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Analysis of Host and Microbiome Responses to Antibiotics Reveals Microbiota-Independent Effects on Gut Immune Response [multiabx]

GSE293180 Mus musculus (mouse) RNA-seq (gene expression) 97 samples Submitted 2026/07/02 Platform GPL21626
At a glance

RNA-seq (gene expression) · mouse · 97 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
97 samples: 18 conditions, mostly with about 6 replicates eachGroups 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)
Antibiotics are thought to affect host physiology by altering the gut microbiota. But which effects of antibiotics are microbiota-dependent or -independent is not clear. Here, we present a resource encompassing host and microbiota response to antibiotic treatment and computational tools to analyze them. We show how antibiotic administration routes affect host and microbiota responses and how combining antibiotics alters their individual activity.
Read the full summary ↓
We developed CompoRes, a tool to determine whether perturbation affects the host directly or via the microbiota, and discovered microbiota-independent effects of antibiotic treatment. We found that circadian expression of MHC class II in the colon is functional in germ-free mice and is controlled by feeding via glucose. This expression pattern was lost during vancomycin treatment as the antibiotic altered feeding behaviour in a microbiota-independent manner. Together, our study provides tools for analysis of host response to different microbiota compositions which can also identify microbiota-independent effects of perturbations.
This dataset
Open GSE293180 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 GSE293180_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 PRJNA1243311 and SRA study SRP574005. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 conditions, mostly with about 6 replicates each
Ampicillin-IP ×6 Ampicillin-IV ×6 Ampicillin-PO ×6 Metronidazole-PO ×6 Mix-IP ×6 Mix-IV ×6 Mix-PO ×6 Neomycin-IP ×6 +10 more

Supports a between-group comparison across 97 samples.

How we read this: 18 replicated groups read from 97 sample titles; they account for 97 of them. 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

+ 57 more — browse all 97 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.