Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111.

IF 1.4 4区生物学 Q4 CELL BIOLOGY

Integrative Biology Pub Date : 2017-10-16 DOI:10.1039/c7ib00077d

Xingxing Jian, Ningchuan Li, Qian Chen, Qiang Hua

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引用次数: 5

Abstract

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed. In this study, a GEM of Escherichia coli NZN111 was employed by the analysis of production and growth coupling (APGC) algorithm to identify genetic strategies for the overproduction of succinate. Through in silico simulation and reaction expression analysis, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), triosephosphate isomerase (TPI), and phosphoenolpyruvate carboxylase (PPC), encoded by gapA, pgk, tpiA, and ppc, respectively, were selected for experimental overexpression. The results showed that overexpressing any of these could improve both growth and succinate production. Specifically, overexpression of GAPDH or PGK showed a significant effect with up to 24% increase in succinate production. These results indicate that the APGC algorithm can be effectively used to guide genetic manipulation for strain design by identifying genome-wide gene amplification targets.

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模型引导鉴定提高大肠杆菌NZN111琥珀酸盐产量的新基因扩增靶点

基因组尺度代谢模型(GEMs)的重建和应用为代谢网络的系统计算分析提供了平台，促进了代谢工程的发展。本研究利用大肠杆菌NZN111的GEM，通过APGC算法分析琥珀酸过量产生的遗传策略。通过计算机模拟和反应表达分析，选择分别由gapA、PGK、tpiA和PPC编码的甘油醛-3-磷酸脱氢酶(GAPDH)、磷酸甘油酸激酶(PGK)、三磷酸异构酶(TPI)和磷酸烯醇丙酮酸羧化酶(PPC)进行实验过表达。结果表明，过表达这些基因中的任何一个都能促进生长和琥珀酸盐的产生。具体来说，GAPDH或PGK的过表达显示出显著的影响，琥珀酸盐的产量可增加24%。这些结果表明，APGC算法可以有效地通过识别全基因组基因扩增靶点来指导菌株设计的遗传操作。

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

Integrative Biology 生物-细胞生物学

CiteScore

4.90

自引率

0.00%

发文量

审稿时长

1 months

期刊介绍： Integrative Biology publishes original biological research based on innovative experimental and theoretical methodologies that answer biological questions. The journal is multi- and inter-disciplinary, calling upon expertise and technologies from the physical sciences, engineering, computation, imaging, and mathematics to address critical questions in biological systems. Research using experimental or computational quantitative technologies to characterise biological systems at the molecular, cellular, tissue and population levels is welcomed. Of particular interest are submissions contributing to quantitative understanding of how component properties at one level in the dimensional scale (nano to micro) determine system behaviour at a higher level of complexity. Studies of synthetic systems, whether used to elucidate fundamental principles of biological function or as the basis for novel applications are also of interest.