Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data

ABSTRACT

Despite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Transcriptome-based approaches are widely used to predict associations between chemicals and disorders. However, the molecular cues regulating gene expression remain unclear. To elucidate the action modes of pollutants, we proposed a data-mining approach, termed “DAR-ChIPEA,” combining epigenome (ATAC-Seq) and large-scale public ChIP-Seq data (human, n = 15,155; mouse, n = 13,156) to identify transcription factors (TFs) that are enriched not only in gene-adjacent domains but also across differentially accessible genomic regions, thereby integratively regulating gene expression upon pollutant exposure. The resultant pollutant–TF matrices are then cross-referenced to a repository of TF–disorder associations to account for pollutant modes of action. For example, TFs that regulate Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; fine particulates (PM_2.5) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms. Thus, our approach has the potential to reveal pivotal TFs that mediate adverse effects of pollutants, thereby facilitating the development of strategies to mitigate environmental pollution damage.