Bilateral NSF/BIO-BBSRC: Synthetic gene circuits to measure and mitigate translational stress during heterologous protein expression

在这个项目中，一个由生物学家和物理学家组成的跨学科团队将建立新的技术，以提高细胞以更高的效率和准确性制造重组蛋白（rp）的能力。生物技术工业的大部分致力于生产用于药品、疫苗和食品添加剂的rp。这些蛋白质是在活细胞中使用正常的蛋白质合成过程制造的，而这些蛋白质的工业生产需要异常高水平的蛋白质-因此所需的蛋白质占细胞中所有蛋白质质量的大部分。在如此高的生产水平下，当不正确的氨基酸插入生长中的蛋白质链时，蛋白质就会产生错误。该项目将研究这些错误是如何积累的，并将创建基因回路，可以感知蛋白质合成错误频率的任何显著增加，并可以部署机制来恢复正常的高精度蛋白质合成。该研究小组由马里兰大学巴尔的摩县的实验室和英国阿伯丁大学的实验室组成，前者研究的是一种模型细菌大肠杆菌，后者研究的是面包酵母。这两个小组还将与英国的一家主要生物技术公司合作，以确保该项目的产品将直接适用于工业规模的蛋白质生产。蛋白质中的氨基酸由mRNA中的三个核苷酸密码子编码；翻译涉及mRNA密码子和tRNA反密码子之间的三个核苷酸碱基对。当解码的tRNA仅与密码子形成两个碱基对时，就会发生错误。对于每个正确的（同源的）tRNA，大约有14个不正确的（近同源的）tRNA可能导致这样的错误。解码过程通常是高度准确的，近同源trna引入错误氨基酸的频率从大约万分之一到低至百万分之一。误译也可以由帧移位引起，其中选择与正确密码子重叠的密码子进行解码。在由低丰度tRNA服务的密码子翻译期间，或在由带电tRNA耗尽引起的核糖体暂停期间，这些误译错误的频率急剧增加。生物技术蛋白的高水平表达恰恰可以创造这些条件，从而增加翻译错误的频率。事实上，许多报告描述了生物技术蛋白表达产生一系列不良的误翻译事件，影响产品的产量和质量，从而影响生物制剂的安全性和有效性。在这个项目中，我们将通过设计和应用新的误译报告基因，能够产生报告酶活性或调节转录因子，来更好地了解翻译错误的系统原因。我们将这些实验方法与翻译和tRNA竞争的全局数学建模相结合，以预测系统压力何时会刺激误译。然后，我们使用合成生物学基因电路对这些新的误译传感器的输出进行转录偶联，以控制rP表达，减少误译并提高rP产品质量。我们的工业合作伙伴将测试这些合成基因电路，以最大限度地实现这项研究对生物技术的影响。本研究将首次揭示rp在引发翻译系统应激中的作用，并确定缓解应激的新途径。

In this project, an interdisciplinary team of biologists and physicists will establish novel technologies to improve the ability of a cell to make recombinant proteins (rPs) at higher efficiency and with greater accuracy. A majority of the biotechnology industry is dedicated to the production of rPs for use as pharmaceuticals, vaccines and food additives. These proteins are made in living cells using the normal process of protein synthesis, whereas the industrial production of these same proteins requires unusually high levels of the protein - such that the desired protein accounts for the majority of all protein mass in the cell. At such a high production level, proteins are made with errors, when incorrect amino acids are inserted into the growing protein chain. This project will investigate how these errors accumulate, and will create genetic circuits that can sense any significant increase in the frequency of protein synthesis errors and can deploy mechanisms to restore normal highly accurate protein synthesis. The research team consists of the lab at University of Maryland Baltimore County, working with a model bacterium E.coli, and the lab at the University of Aberdeen, UK, working with baker`s yeast. The two teams will also collaborate with a major biotechnology company in the United Kingdom to insure that the products of the project will be directly applicable to use in industrial scale protein production.Amino acids in proteins are encoded by three nucleotide codons in mRNA; translation involves three nucleotide base pairs between the mRNA codon and a tRNA anticodon. Errors occur when a decoding tRNA forms only two base pairs with the codon. For each correct (cognate) tRNA there are about fourteen incorrect (near-cognate) tRNAs that could induce such an error. The decoding process is normally highly accurate, with near-cognate tRNAs introducing incorrect amino acids at frequencies from about one in 10,000 to as low as one in 1,000,000. Mistranslation can also result from frameshifting in which a codon overlapping the correct codon is selected for decoding. The frequency of these mistranslation errors increase sharply during translation of codons served by low abundance tRNA, or during ribosomal pausing caused by depletion of charged tRNAs. The high-level expression of biotechnological proteins can creates exactly these conditions and thus increasing translational error frequencies. Indeed, many reports describe biotechnological protein expression generating a range of undesirable mistranslation events, compromising product yield and quality, and thus the safety and efficacy of biologics. In this project we will pursue a better understanding of the system-wide causes of translational error through the design and application of novel reporters of mistranslation, capable of producing either reporter enzyme activities, or regulatory transcription factors. We combine these experimental approaches with global mathematical modelling of translation and tRNA competition to predict when system stress will stimulate mistranslation. We then use synthetic biology gene circuits to transcriptionally couple the output from these new mistranslation sensors to control rP expression, to reduce mistranslation and increase rP product quality. Our industrial partner will test these synthetic gene circuits to maximize the opportunities for realizing the impact of this research on biotechnology. This research will reveal for the first time the role of rPs in triggering translation system stress, and identify novel ways in which stress can be ameliorated.