Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC‐MS

Abstract

We describe here a novel methodology for rapid diagnosis of metabolic changes, which is based on probabilistic equations that relate GC-MS-derived mass distributions in proteinogenic amino acids to in vivo enzyme activities. This metabolic flux ratio analysis by GC-MS provides a comprehensive perspective on central metabolism by quantifying 14 ratios of fluxes through converging pathways and reactions from [1-13 C] and [U-13 C]glucose experiments. Reliability and accuracy of this method were experimentally verified by successfully capturing expected flux responses of Escherichia coli to environmental modifications and seven knockout mutations in all major pathways of central metabolism. Furthermore, several mutants exhibited additional, unexpected flux responses that provide new insights into the behavior of the metabolic network in its entirety. Most prominently, the low in vivo activity of the EntnerDoudoroff pathway in wild-type E. coli increased up to a contribution of 30% to glucose catabolism in mutants of glycolysis and TCA cycle. Moreover, glucose 6-phosphate dehydrogenase mutants catabolized glucose not exclusively via glycolysis, suggesting a yet unidentified bypass of this reaction. Although strongly affected by environmental conditions, a stable balance between anaplerotic and TCA cycle flux was maintained by all mutants in the upper part of metabolism. Overall, our results provide quantitative insight into flux changes that bring about the resilience of metabolic networks to disruption.Keywords: Entner-Doudoroff pathway; flux analysis; fluxome; METAFoR analysis; pentose phosphate pathway.Comprehensive and quantitative understanding of biochemical reaction networks requires not only knowledge about their constituting components, but also information about the behavior of the network in its entirety. Toward this end, systems-oriented methodologies were developed that simultaneously access the level of reaction intermediates [1] or rates of reactions [2][3][4][5], also referred to as the metabolome [6] and the fluxome [7], respectively. The most important property of biochemical networks are the per se nonmeasurable in vivo reaction rates, which may be estimated by so-called metabolic flux analysis that provides a holistic perspective on metabolism.In its simplest form, metabolic flux analysis relies on flux balancing of extracellular consumption and secretion rates within a stoichiometric reaction model [5]. To increase reliability and resolution of such flux balancing analyses, additional information may be derived from 13 C-labeling experiments. In this approach, 13 C-labeled substrates are administered and 13 C-labeled products of metabolism are analyzed by methods that distinguish between different isotope labeling patterns, in particular NMR and MS [2,3,8]. In the most advanced methodology, a comprehensive isotope isomer (isotopomer) model of metabolism is used to map metabolic fluxes in an iterative fitting procedure on the isotopomer pattern of network metabolites that are deduced from NMR or M...

The Soluble and Membrane-bound Transhydrogenases UdhA and PntAB Have Divergent Functions in NADPH Metabolism of Escherichia coli

“…Metabolic Flux Ratio Analysis by GC-MS-Direct analytical interpretation of GC-MS-derived 13 C-labeling pattern in proteinogenic amino acids was used to quantify independent flux partitioning ratios (36,37). Biomass pellets harvested from 13 C-labeling experiments were resuspended and hydrolyzed in 1.5 ml of 6 M HCl at 105°C for 24 h in sealed 2.2-ml Eppendorf tubes.…”

“…After drying in a vacuum centrifuge at room temperature, hydrolysates were derivatized at 85°C in 50 l of dimethylformamide (Fluka) and 50 l of N-(tert-butyldimethylsilyl)-Nmethyl-trifluoroacetamide (Fluka) for 60 min. The mass isotope distribution in the derivatized amino acids was then analyzed by gas chromatography-mass spectrometry (GC-MS), and the GC-MS-derived mass distributions of proteinogenic amino acids were corrected for naturally occurring isotopes (36). The corrected mass distributions were related to in vivo metabolic activities using algebraic equations and statistical data treatment which quantified 14 ratios of fluxes through converging reactions and pathways to the synthesis of eight intracellular metabolites from [1-13 C]glucose and [U- 13 C]glucose experiments (36).…”

“…To elucidate the intracellular carbon flux response, we used metabolic flux ratio analysis by GC-MS (36). By comparing 13 C-labeling pattern in proteinogenic amino acids, this strictly local analysis quantifies ratios of converging fluxes at the junction of two or more pathways or reactions (43,44).…”

Increased TCA cycle activity and reduced oxygen consumption during cytochrome P450‐dependent biotransformation in fission yeast

“…The free amino acids were derivatized at 85 • C for 1 h using 15 µl dimethylformamide and 15 µl N -(tert-butyldimethylsilyl)-N -methyl-trifluoroacetamide (Dauner and Sauer, 2000;Wittmann et al, 2002). GC-MS analysis was carried out as reported recently (Fischer and Sauer, 2003), using a previously described biochemical reaction network (Blank and Sauer, 2004).…”

CreA influences the metabolic fluxes of Aspergillus nidulans during growth on glucose and xylose

“…However, in the exponential phase of batch cultivations, the growth is balanced and hence it can be considered that the internal metabolites are in pseudosteady state, and the use of batch cultures has recently been employed with success for analysis of various yeast (Raghevendran et al, 2004), Escherichia coli (Fischer & Sauer, 2003) and Bacillus subtilis mutants (Zamboni & Sauer, 2003). A potential pitfall associated with the analysis of different mutants in batch cultures is that the mutants may exhibit different maximum specific growth rates, and during unlimited growth in a batch culture the metabolic response is therefore likely to be a function not only of the mutation but also of the variation in the specific growth rate.…”