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Reduced function of the adaptor SH2B3 promotes T1D via altered gc cytokine-regulated, T cell intrinsic immune tolerance

GSE290401 Mus musculus (mouse) RNA-seq (gene expression) 60 samples Submitted 2025/02/27 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 60 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
60 samples: 60 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
Reduced Function of the Adaptor SH2B3 Promotes T1D via Altered Cytokine-Regulated, T-Cell-Intrinsic Immune ToleranceDiabetes · 2025
Summary (written by the submitters)
Genome-wide assocation studie have identified SH2B3 as an important non-MHC gene for islet autoimmunity and type 1 diabetes (T1D). In this study, we found a single haplotype strongly associated with risk for human T1D that includes the sinlge nucleotide variant rs3184505*T in SH2B3. To better characterize the role of SH2B3 in T1D, we used moouse modeling and found a T cell-intrisic role for SH2B3 regulating peripheral tolerance.
Read the full summary ↓
SH2B3 deficiency had minimal effect on TCR signaling or. proliferation across antigen doses, yet enhance cell survival and cytokine signaling including common gamma chain-dependent and interferon-gamma receptor signaling. SH2B3 deficient CD8+T cells showed augmented STAT5-Myc and effector function gene expression partially reversed when blocking autocrine IL-2 in culture. Using the RIP-mOVA model, we found T cells lacking SH2B3 promoted early islet destruction and diasbetes without requiring CD4+ T cell help. SH2B3-deficient cells demontrated increased survival post-transfer compared to control cells despite a similar proliferation profile in the same host. Next we created a spontatneous NOD.Sh2b3-/- mouse model and found markedly increased incidence and accelerated T1D across genders. Collectively, these studies identify SH2B3 as a critical mediator of peripheral T cell tolerance lmiting the T cell response to self-antigens.
Published in
Reduced Function of the Adaptor SH2B3 Promotes T1D via Altered Cytokine-Regulated, T-Cell-Intrinsic Immune Tolerance
Watson TK, Rosen ABI, Drow T et al. · Diabetes 2025 · PMID 40048557 · doi:10.2337/db24-0655
This dataset
Open GSE290401 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 GSE290401_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 PRJNA1227910 and SRA study SRP566174. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40048557) ↗
Study design
60 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 60 sample titles: 60 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

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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