Responses of theCentral Metabolism in Escherichia coli to PhosphoglucoseIsomerase and Glucose-6-Phosphate DehydrogenaseKnockouts

Abstract

The responses of Escherichia coli central carbon metabolism to knockout mutations in phosphoglucose isomerase and glucose-6-phosphate (G6P) dehydrogenase genes were investigated by using glucose-and ammonia-limited chemostats. The metabolic network structures and intracellular carbon fluxes in the wild type and in the knockout mutants were characterized by using the complementary methods of flux ratio analysis and metabolic flux analysis based on [U-13 C]glucose labeling and two-dimensional nuclear magnetic resonance (NMR) spectroscopy of cellular amino acids, glycerol, and glucose. Disruption of phosphoglucose isomerase resulted in use of the pentose phosphate pathway as the primary route of glucose catabolism, while flux rerouting via the Embden-Meyerhof-Parnas pathway and the nonoxidative branch of the pentose phosphate pathway compensated for the G6P dehydrogenase deficiency. Furthermore, additional, unexpected flux responses to the knockout mutations were observed. Most prominently, the glyoxylate shunt was found to be active in phosphoglucose isomerase-deficient E. coli. The Entner-Doudoroff pathway also contributed to a minor fraction of the glucose catabolism in this mutant strain. Moreover, although knockout of G6P dehydrogenase had no significant influence on the central metabolism under glucose-limited conditions, this mutation resulted in extensive overflow metabolism and extremely low tricarboxylic acid cycle fluxes under ammonia limitation conditions.The central carbon pathways constitute the backbone of cell metabolism by providing energy, building blocks, and reducing power for biomass synthesis. Due to the extensive redundancy and the presence of isozymes, most single-gene knockout mutations in central metabolism do not block cell growth on glucose (13, 18). To reveal gene-phenotype relationships, it is important to gain insight into the complex responses of the metabolic network in its entirety to these mutations. The most important properties of biochemical networks are the per se nonmeasurable in vivo reaction rates, which may be estimated by metabolic flux analysis (41).The most common approach is based on flux balancing of extracellular uptake and secretion rates within a stoichiometric reaction model (26,45). This approach usually requires assumptions about redox or energy balances, and the validity of these assumptions strongly affects the flux estimates. To increase the reliability and resolution of such flux balance analyses, additional information may be obtained from 13 C labeling experiments (43,48). In this approach, the isotope labeling patterns of intracellular metabolites are analyzed by either nuclear magnetic resonance (NMR) or mass spectrometry. The data are then used for identification of the metabolic network structure or for quantification of the intracellular carbon fluxes.Direct analytical interpretation of 13 C labeling patterns may provide direct evidence for a particular flux or reaction (24,34). Recently, a more general method of flux ratio analysis has been developed...

Metabolic flux analysis of Escherichia coli in glucose-limited continuous culture. I. Growth-rate-dependent metabolic efficiency at steady state

“…This change in the distribution of the metabolic pathway utilization is connected to an increased energy efficiency of carbon substrate utilization for biomass production. Also, one should recall that phosphoglucoseisomerase-negative mutants of E. coli (r2 not possible) are able to grow on glucose, although at slower rates compared with wild-type cells but with a higher biomass yield (Fraenkel & Vinopal, 1973;Canonaco et al, 2001;Hua et al, 2003), indicating that the function of the PP pathway extends beyond the supply of NADPH and biosynthetic precursors. Moreover, studies on E. coli geneknockout mutants deficient in catabolic enzymes indicate a rather robust and flexible metabolic network, where pathways can substitute for each other, sometimes without significantly affecting growth rates (e.g.…”

Response of the central metabolism of Escherichia coli to modified expression of the gene encoding the glucose‐6‐phosphate dehydrogenase

“…For this purpose, cells were grown in minimal medium containing a mixture of 80% [1-13 C] glucose and 20% [U-13 C] glucose. The specifically labelled [1-13 C] glucose was used, in addition to the uniformly labelled glucose, to obtain direct data concerning flux through the oxidative PPP since CO 2 loss within this pathway involves specific loss of [1][2][3][4][5][6][7][8][9][10][11][12][13] C] glucose [21]. Thus, the M0 abundances will reflect the extent to which glucose is catabolized via PPP.…”

“…The consequences of the lack of G6PDH have been already reported [7][8][9], but there is currently no report on the effects of increased G6PDH activity on the metabolic network. A strain expressing zwf to high levels has been constructed and compared to both its parent strain and an isogenic strain in which zwf has been deleted as regards to the growth characteristics and metabolic flux distribution.…”

Increasing the accuracy of mass isotopomer analysis through calibration curves constructed using biologically synthesized compounds

“…The a i s deviate from 1. The exact C j values of the whole molecule can be computed using Eqns (10), (14) and (6). To simulate the approximations we made in this work, we can first estimate R avg from exact C 1 of the molecules using Eqns (14), (6) and (13), then compute the other C j values of the molecule using the estimated R avg and the binomial distributions.…”