Efficient Autoinducible Expression of Recombinant Proteins via the DegSU Quorum Sensing System in a Robust Bacillus subtilis.

IF 3.9 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
ACS Synthetic Biology Pub Date : 2025-01-17 Epub Date: 2025-01-05 DOI:10.1021/acssynbio.4c00798
Hong Zong, Liya Zhang, Yiwen Cheng, Zhiying Sheng, Bin Zhuge, Xinyao Lu
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引用次数: 0

Abstract

DegSU quorum sensing (QS) system enables autoinducible expression of recombinant proteins in Bacillus subtilis . However, insufficient promoter strength and a complex regulatory circuit limit its practical application. Here, the QS-responsive promoter PaprE was modified by core region mutation, upstream truncation, and addition of activating binding sites, yielding PE742 with a 118.3% increase in strength. A mathematical model was developed to accurately quantify the regulatory process from a comprehensive perspective. Guided by this model, the DegSU QS system was further optimized in a robust B. subtilis by knocking out competitive target genes sacB and amyE, operons pgs and srfA, introducing variants degUL113F and degQ36Hy, and increasing regulatory strength by 84.0%. A 52.5% increase in acetoin titer and a 65.9% increase in extracellular carboxypeptidase activity validated the industrial value of this study. Overall, this study addresses the limitations of the DegSU QS system in practical application and demonstrates its potential for high-level recombinant protein production.

DegSU群体感应系统在枯草芽孢杆菌中高效自诱导表达重组蛋白。
DegSU群体感应(QS)系统实现了重组蛋白在枯草芽孢杆菌中的自诱导表达。然而,启动子强度不足和复杂的调控电路限制了其实际应用。在这里,通过核心区域突变、上游截断和增加激活结合位点对qs响应启动子PaprE进行修饰,得到了PE742,强度增加了118.3%。开发了一个数学模型,从综合的角度准确地量化监管过程。在此模型的指导下,DegSU QS系统在强大的枯草芽孢杆菌中进一步优化,敲除竞争靶基因sacB和amyE,操作子pgs和srfA,引入变异degUL113F和degQ36Hy,调控强度提高了84.0%。乙酰肽滴度增加52.5%,细胞外羧肽酶活性增加65.9%,证实了该研究的工业价值。总的来说,本研究解决了DegSU QS系统在实际应用中的局限性,并展示了其在高水平重组蛋白生产中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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