Literature DB >> 22475504

Gas chromatography-mass spectrometry analysis of 13C labeling in sugars for metabolic flux analysis.

Mohamed Koubaa1, Sarra Mghaieth, Brigitte Thomasset, Albrecht Roscher.   

Abstract

Metabolic flux analysis, using 13C labeled substrates, has become a powerful methodology for quantifying intracellular fluxes. Most often, analysis is restricted to nuclear magnetic resonance or mass spectrometry measurement of 13C label incorporation into protein amino acids. However, amino acid isotopomer distribution insufficiently covers the entire network of central metabolism, especially in plant cells with highly compartmented metabolism, and analysis of other metabolites is required. Analysis of label in saccharides provides complementary data to better define fluxes around hexose, pentose, and triose phosphate pools. Here, we propose a gas chromatography-mass spectrometry (GC-MS) method to analyze 13C labeling in glucose and fructose moieties of sucrose, free glucose, fructose, maltose, inositol, and starch. Our results show that saccharide labeling for isotopomer quantification is better analyzed by chemical ionization than by electron ionization. The structure of the generated fragments was simulated and validated using labeled standards. The method is illustrated by analysis of saccharides extracted from developing rapeseed (Brassica napus L.) embryos. It is shown that glucose 6-phosphate isomerase and plastidial glucose 6-phosphate transport reactions are not at equilibrium, and light is shed on the pathways leading to fructose, maltose, and inositol synthesis.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22475504     DOI: 10.1016/j.ab.2012.03.020

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


  8 in total

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5.  Data documenting the comparison between the theoretically expected values of free sugars mass isotopomer composition with standards using GC-MS and LC-HRMS for Metabolic Flux Analysis.

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Journal:  Data Brief       Date:  2017-03-31

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Authors:  Doug K Allen; Bradley S Evans; Igor G L Libourel
Journal:  PLoS One       Date:  2014-03-13       Impact factor: 3.240

  8 in total

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