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Interleukin-23 promotes a pro-inflammatory Th17 cell state by stabilizing RORγt and suppressing glucocorticoid receptor activity II

GSE296401 Mus musculus (mouse) RNA-seq (gene expression) 38 samples Submitted 2026/06/12 Platform GPL21626Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 38 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
38 samples: 10 conditions, mostly with about 4 replicates eachGroups 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)
Interleukin-23 receptor (IL-23R) signaling is critical for the generation of pro-inflammatory CD4+ IL-17-producing T cells (Th17) that can drive autoimmune tissue inflammation, but the underlying mechanisms are not clear. We integrated phosphoproteomic and transcriptomic data downstream of IL-23R and IL-12R, which share a common subunit, to identify mechanisms engaged specifically by IL-23.
Read the full summary ↓
We identified chromodomain helicase DNA-binding protein 1 (CHD1), an epigenetic regulator, and the glucocorticoid receptor (GR), a transcription factor (TF), as mediators of IL-23R signaling. IL-23R activation promoted CHD1 interaction with TF STAT3 and co-binding at the TF RORγt locus to enforce a pro-inflammatory Th17 state. Conversely, IL-23R signaling altered phosphorylation of the GR, thereby preventing its activation and nuclear translocation, ultimately impairing GR-driven inhibition of pro-inflammatory Th17 gene programs. Our findings uncover two mechanisms by which IL-23 promotes a pro-inflammatory Th17 cell state, offering potential therapeutic targets for treating Th17-driven autoimmune tissue inflammation and restoring homeostasis.
This dataset
Open GSE296401 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 GSE296401_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 PRJNA1259064 and SRA study SRP583186. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly with about 4 replicates each
CHD1_KO_CNS ×4 CHD1_KO_dLN ×4 GR_WT_CNS ×4 GR_cKO_CNS ×4 WT_Ctrl ×4 KO_Ctrl ×4 WT_Dex ×4 KO_Dex ×4 +2 more

Supports a between-group comparison across 38 samples.

How we read this: 10 replicated groups read from 38 sample titles; they account for 38 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

Search all mouse RNA-seq datasets in GEO →

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