(752f) Metabolic Engineering for Terpenoids Overproduction and Discovery

Terpenoids comprise the largest family of natural products with wide applications, and many of them have been engineered for commercial use. Our strategy for engineering terpenoids is based on the instruction of our in vitro reconstitution assay of mevalonate (MVA) pathway. The information from this system guided us to establish an efficient precursor-producing platform for terpenoids. Base on this well optimized platform, a high titer of farnesene was obtained in a short time with little mutant constructed^[1]. Subsequently, we directly extend the application of this strategy and platform to realize a high titer production of lycopene^[2], astaxanthin^[3] and taxadiene^[4] production in E. coli, S. cerevisiae and filamentous fungi quickly and efficiently.

Besides the production of terpenoids with important industrial applications, efficient heterologous terpenoids overproduction platforms also provide the perfect solution for us to activate the silent genes, gene clusters. Or resolve the problem that the production of target compounds is too low to be characterized, which may frequently encounter for natural product discovery. With the help of well-established terpenoids overproduction platforms in E. coli^[5] and S. cerevisiae, we have characterized a large number of sesquiterpene, diterpene and sesterterpene synthase, which were usually silent or with low expression level in filamentous fungi. Some new terpene skeletons and new compounds that were usually difficult to obtain were discovered by this strategy. It's reasonable for us to believe that the our technical platform and strategy can promote the progress of natural products discovery greatly.

References:

1. Zhu F, Zhong X, Hu M, et al. (2014). Biotechnol Bioeng, 111, 1396-1405.

2. Zhu F, Lu L, Fu S, et al. (2015). Process Biochem, 50, 341-346.

3. Ma T, Zhou Y, Li X, et al. (2015). Biotechnol J, 11, 228-237.

4. Bian G, Yuan Y, Tao H, et al. (2017). Biotechnol J, doi: 10.1002/biot.201600697.

5. Bian G, Han Y, Hou A, et al. (2017). BioRxiv, http://biorxiv.org/content/early/2017/02/25/105247.