Literature DB >> 12740935

Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation.

Wouter A van Winden1, Walter M van Gulik, Dick Schipper, Peter J T Verheijen, Preben Krabben, Jacobus L Vinke, Joseph J Heijnen.   

Abstract

At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model. Copyright 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 83: 75-92, 2003.

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Year:  2003        PMID: 12740935     DOI: 10.1002/bit.10648

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  6 in total

Review 1.  It is all about metabolic fluxes.

Authors:  Jens Nielsen
Journal:  J Bacteriol       Date:  2003-12       Impact factor: 3.490

Review 2.  Fluxomics: mass spectrometry versus quantitative imaging.

Authors:  Wolfgang Wiechert; Oliver Schweissgut; Hitomi Takanaga; Wolf B Frommer
Journal:  Curr Opin Plant Biol       Date:  2007-05-03       Impact factor: 7.834

3.  13C-labeled gluconate tracing as a direct and accurate method for determining the pentose phosphate pathway split ratio in Penicillium chrysogenum.

Authors:  Roelco J Kleijn; Wouter A van Winden; Cor Ras; Walter M van Gulik; Dick Schipper; Joseph J Heijnen
Journal:  Appl Environ Microbiol       Date:  2006-07       Impact factor: 4.792

4.  Metabolic flux elucidation for large-scale models using 13C labeled isotopes.

Authors:  Patrick F Suthers; Anthony P Burgard; Madhukar S Dasika; Farnaz Nowroozi; Stephen Van Dien; Jay D Keasling; Costas D Maranas
Journal:  Metab Eng       Date:  2007-05-29       Impact factor: 9.783

5.  Substrate cycles in Penicillium chrysogenum quantified by isotopic non-stationary flux analysis.

Authors:  Zheng Zhao; Angela Ten Pierick; Lodewijk de Jonge; Joseph J Heijnen; S Aljoscha Wahl
Journal:  Microb Cell Fact       Date:  2012-10-25       Impact factor: 5.328

6.  OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis.

Authors:  Lake-Ee Quek; Christoph Wittmann; Lars K Nielsen; Jens O Krömer
Journal:  Microb Cell Fact       Date:  2009-05-01       Impact factor: 5.328

  6 in total

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