The extent of ribosome queuing in budding yeast

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.核糖体排队（ribosome queuing）是一种基础性现象，被认为与基因组进化、合成生物学、基因表达调控、细胞内生物物理学等诸多领域密切相关。然而，该现象尚未在基因组层面完成量化。 尽管如此，现有翻译研究手段（如核糖体足迹技术）通常仅针对单核糖体保护足迹（single ribosome protected footprints，mRPFs）进行校准优化，因此在检测核糖体排队方面的能力存在显著局限。另一方面，该领域的多数模型均假定并分析特定程度的排队现象。 本研究提出一种基于改良型核糖体 profiling 实验方案的实验-计算联用方法，用于核糖体排队研究：该方法通过对从成对核糖体中提取的RNA足迹（dRPFs）进行测序实现。我们将该方法与传统核糖体 profiling 分析相结合，以获取核糖体运动的精细图谱。数据通过翻译动力学计算模型开展分析，并在酿酒酵母（Saccharomyces cerevisiae）转录组上完成了方法的实施验证。 我们的数据表明，核糖体排队的发生频率远超此前预期：测得的成对核糖体足迹（dRPFs）内核糖体数与单核糖体保护足迹（mRPFs）的比值为0.2~0.35，这意味着至少有1~5个正在翻译的核糖体处于拥堵状态；这类拥堵的核糖体无法被传统方法捕获。 我们发现，特定区域富集了拥堵核糖体，例如开放阅读框（ORF）的5'端以及单核糖体保护足迹峰值上游区域等。尽管排队现象与转录本上更高的核糖体密度相关（这是高翻译效率基因的典型特征），但我们仍报道了部分案例：拥堵在低表达基因中相对更为严重，甚至可能受到正向选择。 此外，我们的分析表明，编码区对细胞内tRNA水平的适配性越高，核糖体排队程度越低。我们的分析还提示，酿酒酵母转录组正经历以消除拥堵为目标的选择压力。 综上，我们提出的方法是实现mRNA翻译过程中高分辨率核糖体运动分析、并解析其进化机制的核心工具。