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Antibiotics driven gut microbiome perturbation alters the immune response to the rabies vaccine

GSE294364 Homo sapiens (human) RNA-seq (gene expression) 176 samples Submitted 2025/06/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 176 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
176 samples: 176 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
Antibiotic-induced gut microbiome perturbation alters the immune responses to the rabies vaccineCell host & microbe · 2025
Summary (written by the submitters)
The gut microbiome plays a crucial role in modulating human immunity. Previously, we found that antibiotic-induced microbiome perturbation affected influenza vaccine responses depending on pre-existing immunity levels. However, its impact on primary responses remains unclear. Here, we employed a systems biology approach to analyze the impact of antibiotic administration on primary and secondary immune responses to the rabies vaccine in humans.
Read the full summary ↓
Antibiotic administration disrupted the microbiome, reducing gut bacterial load and causing long-lasting reduction in commensal diversity. This alteration was associated with reduced rabies-specific humoral responses. Multi-omics profiling revealed that antibiotic administration induced: 1) an enhanced pro-inflammatory signature early after vaccination, 2) a shift in the balance of vaccine-specific T-helper 1(Th1) to T-follicular-helper response towards Th1 phenotype, 3) profound alterations in metabolites, particularly in secondary bile acids in blood. By integrating multi-omics datasets, we generated a multi-scale, multi-response network that revealed key regulatory nodes including the microbiota, secondary bile acids, and humoral immunity to vaccination.
Published in
Antibiotic-induced gut microbiome perturbation alters the immune responses to the rabies vaccine
Feng Y, de Jong SE, Oliveira APBN et al. · Cell host & microbe 2025 · PMID 40252648 · doi:10.1016/j.chom.2025.03.015
This dataset
Open GSE294364 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 GSE294364_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 PRJNA1249091 and SRA study SRP578113. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40252648) ↗
Study design
176 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 176 sample titles: 176 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

+ 136 more — browse all 176 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.