COMPREHENSIVE TRANSLATIONAL PROFILING AND STE AI UNCOVER RAPID CONTROL OF PROTEIN BIOSYNTHESIS DURING CELL STRESS
Nikolay E. Shirokikh1, Attila Horvath1, Yoshika Janapala1, Katrina Woodward1, Shafi Mahmud1, Alice Cleynen2, Elizabeth E. Gardiner3, Ross D. Hannan1, 4-7, Eduardo Eyras1,8, Thomas Preiss1,9
1 Division of Genome Sciences and Cancer, The John Curtin School of Medical Research, and The Shine-Dalgarno Centre for RNA Innovation, The Australian National University; Canberra, ACT 2601, Australia
2 CNRS, Université de Montpellier, Montpellier, France
3 Division of Genome Sciences and Cancer, The John Curtin School of Medical Research, and The National Platelet Research and Referral Centre, The Australian National University; Canberra, ACT 2601, Australia
4 Department of Biochemistry and Molecular Biology, University of Melbourne; Parkville 3010, Australia
5 Peter MacCallum Cancer Centre; Melbourne 3000, Australia
6 Department of Biochemistry and Molecular Biology, Monash University; Clayton 3800, Australia
7 School of Biomedical Sciences, University of Queensland; St Lucia 4067, Australia
8 Division of Genome Sciences and Cancer, The John Curtin School of Medical Research, and The Centre for Computational Biomedical Sciences, The Australian National University; Canberra, ACT 2601, Australia
9 Victor Chang Cardiac Research Institute; Darlinghurst, NSW 2010, Australia
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 the ribosomes can co-localise. Here we propose and, using rapid crosslinking-based enhanced translation complex profile sequencing (eTCP-seq), confirm co-localisation of ribosomes on mRNA resulting from diffusional dynamics.
We further demonstrate that the co-localised ribosomes (such as disomes) can be of a different origin: some are related to translation elongation delays, others are reflective of the polysome spatial arrangement or descend from stochastic molecular events. Together with the other types of translational complexes, co-localised ribosomes contain new rich information about the translational state of mRNA in vivo.
Employing unbiased machine learning to the eTCP-seq data in a novel AI-based Stochastic Translation Efficiency (STE) pipeline, we demonstrate accurate prediction of the absolute translation output from footprints and detect strong (>50-fold) and highly specific translational regulation on certain mRNAs in just under 10 minutes of glucose starvation response in yeast.
STE is the first application of AI to decipher high-throughput footprinting data. STE ranks mRNAs by the ‘power’ of their translation in a single experiment or between conditions. Importantly, STE does not utilise bias-inducing normalisation to the RNA abundance or signals of a different type and relies on self-normalised data. STE AI thus has new applications in translationally dissecting cell states in disease pathophysiology and drug development, and will facilitate the design of next-generation synthetic biology constructs and mRNA-based therapeutics.
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Speaker
Nikolay E. Shirokikh, John Curtin School of Medical Research (JCSMR) and The Shine-Dalgarno Centre for RNA Innovation
Australie