Identifying unique exposure-specific transgenerational differentially DNA methylated region epimutations in the genome using hybrid deep learning prediction models.

Abstract

Exposure to environmental toxicants can lead to epimutations in the genome and an increase in differential DNA methylated regions (DMRs) that have been linked to increased susceptibility to various diseases. However, the unique effect of particular toxicants on the genome in terms of leading to unique DMRs for the toxicants has been less studied. One hurdle to such studies is the low number of observed DMRs per toxicants. To address this hurdle, a previously validated hybrid deep-learning cross-exposure prediction model is trained per exposure and used to predict exposure-specific DMRs in the genome. Given these predicted exposure-specific DMRs, a set of unique DMRs per exposure can be identified. Analysis of these unique DMRs through visualization, DNA sequence motif matching, and gene association reveals known and unknown links between individual exposures and their unique effects on the genome. The results indicate the potential ability to define exposure-specific epigenetic markers in the genome and the potential relative impact of different exposures. Therefore, a computational approach to predict exposure-specific transgenerational epimutations was developed, which supported the exposure specificity of ancestral toxicant actions and provided epigenome information on the DMR sites predicted.