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Although transcriptome-based approaches are widely used to predict associations between chemicals and disorders, the molecular cues regulating pollutant-derived gene expression changes remain unclear. Therefore, we developed a data-mining approach, termed \u201cDAR-ChIPEA,\u201d to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Large-scale public ChIP-Seq data (human,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200915,155; mouse,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200913,156) were used to predict TFs that are enriched in the pollutant-induced differentially accessible genomic regions (DARs) obtained from epigenome analyses (ATAC-Seq). The resultant pollutant\u2013TF matrices were then cross-referenced to a repository of TF\u2013disorder associations to account for pollutant modes of action. We subsequently evaluated the performance of the proposed method using a chemical perturbation data set to compare the outputs of the DAR-ChIPEA and our previously developed differentially expressed gene (DEG)-ChIPEA methods using pollutant-induced DEGs as input. We then adopted the proposed method to predict disease-associated mechanisms triggered by pollutants.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The proposed approach outperformed other methods using the area under the receiver operating characteristic curve score. The mean score of the proposed DAR-ChIPEA was significantly higher than that of our previously described DEG-ChIPEA (0.7287 vs. 0.7060;\n                      <jats:italic>Q<\/jats:italic>\n                      \u2009=\u20095.278\u2009\u00d7\u200910\n                      <jats:sup>\u201342<\/jats:sup>\n                      ; two-tailed Wilcoxon rank-sum test). The proposed approach further predicted TF-driven modes of action upon pollutant exposure, indicating that (1) TFs regulating Th1\/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; (2) fine particulates (PM\n                      <jats:sub>2.5<\/jats:sub>\n                      ) inhibit the binding of C\/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and (3) lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Highlighting genome-wide chromatin change upon pollutant exposure to elucidate the epigenetic landscape of pollutant responses outperformed our previously described method that focuses on gene-adjacent domains only. Our approach has the potential to reveal pivotal TFs that mediate deleterious effects of pollutants, thereby facilitating the development of strategies to mitigate damage from environmental pollution.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13072-023-00510-w","type":"journal-article","created":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T22:01:46Z","timestamp":1695592906000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data"],"prefix":"10.1186","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1075-4936","authenticated-orcid":false,"given":"Zhaonan","family":"Zou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2192-9949","authenticated-orcid":false,"given":"Yuka","family":"Yoshimura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2279-8773","authenticated-orcid":false,"given":"Yoshihiro","family":"Yamanishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4767-3259","authenticated-orcid":false,"given":"Shinya","family":"Oki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"issue":"6","key":"510_CR1","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.1016\/j.jaci.2019.08.037","volume":"144","author":"SW Lee","year":"2019","unstructured":"Lee SW, Yon DK, James CC, Lee S, Koh HY, Sheen YH, et al. 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