GEO series

Y chromosome gene KDM5D coordinates epigenetic and metabolic control of sex differences in CD8⁺ T cell immunity [CUT&Run]

GSE331334 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 8 samples Submitted 2026/07/23 Platform GPL19057

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 8 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: 8 samples: 3 × KD_H3K4me vs 3 × WT_H3K4meGroups 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.

Summary (written by the submitters)

Sex differences in anti-tumor immunity remain incompletely understood yet have important implications for tumor biology and immunotherapy. Here, we combine murine colorectal cancer (CRC) models, primary CD8+ T-cell perturbation studies, and single-cell transcriptomic analyses of human CRC to define a male-biased regulatory axis that limits CD8+ T-cell effector function.

Read the full summary ↓

Male CD8+ T cells displayed reduced cytokine production, proliferation, cytotoxicity, TCR abundance, and TCR signaling relative to female CD8+ T-cells. Mechanistically, depletion of histone demethylase KDM5D, encoded by the Y chromosome, enhanced TCR expression and clustering, increased proximal TCR signaling, reduced cholesterol biosynthetic programs, and lowered expression of exhaustion-associated genes. In human CRC single-cell datasets, male tumor-infiltrating CD8+ T-cells showed enrichment for exhaustion and cholesterol-associated programs, supporting the translational relevance of this axis. Consistent with the known role of cholesterol in TCR clustering and activation, lovastatin reduced exhaustion-associated marker expression in male CD8+ T-cells and delayed CRC tumor growth in vivo – findings which align with the male-specific CRC risk reduction role of statins. Together, these findings identify KDM5D as a sex-chromosome-linked regulator of male CD8+ T-cell dysfunction and validate cholesterol metabolism as an actionable target for sex-specific immunotherapeutic modulation in cancer.

This dataset

Open GSE331334 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 GSE331334_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 PRJNA1467701 and SRA study SRP701724. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × KD_H3K4me vs 3 × WT_H3K4me

Supports a between-group comparison across 6 samples.

How we read this: 2 replicated groups read from 8 sample titles; they account for 6 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

GSM9744093 KD_H3K4me1 KD_H3K4me
GSM9744094 KD_H3K4me2 KD_H3K4me
GSM9744095 KD_H3K4me3 KD_H3K4me
GSM9744096 KD_H3K27ac
GSM9744097 WT_H3K4me1 WT_H3K4me
GSM9744098 WT_H3K4me2 WT_H3K4me
GSM9744099 WT_H3K4me3 WT_H3K4me
GSM9744100 WT_H3K27ac

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.