Metabolite and isotopomer balancing in the analysis of metabolic cycles: I. Theory

Klapa, Maria I.; Park, Sung M.; Sinskey, Anthony J.; Stephanopoulos, Gregory

doi:10.1002/(sici)1097-0290(19990220)62:4<375::aid-bit1>3.0.co;2-o

Cited by 79 publications

(31 citation statements)

References 32 publications

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“…In practice, isotope labelling in metabolic balancing is often performed by growing a culture on 13 Cglucose and making time-dependent measurements of the flux of incorporated 13 C by NMR or (less frequently) MS (Szyperski, 1998). If NMR fine structures of 13 C-enriched metabolic intermediates are studied, the analysis of the position of the incorporated 13 C atoms enables mathematical modelling of the contribution of different pathways to the metabolic cycles (Schmidt et al, 1997;Klapa et al, 1999;Parket al, 1999). All these authors emphasized that NMR analyses proved to be more powerful compared to MSbased approaches, since it is much more difficult (and sometimes impossible) to obtain positional information of the incorporated 13 C atoms from interpretation of mass spectral fragmentation.…”

Section: Modelling Based On Metabolic Flux Measurementsmentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

1,624

1,337

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Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and 'proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

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Section: Modelling Based On Metabolic Flux Measurementsmentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

1,624

1,337

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“…An exhaustive examination of these strains was conducted by using a mixture of 13 C-labeled and -unlabeled glucose followed by analysis of protein-bound amino acids via two-dimensional 13 C, 1 H HSQC NMR spectroscopy. Metabolite balancing (31), biosynthetic fractional 13 C labeling of proteinogenic amino acids (29), and isotopomer balancing (30) were employed as a means to evaluate the metabolic differences between MG1655 and Pdh. Metabolite balancing and the analysis of intact and broken carbon bonds in key amino acids provided initial evidence of a potential redistribution of fluxes in response to the PDHC deficiency.…”

Section: Discussionmentioning

confidence: 99%

“…First, intracellular fluxes were estimated based on network stoichiometry and extracellular measurements using a technique known as metabolite balancing (30,31). The stoichiometric model used for this purpose consisted of 80 reactions (fluxes) and 74 balanceable metabolites (see supplemental Table 1a), thus resulting in a system with six degrees of freedom.…”

Section: Methodsmentioning

confidence: 99%

“…A second in vivo metabolic flux analysis technique, based on the use of 13 C-labeled glucose followed by NMR analysis and isotopomer balancing (30), was also used for the estimation of intracellular fluxes as it provides a more comprehensive description of network topology that, unlike metabolite balancing, does not rely on redox and ATP balances. This approach is referred to here as isotopomer balancing.…”

Section: Methodsmentioning

confidence: 99%

“…To this end, the above measurements of growth and formation of extracellular products, in combination with a stoichiometric model of metabolism (see supplemental Table 1a), were used to calculate intracellular fluxes during exponential growth and gain a better understanding of the global response to the PDHC deficiency. This approach, known as metabolite balancing (4,30,31), is a very useful, noninvasive, and cost-effective tool that has been widely used to study microbial metabolism (4,34,35). Selected intracellular fluxes calculated for MG1655 and Pdh cultures are shown in Table 1; the complete set of fluxes is provided in supplemental Table 1a.…”

Section: Kinetics Of Glucose Fermentation In Wild-type Mg1655 and A Pmentioning

confidence: 99%

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Metabolic Analysis of Wild-type Escherichia coli and a Pyruvate Dehydrogenase Complex (PDHC)-deficient Derivative Reveals the Role of PDHC in the Fermentative Metabolism of Glucose

