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Transcriptomic analysis of the impact of a short-term leucine deprivation and evaluation of GCN2 involvement in induced gene differential expression in human primary T cells

GSE279263 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/01/20 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 16 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
Homo sapiens (human)
Samples
16 samples: 16 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
Transcriptomic analysis of human primary T cells after short-term leucine-deprivation and evaluation of kinase GCN2's role in regulating differential gene expressionPloS one · 2025
Summary (written by the submitters)
We previously demonstrated the proof of concept for the functionality of the NUTRIREG system in T cells. This technology allows for the induction of transgene expression following the consumption of a diet deficient in an essential amino acid, by activating the GCN2-ATF4 signaling pathway. To utilize the NUTRIREG technology for therapeutic purposes in cell therapy with short-term EAA deprivation, we explored the general impact of 6-hour leucine deprivation on primary activated human T cells using RNA-seq technology.
Read the full summary ↓
We identified 3,431 differentially expressed genes (adjusted p-value < 0.05) between T cells cultured in control media and those cultured in leucine-deprived media for 6 hours. Gene Set Enrichment Analysis revealed that "TNFα signaling via NFκB", "interferon-γ response", and "unfolded protein response" gene sets were positively enriched, while "mTORC1 signaling", "Myc targets", and "oxidative phosphorylation" gene sets were negatively enriched. T cells were cultured with or without a GCN2 inhibitor, which allowed us to assess the involvement of GCN2 kinase in differential gene expression during the 6-hour leucine deprivation. We found that 59% of the differentially expressed genes (DEGs) in our dataset were dependent on GCN2 kinase (n=2028), with 1,140 up-regulated and 888 down-regulated GCN2-dependent genes.
Published in
Transcriptomic analysis of human primary T cells after short-term leucine-deprivation and evaluation of kinase GCN2's role in regulating differential gene expression
Dougé A, Cueff G, Keime C et al. · PloS one 2025 · PMID 39965008 · doi:10.1371/journal.pone.0317505
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 GSE279263_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 PRJNA1171747 and SRA study SRP537920. Searching any of these in the dataset finder brings you back here.

Study design
16 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 16 sample titles: 16 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
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