Regulation of Protein Synthesis by Synonymous Codon Usage

总结 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， 并且它在人类疾病如癌症和神经紊乱中经常失调。我的研究 该计划旨在开发一个实验约束，生物物理模型的蛋白质合成在整个- 细胞水平，目标是预测正常和患病细胞状态下的蛋白质水平。我们目前的重点是 在破译核糖体运动对mRNA的动力学及其对蛋白质表达的影响。本研究 基于我们最近的发现，同义密码子的使用是核糖体动力学的有效决定因素， 蛋白质丰度在营养有限的细菌生长。值得注意的是，我们的实验观察结果并不是 这可以通过已知的密码子使用偏好或tRNA丰度的等级来解释。我们目前的结果表明， 特定密码子的偏向使用可以调节来自微生物的跨几个生命领域的蛋白质表达 哺乳动物细胞在营养供应波动期间。我们的研究策略旨在建立 这一先前未被怀疑的同义密码子依赖性的机制和基因靶点 调节细菌和哺乳动物细胞中的蛋白质表达。成功完成这项研究 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，新的定量方法 在我们的研究计划中开发的将提供一个严格的建模框架，用于通过实验得出- 从日益复杂的数据集，如核糖体占有率测量可测试的预测。

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.