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Translation-dependent and independent mRNA decay occur through mutually exclusive pathways that are defined by ribosome density during T Cell activation [RNA-Seq]

GSE159295 Mus musculus (mouse) RNA-seq (gene expression) 105 samples Submitted 2024/03/01 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 105 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
105 samples: 35 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.
Paper
Translation-dependent and -independent mRNA decay occur through mutually exclusive pathways defined by ribosome density during T cell activationGenome research · 2024
Summary (written by the submitters)
Post-transcriptional control is crucial for regulating protein expression, both basally and in response to extracellular cues. Proper signal transduction requires tight control of both response induction and termination. One way protein expression might be attenuated is by targeting mRNAs to translation-dependent degradation (TDD), thus making any increase in protein expression self-limiting.
Read the full summary ↓
However, the extent to which TDD is a general mechanism for limiting protein expression is currently unknown. Here we describe a comprehensive analysis of basal and signal-induced TDD in mouse primary CD4 T cells. Our data indicate that most cellular transcripts are decayed to some extent in a translation-dependent manner, both in resting and activated cells. Analysis of transcript features revealed that 3’UTR length and ribosome density are major determinants of the magnitude of TDD as well as GC content and amino acid identity. Consistently, upon T cell activation, all transcripts that undergo changes in ribosome density display a corresponding change in their level of TDD. Surprisingly, the amplitude of translation-independent mRNA decay appears as a mirror image of TDD. Moreover, translation-independent decay also responds to changes in ribosome density upon T cell activation but in the opposite direction to those observed for TDD. Our data demonstrate a strong interconnection between mRNA translation and decay in mammalian cells. Furthermore they indicate that ribosome loading is a major determinant of the pathway by which transcripts are degraded within cells
Published in
Translation-dependent and -independent mRNA decay occur through mutually exclusive pathways defined by ribosome density during T cell activation
Mercier BC, Labaronne E, Cluet D et al. · Genome research 2024 · PMID 38508694 · doi:10.1101/gr.277863.123
This dataset
Open GSE159295 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 GSE159295_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 PRJNA668315 and SRA study SRP286877. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38508694) ↗
Study design
35 conditions, mostly in triplicate
T-cell Activated 1h CHX ×3 T-cell Activated 3h CHX ×3 T-cell Activated 1h Harringtonine ×3 T-cell Activated 3h Harringtonine ×3 T-cell Activated 1h DRB CHX ×3 T-cell Activated 3h DRB CHX ×3 T-cell Activated 1h DRB Harr ×3 T-cell Activated 3h DRB Harr ×3 +27 more

Supports a between-group comparison across 105 samples.

How we read this: 35 replicated groups read from 105 sample titles; they account for 105 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

+ 65 more — browse all 105 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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