Pathway engineering of Escherichia coli for α‐ketoglutaric acid production

2020 ◽  
Vol 117 (9) ◽  
pp. 2791-2801 ◽  
Author(s):  
Xiulai Chen ◽  
Xiaoxiang Dong ◽  
Jia Liu ◽  
Qiuling Luo ◽  
Liming Liu
2005 ◽  
Vol 71 (12) ◽  
pp. 7880-7887 ◽  
Author(s):  
Sang Jun Lee ◽  
Dong-Yup Lee ◽  
Tae Yong Kim ◽  
Byung Hun Kim ◽  
Jinwon Lee ◽  
...  

ABSTRACT 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.


2006 ◽  
Vol 188 (2) ◽  
pp. 587-598 ◽  
Author(s):  
Moshe Herzberg ◽  
Ian K. Kaye ◽  
Wolfgang Peti ◽  
Thomas K. Wood

ABSTRACT YdgG is an uncharacterized protein that is induced in Escherichia coli biofilms. Here it is shown that deletion of ydgG decreased extracellular and increased intracellular concentrations of autoinducer 2 (AI-2); hence, YdgG enhances transport of AI-2. Consistent with this hypothesis, deletion of ydgG resulted in a 7,000-fold increase in biofilm thickness and 574-fold increase in biomass in flow cells. Also consistent with the hypothesis, deletion of ydgG increased cell motility by increasing transcription of flagellar genes (genes induced by AI-2). By expressing ydgG in trans, the wild-type phenotypes for extracellular AI-2 activity, motility, and biofilm formation were restored. YdgG is also predicted to be a membrane-spanning protein that is conserved in many bacteria, and it influences resistance to several antimicrobials, including crystal violet and streptomycin (this phenotype could also be complemented). Deletion of ydgG also caused 31% of the bacterial chromosome to be differentially expressed in biofilms, as expected, since AI-2 controls hundreds of genes. YdgG was found to negatively modulate expression of flagellum- and motility-related genes, as well as other known products essential for biofilm formation, including operons for type 1 fimbriae, autotransporter protein Ag43, curli production, colanic acid production, and production of polysaccharide adhesin. Eighty genes not previously related to biofilm formation were also identified, including those that encode transport proteins (yihN and yihP), polysialic acid production (gutM and gutQ), CP4-57 prophage functions (yfjR and alpA), methionine biosynthesis (metR), biotin and thiamine biosynthesis (bioF and thiDFH), anaerobic metabolism (focB, hyfACDR, ttdA, and fumB), and proteins with unknown function (ybfG, yceO, yjhQ, and yjbE); 10 of these genes were verified through mutation to decrease biofilm formation by 40% or more (yfjR, bioF, yccW, yjbE, yceO, ttdA, fumB, yjiP, gutQ, and yihR). Hence, it appears YdgG controls the transport of the quorum-sensing signal AI-2, and so we suggest the gene name tqsA.


2019 ◽  
Vol 141 ◽  
pp. 252-258 ◽  
Author(s):  
Ping Yu ◽  
Qian Ren ◽  
Xinxin Wang ◽  
Xingxing Huang

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