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Acidity perturbs IL-2 responsiveness, mTORC1 and c-Myc in CD8+ T cells

GSE269681 Mus musculus (mouse) RNA-seq (gene expression) 35 samples Submitted 2024/06/13 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 35 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
35 samples: 35 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.
Summary (written by the submitters)
CD8+T cells play a critical role in tumor control but a range of barriers in the microenvironment including low pH can suppress their function.Here, we demonstrate that acidity dampens T-cell expansion mainly due to impaired IL-2 responsiveness, blunts cytokine secretion upon re-activation, and lowers the cytolytic capacity ofCD8+T cells expressing weak affinity TCR.
Read the full summary ↓
We further reveal decreases in both mTORC1 signaling and c-Myc levels at low pH.Mechanistically, concomitantly to nuclear/cytoplasmic acidification, mTORC1 was disrupted in a Rheb-, Akt/TSC2/PRAS40-, GATOR1- and Lkb1/AMPK -independent manner, and c-Myc levels dropped due to both decreased transcription and higher proteasome-mediated degradation. In addition, lower intracellular levels of glutamine/glutamate/aspartate and elevated proline were observed with no apparent impact on mTORC1 or c-Myc. Overall, we conclude that, due to the broad disruption caused by acidity, multiple interventions will be required to restore T-cell function unless intracellular pH can be effectively restored.
This dataset
Open GSE269681 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 GSE269681_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 PRJNA1123221 and SRA study SRP513513. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
35 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 35 sample titles: 35 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
Datasets on a similar topic

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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.