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Posttranscriptional Expression Regulation: What Determines Translation Rates?

机译:转录后表达调控:什么决定翻译率?

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Recent analyses indicate that differences in protein concentrations are only 20%–40% attributable to variable mRNA levels, underlining the importance of posttranscriptional regulation. Generally, protein concentrations depend on the translation rate (which is proportional to the translational activity, TA) and the degradation rate. By integrating 12 publicly available large-scale datasets and additional database information of the yeast Saccharomyces cerevisiae, we systematically analyzed five factors contributing to TA: mRNA concentration, ribosome density, ribosome occupancy, the codon adaptation index, and a newly developed “tRNA adaptation index.” Our analysis of the functional relationship between the TA and measured protein concentrations suggests that the TA follows Michaelis–Menten kinetics. The calculated TA, together with measured protein concentrations, allowed us to estimate degradation rates for 4,125 proteins under standard conditions. A significant correlation to recently published degradation rates supports our approach. Moreover, based on a newly developed scoring system, we identified and analyzed genes subjected to the posttranscriptional regulation mechanism, translation on demand. Next we applied these findings to publicly available data of protein and mRNA concentrations under four stress conditions. The integration of these measurements allowed us to compare the condition-specific responses at the posttranscriptional level. Our analysis of all 62 proteins that have been measured under all four conditions revealed proteins with very specific posttranscriptional stress response, in contrast to more generic responders, which were nonspecifically regulated under several conditions. The concept of specific and generic responders is known for transcriptional regulation. Here we show that it also holds true at the posttranscriptional level.
机译:最近的分析表明,蛋白质浓度的差异仅归因于可变的mRNA水平,仅为20%至40%,这突显了转录后调控的重要性。通常,蛋白质浓度取决于翻译速率(与翻译活性TA成正比)和降解速率。通过整合啤酒酵母的12个公开可用的大规模数据集和其他数据库信息,我们系统地分析了促成TA的五个因素:mRNA浓度,核糖体密度,核糖体占有率,密码子适应指数和新开发的“ tRNA适应指数” 。”我们对TA和所测蛋白质浓度之间的功能关系的分析表明,TA遵循Michaelis–Menten动力学。计算出的TA以及测得的蛋白质浓度,使我们能够估计标准条件下4,125种蛋白质的降解率。与最近发布的降解率的显着相关性支持我们的方法。此外,基于新开发的评分系统,我们鉴定并分析了受转录后调控机制,按需翻译的基因。接下来,我们将这些发现应用于四种压力条件下蛋白质和mRNA浓度的公开数据。这些测量结果的整合使我们能够在转录后水平比较条件特异性反应。我们对在所有四种条件下测量的所有62种蛋白质的分析表明,与非常普通的应答器相比,该蛋白具有非常特异的转录后应激反应,而后者在几种情况下均非特异性调节。特异性和通用应答者的概念对于转录调节是已知的。在这里,我们证明了它在转录后水平上也适用。

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