Manchester Institute of Biotechnology (MIB), School of Chemistry , The University of Manchester , 131 Princess Street , Manchester M1 7DN , United Kingdom.
School of Biological Sciences , University of Edinburgh , King's Buildings , Edinburgh EH9 3JY , United Kingdom.
Biochemistry. 2019 Mar 19;58(11):1492-1500. doi: 10.1021/acs.biochem.8b01086. Epub 2019 Mar 8.
The field of synthetic biology is already beginning to realize its potential, with a wealth of examples showcasing the successful genetic engineering of microorganisms for the production of valuable compounds. The chassis Saccharomyces cerevisiae has been engineered to function as a microfactory for producing many of these economically and medically relevant compounds. However, strain construction and optimization to produce industrially relevant titers necessitate a wealth of underpinning biological knowledge alongside large investments of capital and time. Over the past decade, advances in DNA synthesis and editing tools have enabled multiplex genome engineering of yeast, permitting access to more complex modifications that could not have been easily generated in the past. These genome engineering efforts often result in large populations of strains with genetic diversity that can pose a significant challenge to screen individually via traditional methods such as mass spectrometry. The large number of samples generated would necessitate screening methods capable of analyzing all of the strains generated to maximize the explored genetic space. In this Perspective, we focus on recent innovations in multiplex genome engineering of S. cerevisiae, together with biosensors and high-throughput screening tools, such as droplet microfluidics, and their applications in accelerating chassis optimization.
合成生物学领域已经开始发挥其潜力,有大量成功的例子展示了微生物的基因工程,用于生产有价值的化合物。底盘酵母已被工程化为生产许多具有经济和医学相关性的化合物的微工厂。然而,为了生产具有工业相关性的效价,需要大量的基础生物学知识,以及大量的资金和时间投入。在过去的十年中,DNA 合成和编辑工具的进步使酵母的多路基因组工程成为可能,从而可以实现过去难以轻易产生的更复杂的修饰。这些基因组工程的努力通常会产生具有遗传多样性的大量菌株,这可能对通过传统方法(如质谱法)逐个筛选构成重大挑战。生成的大量样本需要能够分析所有生成的菌株的筛选方法,以最大限度地利用所探索的遗传空间。在本观点中,我们重点介绍了 S. cerevisiae 多路基因组工程的最新创新,以及生物传感器和高通量筛选工具,如液滴微流控技术,及其在加速底盘优化中的应用。