Abstract
Biosynthetically directed fractional 13C labeling of proteinogenic amino acids is achieved by expression of proteins on a minimal medium which contains a mixture of [13C6]glucose and glucose with natural isotope abundance as the sole carbon source. Subsequent hydrolysis of the proteins yields the free amino acids. The observation of 13C-13C spin-spin scalar coupling fine structures in sensitive two-dimensional heteronuclear [13C,1H]-correlation spectroscopy (2D [13C,1H]-COSY) allows one to identify non-random 13C-labeling patterns arising from the incorporation of intact two-carbon and three-carbon fragments from a single source molecule of glucose into the amino acids. Since 2D [13C,1H]-COSY suffices to resolve all relevant resonances, the mixture of amino acids can be analyzed without further separation of its components. Probabilistic equations relate the observed multiplet intensities of the 13C fine structures to the relative abundance of the intact carbon fragments. They enable a quantitative analysis of the carbon flux in the network of biosynthetic pathways, thus using the proteinogenic amino acids as probes to study intermediary metabolism. This paper shows that biosynthetically directed fractional 13C labeling of amino acids provides an efficient analytical tool to quantitatively investigate glycolysis, pyruvate metabolism, pentose phosphate pathway, tricarboxylic acid cycle and C1 metabolism. Possible applications of the method include both the exploration of unknown biosynthetic pathways and the rapid elucidation of the response of a known biosynthetic reaction network to changes in growth conditions or genetic manipulations. In conjunction with the relatively low costs for isotopes, manpower and NMR instrument time, this makes biosynthetic fractional 13C labeling of proteinogenic amino acids particularly attractive to support process design and metabolic engineering in biotechnology, since screening procedures become feasible which enable a systematic characterization of the cell's metabolic state as a function of parameters that are involved in the optimization of biotechnological processes.
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