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The DNA demethylase TET1 modifies the impact of maternal folic acid status on embryonic brain development

GSE275393 Mus musculus (mouse) RNA-seq (gene expression) 40 samples Submitted 2024/10/24 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 40 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
40 samples: 40 samples, each with a distinct title — no replicated groupsGroups 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
The DNA demethylase TET1 modifies the impact of maternal folic acid status on embryonic brain developmentEMBO reports · 2025
Summary (written by the submitters)
Folic acid (FA) prevents neural tube defects (NTDs), but it remains unclear why many cases of human NTDs still occur despite FA supplementation. Here, we investigate how the DNA demethylase TET1 interacts with maternal dietary FA status to regulate embryonic brain development. We determined that cranial NTDs in Tet1-/- embryos display higher penetrance in non-inbred than in inbred genetic backgrounds and are resistant to FA supplementation across strains.
Read the full summary ↓
Maternal diets that are either too rich or deficient in FA are linked to an increased incidence of mild cranial deformities in wild type and Tet1+/- offspring and altered DNA hypermethylation in Tet1-/- embryos primarily at neurodevelopmental loci. Excess FA in Tet1-/- embryos results in phospholipid metabolite loss and reduced expression of multiple membrane solute carriers, including a FA transporter gene which exhibits increased promoter DNA methylation, thus mimicking a pseudo-FA deficiency. FA deficiency reveals an impact of Tet1 haploinsufficiency that contributes to DNA hypermethylation and susceptibility to NTDs. Overall, our study reveals that epigenetic dysregulation may underlie the resistance of NTDs to FA supplementation.
Published in
The DNA demethylase TET1 modifies the impact of maternal folic acid status on embryonic brain development
Chen L, van der Veer BK, Chen Q et al. · EMBO reports 2025 · PMID 39578553 · doi:10.1038/s44319-024-00316-1
This dataset
Open GSE275393 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 GSE275393_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: 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 PRJNA1150593 and SRA study SRP527867. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39578553) ↗
Study design
40 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 40 sample titles: 40 distinct titles with little repetition. 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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