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Rebalancing Viral and Immune Damage versus Tissue Repair Prevents Death from Lethal Influenza Infection

GSE271812 Mus musculus (mouse) RNA-seq (gene expression) 49 samples Submitted 2025/11/21 Platform GPL21626
At a glance

RNA-seq (gene expression) · mouse · 49 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
49 samples: 13 conditions, mostly with about 4 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.
Paper
Rebalancing viral and immune damage versus repair prevents death from lethal influenza infectionScience (New York, N.Y.) · 2025
Summary (written by the submitters)
Maintaining tissue function while eliminating pathogen-infected cells is a fundamental challenge in host defense. Studies of acute viral respiratory diseases such as influenza and COVID-19 have emphasized the key role of innate inflammatory damage survival on outcome, yet other than high dose steroids, a wide variety of immunomodulatory drugs have only modest positive effects.
Read the full summary ↓
Here we tested more than 50 immunomodulatory regimes in a mouse model of lethal influenza infection, with only the previously reported early depletion of neutrophils showing efficacy. This suggests that the infected host passes an early tipping point in which reduction of further innate immune damage alone cannot rescue adequate physiological tissue function. To re-balance the system around this tipping point, we investigated whether late in infection, partial limitation of viral spread using oseltamivir (Tamiflu) together with enhancement of epithelial repair by blockade of interferon signaling or limitation of further epithelial cell loss mediated by cytotoxic CD8+ T cells would rescue infected animals. These treatments rescued a large fraction of infected animals through the two anticipated mechanisms. These findings provide new insight into the importance of repair processes and the timing of adaptive immune responses in survival from influenza infections.
Published in
Rebalancing viral and immune damage versus repair prevents death from lethal influenza infection
Ichise H, Speranza E, La Russa F et al. · Science (New York, N.Y.) 2025 · PMID 41264690 · doi:10.1126/science.adr4635
This dataset
Open GSE271812 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 GSE271812_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 PRJNA1133664 and SRA study SRP518820. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41264690) ↗
Study design
13 conditions, mostly with about 4 replicates each
PBS_none_4 ×4 IFA_none_4 ×4 IFA_PBS_6 ×4 IFA_Tamiflu_6 ×4 IFA_Tamiflu-CD8_6 ×4 IFA_PBS_8 ×4 IFA_Tamiflu-IFNAR_8 ×4 IFA_Tamiflu_12 ×4 +5 more

Supports a between-group comparison across 49 samples.

How we read this: 13 replicated groups read from 49 sample titles; they account for 49 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

+ 9 more — browse all 49 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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