Poster Presentation 43rd Lorne Genome Conference 2022

Diffusional variance signatures of single molecule dynamics in the translation complex profiling data (#213)

Attila Horvath 1 , Yoshika Janapala 1 , Eduardo Eyras 1 , Ross D. Hannan 1 2 , Thomas Preiss 1 3 , Nikolay E. Shirokikh 1
  1. Division of Genome Sciences and Cancer, The John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
  2. Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
  3. Victor Chang Cardiac Research Institute, Sydney, NSW, Australia

Full-transcriptome methods have brought versatile power to protein biosynthesis research, but remain difficult to apply for the quantification of absolute protein synthesis rates. Here we propose and, using modified translation complex profile sequencing, confirm co-localisation of ribosomes on messenger(m)RNA resulting from individual molecular dynamics events. We demonstrate that the co-localised ribosomes can be of a different origin. Some co-localised ribosomes are related to the well-accepted translation elongation delays on mRNA. Others are reflective of the mRNA spatial arrangement in polysomes and can represent individual, diffusion-defined events.

The stochastically co-localised ribosomes are linked to the translation initiation efficiency and provide a robust variable to model and quantify specific protein output from mRNA. We demonstrate that the stochastic signal, together with other measurements derived from translation complex profile sequencing, allows to predict the absolute translation initiation and protein biosynthesis rates. Using our models, it is possible to rank mRNAs by the absolute protein output and thus, characterise the ‘power’ of translation control elements across transcripts in a single setting or between different conditions. Our modelling does not use bias-inducing normalisation to the RNA abundance or signals of different types and relies on self-normalised signal pairs.

We demonstrate application of our models to the prototypical example of translational control during yeast response to glucose depletion. We find that glucose stress results in a response that is more complex than previously thought, exhibiting a high degree of selective translational control that acts towards both, suppression and activation of different mRNAs. We uncover several unexpected findings, such as the elevated initiation rate for many of mid-power mRNAs under the stress. More can be discovered by broader application of our methods, as they are permissive of finer dynamics dissection and elucidation of very rapid cell responses at the level of RNA translation.