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Akkermansia muciniphila contributes to fasting-mediated radioprotection by inducing epigenetic and transcriptional changes in small intestinal epithelial cells

GSE306673 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 32 samples Submitted 2026/06/08 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 32 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
32 samples: 32 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
Fasting primes small intestinal regeneration after damage via a microbiome-metabolite-chromatin axisProceedings of the National Academy of Sciences of the United States of America · 2026
Summary (written by the submitters)
Fasting protects the small intestine epithelial cells from high-dose radiation injury, yet the mechanisms linking fasting to epithelial regeneration remain incompletely understood. Akkermansia muciniphila (AKK) is both necessary and sufficient for fasting-induced radioprotection. Selective depletion of AKK abolishes fasting-mediated preservation of SI stem cells, while recolonization restores epithelial integrity and survival.
Read the full summary ↓
AKK produces the short-chain fatty acid propionate, which, together with host-derived β-hydroxybutyrate (β-OHB), induces histone H3 acetylation (H3K27ac, H3K9ac) and β-hydroxybutyrylation (H3K9bhb) in crypt epithelial cells, remodeling enhancer–promoter landscapes. CUT&Tag and single-cell ATAC-seq analyses show that fasting, in an AKK-dependent manner, expands a Clu⁺Olfm4⁺ “revival” stem cell (persister cells) population enriched for HNF, FOXA, GATA and KLF transcription factor motifs, establishing a pro-regenerative chromatin state that persists through injury-repair phases. CTCF and BORIS activities persist, suggesting a role in maintaining epigenetic memory for intestinal regeneration after radiation. This microbiota–metabolite–chromatin axis primes persister cells for rapid regeneration after damage and offers a strategy to protect normal tissues during radiation therapy.
Published in
Fasting primes small intestinal regeneration after damage via a microbiome-metabolite-chromatin axis
Barrodia P, Saw AK, Jeter-Jones SL et al. · Proceedings of the National Academy of Sciences of the United States of America 2026 · PMID 42335240 · doi:10.1073/pnas.2529215123
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 GSE306673_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 peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1311610 and SRA study SRP613718. Searching any of these in the dataset finder brings you back here.

Study design
32 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 32 sample titles: 32 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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