利用自控硅学基因敲除策略提高酿酒酵母可持续生产异源萜类化合物的能力

IF 6.8 1区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Metabolic engineering Pub Date : 2024-04-20 DOI:10.1016/j.ymben.2024.04.005
Na Zhang , Xiaohan Li , Qiang Zhou , Ying Zhang , Bo Lv , Bing Hu , Chun Li
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引用次数: 0

摘要

近几十年来,利用宿主细胞中的异源代谢途径生产植物天然产物(PNPs)的微生物生物工程领域不断发展。异源代谢途径被引入宿主细胞后,传统的代谢工程技术被用来提高 PNP 生物合成途径的生产率和产量,以及管理竞争途径。计算生物学的出现标志着通过硅学方法进行菌株设计的新纪元的开始。这些方法利用基因组尺度代谢模型(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,由于内源基因被硬性敲除,硅学策略的实施往往会导致代谢通量分布不均,从而阻碍细胞生长并最终影响目标产物的积累。在本研究中,我们创造性地利用合成生物学来完善高效生产 PNPs 的硅学菌株设计。OptKnock 模拟是在毕赤酵母(Saccharomyces cerevisiae)OA07 的 GEM 上进行的,OA07 是一种用于齐墩果酸(Oleanolic acid,OA)生物生产的工程菌株。模拟预测,单个删除 fol1、fol2、fol3、abz1 和 abz2,或联合敲除 hfd1、ald2 和 ald3 可提高其 OA 产量。因此,构建并培养了EK1∼EK7菌株。与原始菌株OA07相比,EK3(OA07△fol3)、EK5(OA07△abz1)和EK6(OA07△abz2)在批量培养中的OA滴度显著提高。然而,在批量喂养模式下,这些提高并不明显,这表明基因缺失并不支持可持续的 OA 生产。为了解决这个问题,我们在 fol3、abz1、abz2 和乙酰-CoA 羧化酶编码基因 acc1 上设计了一个由丙二酰-CoA(一种与生长相关的中间产物,其合成是 OA 合成的旁路)调控的负反馈回路,以动态、自主地调控 OA07 中这些基因的表达。构建的菌株 R_3A、R_5A 和 R_6A 在批次或喂养批次培养模式下的 OA 滴度均明显高于初始菌株和响应基因敲除突变体。其中,菌株 R_3A 的 OA 滴度最高。在烧瓶分批进行喂养培养时,其 OA 滴度是初始菌株的两倍;在发酵罐分批进行喂养培养时,其 OA 滴度在 96 小时内达到 1.23 ± 0.04 g L-1。这表明,优化算法与合成生物学方法的整合在设计生产 PNP 的菌株方面是有效合理的。
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Self-controlled in silico gene knockdown strategies to enhance the sustainable production of heterologous terpenoid by Saccharomyces cerevisiae

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.

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来源期刊
Metabolic engineering
Metabolic engineering 工程技术-生物工程与应用微生物
CiteScore
15.60
自引率
6.00%
发文量
140
审稿时长
44 days
期刊介绍: Metabolic Engineering (MBE) is a journal that focuses on publishing original research papers on the directed modulation of metabolic pathways for metabolite overproduction or the enhancement of cellular properties. It welcomes papers that describe the engineering of native pathways and the synthesis of heterologous pathways to convert microorganisms into microbial cell factories. The journal covers experimental, computational, and modeling approaches for understanding metabolic pathways and manipulating them through genetic, media, or environmental means. Effective exploration of metabolic pathways necessitates the use of molecular biology and biochemistry methods, as well as engineering techniques for modeling and data analysis. MBE serves as a platform for interdisciplinary research in fields such as biochemistry, molecular biology, applied microbiology, cellular physiology, cellular nutrition in health and disease, and biochemical engineering. The journal publishes various types of papers, including original research papers and review papers. It is indexed and abstracted in databases such as Scopus, Embase, EMBiology, Current Contents - Life Sciences and Clinical Medicine, Science Citation Index, PubMed/Medline, CAS and Biotechnology Citation Index.
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