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Dissection of kinase-dependent and independent functions of RIPK3 in inflammatory disease

GSE305184 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/03/11 Platform GPL17021
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
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
A viable kinase-inactive RIPK3 D143N mouse model reveals its scaffold function in driving TNF-induced inflammatory disorderCell death discovery · 2026
Summary (written by the submitters)
RIPK3 is a key regulator of necroptosis and inflammation, yet the distinct contributions of its kinase-dependent and -independent functions remain poorly understood. Here, we report the generation and characterization of RIPK3 D143N mutant mice, a novel model that selectively ablates kinase activity without inducing apoptosis or altering protein expression.
Read the full summary ↓
Unlike previously described RIPK3 mutants (D161N/K51A), the D143N variant fully rescues Casp8-deficient embryonic lethality while effectively blocking necroptosis induced by multiple stimuli. In TNF-a-induced SIRS, we demonstrated that RIPK3's non-kinase activity exacerbates inflammation and tissue damage through JAK-STAT activation. Similarly, the kinase-independent RIPK3 activity promoted influenza-induced pathogenesis. Our findings establish the D143N mutant as a unique tool for dissecting non-catalytic functions of RIPK3, and provide critical insights for targeting RIPK3 for disease treatment.
Published in
A viable kinase-inactive RIPK3 D143N mouse model reveals its scaffold function in driving TNF-induced inflammatory disorder
Du Y, Li J, Zhao C et al. · Cell death discovery 2026 · PMID 41730853 · doi:10.1038/s41420-026-02962-x
This dataset
Open GSE305184 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 GSE305184_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 PRJNA1304553 and SRA study SRP608217. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41730853) ↗
Samples in this study

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