Metabolic flux analysis takes a stoichiometric model of a metabolic network and aims to quantify all the component fluxes (Wiechert, 2001). In simple systems, these fluxes can be deduced from steady-state rates of substrate consumption and product formation, but in practice this approach of metabolite flux balancing is unable to generate sufficient constraints to provide a full flux analysis in most cases (Bonarius et al., 1997). In particular, metabolite flux balancing is largely defeated by the substrate cycles, parallel pathways, and reversible steps that are commonly encountered in metabolic networks (Wiechert, 2001), and for these and other reasons discussed elsewhere metabolite flux balancing is unlikely to be useful in the quantitative analysis of plant metabolism (Morgan and Rhodes, 2002; Roscher et al., 2000).
A more powerful approach for measuring intracellular fluxes, again developed using microorganisms, is to analyze the metabolic redistribution of the label from one or more 13C-labeled substrates (Wiechert, 2001). While flux information can be deduced from the time course of such a labeling experiment, constructing and analyzing time courses can be demanding, and so it is usually preferable to analyze the system after it has reached an isotopic steady state. Typically, a metabolic flux analysis using this approach would therefore involve incubating the tissue or cell suspension with a 13C-labeled substrate for a period that is sufficient to allow the system to reach a metabolic and isotopic steady state; a mass spectrometric and/or nuclear magnetic resonance analysis of the isotopomeric composition of selected metabolites in tissue extracts; and finally construction of the flux map based on the stoichiometry of the network and the measured redistribution of the label (Wiechert, 2001). The number of fluxes in the final map depends on the labeling strategy, the structure of the network, and the extent to which the redistribution of the label is characterized, but the usual objective in microorganisms is to generate a flux map that covers all the central pathways of metabolism (Szyperski, 1998; Wiechert, 2001; Wiechert et al., 2001).
Metabolic flux analysis generates large-scale flux maps in which forward and reverse fluxes are defined at multiple steps in the metabolic network. This manifestation of the metabolic phenotype provides a quantitative tool for comparing the metabolic performance of different genotypes of an organism, as well as for assessing the metabolic consequences of physiological and environmental perturbations (Emmerling et al., 2002; Marx et al., 1999; Sauer et al., 1999). Most of these studies lead to the conclusion that metabolic networks are flexible and robust, in agreement with much larger-scale theoretical studies (Stelling et al., 2002), and thus emphasize the point that targets for metabolic engineering have to be selected rather carefully if they are to have the intended effect on the flux distribution. The investigation of lysine production in C. glutamicum mentioned earlier provides a good illustration of the way in which an analysis of the flux distribution can be used to identify a rational target for metabolic engineering (Petersen et al., 2000, 2001).
Although the extension of steady-state metabolic flux analysis to plants is complicated by subcellular compartmentation, by duplication of pathways, and by the difficulty of establishing an isotopic and metabolic steady state (Roscher et al., 2000), there is increasing evidence that such analyses are both feasible and physiologically useful (Kruger et al., 2003; Schwender et al., 2004; Ratcliffe and Shachar-Hill, 2006). Some of these investigations measure only a small number of fluxes through specific steps or pathways, while others emulate the large-scale analyses of central metabolism that were pioneered on microorganisms. Examples in the small-scale category include an analysis of the relative contribution of malic enzyme and pyruvate kinase to the synthesis of pyruvate in maize root tips (Edwards et al., 1998); an assessment of the impact of elevated fructose 2,6-bisphosphate levels on pyrophosphate: fructose-6-phosphate 1-phosphotransferase in transgenic tobacco callus (Fernie et al., 2001); and the many applications of retrobiosynthetic flux analysis for assessing the relative importance of the mevalonate and methylerythritol phosphate pathways in terpenoid biosynthesis (Eisenreich et al., 2001).
While these small-scale analyses provide useful information about specific aspects of the metabolic phenotype that may well be directly relevant, as in the case of the transgenic tobacco study (Fernie et al., 2001), to the characterization of engineered genotypes, large-scale analyses of multiple fluxes in extensive networks have the potential to provide a much broader assessment of the impact of genetic manipulation on the metabolic network. It is therefore encouraging to note that steady-state stable isotope labeling is now being used to generate flux maps for central carbon metabolism in several plant systems. The first extensive flux map of this kind, based on the measurement of 20 cytosolic, mitochondrial, and plastidic fluxes, was obtained in a study of excised maize root tips (Dieuaide- Noubhani et al., 1995). This map proved to be useful in physiological experiments, for example, in assessing the impact of sucrose starvation on carbon metabolism (Dieuaide-Noubhani et al., 1997). It also led to the development of a more detailed flux map for a tomato cell suspension culture (Rontein et al., 2002), from which it was concluded that the relative fluxes through glycolysis, the tricarboxylic acid cycle, and the pentose phosphate pathway were unaffected by the progression through the culture cycle, whereas the generally smaller anabolic fluxes were more variable. Steady-state flux maps have also been published for the pathways of primary metabolism in developing embryos of oilseed rape (Schwender et al., 2003) and soybean (Sriram et al., 2004). An interesting feature of the oilseed rape model is that the labeling patterns showed rapid exchange of key intermediates between the cytosolic and plastidic compartments, thus simplifying the analysis and the resulting flux map. This result is in contrast to the situation in maize root tips and tomato cells, where the labeling of the unique products of cytosolic and plastidic metabolism showed that the cytosolic and plastidic hexose and triose phosphate pools were kinetically distinct.
The conclusion to be drawn from these studies is that large-scale flux maps can be generated for plant metabolic networks using steady-state stable isotope labeling and that the problems inherent in the complexity of these networks are not necessarily insuperable. These maps have been mainly used to gain further understanding of the operation of wild-type pathways, but, as already seen in microorganisms, it can only be a matter of time before they are also used to assess the impact of genetic manipulation and to propose potentially useful engineering strategies.
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