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Plasticity of epigenomic aging creates vulnerabilities to early-life environmental exposures, reshaping adult cell- and direction-specific trajectories

GSE289162 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 54 samples Submitted 2026/02/11 Platform GPL19057
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 54 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
54 samples: 50 distinct titles, 4 of them repeatedGroups 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
Plasticity of Epigenomic and Transcriptomic Aging Reveals Common Targets for Reprogramming by Environmental ExposuresResearch square · 2025
Summary (written by the submitters)
To understand how early-life exposures shape health and disease trajectories across the lifecourse, the TaRGET II Consortium exposed mice from pre-conception through weaning to diverse environmental toxicants, followed by longitudinal epigenomic and transcriptomic profiling into adulthood. These studies revealed that early-life exposures caused persistent epigenomic reprogramming and correlative disruption of the transcriptome.
Read the full summary ↓
Notably, for the xenoestrogen BPA, obesogen TBT, dioxin TCDD, and air pollutant PM2.5, 40–60% of the genes disrupted in their exposure signatures overlapped with genes differentially expressed during liver aging. Epigenetic histone modifications at enhancers—and, to a lesser extent, promoters—emerged as key targets for this reprogramming. Despite differing mechanisms of action, these 4 toxicants induced a polarized signature in the liver, characterized by cell- and direction-specific disruption of the aging transcriptome. In hepatocytes, genes that typically increase with age, particularly those in metabolic pathways, were downregulated. Conversely, in non-parenchymal cells, genes typically downregulated with age, including those involved in extracellular matrix production, were upregulated. This polarized aging signature correlated strongly with liver transcriptomes from patients with liver disease and hepatocellular carcinoma (HCC), effectively distinguishing healthy from diseased human livers. These findings demonstrate that the plasticity of epigenomic aging is vulnerable to early life environmental exposures, which can reprogram the epigenome, with lasting impacts on the transcriptome, and disease risk, later in life.
Published in
Plasticity of Epigenomic and Transcriptomic Aging Reveals Common Targets for Reprogramming by Environmental Exposures
Walker C, Grimm S, Jangid R et al. · Research square 2025 · PMID 41001535 · doi:10.21203/rs.3.rs-7368222/v1
This dataset
Open GSE289162 in finder → Find more mouse ChIP / ATAC / CUT&Tag datasets →
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.

Series matrix ↓
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 GSE289162_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1221832 and SRA study SRP562900. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41001535) ↗
Study design
50 distinct titles, 4 of them repeated
M_liver_3wk_VEH_2M3V_4me ×2 M_liver_3wk_VEH_3M3V_4me ×2 M_liver_3wk_VEH_4M3V_4me ×2 M_liver_3wk_VEH_1M3V_4me ×2

How we read this: Read from 54 sample titles: 50 distinct titles with little repetition. 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

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