Murarka

Clomburg

Moran

et al. 2010

Journal of Biological Chemistry

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Pyruvate is located at a metabolic junction of assimilatory and dissimilatory pathways and represents a switch point between respiratory and fermentative metabolism. In Escherichia coli, the pyruvate dehydrogenase complex (PDHC) and pyruvate formate-lyase are considered the primary routes of pyruvate conversion to acetyl-CoA for aerobic respiration and anaerobic fermentation, respectively. During glucose fermentation, the in vivo activity of PDHC has been reported as either very low or undetectable, and the role of this enzyme remains unknown. In this study, a comprehensive characterization of wild-type E. coli MG1655 and a PDHC-deficient derivative (Pdh) led to the identification of the role of PDHC in the anaerobic fermentation of glucose. The metabolism of these strains was investigated by using a mixture of 13 C-labeled and -unlabeled glucose followed by the analysis of the labeling pattern in protein-bound amino acids via two-dimensional 13 C, 1 H NMR spectroscopy. Metabolite balancing, biosynthetic 13 C labeling of proteinogenic amino acids, and isotopomer balancing all indicated a large increase in the flux of the oxidative branch of the pentose phosphate pathway (ox-PPP) in response to the PDHC deficiency. Because both ox-PPP and PDHC generate CO 2 and the calculated CO 2 evolution rate was significantly reduced in Pdh, it was hypothesized that the role of PDHC is to provide CO 2 for cell growth. The similarly negative impact of either PDHC or ox-PPP deficiencies, and an even more pronounced impairment of cell growth in a strain lacking both ox-PPP and PDHC, provided further support for this hypothesis. The three strains exhibited similar phenotypes in the presence of an external source of CO 2 , thus confirming the role of PDHC. Activation of formate hydrogen-lyase (which converts formate to CO 2 and H 2 ) rendered the PDHC deficiency silent, but its negative impact reappeared in a strain lacking both PDHC and formate hydrogen-lyase. A stoichiometric analysis of CO 2 generation via PDHC and ox-PPP revealed that the PDHC route is more carbon-and energy-efficient, in agreement with its beneficial role in cell growth.The fermentative metabolism of glucose has been extensively studied in many organisms, especially in the model bacterium Escherichia coli (1). As shown in Fig. 1, glucose is concomitantly transported and phosphorylated by the phosphoenolpyruvatedependent phosphotransferase system (2, 3). The resulting glucose 6-phosphate is processed via the Embden-Meyerhof-Parnas (EMP) 2 pathway and the pentose phosphate pathway (PPP) (4) to provide ATP, reducing power and carbon skeletons for biosynthesis. In the absence of external electron acceptors (i.e. under fermentative conditions), E. coli converts most of the glucose to a mixture of organic acids (acetate, formate, lactate, and succinate), ethanol, carbon dioxide, and hydrogen ( Fig. 1) (1). Most of these fermentation products are generated from pyruvate ( Fig. 1).Pyruvate is located at a major metabolic node linking carbohydrate catabolism to energy...

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Bidirectional reaction steps in metabolic networks: III. Explicit solution and analysis of isotopomer labeling systems

Wiechert

Möllney

Isermann

et al. 1999

Biotechnol. Bioeng.

267

160

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The last few years have brought tremendous progress in experimental methods for metabolic flux determination by carbon‐labeling experiments. A significant enlargement of the available measurement data set has been achieved, especially when isotopomer fractions within intracellular metabolite pools are quantitated. This information can be used to improve the statistical quality of flux estimates. Furthermore, several assumptions on bidirectional intracellular reaction steps that were hitherto indispensable may now become obsolete. To make full use of the complete measurement information a general mathematical model for isotopomer systems is established in this contribution. Then, by introducing the important new concept of cumomers and cumomer fractions, it is shown that the arising nonlinear isotopomer balance equations can be solved analytically in all cases. In particular, the solution of the metabolite flux balances and the positional carbon‐labeling balances presented in part I of this series turn out to be just the first two steps of the general solution procedure for isotopomer balances. A detailed analysis of the isotopomer network structure then opens up new insights into the intrinsic structure of isotopomer systems. In particular, it turns out that isotopomer systems are not as complex as they appear at first glance. This enables some far‐reaching conclusions to be drawn on the information potential of isotopomer experiments with respect to flux identification. Finally, some illustrative examples are examined to show that an information increase is not guaranteed when isotopomer measurements are used in addition to positional enrichment data. © 1999 John Wiley & Sons, Inc. Biotechnol Bioeng 66: 69–85, 1999.

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Metabolite and isotopomer balancing in the analysis of metabolic cycles: I. Theory

Cited by 79 publications

References 32 publications

Metabolomics — the link between genotypes and phenotypes

Metabolomics — the link between genotypes and phenotypes

Metabolic Analysis of Wild-type Escherichia coli and a Pyruvate Dehydrogenase Complex (PDHC)-deficient Derivative Reveals the Role of PDHC in the Fermentative Metabolism of Glucose

Bidirectional reaction steps in metabolic networks: III. Explicit solution and analysis of isotopomer labeling systems

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