Journal of Chromatography B 2015-01-01

Isotope labeling pattern study of central carbon metabolites using GC/MS.

Joon-Young Jung, Min-Kyu Oh

Index: J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 974 , 101-8, (2014)

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Abstract

Determination of fluxes by (13)C tracer experiments depends on monitoring the (13)C labeling pattern of metabolites during isotope experiments. In metabolome-based (13)C metabolic flux analysis, liquid chromatography combined with mass spectrometry or tandem mass spectrometry (LC/MS or LC/MS/MS, respectively) has been mainly used as an analytical platform for isotope pattern studies of central carbon metabolites. However, gas chromatography with mass spectrometry (GC/MS) has several advantages over LC/MS, such as high sensitivity, low cost, ease of operation, and availability of mass spectra databases for comparison. In this study, analysis of isotope pattern for central carbon metabolites using GC/MS was demonstrated. First, a proper set of mass ions for central carbon metabolites was selected based on carbon backbone information and structural isomers of mass fragment ions. A total of 34 mass fragment ions was selected and used for the quantification of 25 central carbon metabolites. Then, to quantify isotope fractions, a natural mass isotopomer library for selected mass fragment ions was constructed and subtracted from isotopomer mass spectra data. The results revealed a surprisingly high abundance of partially labeled (13)C intermediates, such as 56.4% of fructose 6-phosphate and 47.6% of dihydroxyacetone phosphate at isotopic steady state, which were generated in the pentose phosphate pathway. Finally, dynamic changes of isotope fragments of central metabolites were monitored with a U-(13)C glucose stimulus response experiment in Kluyveromyces marxianus. With a comprehensive study of isotope patterns of central carbon metabolites using GC/MS, 25 central carbon metabolites and their isotopic fractions were successfully quantified. Dynamic and precise acquisition of isotope pattern can then be used in combination with proper kinetic models to calculate metabolic fluxes. Copyright © 2014 Elsevier B.V. All rights reserved.