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Dietary restriction mitigates vascular aging, modulates the cGAS-STING pathway and reverses macrophage-like VSMC phenotypes in progeroid DNA-repair-deficient Ercc1Δ/- mice

GSE294678 Mus musculus (mouse) RNA-seq (gene expression) 29 samples Submitted 2025/04/16 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 29 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
29 samples: 29 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
Dietary Restriction Mitigates Vascular Aging, Modulates the cGAS-STING Pathway and Reverses Macrophage-Like VSMC Phenotypes in Progeroid DNA-Repair-Deficient Ercc1(Δ) (/-) MiceAging cell · 2025
Summary (written by the submitters)
Aging is a major risk factor for cardiovascular diseases and the accumulation of DNA damage significantly contributes to the aging process. This study aimed to identify the underlying molecular mechanisms of vascular aging in DNA-repair-deficient progeroid Ercc1Δ/- mice and to explore the therapeutic effect of dietary restriction (DR).
Read the full summary ↓
RNA sequencing analysis revealed that DR reversed gene expression of vascular aging processes including extracellular matrix remodeling in the Ercc1Δ/- aorta. Notably, this analysis indicated the presence of macrophage-like vascular smooth muscle cells (VSMCs) and suggested cGAS-STING pathway activation. The presence of macrophage-like VSMCs and increased STING1 expression were confirmed in Ercc1Δ/- aortic tissue and were both reduced by DR. In vitro, cisplatin-induced DNA damage activated the cGAS-STING pathway in Ercc1Δ/- VSMCs but not in wildtype VSMCs. These findings identify involvement of the cGAS-STING pathway in DNA damage driven vascular aging and underscore the therapeutic benefits of DR for vascular aging. Furthermore, upstream regulator analysis revealed compounds that may replicate the beneficial effects of DR, providing promising leads for further investigation.
Published in
Dietary Restriction Mitigates Vascular Aging, Modulates the cGAS-STING Pathway and Reverses Macrophage-Like VSMC Phenotypes in Progeroid DNA-Repair-Deficient Ercc1(Δ) (/-) Mice
Stefens SJM, van der Linden J, Heredia-Genestar JM et al. · Aging cell 2025 · PMID 40279334 · doi:10.1111/acel.70062
This dataset
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.

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 GSE294678_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1251075 and SRA study SRP578834. Searching any of these in the dataset finder brings you back here.

Study design
29 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 29 sample titles: 29 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
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