Metabolic flux analysis at ultra short time scale: Isotopically non-stationary 13C labeling experiments

Nöh, Katharina; Grönke, Karsten; Luo, Baozhu; Takors, Ralf; Oldiges, Marco; Wiechert, Wolfgang

doi:10.1016/j.jbiotec.2006.11.015

Cited by 183 publications

(119 citation statements)

References 59 publications

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“…Detailed information on separation methods have been reported previously (13,17). The LC-MS data were analyzed as described previously (18). Briefly, the mass isotopomer fractions of the intermediates 1,3-bisphosphoglycerate, 2-phosphoglycerate, 2-oxoglutarate, 6-phosphogluconate, aconitate, citrate-isocitrate, dihydroxyacetone phosphate, erythrose 4-phosphate, fructose 1,6-diphosphate, fructose 6-phosphate, fumarate, gluconolactone, glucose 6-phosphate, glyceraldehyde 3-phosphate, malate, phosphoenolpyruvate, pyruvate, ribose 5-phosphate, ribulose 5-phosphate/xylulose 5-phosphate, sedoheptulose 7-phosphate, succinate, and the free amino acids alanine, arginine, aspartate, glutamate, histidine, leucine, lysine, methionine, phenylalanine, proline, tyrosine, and valine were determined from the respective mass spectra (see Table S2.1, Table S2.2, and Fig.…”

Section: Methodsmentioning

confidence: 99%

Combined Fluxomics and Transcriptomics Analysis of Glucose Catabolism via a Partially Cyclic Pentose Phosphate Pathway in Gluconobacter oxydans 621H

Hanke¹,

Nöh²,

Noack³

et al. 2013

Appl Environ Microbiol

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In this study, the distribution and regulation of periplasmic and cytoplasmic carbon fluxes in Gluconobacter oxydans 621H with glucose were studied by 13 C-based metabolic flux analysis ( 13 C-MFA) in combination with transcriptomics and enzyme assays. For 13 C-MFA, cells were cultivated with specifically 13 C-labeled glucose, and intracellular metabolites were analyzed for their labeling pattern by liquid chromatography-mass spectrometry (LC-MS). In growth phase I, 90% of the glucose was oxidized periplasmically to gluconate and partially further oxidized to 2-ketogluconate. Of the glucose taken up by the cells, 9% was phosphorylated to glucose 6-phosphate, whereas 91% was oxidized by cytoplasmic glucose dehydrogenase to gluconate. Additional gluconate was taken up into the cells by transport. Of the cytoplasmic gluconate, 70% was oxidized to 5-ketogluconate and 30% was phosphorylated to 6-phosphogluconate. In growth phase II, 87% of gluconate was oxidized to 2-ketogluconate in the periplasm and 13% was taken up by the cells and almost completely converted to 6-phosphogluconate. Since G. oxydans lacks phosphofructokinase, glucose 6-phosphate can be metabolized only via the oxidative pentose phosphate pathway (PPP) or the Entner-Doudoroff pathway (EDP).13 C-MFA showed that 6-phosphogluconate is catabolized primarily via the oxidative PPP in both phases I and II (62% and 93%) and demonstrated a cyclic carbon flux through the oxidative PPP. The transcriptome comparison revealed an increased expression of PPP genes in growth phase II, which was supported by enzyme activity measurements and correlated with the increased PPP flux in phase II. Moreover, genes possibly related to a general stress response displayed increased expression in growth phase II.

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Section: Methodsmentioning

confidence: 99%

Combined Fluxomics and Transcriptomics Analysis of Glucose Catabolism via a Partially Cyclic Pentose Phosphate Pathway in Gluconobacter oxydans 621H

Hanke¹,

Nöh²,

Noack³

et al. 2013

Appl Environ Microbiol

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“…This can be partly overcome by the application of power law or lin-log kinetics in the underlying equations [79,80]. Dynamic models are also needed for dynamic labeling experiments for flux analysis as has been shown for E. coli [81]. The current progress in method development for metabolomics of C. glutamicum will surely stimulate the future generation of dynamic models for such organisms by providing extended data sets on intracellular metabolite concentrations required to derive in vivo kinetics [82][83][84][85].…”

