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Defective prelamin A processing promotes RIPK1-driven necroptosis and inflammation in premature aging

GSE183102 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/12/31 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 24 × Sample_R21071403-ZWGroups 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)
Alterations in the architecture and dynamics of the nuclear lamina have a causal role in normal and premature aging. Accumulation of progerin, a toxic isoform of lamin A, or prelamin A, the precursor protein of lamin A, is the major underlying cause of premature aging syndromes. However, the molecular mechanism that mediates premature aging remains poorly defined.
Read the full summary ↓
Here we report that the accumulation of farnesylated forms of prelamin A at the nuclear lamina triggers RIPK1 kinase-dependent signaling that leads to inflammation and RIPK3-mediated necroptosis in multiple tissues of a mouse model of accelerated aging (Zmpste24-/- mice). We show that prelamin A recruits RIPK1 in the nucleus to facilitate its activation in response to TNFα stimulation. Inhibition of prelamin A farnesylation prevents its accumulation at the nuclear envelope, and consequently reduces RIPK1 activation and necroptosis. The causality of RIPK1-driven necroptosis and inflammation in premature aging was demonstrated by genetic inactivation of RIPK1 or knockout of RIPK3, which prevented the premature degeneration of multiple tissues and organs in Zmpste24-/- mice and significantly extended their longevity. Our findings provide in vivo proof of principle for the feasibility of inhibition of RIPK1 kinase to slow down the progression of physiological and pathological aging.
This dataset
Open GSE183102 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 GSE183102_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 PRJNA759384 and SRA study SRP335275. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 × Sample_R21071403-ZW

Supports a between-group comparison across 24 samples.

How we read this: 1 replicated groups read from 24 sample titles; they account for 24 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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