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Ribosomal S6 kinase 1 regulates 'inflammaging' via the senescence secretome II.

GSE218683 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2024/07/09 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 samples: 18 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.
Summary (written by the submitters)
Inhibition of the nutrient-responsive mTOR (mammalian target of rapamycin) signalling pathway including the key downstream S6 kinase 1 (S6K1) extends lifespan and improves healthspan in mice. However, the underlying mechanisms contributing to the profound age-related benefits observed with loss of S6K1 signalling are unclear. Cellular senescence is a stable growth arrest accompanied by an inflammatory phenotype (termed the senescence-associated secretory phenotype, or SASP).
Read the full summary ↓
Whileboth cellular senescence and SASP-mediated chronic inflammation contribute to age-related pathology the specific role of S6K1 signalling in these process have not been determined . Here, focussing on mouse liver a key target tissue for the beneficial health effects of loss of S6K1 signalling, we show that S6K1 deletion does not reduce senescence but ameliorates inflammation and immune cell infiltration in aged livers. Using human and mouse models of senescence, we demonstrated that reduced inflammation is a liver intrinsic effect associated with S6K deletion. Furthermore, gene expression analysis suggested that downregulated cGAS/STING and IRF3 activation might mediate the impaired SASP observed upon S6K deletion. Using a hepatic oncogene induced senescence model, we showed in vivo that S6K1 deletion results in reduced IRF3 activation, impaired production of cytokines such as IL1 and reduced immune infiltration. Overall, deletion of S6K reduces inflammation in the liver suggesting that suppression of the inflammatory SASP by S6K could contribute to explain the beneficial effects of inhibiting these pathways on healthspan and lifespan.
This dataset
Open GSE218683 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 GSE218683_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 PRJNA904792 and SRA study SRP409528. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 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 18 sample titles: 18 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
Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.