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Commensal-derived acetylcholine enhances mucosal immune education [PRESTO-Salsa_screen_InVivo_Ea_corrected]

GSE326298 Homo sapiens (human) RNA-seq (gene expression) 309 samples Submitted 2026/05/29 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 309 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
Homo sapiens (human)
Samples
309 samples: 309 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.
Summary (written by the submitters)
The microbiota produces thousands of potentially bioactive small molecules1-3. High-throughput bioactivity screens of in vitro commensal cultures have exposed microbiota metabolites that shape host physiology by activating diverse G protein-coupled receptors (GPCR)4-7. However, due to technical limitations, the GPCRome-wide bioactivities of in vivo metabolomes, which result from complex diet-microbe-host interactions, remain unexplored.
Read the full summary ↓
Here we leveraged a novel multiplexed GPCR screening technology to assess GPCRome-wide bioactivities of 100 commensal strains grown in vivo in mono-associated germ-free mice or in vitro in bacterial culture media. In vivo and in vitro commensal metabolomes exhibited distinct GPCR activation patterns due to: 1) host-mediated metabolite degradation, 2) in vivo microbial metabolic reprogramming, and 3) biotransformation of dietary substrates. Notably, we identified multiple commensal strains that produced acetylcholine (ACh) in vivo via the conversion of dietary choline, including select Bifidobacteria strains that dominate the microbiome in early life and a probiotic Pediococcus strain. Mechanistically, we identified and characterized the bacterial enzymes mediating this biotransformation in Bifidobacterium breve and Pediococcus pentosaceus, and generated an isogenic mutant B. breve strain lacking ACh production. Mice colonized with an ACh-producing B. breve strain and treated with an FDA-approved acetylcholinesterase inhibitor exhibited enhanced intestinal immunoglobulin A (IgA) production, altered microbiota composition, and increased resistance to enteric infection. Together, these findings suggest that commensal-derived ACh can enhance intestinal IgA production, strengthen mucosal immune defenses, and reinforce host-microbiota mutualism.
This dataset
Open GSE326298 in finder → Find more human 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 GSE326298_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 PRJNA1445077 and SRA study SRP687669. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
309 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 309 sample titles: 309 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

+ 269 more — browse all 309 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.