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UHRF1-Mediated Maintenance DNA Methylation is Required for Induced Regulatory T Cell Function Following Influenza Induced Lung Injury in Young Adult Mice [invitro RNA-seq]

GSE290602 Mus musculus (mouse) DNA methylation 58 samples Submitted 2025/09/03 Platform GPL30172
At a glance

DNA methylation · mouse · 58 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Mus musculus (mouse)
Samples
58 samples: 16 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 (Tregs) promote resolution of inflammation and repair of epithelial damage following lung injury, thus representing a possible cellular therapeutic for ARDS. Foxp3+ natural regulatory T cells (nTregs) require specific DNA methylation patterns maintained by the epigenetic regulator, Ubiquitin-like with PHD and RING finger domains 1, (UHRF1), to function, but the DNA methylation requirements of in vitro induced Tregs (iTregs) remain unknown.
Read the full summary ↓
Here we tested whether the loss of Uhrf1 would augment the pro-recovery function of iTregs during viral pneumonia. We found that the loss of maintenance DNA methylation in iTregs results in reduced engraftment and a delayed repair response after experimental influenza pneumonia. Transcriptional and DNA methylation profiling of sorted Uhrf1-deficient iTregs post infection provide insight into the mechanisms underlying their impaired ability to promote repair, with Uhrf1-defiicent iTregs demonstrating greater instability via gain of alternate effector T cell lineage-defining transcription factors. Strategies to promote stability iTregs could be leveraged to further augment their pro-recovery function during pneumonia and ARDS.
This dataset
Open GSE290602 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 GSE290602_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1228959 and SRA study SRP566573. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in triplicate
Day5_Early_pos_pos_iTreg ×6 Day12_Early_pos_pos_iTreg ×6 Day5_EarlyfliTreg ×6 Day12_EarlyfliTreg ×6 Day12_Del_pos_pos_nTreg ×3 Day5_Early_pos_pos_nTreg ×3 Day12_Early_pos_pos_nTreg ×3 Day5_DelflnTreg ×3 +8 more

Supports a between-group comparison across 58 samples.

How we read this: 16 replicated groups read from 58 sample titles; they account for 58 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

+ 18 more — browse all 58 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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