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Cell senescence emerges as a hallmark and therapeutic target of chronic intracellular infection

GSE338180 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/07/14 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples: 4 × MPI-2 exposed to Mycobacterium abscessus cells at day 1 biol vs 4 × MPI-2 exposed to Mycobacterium abscessus cells at day 5 biol vs 4 × MPI-2 uninfected cells at day 1 biol vs 4 × MPI-2 uninfected cells at day 5 biolGroups 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)
Intracellular pathogens are ideal candidates for modelling the pathophysiology of chronic infection, as they hide within host cells and avoid immune clearance by reshaping cellular fate. This study unveils how the intracellular pathogen Mycobacterium abscessus (Mab) reprograms alveolar macrophages towards a senescent state -a multifaced phenotype marked by proliferative arrest, distinctive morphological shifts, activation of DNA damage signalling cascade, and secretion of senescence-associated secretory phenotype (SASP) factors.
Read the full summary ↓
Mab-induced DNA damage emerged as the primary driver of senescence, while SASP secretion acts as a molecular broadcaster, propagating secondary senescence in neighbouring, uninfected cells through paracrine signalling. This cascading wave of amplified senescence underscores a detrimental effect on the tissue microenvironment, turning it into a chronic inflammatory niche that facilitates Mab persistence and immune evasion. By leveraging evidence of chronic infection-induced senescence, we prototyped a therapeutic approach where senolytic drugs selectively eradicated senescent cells and significantly reduced bacterial burden, similarly to antibiotics.
This dataset
Open GSE338180 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 GSE338180_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 PRJNA1492668 and SRA study SRP716753. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × MPI-2 exposed to Mycobacterium abscessus cells at day 1 biol vs 4 × MPI-2 exposed to Mycobacterium abscessus cells at day 5 biol vs 4 × MPI-2 uninfected cells at day 1 biol vs 4 × MPI-2 uninfected cells at day 5 biol

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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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