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UHRF2 mediates resistance to DNA methylation reprogramming in primordial germ cells [RNAseq_PGCs]

GSE240967 Mus musculus (mouse) DNA methylation 16 samples Submitted 2025/08/12 Platform GPL21103
At a glance

DNA methylation · mouse · 16 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Mus musculus (mouse)
Samples
16 samples: 4 × RNAseq_PGCs_E13.5m_Uhrf2_WT vs 4 × RNAseq_PGCs_E13.5m_Uhrf2_KO vs 4 × RNAseq_PGCs_E13.5f_Uhrf2_WT vs 4 × RNAseq_PGCs_E13.5f_Uhrf2_KOGroups 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
UHRF2 mediates resistance to DNA methylation reprogramming in primordial germ cellsNature communications · 2025
Summary (written by the submitters)
In mammals, primordial germ cells (PGCs) undergo global erasure of DNA methylation with delayed demethylation of germline genes and selective retention of DNA methylation at young retrotransposons. However, the molecular mechanisms of persistent DNA methylation in PGCs remain unclear. Here we report that resistance to DNA methylation reprogramming in PGCs requires UHRF2, the paralog of the DNMT1 cofactor UHRF1.
Read the full summary ↓
PGCs from Uhrf2 knock-out mice show loss of retrotransposon DNA methylation, while DNA methylation is unaffected in somatic cells of Uhrf2-/- mice. Furthermore, Uhrf2-deficient PGCs show precocious demethylation of germline genes and overexpress meiotic genes in females.
Published in
UHRF2 mediates resistance to DNA methylation reprogramming in primordial germ cells
Bender A, Morel M, Dumas M et al. · Nature communications 2025 · PMID 40783491 · doi:10.1038/s41467-025-61954-0
This dataset
Open GSE240967 in finder → Find more mouse 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 GSE240967_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 PRJNA1005954 and SRA study SRP455368. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40783491) ↗
Study design
4 × RNAseq_PGCs_E13.5m_Uhrf2_WT vs 4 × RNAseq_PGCs_E13.5m_Uhrf2_KO vs 4 × RNAseq_PGCs_E13.5f_Uhrf2_WT vs 4 × RNAseq_PGCs_E13.5f_Uhrf2_KO

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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
Datasets on a similar topic

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