Application of Translation Complex Profile sequencing (TCP-seq) to track the course of translational reprogramming in the exponentially growing culture of budding yeast (Saccharomyces cerevisiae, BY4741) subjected to glucose starvation for 10 minutes.

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 profiling, confirm co-localisation of ribosomes on messenger(m)RNA resulting from the ribosomal diffusional dynamics. We demonstrate that the stochastically co-localised ribosomes are linked with the translation initiation rate and provide a robust variable to model and quantify specific absolute protein output from mRNA. The new type of signal originating from the stochastic co-localisation of ribosomes on mRNA is evidenced by long disome-derived (potentially but rarely, multisome-derived) footprints outside the regions of non-random elongation stalls. Using the stochastic co-localisation component of the translation complex profiling data, we propose a new approach to assess absolute translation rates across mRNA, based on the calculation of Stochastic Translation Efficiency (STE) measure. STE employs a machine learning model trained with multiple TCP-seq-sourced variables. The variables, among others, include stochastic disome signal derived from the total disome footprint data and normalised to the ribosome (RS) footprint abundance across the respective open reading frames (ORFs). The variables also include SSU footprint occurrences over the respective start codons normalised to the RS signal averaged and normalised by the ORF length, to better distinguish between the cases of actively initiated or densely populated but slowly initiated mRNAs. Importantly, STE does not employ any variables resulting from normalisation of the signals of different nature, such as any normalisation of the footprint data by relative RNA abundance measured by RNA-seq. We propose STE as a more robust, sometimes more convenient, alternative to the classical TE (translation efficiency). The main distinguishing features of the STE are the inherent single-molecule-event nature of the main measure component (the long stochastic disome footprint signal), similar type of data used for the numerator(s) and denominator(s) of the calculated variables, resistance to the abundance changes and variations in the differential localisation of RNA and insensitivity to the library preparation techniques. We further hypothesise that similar high-throughput data-derived measures based on parameters dependent on random individual molecular co-incidences can be utilised to provide robust assessment of many molecular processes in dynamic biological systems.全转录组方法为蛋白质生物合成研究赋予了多维度的研究能力，但在绝对蛋白质合成速率的定量分析中仍存在应用瓶颈。本文提出了一种全新的分析策略，并借助改良型翻译复合体谱分析（translation complex profiling, TCP），证实了核糖体扩散动力学介导的核糖体在信使RNA（messenger RNA, mRNA）上的共定位现象。研究表明，随机共定位的核糖体与翻译起始速率密切相关，且可作为可靠变量用于建模并定量特定mRNA的绝对蛋白质产出量。由mRNA上核糖体随机共定位所产生的新型信号，可通过非随机延伸停滞区域外的长片段二核糖体足迹（少数情况下为多核糖体来源的足迹）得到验证。基于翻译复合体谱分析数据中的随机共定位组分，本文提出一种全新的mRNA跨转录本绝对翻译速率评估方法，该方法基于随机翻译效率（Stochastic Translation Efficiency, STE）指标的计算。STE采用经多组TCP-seq来源变量训练得到的机器学习模型，此类变量包括：源自总二核糖体足迹数据、并以对应开放阅读框（open reading frames, ORFs）内的核糖体（RS）足迹丰度进行标准化的随机二核糖体信号；此外，变量还涵盖对应起始密码子处的小亚基（SSU）足迹出现频次，该频次经开放阅读框长度标准化后的平均核糖体（RS）信号校正，以更好区分主动起始或转录本密集但起始缓慢的mRNA样本。值得注意的是，STE未引入任何基于不同性质信号标准化得到的变量，例如通过RNA测序（RNA-seq）测得的相对RNA丰度对足迹数据进行的标准化处理。本文提出STE可作为经典翻译效率（translation efficiency, TE）的更可靠、部分场景下更便捷的替代方案。STE的核心优势在于：其核心度量组分（长片段随机二核糖体足迹信号）具有天然的单分子事件本质，计算变量的分子与分母均采用同类数据，不受转录本丰度变化影响，可抵御RNA差异化定位带来的波动，且不受文库制备技术的干扰。本文进一步推测，基于随机单个分子共发生事件参数的同类高通量数据衍生指标，可用于稳健评估动态生物系统中的众多分子过程。