Ribosome traffic flow on the mRNA as a regulator of cellular protein production: an integrated modelling and experimental analysis

mRNA 上的核糖体流量作为细胞蛋白质生产的调节剂：综合建模和实验分析

• 批准号: BB/G010722/1
• 负责人: Ian Stansfield
• 金额: $ 68.49万
• 依托单位: University of Aberdeen
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FG010722%2F1
• 关键词: Ribosome; traffic; flow; mRNA; regulator

In this proposal, an interdisciplinary team of biologists and physicists will establish novel technologies to predict the protein composition of a cell. Proteins are used by the cells within all organisms to carry out the essential biochemical processes that constitute life. Knowing which proteins and in what quantities are being made by a cell, defines the properties of that particular cell. Being able to predict the protein composition of a cell therefore represents a very powerful tool to understand cell biology. Proteins themselves are made of a string of chemical building blocks called amino acids, of which there are twenty different types. It is the distinct sequence of the amino acids in the protein chain that gives the protein its biochemical and catalytic properties. Even a relatively simple organism such as baker's yeast, the subject of this proposal, can have about 6,000 different varieties of protein, each with its own specific amino acid sequence. The cell makes proteins of the correct amino acid sequence using information encoded in its genes. Each gene codes for a single protein type, so baker's yeast has 6,000 genes encoding the same number of distinct proteins. To make a protein, the coding information in a gene is first copied into a short linear molecule termed a messenger RNA, or mRNA. Then an assembly of bio-molecules called ribosome reads the information within the mRNA, a process called translation. The ribosome moves along the mRNA from one end to the other, reading the information coded in the mRNA, and translating it by sequentially adding the amino acids to make a protein chain. The amino acids are brought to the ribosomes by transfer RNA molecules (tRNAs). The protein is then released to carry out its function in the cell. In fact, the mRNA can by translated by multiple ribosomes at the same time, with ribosomes following each other like cars down a road. This traffic analogy is rather apt; sometimes, just as cars get stuck in a traffic jam, so ribosomes can slow down or even pause completely as they translate the mRNA, usually in response to a section of the mRNA that is difficult to translate. When this happens, queues of ribosomes can build up, reducing the rate at which that protein is produced. All mRNAs are comprised of many different slowly and rapidly translated regions, for instance, caused by different abundances of distinct tRNA species. Ribosome queues can then begin to merge, sometimes extending back to the beginning of the mRNA and preventing ribosomes from joining the mRNA. This will reduce the amount of protein synthesis directed by that mRNA. Ribosomal traffic flow on mRNAs is therefore a key regulator of the quantities of the different proteins being made. To understand which population of proteins a cell will express, and in which quantities, therefore requires an ability to predict ribosomal traffic flow on the mRNA, and how whole populations of ribosomes interact with each of the 6,000 mRNAs in yeast. Predicting exactly how ribosomes interact and queue as they translate is a challenging task that requires joint application of both mathematical and biological techniques. In work leading up to this proposal, we have developed a mathematical model to simulate ribosome traffic on mRNAs. This model makes a number of important predictions about how ribosome traffic flow affects the translation of mRNAs, predictions that will be tested in this proposal. The proposed research will also develop the model much further, incorporating detailed mathematical descriptions of the translation process. The model will be tested and validated by experimentally analysing translation reactions in yeast. Overall, the interdisciplinary approach will not only provide genuine insight into the fundamental mechanisms a cell uses to express its genes, but will have implications for the study of many other traffic flow systems in Biology and Physics.

