GEO series
Maternal Vitamin C Deficiency and Genetic Risk Factors Contribute to Congenital Malformations through Dysregulation of DNA Methylation. [RNA-Seq E8.5]
At a glance
DNA methylation · mouse · 29 samples
- What it is
- DNA methylationChemical marks on DNA that help switch genes on or off.
- Organism
- Mus musculus (mouse)
- Samples
- 29 samples: 22 distinct titles, 6 of them repeatedGroups 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
- Maternal Vitamin C Deficiency and Genetic Risk Factors Contribute to Congenital Defects through Dysregulation of DNA MethylationbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Maternal dietary insufficiencies can reshape the offspring epigenome during gestation, contributing to birth defects and developmental disorders. Vitamin C (VitC) is a critical co-factor for Ten-Eleven-Translocation (TET) DNA demethylases, but the impact of its deficiency on embryonic development remains unclear. Here, we show that insufficient maternal VitC combined with genetic susceptibility can give rise to congenital malformations, including neural tube defects (NTDs), through dysregulation of DNA methylation.
Read the full summary ↓
We previously reported NTDs in Tet1 knockout (KO) mice at low penetrance in C57BL/6J (B6) congenic inbred strains, but at two- to three-fold higher rates when outbred or highly backcrossed (incipient congenic) on a 129S6.B6 background. Similarly, maternal VitC deficiency in L-gulonolactone oxidase (Gulo) KO mice, which like humans are unable to synthesize VitC, resulted in highly penetrant congenital malformations in non-inbred mice, during a vulnerable window coinciding with gastrulation. DNA hypermethylation is a signature of VitC-deprived 129S6.B6-Gulo-/- embryonic headfold tissues, being absent in B6 embryos which are grossly normal. In outbred Gulo-/- embryonic brains, genomic regions harboring hypermethylation – hallmarks of TET dysfunction - increased with the severity of embryonic pathologies. A moderate reduction in VitC status is sufficient to induce hypermethylated regions and cause NTDs. Severe embryonic defects in VitC-deprived embryos can be rescued by timely re-supplementation of VitC at the onset of gastrulation, which normalized DNA methylation at most loci. Our results suggest that promoting timely VitC supplementation by at-risk pregnant mothers may prevent many birth defects currently refractory to folic acid supplementation and enhance the “health-span” of future generations.
Published in
van der Veer BK, Custers C, Brangers W et al.
· bioRxiv : the preprint server for biology 2025
· PMID 40502204
· doi:10.1101/2025.05.27.656260
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 GSE295923_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: Look for beta-value tables or raw IDAT files among the supplementary files.
Also filed as BioProject PRJNA1256791 and SRA study SRP582123. Searching any of these in the dataset finder brings you back here.
Study design
22 distinct titles, 6 of them repeated
RNA-E85-HF_B6_AAmin_female_TP159 ×3
RNA-E85-HF_129_AAplus_female_TP160 ×2
RNA-E85-HF_129_AAplus_male_TP114 ×2
RNA-E85-HF_B6_AAmin_male_TP140 ×2
RNA-E85-HF_B6_AAplus_female_TP158 ×2
RNA-E85-HF_B6_AAplus_male_TP158 ×2
How we read this: Read from 29 sample titles: 22 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
- GSM8960967 RNA-E85-HF_129_AAmin_female_TP153-6
- GSM8960968 RNA-E85-HF_129_AAmin_female_TP197-3
- GSM8960969 RNA-E85-HF_129_AAmin_female_TP198-2
- GSM8960970 RNA-E85-HF_129_AAmin_male_TP145-6
- GSM8960971 RNA-E85-HF_129_AAmin_male_TP153-5
- GSM8960972 RNA-E85-HF_129_AAmin_male_TP197-4
- GSM8960973 RNA-E85-HF_129_AAmin_male_TP198-4
- GSM8960974 RNA-E85-HF_129_AAplus_female_TP152-4
- GSM8960975 RNA-E85-HF_129_AAplus_female_TP156-6
- GSM8960976 RNA-E85-HF_129_AAplus_female_TP160-1
- GSM8960977 RNA-E85-HF_129_AAplus_female_TP160-6
- GSM8960978 RNA-E85-HF_129_AAplus_male_TP114-2
- GSM8960979 RNA-E85-HF_129_AAplus_male_TP114-5
- GSM8960980 RNA-E85-HF_129_AAplus_male_TP152-5
- GSM8960981 RNA-E85-HF_129_AAplus_male_TP160-2
- GSM8960982 RNA-E85-HF_B6_AAmin_female_TP149-9
- GSM8960983 RNA-E85-HF_B6_AAmin_female_TP159-3
- GSM8960984 RNA-E85-HF_B6_AAmin_female_TP159-4
- GSM8960985 RNA-E85-HF_B6_AAmin_female_TP159-7
- GSM8960986 RNA-E85-HF_B6_AAmin_male_TP140-1
- GSM8960987 RNA-E85-HF_B6_AAmin_male_TP140-2
- GSM8960988 RNA-E85-HF_B6_AAmin_male_TP149-6
- GSM8960989 RNA-E85-HF_B6_AAmin_male_TP159-5
- GSM8960990 RNA-E85-HF_B6_AAplus_female_TP155-5
- GSM8960991 RNA-E85-HF_B6_AAplus_female_TP158-6
- GSM8960992 RNA-E85-HF_B6_AAplus_female_TP158-7
- GSM8960993 RNA-E85-HF_B6_AAplus_male_TP154-7
- GSM8960994 RNA-E85-HF_B6_AAplus_male_TP158-4
- GSM8960995 RNA-E85-HF_B6_AAplus_male_TP158-5
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