Jones J Andrew, Vernacchio Victoria R, Sinkoe Andrew L, Collins Shannon M, Ibrahim Mohammad H A, Lachance Daniel M, Hahn Juergen, Koffas Mattheos A G
Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Metab Eng. 2016 May;35:55-63. doi: 10.1016/j.ymben.2016.01.006. Epub 2016 Feb 6.
Metabolic engineering and synthetic biology have enabled the use of microbial production platforms for the renewable production of many high-value natural products. Titers and yields, however, are often too low to result in commercially viable processes. Microbial co-cultures have the ability to distribute metabolic burden and allow for modular specific optimization in a way that is not possible through traditional monoculture fermentation methods. Here, we present an Escherichia coli co-culture for the efficient production of flavonoids in vivo, resulting in a 970-fold improvement in titer of flavan-3-ols over previously published monoculture production. To accomplish this improvement in titer, factors such as strain compatibility, carbon source, temperature, induction point, and inoculation ratio were initially optimized. The development of an empirical scaled-Gaussian model based on the initial optimization data was then implemented to predict the optimum point for the system. Experimental verification of the model predictions resulted in a 65% improvement in titer, to 40.7±0.1mg/L flavan-3-ols, over the previous optimum. Overall, this study demonstrates the first application of the co-culture production of flavonoids, the most in-depth co-culture optimization to date, and the first application of empirical systems modeling for improvement of titers from a co-culture system.
代谢工程和合成生物学已使微生物生产平台能够用于可再生地生产多种高价值天然产物。然而,其滴度和产量往往过低,无法实现具有商业可行性的生产流程。微生物共培养能够分散代谢负担,并以传统单培养发酵方法无法实现的方式进行模块化的特定优化。在此,我们展示了一种用于在体内高效生产类黄酮的大肠杆菌共培养体系,与之前发表的单培养生产相比,黄烷 - 3 - 醇的滴度提高了970倍。为实现滴度的这种提高,我们首先对菌株兼容性、碳源、温度、诱导点和接种比例等因素进行了优化。然后基于初始优化数据开发了一个经验性的缩放高斯模型,以预测该系统的最佳点。对模型预测的实验验证使滴度比之前的最佳值提高了65%,达到40.7±0.1mg/L的黄烷 - 3 - 醇。总体而言,本研究展示了类黄酮共培养生产的首次应用、迄今为止最深入的共培养优化以及经验性系统建模在提高共培养体系滴度方面的首次应用。