Séminaire de Probabilités et Statistique :

Le 12 février 2024 à 13:45 - UM - Bât 09 - Salle de conférence (1er étage)

Présentée par Shirokikh Nikolay - Australian National University

Comprehensive translational profiling and STE AI to measure absolute protein biosynthesis rates and rapid changes in mRNA usage

Translational control is important in all life but remains a challenge to accurately quantify. When ribosomes translate messenger (m)RNA into proteins, they attach to the mRNA in series, forming poly(ribo)somes, in which ribosomes can co-localise. By analysing ribosomal co-localisation on mRNA using enhanced translation complex profile sequencing (TCP-seq) based on rapid in vivo crosslinking, we detect long disome footprints outside areas of non-random elongation stalls. We further show that these footprints are linked to translation initiation and protein bioproduction rates, providing a previously-missed feature of functional significance.

Applying advanced machine learning to the comprehensive ribosome localisation patterns we derive from the rich in vivo footprinting data for the first time, we create a novel, accurate and self-normalised measure of translation (stochastic translation efficiency, STE). STE has new applications in interrogating mRNA function and performance in live cells, and dissecting cell states in disease pathophysiology and drug development.

Using STE to study nutrient starvation in yeast as a model system with extremely fast response, we refine translational control from other rapid RNA changes, and highlight metabolic rearrangements invoked by the cells solely at the translational level. In this system, we show that STE is invaluable for identifying the absolute translational ranking of mRNA and its control elements under specific conditions. We envisage STE will aid the development of next-generation synthetic biology designs and mRNA-based therapeutics.

Zoom link: https://umontpellier-fr.zoom.us/j/94087408185