Biosynthetically Directed Fractional 13C-labeling of Proteinogenic Amino Acids. An Efficient Analytical Tool to Investigate Intermediary Metabolism

Szyperski, Thomas

doi:10.1111/j.1432-1033.1995.433zz.x

Cited by 155 publications

(350 citation statements)

References 55 publications

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“…Although similar in scope to previously described metabolic flux ratio analysis by NMR [16,17], GC-MS-based analysis provides a significant advance in handling and sensitivity, so that biomass samples as low as 1 mg cellular dry weight may be analyzed. Without the need for time-consuming quantitative physiological analysis, this methodology thus paves the road to rapid diagnosis of metabolic changes in culture volumes below 1 mL.…”

Section: Discussionmentioning

confidence: 99%

“…As exchange fluxes between serine and glycine [16] clearly influence the mass distribution of serine, PEP (1)2) was used to determine the fraction of trioses that were cleaved and rearranged between C 1 -C 2 by the action of transketolase, and compared to the fraction that originates from an unbroken two carbon unit of glucose according to Eqn (7). An upper bound for PEP molecules that were generated from P5P can be calculated assuming that five trioses are produced from three pentoses and that at least two trioses are rearranged by transketolase.…”

Section: Calculation Of Metabolic Flux Ratiosmentioning

confidence: 99%

“…1). The amino-acidcarbon skeletons were derived from the metabolic intermediates PEP, Pyruvate, P5P, E4P, OAA, and OGA as described [16]. Fumarase A-deficient K12 (EJ1535) [30] Correction for naturally occurring isotopes…”

Section: Bioreaction Networkmentioning

confidence: 99%

“…Such analytically deduced flux ratios were also used successfully as constraints for metabolic flux analysis [13][14][15]. Based on probabilistic equations, a more general methodology was developed to simultaneously identify network topology and multiple flux partitioning ratios [16,17]. This metabolic flux ratio analysis was based on the detection of 13 C-labeling patterns in proteinogenic amino acids by NMR analysis, and provides direct evidence for a particular flux.…”

mentioning

confidence: 99%

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

Fischer

Sauer

2003

European Journal of Biochemistry

348

418

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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...

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Section: Discussionmentioning

confidence: 99%

Section: Calculation Of Metabolic Flux Ratiosmentioning

confidence: 99%

Section: Bioreaction Networkmentioning

confidence: 99%

mentioning

confidence: 99%

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

Fischer

Sauer

2003

European Journal of Biochemistry

348

418

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“…In both batch and fed-batch cultures there are increases in the biomass yield, and even after IPTG induction, cells coexpressing the PGL enzyme can sustain higher catabolic and anabolic fluxes through the PP pathway based on metabolic flux analysis estimates. Moreover, 13 C-labeling and nuclear magnetic resonance studies have shown that when cells are growing aerobically in minimal medium with glucose as the carbon source, the main biosynthetic pathway in E. coli for ribose-5-phosphate and erythrose-4-phosphate, both of which are precursors for nucleotide and aromatic amino acid biosynthesis, is the oxidative branch of the PP pathway (22). It has been reported that 22% of the glucose transported into E. coli is driven by the glucose-6-phosphate dehydrogenase and that part is diverted to nucleotide, amino acid, and vitamin biosynthesis (7).…”

Section: Figmentioning

confidence: 99%

Suppressing Posttranslational Gluconoylation of Heterologous Proteins by Metabolic Engineering of Escherichia coli

Aon¹,

Caimi²,

Taylor³

et al. 2008

Appl Environ Microbiol

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Minimization of chemical modifications during the production of proteins for pharmaceutical and medical applications is of fundamental and practical importance. The gluconoylation of heterologously expressed protein which is observed in Escherichia coli BL21(DE3) constitutes one such undesired posttranslational modification. We postulated that formation of gluconoylated/phosphogluconoylated products of heterologous proteins is caused by the accumulation of 6-phosphogluconolactone due to the absence of phosphogluconolactonase (PGL) in the pentose phosphate pathway. The results obtained demonstrate that overexpression of a heterologous PGL in BL21(DE3) suppresses the formation of the gluconoylated adducts in the therapeutic proteins studied. When this E. coli strain was grown in high-cell-density fed-batch cultures with an extra copy of the pgl gene, we found that the biomass yield and specific productivity of a heterologous 18-kDa protein increased simultaneously by 50 and 60%, respectively. The higher level of PGL expression allowed E. coli strain BL21(DE3) to satisfy the extra demand for precursors, as well as the energy requirements, in order to replicate plasmid DNA and express heterologous genes, as metabolic flux analysis showed by the higher precursor and NADPH fluxes through the oxidative branch of the pentose phosphate shunt. This work shows that E. coli strain BL21(DE3) can be used as a host to produce three different proteins, a heterodimer of liver X receptors, elongin C, and an 18-kDa protein. This is the first report describing a novel and general strategy for suppressing this nonenzymatic modification by metabolic pathway engineering.

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Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices

Schmidt

Carlsen

Nielsen

et al. 1997

Biotechnol. Bioeng.

291

156

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Biosynthetically Directed Fractional 13C-labeling of Proteinogenic Amino Acids. An Efficient Analytical Tool to Investigate Intermediary Metabolism

Cited by 155 publications

References 55 publications

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

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

Suppressing Posttranslational Gluconoylation of Heterologous Proteins by Metabolic Engineering of Escherichia coli

Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices

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