Optimizing HMG-CoA Synthase Expression for Enhanced Limonene Production in Escherichia coli through Temporal Transcription Modulation Using Optogenetics

IF 3.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS ACS Synthetic Biology Pub Date : 2024-11-05 DOI:10.1021/acssynbio.4c0043210.1021/acssynbio.4c00432
Ari Dwijayanti, Jing Wui Yeoh, Congqiang Zhang, Chueh Loo Poh* and Thomas Lautier*, 
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Abstract

Overexpression of a single enzyme in a multigene heterologous pathway may be out of balance with the other enzymes in the pathway, leading to accumulated toxic intermediates, imbalanced carbon flux, reduced productivity of the pathway, or an inhibited growth phenotype. Therefore, optimal, balanced, and synchronized expression levels of enzymes in a particular metabolic pathway is critical to maximize production of desired compounds while maintaining cell fitness in a growing culture. Furthermore, the optimal intracellular concentration of an enzyme is determined by the expression strength, specific timing/duration, and degradation rate of the enzyme. Here, we modulated the intracellular concentration of a key enzyme, namely HMG-CoA synthase (HMGS), in the heterologous mevalonate pathway by tuning its expression level and period of transcription to enhance limonene production in Escherichia coli. Facilitated by the tuned blue-light inducible BLADE/pBad system, we observed that limonene production was highest (160 mg/L) with an intermediate transcription level of HMGS from moderate light illumination (41 au, 150 s ON/150 s OFF) throughout the growth. Owing to the easy penetration and removal of blue-light illumination from the growing culture which is hard to obtain using conventional chemical-based induction, we further explored different induction patterns of HMGS under strong light illumination (2047 au, 300 s ON) for different durations along the growth phases. We identified a specific timing of HMGS expression in the log phase (3–9 h) that led to optimal limonene production (200 mg/L). This is further supported by a mathematical model that predicts several periods of blue-light illumination (3–9 h, 0–9 h, 3–12 h, 0–12 h) to achieve an optimal expression level of HMGS that maximizes limonene production and maintains cell fitness. Compared to moderate and prolonged transcription (41 au, 150 s ON/150 s OFF, 0–73 h), strong but time-limited transcription (2047 au, 300 s ON, 3–9 h) of HMGS could maintain its optimal intracellular concentration and further increased limonene production up to 92% (250 mg/L) in the longer incubation (up to 73 h) without impacting cell fitness. This work has provided new insight into the “right amount” and “just-in-time” expression of a critical metabolite enzyme in the upper module of the mevalonate pathway using optogenetics. This study would complement previous findings in modulating HMGS expression and potentially be applicable to heterologous production of other terpenoids in E. coli.

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利用光遗传学的时序转录调控优化大肠杆菌中 HMG-CoA 合成酶的表达以提高柠檬烯产量
多基因异源途径中单个酶的过度表达可能会与途径中的其他酶失去平衡,导致有毒中间产物积累、碳通量失衡、途径生产率降低或生长表型受抑制。因此,特定代谢途径中酶的最佳、平衡和同步表达水平对于最大限度地生产所需化合物,同时保持细胞在生长培养过程中的活力至关重要。此外,酶的最佳胞内浓度由酶的表达强度、特定时间/持续时间和降解率决定。在这里,我们通过调整异源甲羟戊酸途径中一种关键酶(即 HMG-CoA 合酶(HMGS))的表达水平和转录周期来调节其细胞内浓度,从而提高大肠杆菌中柠檬烯的产量。在调谐蓝光诱导 BLADE/pBad 系统的帮助下,我们观察到,在整个生长过程中,中等光照(41 au,150 s ON/150 s OFF)下,HMGS 的中间转录水平的柠檬烯产量最高(160 mg/L)。由于蓝光光照易于从生长培养物中穿透和移除,而传统的化学诱导很难做到这一点,因此我们进一步探索了强光光照(2047 au,300 s ON)在生长阶段不同持续时间内对 HMGS 的不同诱导模式。我们确定了对数期(3-9 小时)HMGS 表达的特定时间,该时间可使柠檬烯产量达到最佳水平(200 毫克/升)。数学模型进一步证明了这一点,该模型预测了几个蓝光照明期(3-9 小时、0-9 小时、3-12 小时、0-12 小时),以达到 HMGS 的最佳表达水平,从而最大限度地提高柠檬烯产量并保持细胞活力。与中度和长时间转录(41 au,150 s ON/150 s OFF,0-73 h)相比,强转录但有时间限制(2047 au,300 s ON,3-9 h)的HMGS能维持其最佳胞内浓度,并在更长时间的培养(长达73 h)中进一步提高柠檬烯产量达92%(250 mg/L),而不影响细胞活力。这项工作为利用光遗传学 "适量 "和 "适时 "表达甲羟戊酸途径上部模块中的一种关键代谢物酶提供了新的视角。这项研究将补充之前在调节 HMGS 表达方面的发现,并有可能适用于在大肠杆菌中异源生产其他萜类化合物。
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来源期刊
CiteScore
8.00
自引率
10.60%
发文量
380
审稿时长
6-12 weeks
期刊介绍: The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.
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