Regulation of Protein Synthesis by Synonymous Codon Usage

摘要 蛋白质的生物合成占增殖细胞消耗的营养和能量的30%或更多， 而且，在癌症和神经疾病等人类疾病中，它经常被失调。我的研究 该计划旨在开发一种受实验限制的整体蛋白质合成生物物理模型- 细胞水平，目的是预测正常和疾病细胞状态下的蛋白质水平。我们目前的重点是 关于解释核糖体在mRNAs上运动的动力学及其对蛋白质表达的影响。这项研究 基于我们最近的发现，同义密码子的使用是核糖体动力学和核糖体动力学的一个强有力的决定因素 细菌营养限制生长过程中的蛋白质丰度。值得注意的是，我们的实验观察并不是 用密码子使用偏向或tRNA丰度的已知等级来解释。我们目前的结果表明 特定密码子的偏向使用可以调节微生物在生命的几个领域的蛋白质表达 在营养可获得性波动期间对哺乳动物细胞的影响。我们的研究策略旨在建立 这种先前未知的同义密码子依赖的机制和基因靶点 细菌和哺乳动物细胞中蛋白质表达的调控。顺利完成这项研究 将为理解数百个同义突变的后果提供分子基础 最近被认为是癌症的驱动因素。从长远来看，新的量化方法 在我们的研究计划中开发的将提供一个严格的建模框架，用于从实验中推导- 来自日益复杂的数据集的可测试预测，例如核糖体占有率测量。

Summary Biosynthesis of proteins accounts for 30% or more of the nutrients and energy consumed by proliferating cells, and it is often dysregulated in human diseases such as cancer and neurological disorders. My research program aims to develop an experimentally-constrained, biophysical model for protein synthesis at the whole- cell level with the goal of predicting protein levels in normal and diseased cellular states. Our current focus is on deciphering the kinetics of ribosome motion on mRNAs and its effect on protein expression. This research builds on our recent discovery that synonymous codon usage is a potent determinant of ribosome kinetics and protein abundance during nutrient-limited growth of bacteria. Notably, our experimental observations are not explained by known hierarchies of codon usage bias or tRNA abundance. Our current results suggest that biased usage of specific codons can regulate protein expression across several domains of life from microbes to mammalian cells during fluctuations in nutrient availability. Our research strategy aims to establish the mechanism and gene targets for this previously unsuspected, synonymous codon dependent regulation of protein expression in bacteria and mammalian cells. Successful completion of this research will provide a molecular basis for understanding the consequences of several hundred synonymous mutations that have been recently implicated as drivers of cancer. In the longer term, the novel quantitative methods developed in our research program will provide a rigorous modeling framework for deriving experimentally- testable predictions from increasingly complex datasets such as ribosome occupancy measurements.