ArticleEpigenetics & chromatin2023
Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data.
Article in Epigenetics & chromatin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Deciphering Hierarchical Transcriptional Control of Fatty Acid Metabolism in Megalobrama Amblycephala: Insights from Promoter Architecture, PPAR Synergy, and Network Biology.Marine biotechnology (New York, N.Y.) · 2026Article
- Immune-Epigenetic Effects of Environmental Pollutants: Mechanisms, Biomarkers, and Transgenerational Impact.Current issues in molecular biology · 2025Review
- Unmeasured human transcription factor ChIP-seq data shape functional genomics and demand strategic prioritization.Briefings in functional genomics · 2025Article
- ChIP-Atlas 3.0: a data-mining suite to explore chromosome architecture together with large-scale regulome data.Nucleic acids research · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
backgroundDespite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. 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 "DAR-ChIPEA," to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants.
methodsLarge-scale public ChIP-Seq data (human, n = 15,155; mouse, n = 13,156) 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-TF matrices were then cross-referenced to a repository of TF-disorder 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.
resultsThe 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; Q = 5.278 × 10
conclusionsHighlighting 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.
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