NMR in systems biology research: Mmthods for metabolomics and fluxomics

Paula Jouhten, Minna Perälä, Eija Rintala, Laura Ruohonen, Perttu Permi, Merja Penttilä, Hannu Maaheimo

    Research output: Contribution to conferenceConference articleScientific


    Metabolism studies have an important role in systems biology research since the metabolome and the fluxome, the complement of the metabolic fluxes, form the physiological phenotype of the cell. The regulation of the physiological state of the cell is distributed to all the levels of cell function that also communicate and interact constantly. The analysis of only transcriptome and proteome levels would provide vastly incomplete information of the system level function. The strength of NMR methods in metabolomics is their versatility. 1H NMR is superior in being unbiased since all low-molecular weight compounds containing protons can be detected with a single run. On the other hand, an NMR analysis can be tailored to a precisely targeted analysis by applying detection of other nuclei and more sophisticated methodology. Furthermore, In vivo –NMR enables the non-invasive monitoring of the metabolite pools in the system. We are currently applying NMR methods both in rapid profiling of the metabolome and in identification and quantification of the metabolites. NMR suffers from an intrinsic lack of sensitivity when compared to MS. However, we have recently started using Varian’s cryogenic probe that has provided a huge increase in sensitivity to our metabolome analyses. We are also building a spectral library of the intermediates of the central carbon metabolism of yeast for speeding up the identification of signals. For conversion of the metabolome profiles as NMR spectra to multivariate data sets we have PERCH NMR software that performs fuzzy integral transformation and also other software for conventional bucketing (Laatikainen et al., 1996). The fuzzy integral transform method is robust against peak shifting in the spectra. Multivariate data analyses such as principal component analysis (PCA) are being applied to extract hidden correlations from the data. Carbon-13 labelling experiments are currently the only method that gives direct information on the actual metabolic pathways that have been active in the system at a given time point. One of the most effective carbon-13 tracer protocols, in terms of both cost and experimental efficiency, is metabolic flux ratio (METAFoR) analysis (Szyperski et al., 1999) that is based on growing the cells on a mixture of uniformly labelled (≈10%) and unlabelled carbon source and subsequently analysing the labelling of proteinogenic amino acids. The software FCAL (Glaser 1999; Szyperski et al., 1999) is being used in flux ratio calculations in METAFoR experiments. In addition to extending the method to the eukaryotic organism Saccharomyces cerevisiae, with compartmentalised metabolism, under glucose repressing conditions (Maaheimo et al., 2001), we have also included the glyoxylate shunt to our current metabolic model network and METAFoR formalism. Metabolic flux ratios can be used as constraints in metabolic flux analysis (MFA) to obtain net flux data from the system (Fischer et al., 2004). METAFoR analysis provides a global profile of the flux state of the system but experiments employing both positionally and uniformly labelled carbon source molecules in a same experiment possess the highest information content. In such experiments, one needs to be able to measure fractional enrichments from amino acids and metabolic intermediates. A phosphorus-31 NMR based method, H1-P31 HSQC-TOCSY, for detection of positional fractional enrichments in sugar phosphate intermediates has been developed. Integration of data from different levels of cell function is necessary for system level understanding. Metabolomics and fluxomics data will be studied along with transcriptional and proteomics data from the same biological experiments to find correlations and patterns in the system level function. With this approach we aim to obtain novel information on the regulation of physiological changes in yeast. References: Fischer, E., Zamboni, N., and Sauer, U., High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13C constraints, Anal. Biochem. 325 (2004) 308-316. Laatikainen, R., Niemitz, M., Weber, U., Sundelin, J., Hassinen, T., and Vepsäläinen, J., General strategies for total-lineshape-type spectral analysis of NMR spectra using integral-transform iterator. J. Magn. Res. A 120 (1996) 1-10. Glaser (1999) FCAL, Ver.2.3.0. ETH Zürich Maaheimo, H., Fiaux, J., Çakar, Z. P., Bailey, J. E., Sauer, U., and Szyperski, T., Central carbon metabolism of Saccharomyces cerevisiae explored by biosynthetic fractional 13C labelling of common amino acids, Eur. J. Biochem. 268 (2001) 2464-2479. Szyperski, T., Glaser, R. W., Hochuli, M., Fiaux, J., Sauer, U., Bailey, J. E., and Wütrich, K., Bioreaction network topology and metabolic flux ratio analysis by biosynthetic fractional 13C labelling and two-dimensional NMR spectroscopy, Metab. Eng. 1 (1999) 189-197.
    Original languageEnglish
    Publication statusPublished - 2004
    EventNORFA Yeast Systems Biology Workshop - Copenhagen, Denmark
    Duration: 17 Nov 200421 Nov 2004


    WorkshopNORFA Yeast Systems Biology Workshop


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