In the previous section, we have described how network analysis has contributed to our understanding of network architecture and how this architecture can be used to explain and investigate network behaviour and regulation. Clearly, the first step in such an endeavour is accurately describing the network under consideration in its entirety. This is why much of the network analysis of biological systems has concentrated on organisms such as E. coli and yeast that are extensively characterised at the genomic and molecular level and for which sufficient information is available to describe fully their molecular networks. However, the genomic and molecular information available for more complex eukaryotes is beginning to rival that of E. coli and yeast. This is particularly true for the model plant species, Arabidopsis thaliana. Although network tools have yet to be applied to higher plants, a wealth of global data sets has been accumulated. In this section, we will discuss what needs to be put into place to undertake a systems analysis of the plant metabolic network. We will also review what can be learnt from the global profiling studies that have already been completed.
1. Getting the right network structure
Before interrogation of network functionality can be attempted, it is clearly essential that the structure of the network is fully elucidated. This is by no means a trivial task. Even the most classical of biological networks, metabolic pathways and signal transduction cascades, are only partially defined. Evaluation of the cellular metabolism of plants (Buchanan et al., 2001) reveals how daunting a task network structure elucidation is likely to be. A huge amount of research effort has been placed into metabolic pathway definition, and recent genome sequencing efforts have allowed the definition of the majority of proteins associated with metabolism. However, it is important to note that, contrary to conventional wisdom, our current knowledge of the structure of plant cellular metabolism is far from complete. Whereas some pathways of primary metabolism have been carefully and systematically elucidated, the majority of secondary metabolic pathways have not. Moreover, the participation of enzymes in previously undefined metabolic contexts (even for enzymes that are components of well-defined pathways) remains a genuine possibility (Schwender et al., 2004).
Part of the problem lies in the amount of ‘assumed knowledge’ of gene function in plants. A large proportion of Arabidopsis genes are still classified merely on the basis of their homology to genes from other species. Moreover, the role of many proteins and enzymes are assumed on the basis of their functionality in better studied, less complex systems. Emergent databases of metabolic pathways (for example, the Kyoto Encyclopedia of Genes and Genomes Metabolic Descriptions, http://www.genome.ad.jp/kegg, and MetaCyc, http://metacyc.org/), whilst invaluable resources in their own right, have only exacerbated the perception that pathway structure is established in cases where no empirical evidence for cross-kingdom pathway homologies exists. This over-reliance on structural information from microbial systems is problematic because it is clear that a network model that is a poor resemblance of the in vivo situation has limited use. The situation should be improved by the development of curated plant-specific databases such as AraCyc (Mueller et al., 2003) and by pathway visualisation tools such as MAPMAN (Thimm et al., 2004), that allow the experimenter to easily construct their own pathways de novo. However, even with a plant-specific database in place, caution must still be exercised: it remains likely that the Arabidopsis metabolic network structure will be dramatically different from that of quaking aspen, for example. Not only do plant species have widely different genome sizes and numbers of genes, but they also display staggering metabolic diversity. It has been estimated that over 200 000 metabolites exist in the plant kingdom, with any single plant species displaying only a fraction of this diversity in its metabolome (De Luca & St Pierre, 2000).
With the above caveats in mind, it is clear that there is a great need for empirical testing of metabolic network structures. Although network analysis is very much in vogue at the moment, it is important to note that the analysis of pathway structure has been a fundamental aspect of biochemistry for many decades. Important historical examples include, but are by no means limited to, the discovery of the tricarboxylic acid (TCA) and Calvin cycles (Krebs & Johnson, 1937; Calvin, 1962). The elucidation of these cardinal pathways facilitated much further research effort concerned with metabolic regulation, (micro)compartmentation and pathway interaction (detailed in Section III.3), which has allowed high-level understanding of these pathways. However, even for well-studied pathways such as the TCA cycle, our understanding remains somewhat limited. Despite the fact that the reactions described by Krebs were identified to occur in plants in the 1960s (Beevers, 1961), relatively few studies have addressed the function or regulation of this pathway in plants (for a detailed discussion, see Fernie et al., 2004a).
