Evidence map›Paper›PMID 37743474›Full record

ArticleEpigenetics & chromatin2023

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

Zhaonan Zou, Yuka Yoshimura, Yoshihiro Yamanishi, Shinya Oki

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Zhaonan ZouDepartment of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, 53 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.ORCID http://orcid.org/0000-0002-1075-4936
Yuka YoshimuraDepartment of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, 53 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.ORCID http://orcid.org/0009-0001-2192-9949
Yoshihiro YamanishiDepartment of Complex Systems Science, Graduate School of Informatics, Nagoya University, Furo-Cho, Chikusa-Ku, Nagoya, 464-8602, Japan.ORCID http://orcid.org/0000-0003-2279-8773
Shinya OkiDepartment of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, 53 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan. oki.shinya.3w@kyoto-u.ac.jp.ORCID http://orcid.org/0000-0002-4767-3259
Kyoto University · JPNagoya University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Environmental PollutantsAnimalsChromatin Immunoprecipitation SequencingEpigenesis, GeneticEpigenomicsGenomicsHumansMiceEnvironmental PollutantsAction modes of environmental pollutantsATAC-seqChIP-SeqDifferentially accessible genomic regionEpigenetic landscapeTranscription factor

Identifiers

PMID37743474
PMCPMC10518938
OpenAlexW4386994303

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.