可检测由外质蛋白引起的压力的生物传感器

IF 3.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS ACS Synthetic Biology Pub Date : 2024-04-27 DOI:10.1021/acssynbio.3c00720
Alister J. Cumming, Diana Khananisho, Mateusz Balka, Nicklas Liljestrand and Daniel O. Daley*, 
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引用次数: 0

摘要

大肠杆菌通常被用作生产重组蛋白质的工厂。在许多情况下,重组蛋白需要二硫键才能正确折叠和发挥作用。这些蛋白质在基因上与信号肽融合,从而被分泌到外质的氧化环境中(那里有形成二硫键所需的酶类)。目前,很难在体内确定重组蛋白是有效地从细胞质中分泌出来并在细胞周质中折叠,还是因为超出了细胞能力而在其中一个步骤中出现了瓶颈。为了解决这个问题,我们开发了一种生物传感器,用于检测由以下原因造成的细胞压力:(1) 蛋白质从细胞质中的分泌效率低下;(2) 蛋白质在细胞质周围聚集。我们展示了如何利用从生物传感器获得的荧光指纹来识别不超过细胞能力、因而不会造成细胞应激的诱导条件。这些诱导条件能产生更有效的生物量,在某些情况下还能提高可溶性重组蛋白的滴度。
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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Biosensor that Detects Stress Caused by Periplasmic Proteins

Escherichia coli is often used as a factory to produce recombinant proteins. In many cases, the recombinant protein needs disulfide bonds to fold and function correctly. These proteins are genetically fused to a signal peptide so that they are secreted to the oxidizing environment of the periplasm (where the enzymes required for disulfide bond formation exist). Currently, it is difficult to determine in vivo whether a recombinant protein is efficiently secreted from the cytoplasm and folded in the periplasm or if there is a bottleneck in one of these steps because cellular capacity has been exceeded. To address this problem, we have developed a biosensor that detects cellular stress caused by (1) inefficient secretion of proteins from the cytoplasm and (2) aggregation of proteins in the periplasm. We demonstrate how the fluorescence fingerprint obtained from the biosensor can be used to identify induction conditions that do not exceed the capacity of the cell and therefore do not cause cellular stress. These induction conditions result in more effective biomass and in some cases higher titers of soluble recombinant proteins.

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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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