Section: Dynamic Modeling Approachesmentioning

confidence: 99%

“…Respirometric flux analysis, conceptually based on sole labeling measurement of CO 2 extended the application of flux analysis to nongrowing cells [97][98][99]. Additionally, labeling measurement of intracellular metabolites has also received increasing attraction since it offers the possibility to resolve fluxes under dynamic conditions [81,100]. After a decade of intense research and development, such 13 C-based flux methods can routinely track steady-state fluxes in microbes [92].…”

Section: State-of-the-art 13 C Metabolic Flux Analysis and Current Dementioning

confidence: 99%

Analysis and Engineering of Metabolic Pathway Fluxes in Corynebacterium glutamicum

Wittmann

2010

Biosystems Engineering I

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The Gram-positive soil bacterium Corynebacterium glutamicum was discovered as a natural overproducer of glutamate about 50 years ago. Linked to the steadily increasing economical importance of this microorganism for production of glutamate and other amino acids, the quest for efficient production strains has been an intense area of research during the past few decades. Efficient production strains were created by applying classical mutagenesis and selection and especially metabolic engineering strategies with the advent of recombinant DNA technology. Hereby experimental and computational approaches have provided fascinating insights into the metabolism of this microorganism and directed strain engineering. Today, C. glutamicum is applied to the industrial production of more than 2 million tons of amino acids per year. The huge achievements in recent years, including the sequencing of the complete genome and efficient post genomic approaches, now provide the basis for a new, fascinating era of research -analysis of metabolic and regulatory properties of C. glutamicum on a global scale towards novel and superior bioprocesses.

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“…The concept of a cumomer introduced by Wiechert et al has also been adopted to simplify the computational complexity of the isotopomer [3] and develop the most widely used software 13CFlux [4]. A flux estimation framework has recently been proposed and developed with regard to dynamic 13C labeling [5]. Antoniewicz et al proposed a computational framework for elementary metabolite units, which has greatly reduced the amount of computation flux [6].…”

Section: Introductionmentioning

confidence: 99%

Interpretation and Integration of 13C-Fluxomics Data

T¹

2015

Metabolomics

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al. and Young et al. have achieved good results by using LC-MS or LC-MS/MS to detect the state of 13C labeling [11,12].Based on their work and the increasingly wide use of 13C metabolic flux methods in recent years, more and more metabolic flux data have been accumulated, with fluxomics itself emerging as a new way to describe cell systems. Thus, fluxomics has gradually matured into a new -omics technology that can be mentioned in the same breath with proteomics and metabolomics. What follows then is that the interpretation of metabolic fluxomics data is becoming more and more important. The interpretation of fluxomics data currently relies predominantly on Genome-Scale Metabolic Network Models (GSMR) [13]. Burgard et al., relying on GSMR and a Flux Balance Analysis framework, developed a method of predicting the biological significance of specific metabolic flux [14]. The method uses bi-level linear programming and can identify an objective function that leads to a specific metabolic flux distribution. Cell growth under either aerobic or anaerobic conditions has the same metabolic objective, but different metabolic fluxes due to the differences in input conditions. Schuetz et al. projected metabolic flux data from the Sauer laboratory onto a threedimensional flux space, and using the multi-objective optimization method, they found that these flux data were distributed on the Paretofront produced by several different objective functions, indicating that the evolution of the flux state was achieved by a shift among different objectives [15]. By combining metabolic flux data, GSMR and multiobjective optimization, we found that, in the case of oxidative stress, the central carbon metabolism of E. coli could transit from the normal state into a suboptimal state, resulting in more NADPH to fight against oxygen free radicals [16].Corresponding with the interpretation of fluxomics data is the integration of fluxomics data with other -omics data to provide greater biological knowledge. Progress in this area has been slow in recent years or to put it in another way, has not yet attracted enough attention from relevant researchers. However, the number of metabolic fluxomic data

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Metabolic flux analysis at ultra short time scale: Isotopically non-stationary 13C labeling experiments

Cited by 183 publications

References 59 publications

Combined Fluxomics and Transcriptomics Analysis of Glucose Catabolism via a Partially Cyclic Pentose Phosphate Pathway in Gluconobacter oxydans 621H

Combined Fluxomics and Transcriptomics Analysis of Glucose Catabolism via a Partially Cyclic Pentose Phosphate Pathway in Gluconobacter oxydans 621H

Analysis and Engineering of Metabolic Pathway Fluxes in Corynebacterium glutamicum

Interpretation and Integration of 13C-Fluxomics Data

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