基于生物传感器的酶工程方法应用于双糖生物合成。

IF 2.6 Q2 BIOCHEMICAL RESEARCH METHODS Synthetic biology (Oxford, England) Pub Date : 2019-12-02 eCollection Date: 2019-01-01 DOI:10.1093/synbio/ysz028
Jeremy Armetta, Rose Berthome, Antonin Cros, Celine Pophillat, Bruno Maria Colombo, Amir Pandi, Ioana Grigoras
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引用次数: 0

摘要

化合物的生物生产对现代工业具有重大意义,因为它可以降低生产成本,减少对生态的影响。利用合成生物学、代谢工程和酶工程工具,可以提高产量,达到大规模生产和成本效益的预期。在本研究中,我们探讨了 D-水苏糖(又称 D-阿洛酮糖)的生物生产,D-水苏糖是一种罕见的无毒糖类,也是一种甜味剂,在自然界中的含量很低。D- 菊糖具有有趣的特性,似乎有能力对抗肥胖和 2 型糖尿病。我们开发了一种基于生物传感器的酶筛选方法,作为酶筛选的工具,并以纤维素溶解梭菌 D-木糖 3-酰亚胺酶为基准,从 D-果糖生产 D-木糖。为此,我们根据以前未表征的转录因子及其预测启动子或工程启动子,构建并表征了 7 种对木糖反应的生物传感器。为了使我们的系统标准化,我们创建了通用生物传感器底盘(Universal Biosensor Chassis),它具有高度模块化的结构,可以快速设计任何基于转录因子的生物传感器。在七种生物传感器中,我们选择了线性度最高、荧光折叠变化增幅最大的一种。接下来,我们用易出错的 PCR 方法生成了一个 D-草糖 3-酰亚胺酶突变体库,并用该生物传感器筛选出了功能增益酶突变体,从而证明了该框架的高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Biosensor-based enzyme engineering approach applied to psicose biosynthesis.

Bioproduction of chemical compounds is of great interest for modern industries, as it reduces their production costs and ecological impact. With the use of synthetic biology, metabolic engineering and enzyme engineering tools, the yield of production can be improved to reach mass production and cost-effectiveness expectations. In this study, we explore the bioproduction of D-psicose, also known as D-allulose, a rare non-toxic sugar and a sweetener present in nature in low amounts. D-psicose has interesting properties and seemingly the ability to fight against obesity and type 2 diabetes. We developed a biosensor-based enzyme screening approach as a tool for enzyme selection that we benchmarked with the Clostridium cellulolyticum D-psicose 3-epimerase for the production of D-psicose from D-fructose. For this purpose, we constructed and characterized seven psicose responsive biosensors based on previously uncharacterized transcription factors and either their predicted promoters or an engineered promoter. In order to standardize our system, we created the Universal Biosensor Chassis, a construct with a highly modular architecture that allows rapid engineering of any transcription factor-based biosensor. Among the seven biosensors, we chose the one displaying the most linear behavior and the highest increase in fluorescence fold change. Next, we generated a library of D-psicose 3-epimerase mutants by error-prone PCR and screened it using the biosensor to select gain of function enzyme mutants, thus demonstrating the framework's efficiency.

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