Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation
Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation
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DOI：
10.1128/aem.71.12.7880-7887.2005
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发表时间：
2005-12-01
影响因子：
4.4
通讯作者：
Lee, SY
中科院分区：
文献类型：
--
作者：
Lee, SJ;Lee, DY;Lee, SY
对混合酸发酵大肠杆菌和琥珀酸高产Mannheimia succiniciproducens的基因组进行了比较分析，以确定在E.杆菌这导致了五个基因或操纵子的鉴定，包括ptsG，pykF，sdhA，mqo和aceBA，它们可能在E.杆菌然而，这些合理选择的基因的组合破坏并不允许提高琥珀酸生产在E。杆菌因此，进行基于线性规划的计算机代谢分析，以评估各种组合敲除菌株的最大生物量和琥珀酸产量之间的相关性。该计算机模拟分析预测，破坏三种丙酮酸形成酶ptsG、pykF和pykA的基因允许增强的琥珀酸生产。事实上，这种三重突变使琥珀酸产量增加了7倍以上，琥珀酸与发酵产物的比率增加了9倍。因此，减少丙酮酸的代谢通量是实现高效琥珀酸生产的关键。杆菌这些结果表明，比较基因组分析与计算机代谢分析相结合，可以是一种有效的方法，发展战略的菌株改良。
Comparative analysis of the genomes of mixed-acid-fermenting Escherichia coli and succinic acid-overproducing Mannheimia succiniciproducens was carried out to identify candidate genes to be manipulated for overproducing succinic acid in E. coli. This resulted in the identification of five genes or operons, including ptsG, pykF, sdhA, mqo, and aceBA, which may drive metabolic fluxes away from succinic acid formation in the central metabolic pathway of E. coli. However, combinatorial disruption of these rationally selected genes did not allow enhanced succinic acid production in E. coli. Therefore, in silico metabolic analysis based on linear programming was carried out to evaluate the correlation between the maximum biomass and succinic acid production for various combinatorial knockout strains. This in silico analysis predicted that disrupting the genes for three pyruvate forming enzymes, ptsG, pykF, and pykA, allows enhanced succinic acid production. Indeed, this triple mutation increased the succinic acid production by more than sevenfold and the ratio of succinic acid to fermentation products by ninefold. It could be concluded that reducing the metabolic flux to pyruvate is crucial to achieve efficient succinic acid production in E. coli. These results suggest that the comparative genome analysis combined with in silico metabolic analysis can be an efficient way of developing strategies for strain improvement.