Biosensor-based enzyme engineering approach applied to psicose biosynthesis. Issue 1 (2nd December 2019)

• Record Type: Journal Article
• Authors: Armetta, Jeremy; Berthome, Rose; Cros, Antonin; Pophillat, Celine; Colombo, Bruno Maria; Pandi, Amir; Grigoras, Ioana
• Is Part Of: Synthetic biology. Volume 4:Issue 1(2019)
• Publication Date: 2019-12-02
• Subjects: transcription factor-based biosensor -- rare sugars -- psicose -- enzyme engineering -- Universal Biosensing Chassis; Synthetic biology -- Periodicals; 660.6
• DOI: 10.1093/synbio/ysz028
• Languages: English
• ISSNs: 2397-7000

Abstract

Abstract: 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.