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Global Chromatin Remodeling and Widespread Differential Methylation Changes Underlie Brown Adipose Dysfunction and Dysregulated Metabolism in Response to Chronic Air Pollution Exposure: A Key Role for Reciprocal Regulation by HDAC9 and KDN2b

GSE255961 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/07/30 Platform GPL21273
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 12 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
12 samples: 3 × WGBS_FA vs 3 × WGBS_PM vs 3 × ATAC_FA vs 3 × ATAC_PMGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Air pollution modulates brown adipose tissue function through epigenetic regulation by HDAC9 and KDM2BJCI insight · 2025
Summary (written by the submitters)
The Global Burden of Disease implicates air pollution in mediating as much as 20% of Type 2 Diabetes mellitus globally. This profound impact of air pollution is supported by experimental studies suggesting effects on whole body metabolism and insulin resistance.
Read the full summary ↓
We explored the effect of chronic inhalational air pollution exposure for 12 weeks, using a whole body inhalation device in mice that allows for daily exposure to concentrated ambient particulate matter <2.5 microns (6 hours/day, 5 days a week) on whole body metabolism and brown adipose tissue (BAT) function, with detailed investigation of differentially methylated regions (DMRs), chromatin accessibility (differential accessible regions or DARs), and differential gene expression (DEGs) levels using a combination of whole genome bisulfite sequencing, ATAC-sequencing, and RNA-sequencing respectively. A significant impact of chronic PM2.5 exposure on BAT function and whole-body metabolism through alterations in key BAT transcriptional programs resulting in increased redox stress, lipid deposition, fibrosis and reduced thermogenic function of BAT was observed. Significant differential methylation with evidence for both hypomethylation and hypermethylation involving target regions corresponding to both circadian and Nrf2 target genes including sites enriched for transcription factor binding sites implicated in redox stress, insulin resistance and BAT function in response to PM2.5. ATAC sequencing revealed widespread chromatin remodeling involving intronic regions and enhancer elements and loss of DARs in Ucp1 and Gclc major regulatory genes associated with thermogenesis and glutathione synthesis. DARs-DEGs interaction analysis pointed to functional pathways linked to diabetes development, fatty acid biosynthesis activation and activation of JAK-STAT, and MAPK in PM-exposed mice. Integrated analysis of DARs, DEGs and DMRs uncovered the Histone de-acetylase Hdac9 and Kdm2b, a Histone demethylase that demethylates 'Lys-4' and 'Lys-36' of histone H3. ChiP studies demonstrated that Hdac9 physically interacts with the BAT transcription factor Prdm16 and rorα in BAT decreasing their expression and providing at least one direct mechanism for our observations. Collectively our results results link DNA methylation with control of chromatin accessibility and gene expression changes involved in the alteration of brown fat metabolism linking chronic air pollution exposure with alterations in BAT and whole-body metabolism
Published in
Air pollution modulates brown adipose tissue function through epigenetic regulation by HDAC9 and KDM2B
Palanivel R, Dazard JE, Park B et al. · JCI insight 2025 · PMID 40985891 · doi:10.1172/jci.insight.187023
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE255961_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1077306 and SRA study SRP490033. Searching any of these in the dataset finder brings you back here.

Study design
3 × WGBS_FA vs 3 × WGBS_PM vs 3 × ATAC_FA vs 3 × ATAC_PM

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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