Research Article: The extent of ribosome queuing in budding yeast

Date Published: January 29, 2018

Publisher: Public Library of Science

Author(s): Alon Diament, Anna Feldman, Elisheva Schochet, Martin Kupiec, Yoav Arava, Tamir Tuller, John McCarthy

Abstract: Ribosome queuing is a fundamental phenomenon suggested to be related to topics such as genome evolution, synthetic biology, gene expression regulation, intracellular biophysics, and more. However, this phenomenon hasn’t been quantified yet at a genomic level. Nevertheless, methodologies for studying translation (e.g. ribosome footprints) are usually calibrated to capture only single ribosome protected footprints (mRPFs) and thus limited in their ability to detect ribosome queuing. On the other hand, most of the models in the field assume and analyze a certain level of queuing. Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of ribosomes (dRPFs) using a modified ribosome profiling protocol. We combine our approach with traditional ribosome profiling to generate a detailed profile of ribosome traffic. The data are analyzed using computational models of translation dynamics. The approach was implemented on the Saccharomyces cerevisiae transcriptome. Our data shows that ribosome queuing is more frequent than previously thought: the measured ratio of ribosomes within dRPFs to mRPFs is 0.2–0.35, suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods. We found that specific regions are enriched with queued ribosomes, such as the 5’-end of ORFs, and regions upstream to mRPF peaks, among others. While queuing is related to higher density of ribosomes on the transcript (characteristic of highly translated genes), we report cases where traffic jams are relatively more severe in lowly expressed genes and possibly even selected for. In addition, our analysis demonstrates that higher adaptation of the coding region to the intracellular tRNA levels is associated with lower queuing levels. Our analysis also suggests that the Saccharomyces cerevisiae transcriptome undergoes selection for eliminating traffic jams. Thus, our proposed approach is an essential tool for high resolution analysis of ribosome traffic during mRNA translation and understanding its evolution.

Partial Text: Understanding the dynamics of protein translation is a fundamental question in biology, and has been extensively studied using experimental and computational methods in recent years [1,2]. During translation, multiple ribosomes may translate the same mRNA. The density of ribosomal traffic across the transcript poses several open questions, such as how often a ribosome’s path is blocked by a second ribosome, do queues of multiple ribosomes typically form on mRNAs and what is their effect on the overall translation rate of an mRNA.

Ribosome traffic jam is a phenomenon that has been mentioned many times in the context of genome evolution, biophysics of translation, translation modeling, biotechnology, and more. To the best of our knowledge, this study provides the first comprehensive experimental quantification of traffic jams during translation. We estimated that at least 20% of the ribosomes in the budding yeast S. cerevisiae are positioned close enough to another ribosome on the transcript to be collected as a single footprint in a ribosome profiling experiment. This is a direct indication for the level of queuing in transcripts. Our estimates using sucrose gradient analyses have been consistent across experiments, and with simulated ribosome traffic based on empirical parameters. The observed variance in results obtained from different datasets may have been related to the experiment’s particular conditions and protocol. The current experimental resolution limits the accuracy of analysis to footprints containing a single (mRPF) or a pair (dRPF) of ribosomes, but future Ribo-seq experiments may enable the analysis of larger footprints.

Source:

http://doi.org/10.1371/journal.pcbi.1005951