Microbial bioengineering is a growing field for producing plant natural products (PNPs) in recent decades, using heterologous metabolic pathways in host cells. Once heterologous metabolic pathways have been introduced into host cells, traditional metabolic engineering techniques are employed to enhance the productivity and yield of PNP biosynthetic routes, as well as to manage competing pathways. The advent of computational biology has marked the beginning of a novel epoch in strain design through in silico methods. These methods utilize genome-scale metabolic models (GEMs) and flux optimization algorithms to facilitate rational design across the entire cellular metabolic network. However, the implementation of in silico strategies can often result in an uneven distribution of metabolic fluxes due to the rigid knocking out of endogenous genes, which can impede cell growth and ultimately impact the accumulation of target products. In this study, we creatively utilized synthetic biology to refine in silico strain design for efficient PNPs production. OptKnock simulation was performed on the GEM of Saccharomyces cerevisiae OA07, an engineered strain for oleanolic acid (OA) bioproduction that has been reported previously. The simulation predicted that the single deletion of fol1, fol2, fol3, abz1, and abz2, or a combined knockout of hfd1, ald2 and ald3 could improve its OA production. Consequently, strains EK1∼EK7 were constructed and cultivated. EK3 (OA07△fol3), EK5 (OA07△abz1), and EK6 (OA07△abz2) had significantly higher OA titers in a batch cultivation compared to the original strain OA07. However, these increases were less pronounced in the fed-batch mode, indicating that gene deletion did not support sustainable OA production. To address this, we designed a negative feedback circuit regulated by malonyl-CoA, a growth-associated intermediate whose synthesis served as a bypass to OA synthesis, at fol3, abz1, abz2, and at acetyl-CoA carboxylase-encoding gene acc1, to dynamically and autonomously regulate the expression of these genes in OA07. The constructed strains R_3A, R_5A and R_6A had significantly higher OA titers than the initial strain and the responding gene-knockout mutants in either batch or fed-batch culture modes. Among them, strain R_3A stand out with the highest OA titer reported to date. Its OA titer doubled that of the initial strain in the flask-level fed-batch cultivation, and achieved at 1.23 ± 0.04 g L−1 in 96 h in the fermenter-level fed-batch mode. This indicated that the integration of optimization algorithm and synthetic biology approaches was efficiently rational for PNP-producing strain design.

中文翻译：

---

自我控制的计算机基因敲低策略，以增强酿酒酵母异源萜类化合物的可持续生产


近几十年来，微生物生物工程是一个不断增长的领域，利用宿主细胞中的异源代谢途径生产植物天然产物 （PNP）。一旦异源代谢途径被引入宿主细胞，传统的代谢工程技术就被用于提高 PNP 生物合成途径的生产力和产量，以及管理竞争途径。计算生物学的出现标志着通过计算机方法进行菌株设计的新时代的开始。这些方法利用基因组规模的代谢模型 （GEM） 和通量优化算法来促进整个细胞代谢网络的合理设计。然而，由于内源性基因的刚性敲除，计算机模拟策略的实施通常会导致代谢通量分布不均匀，这会阻碍细胞生长并最终影响靶产物的积累。在这项研究中，我们创造性地利用合成生物学来改进计算机菌株设计，以实现高效的 PNP 生产。在酿酒酵母 OA07 的 GEM 上进行 OptKnock 模拟，这是一种先前报道的用于齐墩果酸 （OA） 生物生产的工程菌株。模拟预测 fol1 、 fol2 、 fol3 、 abz1 和 abz2 的单个缺失，或 hfd1 、 ald2 和 ald3 的联合敲除可以提高其 OA 的产生。因此，构建和培养菌株 EK1∼EK7。与原始菌株 OA07 相比，EK3 （OA07△fol3） 、EK5 （OA07△abz1） 和 EK6 （OA07△abz2） 在批量培养中具有显著更高的 OA 滴度。然而，这些增加在补料分批模式下不太明显，表明基因缺失不支持可持续的 OA 生产。 为了解决这个问题，我们设计了一个由丙二酰辅酶 A 调节的负反馈回路，丙二酰辅酶 A 是一种生长相关中间体，其合成作为 OA 合成的旁路，在 fol3 、 abz1 、 abz2 和乙酰辅酶 A 羧化酶编码基因 acc1 处，以动态和自主调节这些基因在 OA07 中的表达。在分批或补料分批培养模式下，构建菌株 R_3A 、 R_5A 和 R_6A 的 OA 滴度显著高于初始菌株和响应基因敲除突变体。其中，菌株 R_3A 以迄今为止报道的最高 OA 滴度脱颖而出。其 OA 滴度是培养瓶水平补料分批培养中初始菌株的两倍，在发酵罐水平补料分批模式下，在 96 小时内达到 1.23 ± 0.04 g L-1。这表明优化算法和合成生物学方法的整合对于 PNP 生产菌株设计是有效的合理。