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H3K36 Methylation - a Guardian of Epigenome Integrity [RNA-seq]

GSE274364 Homo sapiens (human) DNA methylation 26 samples Submitted 2025/10/23 Platform GPL21103Platform GPL20301
At a glance

DNA methylation · human · 26 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
26 samples: 10 conditions, mostly in triplicateGroups 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
H3K36 Methylation as a Guardian of Epigenome IntegrityNature communications · 2025
Summary (written by the submitters)
H3K36 methylation is a key epigenetic mark with critical roles in development and disease, yet its genome-wide functions remain incompletely understood. Here, we systematically dissect the role of H3K36 methylation using a series of CRISPR-engineered mouse mesenchymal stem cell lines with progressive knockouts of the five H3K36 methyltransferases: NSD1, NSD2, NSD3, SETD2 and ASH1L—culminating in quintuple knockout (QuiKO) cells entirely lacking H3K36me2/3.
Read the full summary ↓
We demonstrate that H3K36me2 enriched at active cis-regulatory elements predominantly influences enhancer activity and supports the expression of enhancer-dependent genes. Within gene bodies, loss of H3K36me2/3 permits invasion by the repressive H3K27me2/3 modifications, thereby disrupting transcriptional fidelity. Unexpectedly, we uncover a previously undescribed relationship between H3K36me and H3K9me3: upon H3K36me2 depletion, SUV39H1-mediated H3K9me3 redistributes from large constitutive heterochromatic domains into euchromatin, leading to widespread epigenome restructuring and transcriptional changes. This redistribution is accompanied by a collapse of 3D genome organization, including weakened compartment boundaries and a profound loss of long-range interactions. Finally, we validate key findings in two human HNSCC cell lines overexpressing the H3K36M oncohistone, highlighting the conserved role of H3K36 methylation in maintaining epigenome integrity across species and cell types, and providing further mechanistic insights into the downstream consequences of this oncogenic mutation. Together, these data reveal that H3K36me is essential not only for local gene regulation but also for safeguarding the higher-order structure and functional balance of the genome.
Published in
H3K36 Methylation as a Guardian of Epigenome Integrity
Padilla R, Shipman GA, Horth C et al. · Nature communications 2025 · PMID 41381501 · doi:10.1038/s41467-025-66365-9
This dataset
Open GSE274364 in finder → Find more RNA-seq 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 GSE274364_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 beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1146048 and SRA study SRP525143. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41381501) ↗
Study design
10 conditions, mostly in triplicate
C3H10T1-2_ASH1L-KO_RNAseq ×3 C3H10T1-2_NSD1-2-3-SETD2-ASH1L-QuiKO_RNAseq ×3 C3H10T1-2_NSD1-2-3-SETD2-QKO_RNAseq ×3 C3H10T1-2_NSD1-2-SETD2-TKO_RNAseq ×3 C3H10T1-2_NSD3-KO_RNAseq ×3 C3H10T1-2_Parental_sgCtrl_RNAseq ×3 Cal27_H3K36M_OE_RNAseq ×2 Cal27_PA_RNAseq ×2 +2 more

Supports a between-group comparison across 26 samples.

How we read this: 10 replicated groups read from 26 sample titles; they account for 26 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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