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Systematic perturbations of SETD2, NSD1, NSD2, NSD3 and ASH1L reveal their distinct contributions to H3K36 methylation

GSE243566 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 125 samples Submitted 2024/09/25 Platform GPL21103Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 125 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
125 samples: 45 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
Systematic perturbations of SETD2, NSD1, NSD2, NSD3, and ASH1L reveal their distinct contributions to H3K36 methylationGenome biology · 2024
Summary (written by the submitters)
Methylation of histone 3 lysine 36 (H3K36me) has emerged as an essential epigenetic component for the faithful regulation of gene expression. Despite its importance in development, disease, and cancer, how the molecular agents collectively shape the genome-wide deposition of H3K36me is unclear. Here, we use mouse mesenchymal stem cells to comprehensively perturb the components of the H3K36me deposition machinery and infer the activities of the five most prominent players: SETD2, NSD1, NSD2, NSD3, and ASH1L.
Read the full summary ↓
We find that H3K36me2 is the most abundant of the three methylation states and that it is predominantly deposited at intergenic regions by NSD1, and in part by NSD2. In contrast, H3K36me1/3 are most abundant within exons and are positively correlated with gene expression. We demonstrate that while SETD2 is responsible for depositing most H3K36me3, it also deposits H3K36me2 within transcribed genes. Additionally, loss of SETD2 results in an increase of exonic H3K36me1, suggesting that other H3K36 methyltransferases may prime gene bodies with lower methylation states ahead of transcription. Through a reductive approach, we uncover the distribution patterns of NSD3- and ASH1L-catalyzed H3K36me2. While NSD1/2 establish broad intergenic H3K36me2 domains, NSD3 deposits H3K36me2 peaks centered on active promoter and enhancer regions. Meanwhile, the activity of ASH1L is restricted to regulatory elements of developmentally relevant genes, and our analyses implicate PBX2 as a potential recruitment factor. Overall, our study provides new insights into the regulation of H3K36me and helps to consolidate the wealth of previous observations in the context of structured analyses.
Published in
Systematic perturbations of SETD2, NSD1, NSD2, NSD3, and ASH1L reveal their distinct contributions to H3K36 methylation
Shipman GA, Padilla R, Horth C et al. · Genome biology 2024 · PMID 39390582 · doi:10.1186/s13059-024-03415-3
This dataset
Open GSE243566 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 GSE243566_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 PRJNA1018940 and SRA study SRP461879. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39390582) ↗
Study design
45 conditions, mostly in triplicate
C3H10T1-2_Parental_H3K36me1 ×3 C3H10T1-2_Parental_H3K36me2 ×3 C3H10T1-2_Parental_H3K36me3 ×3 C3H10T1-2_Parental_input ×3 C3H10T1-2_ASH1L_KO_H3K36me1 ×3 C3H10T1-2_ASH1L_KO_H3K36me2 ×3 C3H10T1-2_ASH1L_KO_H3K36me3 ×3 C3H10T1-2_ASH1L_KO_input ×3 +38 more

Supports a between-group comparison across 124 samples.

How we read this: 45 replicated groups read from 125 sample titles; they account for 124 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

+ 85 more — browse all 125 samples with per-sample file links →

Datasets on a similar topic

Search all mouse ChIP / ATAC / CUT&Tag datasets in GEO →

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