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NRF1-mediated Innate Immune Response Drives Cellular Senescence and Aging

GSE294536 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/12/31 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 3 × Prol_wt, vs 3 × Prol_ko, vs 3 × Sen_wt, vs 3 × Sen_ko,Groups 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
NRF1-mediated innate immune response drives inflammagingNature communications · 2025
Summary (written by the submitters)
Senescent cell accumulation in organs is a hallmark of aging, leading to diminished regenerative capacity and organ dysfunction. Aberrant innate immune responses contribute significantly to cellular senescence, yet the precise interplay between innate immunity and senescence remains poorly characterized. Here, we elucidate the pivotal role of nuclear respiratory factor 1 (NRF1) in orchestrating innate immune responses that drive senescence and the senescence-associated secretory phenotype (SASP).
Read the full summary ↓
NRF1 deficiency delayed cellular senescence and ameliorated age-related deterioration in multiple organs. Mechanistically, NRF1 enhanced SASP by transcriptionally regulating TBK1 and IRF3, critical nodes in innate immunity essential for senescence induction. Conversely, NRF1 deficiency suppressed innate immune activation, thereby attenuating inflammation associated with senescence and aging. Additionally, DNA damage activated ATM kinase, which phosphorylated NRF1 at Ser393, augmenting the NRF1-TBK1/IRF3-type I interferon axis and exacerbating cellular senescence. Furthermore, NRF1 knockdown treatment effectively mitigated aging phenotypes and extended lifespan in aged mice. Collectively, our findings underscore the essential role of the ATM-NRF1-TBK1/IRF3-type I interferon axis in DNA damage-induced senescence, suggesting that targeted NRF1 modulation holds therapeutic promise for improving the aging process.
Published in
NRF1-mediated innate immune response drives inflammaging
Lei H, Zhao T, Zhang J et al. · Nature communications 2025 · PMID 41381492 · doi:10.1038/s41467-025-66368-6
This dataset
Open GSE294536 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 GSE294536_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 PRJNA1250455 and SRA study SRP578554. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41381492) ↗
Study design
3 × Prol_wt, vs 3 × Prol_ko, vs 3 × Sen_wt, vs 3 × Sen_ko,

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
Datasets on a similar topic

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