Dispelling the idea that plant metabolism is a ‘done deal’ is the fact that we continue to elucidate new pathways and refine our understanding of existing ones. In recent years, important discoveries include the plant pathways for ascorbate metabolism (Wheeler et al., 1998; Green & Fry, 2005) and isoprenoid biosynthesis (Masse et al., 2004; Wolferetz et al., 2004) and the demonstration of the role of ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) in a previously undefined metabolic context (Schwender et al., 2004). These examples, alongside recent elegant studies on carbon nitrogen interactions (reviewed in Galili, 2002; Stitt et al., 2002; Stitt & Fernie, 2003) are excellent illustrations of foundation studies on which plant metabolic network analysis can be developed. Isotope labelling studies of the mevalonate pathways of diatoms and higher plants allowed clarification not only of the network structures of these species but also functional analysis of the relative importance of different routes to the same end. Whereas the elucidation of the pathways of synthesis (Wheeler et al., 1998; Agius et al., 2003) and degradation (Green & Fry, 2005) of ascorbate placed previously identified enzymes in a novel metabolic context. Similarly, a recent study demonstrated that Rubisco can function independently of the Calvin cycle to improve the carbon efficiency of developing green seeds (see Fig. 2). In doing so, this study solved a puzzle that has been perplexing plant biochemists for many years. The storage of carbon as oil was thought to be intrinsically inefficient because one carbon is lost in the form of carbon dioxide for each triacylglycerol incorporated into fatty acids owing to the action of pyruvate dehydrogenase. The metabolic fate of uniformly labelled carbon sources following feeding to Brassica napus seeds was compared to theoretically calculated values following elemental flux mode analysis (Schuster et al., 1999) of textbook pathways. Surprisingly, a 3 : 1 ratio of carbon in oil to carbon liberated as carbon dioxide was experimentally determined. This ratio is higher than the 2 : 1 ratio expected, given the action of pyruvate dehydrogenase. Further flux experimentation utilising [1–13C] or [U-13C]alanine revealed that Rubisco was responsible for the fixation of carbon dioxide and the absence of label randomisation suggested that this enzyme was acting in isolation from the Calvin cycle. In establishing this route, the authors were able to demonstrate that the formation of acetyl CoA formation was in fact more efficient than previously thought and that this pathway is responsible for 62% of 3-phosphoglycerate (3PGA) production in developing B. napus embryos. This example highlights the power of combining mathematical approaches with traditional isotope tracer studies to aid the elucidation of metabolic networks. The fact that a previously unrecognised role has been established for such a fundamental enzyme also amply highlights the paucity of our current knowledge of plant metabolism.
Figure 2. The metabolic transformation from sugars into fatty acids. (a) Conversion of hexose phosphate to pentose phosphate through the nonoxidative steps of the pentose phosphate pathway and the subsequent formation of 3PGA by Rubisco bypasses the glycolytic enzymes glyceraldehyde 3-phosphate dehydrogenase and phosphoglycerate kinase while recycling half of the carbon dioxide released by pyruvate dehydrogenase. 3PGA is then further processed to pyruvate, acetyl CoA and fatty acids. (b) An expansion of part of (a) to indicate carbon skeletons and to define relationships between VPDH (flux through the PDH complex), VX (additional cellular carbon dioxide production) and VRub (carbon dioxide refixation by Rubisco). Metabolites: AcCoA, acetyl CoA; DHAP, dihydroacetone 3-phosphate; E4P, erythrose 4-phosphate; Fru 6-P, fructose 6-phosphate; GAP, glycealdehyde 3-phosphate; Glc 6-P, glucose 6-phosphate; 3PGA, 3-phosphoglycerate; Pyr, pyruvate; Ri 5-P, ribose 5-phosphate; Ri 1,5-P, ribulose 1,5-bisphosphate; Ri 5-P, ribulose 5-phosphate; S 7-P, sedoheptulose 7-phosphate; Xu 5-P xylulose 5-phosphate. Enzymes: Aldo, fructose 1,6-bisphosphatase aldolase; Eno, 2-phosphoglycerate enolase; Xepi, xylulose 5-phosphate epimerase; FAS, fatty acid synthase, PGI, phosphoglucose isomerase; PGM, phosphoglyceromutase; GAPDH, glyceraldehydes 3-phosphate dehydrogenase; Riso, ribulose 5-phosphate isomerase; PDH, pyruvate dehydrogenase; PFK, phosphofructokinase; PK, pyruvate kinase; PGK, phosphoglycero kinase; PRK, phosphoribulokinase; TA, transaldolase; TK, transketolase; TPI, triose phosphate isomerase. Redrawn from Schwender et al. (2004).
