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The immune checkpoint TIGIT is upregulated on T cells during bacterial infection and is a potential target for immunotherapy

GSE234086 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2024/10/31 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 6 × Infected KO vs 6 × Infected WT vs 5 × Naïve KO vs 5 × Naïve WTGroups 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
The immune checkpoint TIGIT is upregulated on T cells during bacterial infection and is a potential target for immunotherapyImmunology and cell biology · 2024
Summary (written by the submitters)
Antibiotic resistance is a major public health threat, and alternatives to antibiotic therapy are urgently needed. Immunotherapy, particularly the blockade of inhibitory immune checkpoints, is a leading treatment option in cancer and autoimmunity. In this study, we used a murine model of Salmonella Typhimurium infection to investigate whether immune checkpoint blockade could be applied to bacterial infection.
Read the full summary ↓
We found that the immune checkpoint T-cell immunoglobulin and ITIM domain (TIGIT) was significantly upregulated on lymphocytes during infection, particularly on CD4+ T cells, drastically limiting their proinflammatory function. Blockade of TIGIT in vivo using monoclonal antibodies was able to enhance immunity and improve bacterial clearance. The efficacy of anti-TIGIT was dependent on the capacity of the antibody to bind to Fc (fragment crystallizable) receptors, giving important insights into the mechanism of anti-TIGIT therapy. This research suggests that targeting immune checkpoints, such as TIGIT, has the potential to enhance immune responses toward bacteria and restore antibacterial treatment options in the face of antibiotic resistance. In this experiment, we investigated the effect of TIGIT on CD4+ T cells by performing bulk RNA-sequencing on WT or TIGIT-/- CD4+ T cells in the context of S. Typhimurium infection.
Published in
The immune checkpoint TIGIT is upregulated on T cells during bacterial infection and is a potential target for immunotherapy
McCulloch TR, Rossi GR, Miranda-Hernandez S et al. · Immunology and cell biology 2024 · PMID 38873699 · doi:10.1111/imcb.12794
This dataset
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.

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 GSE234086_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA980088 and SRA study SRP441092. Searching any of these in the dataset finder brings you back here.

Study design
6 × Infected KO vs 6 × Infected WT vs 5 × Naïve KO vs 5 × Naïve WT

Supports a case/control comparison: 6 samples read as cases, 7 as controls.

How we read this: 4 replicated groups read from 24 sample titles; they account for 22 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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