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Transcriptomic alterations including p53 pathway dysregulation prime DNMT3A mutant cells for transformation

GSE275532 Mus musculus (mouse) RNA-seq (gene expression) 13 samples Submitted 2025/06/18 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 13 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
13 samples: 4 × Untreated Dnmt3a mutant, vs 4 × Untreated wild type, vs 3 × Irradiated Dnmt3a mutant, vs 2 × Irradiated wild type,Groups 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
Transcriptomic changes including p53 dysregulation prime DNMT3A mutant cells for transformationEMBO reports · 2025
Summary (written by the submitters)
DNMT3A mutations are prevalent in haematologic malignancies. Our mouse model introduced the murine homologue (R878H) of the human ‘hotspot’ R882H mutation into the mouse Dnmt3a locus, resulting in globally reduced DNA methylation in all tissues. Mice with heterozygous R878H mutations developed g-radiation induced thymic lymphoma more rapidly than control mice, suggesting a vulnerability to stress stimuli in Dnmt3aR878H/+ cells.
Read the full summary ↓
In competitive transplantations, Dnmt3aR878H/+ Lin-Sca-1+Kit+ (LSK) cells had a competitive advantage over WT cells, indicating a self-renewal phenotype at the expense of differentiation. RNA-sequencing of Dnmt3aR878H/+ LSKs exposed to low dose g-radiation showed downregulation of the p53 pathway. Accordingly, reduced PUMA expression was observed by flow cytometry in the bone marrow of g-irradiated Dnmt3aR878H/+ mice due to altered p53 signalling. These findings provide new insights as to how DNMT3A mutations cause subtle changes in the transcriptome of LSK cells which contribute to their increased self-renewal and propensity for malignant transformation.
Published in
Transcriptomic changes including p53 dysregulation prime DNMT3A mutant cells for transformation
Lawrence EM, Cooray A, Kueh AJ et al. · EMBO reports 2025 · PMID 40307617 · doi:10.1038/s44319-025-00450-4
This dataset
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.

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 GSE275532_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1151635 and SRA study SRP528302. Searching any of these in the dataset finder brings you back here.

Study design
4 × Untreated Dnmt3a mutant, vs 4 × Untreated wild type, vs 3 × Irradiated Dnmt3a mutant, vs 2 × Irradiated wild type,

Supports a case/control comparison: 3 samples read as cases, 2 as controls.

How we read this: 4 replicated groups read from 13 sample titles; they account for 13 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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