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Inflammation Impacts Androgen Receptor Signaling in Basal Prostate Stem Cells Through Interleukin 1 Receptor Antagonist

GSE271146 Mus musculus (mouse) RNA-seq (gene expression) 282 samples Submitted 2024/10/09 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 282 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
282 samples: 282 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.
Paper
Inflammation impacts androgen receptor signaling in basal prostate stem cells through interleukin 1 receptor antagonistCommunications biology · 2024
Summary (written by the submitters)
Chronic prostate inflammation in patients with benign prostate hyperplasia (BPH) correlates with the severity of symptoms. How inflammation contributes to prostate enlargement and/or BPH symptoms and the underlying mechanisms remain unclear. In this study, we utilized a unique transgenic mouse model that mimics chronic non-bacterial prostatitis in men and investigated the impact of inflammation on androgen receptor (AR) in basal prostate stem cells (bPSC) and their differentiation in vivo.
Read the full summary ↓
We found that inflammation significantly enhanced AR levels and activity in bPSC. More importantly, we identified interleukin 1 receptor antagonist (IL-1RA) as a crucial regulator of AR in bPSC during inflammation. IL-1RA was one of the top genes upregulated by inflammation, and inhibiting IL-1RA reversed the enhanced AR activity in organoids derived from inflamed bPSC. Additionally, IL-1RA appeared to activate AR by counteracting IL-1's inhibitory effect. Furthermore, using a lineage tracing model, we observed that inflammation induced bPSC proliferation and differentiation into luminal cells even under castrate conditions, indicating that AR activation driven by inflammation is sufficient to promote bPSC proliferation and differentiation. Taken together, our study uncovered novel mechanisms through which inflammation modulates AR signaling in bPSC and induces bPSC luminal differentiation that may contribute to prostate hyperplasia.
Published in
Inflammation impacts androgen receptor signaling in basal prostate stem cells through interleukin 1 receptor antagonist
Cooper PO, Yang J, Wang HH et al. · Communications biology 2024 · PMID 39455902 · doi:10.1038/s42003-024-07071-y
This dataset
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.

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 GSE271146_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1130057 and SRA study SRP517124. Searching any of these in the dataset finder brings you back here.

Study design
282 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 282 sample titles: 282 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

+ 242 more — browse all 282 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.