Flux analyses in plants are generally still performed following either incubation of plants (or plant material) in radiolabelled precursors and subsequent chemical fractionation by a series of ion- exchange and thin layer chromatographic separations in tandem with enzymatic digestions, or NMR-based approaches. Although very useful each of these approaches suffers from drawbacks. The fractionation of radiolabel is highly labour intensive and generally only provides a low level of resolution that is limited either to end-products (such as sucrose and starch) or compound classes groups (e.g. organic and amino acids represent single pools see Figure 1a). That said this technique has proven vitally important in previous studies of starch metabolism (Coates and ap Rees, 1994; Hill and Smith, 1991; Tauberger et al., 2000) and has found both historical (Graham and ap Rees, 1965; Lips and Beevers, 1966) and recent (Gage et al., 1997; Wheeler et al., 1998) utility in the elucidation of metabolic pathways in plants. In contrast, NMR analysis allows the evaluation of specific compounds and importantly, as it detects atomic as opposed to chemical properties, can yield important information as to the position at which a molecule is labelled. For this reason it has long been used in the analysis of bacterial and mammalian fluxes (Des Rosiers et al., 1991; Katz et al., 1989; Szyperski, 1998), and has more recently been adopted by a number of researchers in the plant field (see Roscher et al., 2000). The major disadvantage that NMR spectroscopy has in comparison with mass spectrometric methodologies is that of sensitivity with NMR generally being restricted to determinations of the more abundant cellular constituents. Given these constraints we decided to develop a method for analysis of 13C-labelled plant extracts using mass-spectrometry. Our first attempt was to prepare methanol extracts as described in Roessner et al. (2000), and to ionize these extracts chemically using NH3. As chemical ionization is less harsh than electron impact ionization it would be expected to leave more metabolites in their in vivo form. Unfortunately, despite repeated trials, following this approach we were unable to detect a suitable range of 13C-labelled analytes for the purposes of this work. Therefore, we next attempted to adapt a pre-established electron impact-based GC-MS method for analysis of steady-state metabolite levels of potato tuber tissue that we know exhibits high reproducibility of both analyte retention time and peak responses (Roessner et al., 2000, 2001a). Using this method allowed the detection of labelled metabolites with relative ease, however, given that one of the derivitizing agents we use – (N-methyl-N-trimethylsilyl)trifluoroacetanide (MSTFA) – consists of a mixture of isotopes great caution must be taken to ensure the labelling pattern determined reflects the redistribution of isotope during the incubation process rather than the derivitization itself. For this reason we decided to fully process control samples that had been incubated in unlabelled glucose (exhibiting only the natural abundance of 1.11%13C) alongside every sample incubated in 13C glucose. As the derivitization process has already been optimized and proven to be robust (Roessner et al., 2000), these control samples allow a valid measurement of the amount of analyte that has been labelled. The identity of each unlabelled peak was established by comparison of the mass spectra with those stored in our in-house library (http://www.mpimp-golm.mpg.de/fernie), once these were assigned peaks were searched for that indicated the presence of label (i.e. between m + 1 and m + 12 of each unlabelled peak). As electron impact ionization was used not every peak corresponded to the molecular ion of the metabolite in question, however, in such instances the natural abundance pattern of the fragment analysed was studied to validate the assumption that the labelling of this fragment was representative of the labelling within the entire molecule. In instances where it was impossible to quantify the labelling pattern of the molecular ion we routinely used the highest mass fragment. It is, theoretically, possible that averaging the labelling of multiple fragments could yield higher accuracy. However, given that the evaluation of replicate samples using a single ion for quantification yielded highly similar results we chose to pursue this simpler method as it obviates the need for intensive computer and manual interrogation of the datasets. Following these analyses we found that we were able to reliably detect labelling in 18 amino acids, five organic acids and a total of eight sugars, sugar alcohols and phosphoesters. From this list we constructed a metabolic scheme of the unidirectional exchange rates that are theoretically determinable using this GC-MS protocol (Figure 1b). It is worth noting, however, that these metabolites are by no means the only ones in which label can be reliably detected in, merely the ones we were interested in for the purposes of this study. We routinely use a very similar protocol to determine the levels of some 80 known and 40 unknown metabolites in potato (Roessner et al., 2001a,b) and 90 known and 80 unknown metabolites in tomato (Roessner-Tunali et al., 2003b). Whilst detection of label is some extent dependent on metabolite abundance the exact number of metabolites for which reliable isotope quantification is possible will be conditional. However, it should be possible to detect labelling in over 50% of these compounds, under normal physiological conditions, using this exact same method. When throughput is severely compromised and the GC-MS runs in single ion detection mode it should be possible to detect the labelling pattern of all these compounds.
Figure 1. Schematic diagrams of reaction rates that can be studied following incubation in [U14-C]glucose and [U13-C]glucose. Compound classes of metabolites that can be determined following [U14-C]glucose feeding (a), represent bulk fluxes all of which derive from the hexose-P pool (the summed carbon in glucose 6-phosphate, glucose 1-phosphate, fructose 6-phosphate and 3-phosphoglycerate) and allow calculation of the absolute flux to starch, cell wall, sucrose and glycolysis. Metabolites that can be determined following [U13-C]glucose feeding (b), arrows represent unidirectional carbon exchange rates between metabolic precursors and individual sugars, organic acids and amino acids that can be studied (those metabolites set in italic type can not be directly measured by the techniques reported in this paper). Those metabolites sharing a common precursor are grouped together.
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