Genome ResearchGenomicsPeer-reviewed journal article
Translation-dependent and independent mRNA decay occur through mutually exclusive pathways that are defined by ribosome density during T Cell activation.
Blandine C. Mercier, Emmanuel LabaronneiD, David CluetiD, Laura Guiguettaz, Nicolas Fontrodona, Alicia A. BicknelliD, Antoine CorbiniD, Mélanie WenckeriD, Fabien AubéiD, Laurent ModoloiD, Karina JouravlevaiD, Didier AuboeufiD, Melissa J. MooreiD, Emiliano P. RicciiD
Abstract
mRNA translation and degradation are strongly interconnected processes that participate in the fine tuning of gene expression. Particularly, targeting mRNAs to translation-dependent degradation (TDD) could attenuate protein expression by making any increase in mRNA translation self-limiting. 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. Our analysis further identifies the length of untranslated regions, the density of ribosomes and the GC content of the coding region as major determinants of TDD magnitude. Consistent with this, all transcripts that undergo changes in ribosome density upon T cell activation display a corresponding change in their TDD level. Surprisingly, the amplitude of translation-independent mRNA decay (TID) appears as a mirror image of TDD. Moreover, TID also responds to changes in ribosome density upon T cell activation but in the opposite direction from the one observed for TDD. Our data demonstrate a strong interconnection between mRNA translation and decay in mammalian cells. Furthermore, they indicate that ribosome density is a major determinant of the pathway by which transcripts are degraded within cells.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 11
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