在这项提案中，由生物学家和物理学家组成的跨学科团队将建立预测细胞蛋白质组成的新技术。所有生物体内的细胞都利用蛋白质来执行构成生命的基本生化过程。了解细胞产生哪些蛋白质以及数量，可以定义该特定细胞的特性。因此，能够预测细胞的蛋白质组成是理解细胞生物学的一个非常强大的工具。蛋白质本身由一系列称为氨基酸的化学结构单元组成，其中有二十种不同的类型。蛋白质链中氨基酸的独特序列赋予了蛋白质生化和催化特性。即使是相对简单的生物体，例如本提案的主题面包酵母，也可以含有大约 6,000 种不同的蛋白质，每种都有自己特定的氨基酸序列。细胞利用其基因编码的信息制造具有正确氨基酸序列的蛋白质。每个基因编码一种蛋白质类型，因此面包酵母有 6,000 个基因编码相同数量的不同蛋白质。为了制造蛋白质，基因中的编码信息首先被复制到称为信使 RNA 或 mRNA 的短线性分子中。然后，称为核糖体的生物分子组装体读取 mRNA 中的信息，这一过程称为翻译。核糖体沿着 mRNA 从一端移动到另一端，读取 mRNA 中编码的信息，并通过顺序添加氨基酸来翻译它，形成蛋白质链。氨基酸通过转移 RNA 分子 (tRNA) 被带到核糖体。然后蛋白质被释放以在细胞中发挥其功能。事实上，mRNA 可以同时由多个核糖体翻译，核糖体就像道路上的汽车一样相互跟随。这个交通类比相当贴切。有时，就像汽车陷入交通堵塞一样，核糖体在翻译 mRNA 时会减慢速度甚至完全暂停，通常是为了响应 mRNA 中难以翻译的部分。当这种情况发生时，核糖体队列就会堆积起来，从而降低蛋白质的产生速度。所有 mRNA 均由许多不同的缓慢和快速翻译区域组成，例如，由不同 tRNA 种类的不同丰度引起。然后核糖体队列开始合并，有时会延伸回 mRNA 的开头并阻止核糖体加入 mRNA。这将减少该 mRNA 指导的蛋白质合成量。因此，mRNA 上的核糖体流量是不同蛋白质生成量的关键调节因素。因此，为了了解细胞将表达哪些蛋白质群体以及表达量，需要能够预测 mRNA 上的核糖体流量，以及整个核糖体群体如何与酵母中 6,000 个 mRNA 中的每一个相互作用。准确预测核糖体在翻译时如何相互作用和排队是一项具有挑战性的任务，需要数学和生物技术的联合应用。在提出这一建议的工作中，我们开发了一个数学模型来模拟 mRNA 上的核糖体运输。该模型对核糖体流量如何影响 mRNA 的翻译做出了许多重要的预测，这些预测将在本提案中进行测试。拟议的研究还将进一步开发该模型，结合翻译过程的详细数学描述。该模型将通过实验分析酵母中的翻译反应进行测试和验证。总体而言，跨学科方法不仅将提供对细胞表达其基因的基本机制的真正见解，而且将对生物学和物理学中许多其他交通流系统的研究产生影响。

项目成果

Limited resources in a driven diffusion process.

• DOI: 10.1103/physrevlett.105.078102
• 发表时间: 2010-08-13
• 期刊: Physical review letters
• 影响因子: 8.6
• 作者: Brackley CA;Romano MC;Grebogi C;Thiel M
• 通讯作者: Thiel M

Mixed population of competing totally asymmetric simple exclusion processes with a shared reservoir of particles.

• DOI: 10.1103/physreve.85.011142
• 发表时间: 2012-01
• 期刊: Physical review. E, Statistical, nonlinear, and soft matter physics
• 作者: Greulich P;Ciandrini L;Allen RJ;Romano MC
• 通讯作者: Romano MC

Ribosome traffic on mRNAs maps to gene ontology: genome-wide quantification of translation initiation rates and polysome size regulation.

• DOI: 10.1371/journal.pcbi.1002866
• 发表时间: 2013
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Ciandrini L;Stansfield I;Romano MC
• 通讯作者: Romano MC

Identification of the mRNA targets of tRNA-specific regulation using genome-wide simulation of translation.

• DOI: 10.1093/nar/gkw630
• 发表时间: 2016-11-02
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Gorgoni B;Ciandrini L;McFarland MR;Romano MC;Stansfield I
• 通讯作者: Stansfield I