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Characterization of Subchronic Lung and Brain Consequences Caused by Mouse-Adapted SARS-CoV-2 and Influenza A Infection of C57BL6 mice

GSE315893 Mus musculus (mouse) RNA-seq (gene expression) 44 samples Submitted 2026/01/12 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 44 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
44 samples: 44 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
Characterization of subchronic lung and brain consequences caused by mouse-adapted SARS-CoV-2 and influenza A infection of C57BL6 miceFrontiers in immunology · 2026
Summary (written by the submitters)
SARS-CoV-2 and, to a lesser extent, influenza A can lead to long-term complications in the respiratory and nervous systems. However, the mechanisms driving post-viral sequelae remain poorly understood. To address this gap, we longitudinally characterized C57BL/6 mice infected with sublethal doses of mouse-adapted SARS-CoV-2 (MA30) or influenza A (PR8).
Read the full summary ↓
Lung and brain tissues were analyzed at 14-, 21-, and 28-days post-infection (DPI) using histological analysis and bulk-RNA sequencing. In the lungs, both infections caused prolonged inflammation and fibrosis. MA30-infected lungs showed persistent upregulation of inflammatory, coagulation, complement, as well as fibrotic, and extracellular matrix (ECM) remodeling pathways at 21 DPI, alongside downregulation of epithelial junction and metabolic program pathways. In contrast, PR8-infected lungs exhibited a strong acute interferon response and chronic upregulation of basal epithelial markers (e.g., Krt5, Krt14), consistent with epithelial regeneration. Notably, only PR8-infected mice displayed KRT5+ progenitor cell migration into damaged lung regions, indicating divergence in repair mechanisms. Neither MA30-infected, nor PR8-infected mice had detectable brain infection. However, MA30 mice, but not PR8-infected mice exhibited an elevated frequency of microhemorrhages at early timepoints and marked neuroinflammation at all timepoints. Transcriptomic profiling of MA30-infected brains showed enrichment for up-regulation of ECM remodeling, vascular dysfunction, IL6-signaling pathways along with a virus-specific disruption of the hypothalamic–pituitary axis with MA30 infection not seen in PR8-infected brains. These included genes linked to neuroinflammation, sensory processing disruption, and microvascular injury, mirroring clinical features of Long COVID. Together, these findings establish distinct tissue-specific trajectories of long-term pathology following SARS-CoV-2 and influenza infection and provide a foundation for dissecting the mechanisms of post-viral lung and brain disease.
Published in
Characterization of subchronic lung and brain consequences caused by mouse-adapted SARS-CoV-2 and influenza A infection of C57BL6 mice
Currey J, Wang C, Mayer MG et al. · Frontiers in immunology 2026 · PMID 41822471 · doi:10.3389/fimmu.2026.1755141
This dataset
Open GSE315893 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 GSE315893_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 PRJNA1399744 and SRA study SRP660606. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41822471) ↗
Study design
44 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 44 sample titles: 44 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

+ 4 more — browse all 44 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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