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Nutrient-driven histone code determines exhausted CD8+ T cell fates [ChIP-seq]

GSE235005 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 50 samples Submitted 2024/12/11 Platform GPL30172
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 50 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
50 samples: 23 conditions, mostly in duplicateGroups 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
Nutrient-driven histone code determines exhausted CD8(+) T cell fatesScience (New York, N.Y.) · 2025
Summary (written by the submitters)
To investigate the role of ACSS2 and ACLY in the regulation of epigenetic states of CD8+ T cells in cancer and chronic viral infections Exhausted T cells (TEX) in cancer and chronic viral infections undergo metabolic and epigenetic remodeling, impairing protective capabilities. However, the impact of nutrient metabolism on epigenetic modifications that control TEX differentiation remains unclear.
Read the full summary ↓
Our study reveals that TEX cells shift from acetate to citrate metabolism by downregulating acetyl-CoA synthetase 2 (ACSS2) while maintaining ATP-citrate lyase (ACLY) activity. This metabolic switch increases citrate-dependent histone acetylation, mediated by histone acetyltransferase KAT2A-ACLY interactions, at TEX signature-genes while reducing acetate-dependent histone acetylation, dependent on p300-ACSS2 complexes, at effector and memory T cell genes. Nuclear ACSS2 overexpression or ACLY inhibition prevents TEX differentiation and enhances tumor-specific T cell responses. These findings unveil a nutrient-driven histone code governing CD8+ T cell differentiation, with implications for metabolic- and epigenetic-based T cell therapies
Published in
Nutrient-driven histone code determines exhausted CD8(+) T cell fates
Ma S, Dahabieh MS, Mann TH et al. · Science (New York, N.Y.) 2025 · PMID 39666821 · doi:10.1126/science.adj3020
This dataset
Open GSE235005 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 GSE235005_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 PRJNA984164 and SRA study SRP445900. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39666821) ↗
Study design
23 conditions, mostly in duplicate
LCMV-Cl13_TEXprog_H3K27ac ×2 LCMV-Cl13_TEXterm_H3K27ac ×2 LCMV-Cl13_TEXprog_H3K9ac ×2 LCMV-Cl13_TEXterm_H3K9ac ×2 LCMV-Cl13_TEXprog_H4ac ×2 LCMV-Cl13_TEXterm_H4ac ×2 TEX_sgscramble_H3K27ac ×2 TEX_sgAcly_H3K27ac ×2 +19 more

Supports a between-group comparison across 46 samples.

How we read this: 23 replicated groups read from 50 sample titles; they account for 46 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

+ 10 more — browse all 50 samples with per-sample file links →

Datasets on a similar topic

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