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Sustained activation of the integrated stress response is a mediator of Long Covid.

GSE336805 Mus musculus (mouse) RNA-seq (gene expression) 40 samples Submitted 2026/07/31 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 40 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
40 samples: 40 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)
Why some individuals recover from COVID-19, while others do not, remains unknown. Mitochondrial dysfunction has been identified as a hallmark of Long COVID, however, multiple mitochondria haplotypes exist in the human population. Here, we used two mouse models that share the same nuclear genome but differ in mitochondrial genomes and compare their recovery rate after SARS-CoV-2 infection.
Read the full summary ↓
While in one model, mice recovered, in the other model, male mice did not recover. RNAseq analysis, over a post-infection time course, revealed activation of the integrated stress response (ISR), which is known to be activated during acute viral infection through the PKR kinase. Therefore, and as expected, in recovered mice, activation of the ISR is transient and restricted to the acute phase of infection. However, in mice that did not recover, the ISR shows a sustained activation at time points where viral particles are no longer detectable. Rather, the activation of the ISR correlates with the up-regulation of endogenous mitochondria derived dsRNA, which were recently shown to be the preferred substrate of the PKR kinase. Sustained activation of the ISR was confirmed in 3 independent datasets of Long COVID patients in human. We show that treatment with the ISR inhibitor, ISRIB, correlates with recovery in mice affected by Long COVID. Since ISRIB is currently tested in clinical trials for other conditions and is reported to improve symptoms associated with Long COVID such as memory lost, gut inflammation and cardiomyopathy in preclinical models, our data opens the possibility to explore ISRIB as an intervention for the treatment of Long COVID.
This dataset
Open GSE336805 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 GSE336805_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 PRJNA1483383 and SRA study SRP713363. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
40 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 40 sample titles: 40 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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