GEO series

Defective autophagy in CD4 T cells drives liver fibrosis via type 3 inflammation

GSE292735 Mus musculus (mouse) RNA-seq (gene expression) 11 samples Submitted 2025/04/14 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 11 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: 11 samples: 6 × FK vs 5 × FWGroups 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: Defective autophagy in CD4 T cells drives liver fibrosis via type 3 inflammationNature communications · 2025

Summary (written by the submitters)

Abstract: Conventional CD4 T cells represent a major source of inflammatory mediators that drive progression of chronic liver disease to fibrosis and to end-stage cirrhosis. Identification of T cell pathways that limits the inflammatory response could thus have therapeutic relevance.

Read the full summary ↓

Here we show, using both human samples and mouse models, that autophagy is deficient in CD4 T cells from patients with advanced fibrosis, and that loss of autophagy following genomic deletion of ATG5 in T cells is associated with the emergence of pathogenic IL-17A+IFN-g+ Th17 T cells that drive liver fibrosis in mice. Mechanistically, liver CD4 T cells lacking autophagy display a Th17 glycolytic phenotype associated with enhanced type 3 cytokine (i.e. IL-17A and GM-CSF) release, shifting hepatic myofibroblasts, hepatocytes and macrophages toward a proinflammatory phenotype. We also show that autophagy can be rescued in CD4 T cells from patients with extensive liver fibrosis, leading to decreased frequency of pathogenic Th17 cells and reduced GM-CSF levels; in addition, limited fibrosis is observed in mice in which Rubicon, a negative regulator of autophagy, is deleted specifically in their T cells. Our findings thus implicate autophagy in CD4 T cells as a key therapeutic target to control inflammation-driven fibrosis during chronic liver injury.

Published in

Defective autophagy in CD4 T cells drives liver fibrosis via type 3 inflammation

Al Sayegh R, Wan J, Caër C et al. · Nature communications 2025 · PMID 40274816 · doi:10.1038/s41467-025-59218-y

This dataset

Open GSE292735 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 GSE292735_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 PRJNA1241174 and SRA study SRP572779. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40274816) ↗

Study design

6 × FK vs 5 × FW

Supports a between-group comparison across 11 samples.

How we read this: 2 replicated groups read from 11 sample titles; they account for 11 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

GSM8864513 FK75108 FK
GSM8864514 FK75110 FK
GSM8864515 FK75111 FK
GSM8864516 FK75112 FK
GSM8864517 FK75113 FK
GSM8864518 FK75264 FK
GSM8864519 FW33335 FW
GSM8864520 FW75106 FW
GSM8864521 FW75107 FW
GSM8864522 FW75109 FW
GSM8864523 FW75262 FW

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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.