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Effect of acute and chronic ATF6 activation on gene expression in the colonic epithelium, in specific pathogen-free and germ-free mice.

GSE247122 Mus musculus (mouse) RNA-seq (gene expression) 37 samples Submitted 2025/07/02 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 37 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
37 samples: 37 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
ATF6 activation alters colonic lipid metabolism causing tumour-associated microbial adaptationNature metabolism · 2025
Summary (written by the submitters)
The induction of endoplasmic reticulum unfolded protein responses (UPRER) contributes to cancer development and progression. We recently linked microbiota-related triggers to the tumor-promoting role of signal transducer activating transcription factor 6 (ATF6) in the colon. Here we substantiate the clinical relevance of ATF6 and related bacterial genera in colorectal cancer patient cohorts.
Read the full summary ↓
Spatial and longitudinal bacterial profiling in ATF6 transgenic mice (nATF6IEC) identified tumor-initiating and tumor-progressing shifts in the mucosa-associated microbiota. Transcriptional analysis in intestinal epithelial cells (IEC) of germ-free and specific pathogen-free nATF6IEC mice defined bacteria-specific changes in cellular metabolism enriched for fatty acid biosynthesis. Untargeted metabolomics, isotope-labeling in intestinal organoids and FASN inhibition confirmed ATF6-mediated involvement of long-chain fatty acids in tumorigenesis. Multi-omics data integration identified a bacteria-lipid network characterized by fatty acid efflux, catabolism and detoxification. We postulate chronic ATF6 signaling to drive a clinically relevant pathologic response altering lipid metabolism to select for a tumor-promoting microbiota.
Published in
ATF6 activation alters colonic lipid metabolism causing tumour-associated microbial adaptation
Coleman OI, Sorbie A, Riva A et al. · Nature metabolism 2025 · PMID 40890536 · doi:10.1038/s42255-025-01350-6
This dataset
Open GSE247122 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 GSE247122_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 PRJNA1036249 and SRA study SRP470398. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40890536) ↗
Study design
37 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 37 sample titles: 37 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.