expression–genotype–dose relationships revealed some signi?cant associations; however, the e?ects of TCE on gene expression in liver appear to be highly polygenic traits that are challenging to positionally map. This study highlights the usefulness of mouse population-based studies in assessing inter-individual variation in toxicological responses, but cautions that genetic mapping may be challenging because of the complexity in gene exposure–dose relationships.