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Premature aging effects on COVID-19 pathogenesis: new insights from mouse models

GSE264189 Mus musculus (mouse) RNA-seq (gene expression) 42 samples Submitted 2024/08/28 Platform GPL28330
At a glance

RNA-seq (gene expression) · mouse · 42 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
42 samples
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
Premature aging effects on COVID-19 pathogenesis: new insights from mouse modelsScientific reports · 2024
Summary (written by the submitters)
Aging is identified as a significant risk factor for severe coronavirus disease-2019 (COVID-19), often resulting in profound lung damage and mortality. Yet, the biological relationship between aging, aging-related comorbidities, and COVID-19 remains incompletely understood. This study aimed to elucidate the age-related COVID19 pathogenesis using a Hutchinson-Gilford progeria syndrome (HGPS) mouse model with humanized ACE2 receptors.
Read the full summary ↓
Pathological features were compared between young, aged, and HGPS hACE2 mice following SAR-CoV-2 challenge. We demonstrated that young mice display robust interferon response and antiviral activity, whereas this response is attenuated in aged mice. Viral infection in aged mice results in severe respiratory tract bleeding, likely contributing a higher mortality rate. In contrast, HGPS hACE2 mice exhibit milder disease manifestations characterized by minor immune cell infiltration and dysregulation of multiple metabolic processes. Comprehensive transcriptome analysis revealed both shared and unique gene expression dynamics among different mouse groups. Collectively, our studies evaluated the impact of SARS-CoV-2 infection on progeroid syndromes using a HGPS hACE2 mouse model, which holds promise as a valuable tool for investigating COVID-19 pathogenesis in individuals with premature aging.
Published in
Premature aging effects on COVID-19 pathogenesis: new insights from mouse models
Haoyu W, Meiqin L, Jiaoyang S et al. · Scientific reports 2024 · PMID 39181932 · doi:10.1038/s41598-024-70612-2
This dataset
Open GSE264189 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 GSE264189_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 PRJNA1101424 and SRA study SRP502276. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39181932) ↗
Samples in this study

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+ 42 more — browse all 42 samples with per-sample file links →

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