Abstract
NMR spectroscopy is an efficient method for analyzing 13C labelling of cellular metabolites. The strength of it is especially the ability to provide direct quantitative positional information on the 13C labelling status of carbon atoms in metabolites. NMR spectroscopic methods allow also for detection of contiguously 13C-labelled fragments in the carbon backbones of the metabolites. Furthermore, the recent developments of NMR spectroscopy hardware have substantially improved the sensitivity of the methods. In this chapter we describe a method for analyzing the 13C labelling of the biomass amino acids for metabolic flux analysis, sample preparation for NMR spectroscopy, acquiring and processing the NMR spectra, and extracting the 13C labelling information from the NMR data. Different NMR methods are applied depending on the 13C labelling strategy chosen. These strategies include uniform 13C labelling, positional 13C labelling, or a combination of both. Not only the preparation of sample for analysis of 13C labelling in proteinogenic amino acids in biomass is described, but also the necessary modifications to the method when analysis of 13C labelling in free metabolic intermediates is of interest. Finally the strategies for using the different NMR-detected 13C labelling data in 13C MFA are discussed.
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Acknowledgements
This study was financially supported by the Academy of Finland through the Centre of Excellence in White Biotechnology Green Chemistry (grant 118573). The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement number 222716—SMARTCELL. P.J. wants to acknowledge funding from the Academy of Finland for a postdoctoral researcher’s project (grant 140380).
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Jouhten, P., Maaheimo, H. (2014). Labelling Analysis for 13C MFA Using NMR Spectroscopy. In: Krömer, J., Nielsen, L., Blank, L. (eds) Metabolic Flux Analysis. Methods in Molecular Biology, vol 1191. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1170-7_9
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DOI: https://doi.org/10.1007/978-1-4939-1170-7_9
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