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TIF1γ regulates stability of T regulatory cells during inflammation [RNA-seq]

GSE308190 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2026/05/21 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 15 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
15 samples: 6 conditions, mostly in triplicateGroups 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)
Regulatory T cells are a suppressor population critical to control inflammation and maintain tissue homeostasis. The TGF-β pathway is a fundamental signal for regulatory T cell differentiation, yet the molecular determinants of how TGF-β signals regulate all these processes are not completely understood. TIF1γ promotes a noncanonical TGF-β/Smad pathway in HSC, however the role of TIF1γ in Treg function has been largely ignored.
Read the full summary ↓
Here we show that TIF1γ deficient Tregs lose stability and acquire an effector phenotype upon an inflammatory stimulus. TIF1γ deficient Tregs gain a Th1-like Treg phenotype or become pro-inflammatory exTregs in an autoimmune model; while in a helminth infection model, Tregs lacking TIF1γ expression acquire a Th2-like phenotype. Loss of Foxp3 in TIF1γ deficient Tregs is cell-intrinsic, increases proliferation and enhances glycolytic capacity upon activation. Upon activation, TIF1g deficient Tregs upregulate a transcriptional profile associated with proliferation, T cell activation and T helper differentiation. Additionally, these cells acquire increased chromatin accessibility motifs for transcriptional regulators such as NFKb and different STATs; strongly suggestive of enhanced responsiveness to inflammatory cues. TIF1γ deficient Tregs show enhanced proliferation regardless TGF-β was present in the media or not; however, the T helper-like bias a Treg has in the absence of TIF1γ, occurs only in the absence of TGF-β signaling. Finally, we identified the beta-catenin pathway as the molecular mechanism driving both TIF1γ-dependent Treg stability and proliferation upon inflammation. Unlike any other previous reports eliminating different TGF-β molecular components in Tregs, deletion of TIF1γ severely impaired Treg function and stability upon inflammation. Altogether, our data demonstrates that TIF1γ is required for the maintenance of a suppressor phenotype and stability of Treg lymphocytes during inflammatory conditions in vivo and represents a new modulatory pathway to manipulate Treg cells for therapeutic purposes.
This dataset
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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 GSE308190_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 PRJNA1329192 and SRA study SRP620739. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
Treg cells, TIFγ ΔFoxp3, Control, RNA-seq ×3 Treg cells, TIFγ ΔFoxp3, TGF-β, RNA-seq ×3 Treg cells, WT, anti-TGF-β, RNA-seq ×3 Treg cells, WT, Control, RNA-seq ×2 Treg cells, WT, TGF-β, RNA-seq ×2 Treg cells, TIFγ ΔFoxp3, anti-TGF-β, RNA-seq ×2

Supports a between-group comparison across 15 samples.

How we read this: 6 replicated groups read from 15 sample titles; they account for 15 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
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