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2. What regulatory aspects do we need to consider within the plant metabolic network?
Mechanisms of metabolic regulation have been established for many years and require little comment here, save to say that in a network context all of them are of important. Given that recent theoretical and experimental studies indicate that the majority of control occurs at the post-transcriptional level (Ter Kuile & Westerhoff, 2001; Urbanczyk-Wochniak et al., 2003a), understanding of metabolic network regulation will ultimately involve systematic characterisation not only of transcript and protein abundance but also of post-translational modification of enzymes, protein–protein interaction and kinetic characterisation of every enzyme of the cell. Furthermore, a large amount of experimental data suggests that the canonical ‘pyramid of life’ (Oltvai & Barabasi, 2002), whereby information is passed from gene to RNA to protein to function, is somewhat misleading because this hierarchy is clearly not unidirectional. Examples of feedback in this hierarchy include, but are not limited to, the stabilisation of RNA by metabolites (Fafournoux et al., 2000) and the metabolite-mediated regulation of gene expression (Sheen, 1990; Templeton & Moorhead, 2004). Thus it is probably imperative to analyse as many interacting elements as possible in order to establish network regulation. To date, very few large-scale studies of network regulation have been carried out in plants. Therefore, in this section we intend to discuss in general the approaches that can yield important information on aspects of metabolic regulation before detailing a few case studies in which metabolic regulation has been elucidated at the pathway and/or subnetwork level.
Network thinking in plants has focussed mainly on the elucidation of signal transduction cascades. It is perhaps apt that many of the studies that have highlighted the complexity of metabolite regulation also originated in this research field. Indeed, work on cellular sensors of ATP and ADP and of sugars (Sheen, 1990; Hardie, 2003) has revealed that metabolites not only act as intermediates in pathways but can also be important integrators of metabolic status with other fundamental cellular events including transcription, translation and covalent modification of proteins (Templeton & Moorhead, 2004). The identification of the mechanism of the Trp RNA binding attenuation protein (TRAP) in Bacillus subtilis, responsible for both transcriptional attenuation and translational control of Trp synthetic pathway genes (Babitzke & Gollnick, 2001), together with the subsequent finding that such riboswitches are prevalent across biology (Sudarasan et al., 2003), suggests that other aspects in addition to RNA and transcription factor mediated control of gene expression require intensive investigation in the future.
The rapid emergence of proteomics as one of the mainstays of postgenomic research also opens up another level of the regulatory hierarchy. The proteomics field is currently undergoing a shift away from techniques that merely generate protein catalogues towards those that are more quantitative in nature (Ong et al., 2002; Tyers & Mann, 2003), those that allow the definition of post-translational modifications (Mann & Jensen, 2003) and protein–protein interactions (Rohila et al., 2004). Analysis of these aspects of the plant proteome is still in its infancy. Moreover, the greater experimental input to introduce tagged genes into plants in a systematic fashion in comparison to yeast has limited progress. Thus, there are currently no plant protein–protein interaction models of the same scale that have been achieved for yeast (reviewed in Cornell et al., 2004).
3. The importance of protein–protein interactions
Although we lack the systematic maps of protein–protein interactions that are available for yeast, many intriguing protein–protein interactions have been functionally characterised in plants and the transient association of enzymes into functional complexes is being increasingly recognised as an important part of the regulatory hierarchy (for comprehensive reviews, see Winkel, 2004 or Jorgensen et al., 2005). The realisation that adjacent enzymes in metabolic pathways can associate with one another to form a functional complex has important implications for our understanding of the regulation and organisation of the plant metabolic network.
The groundbreaking work of Paul Srere and coworkers demonstrated the presence and functionality of several such multienzyme complexes within the TCA cycle of several microbial and also of mammalian species (Srere, 1985; Robinson et al., 1987). Through these studies, he was able to demonstrate that enzymes in a pathway functioned far more efficiently when associated together due to the channelling of the connecting metabolic intermediates between the enzymes in question. Srere called such channels metabolons, and argued that they were a common motif in cellular metabolism. Indeed, earlier studies sugggest that in Neurospora crossa all measureable enzyme activities were associated with organelles, membranes or cytoskeleton, with little or no protein present in the aqueous soluble fraction (Zalokar, 1960). The demonstration by Yanofsky & Rachmeier in 1958 that free indole was not an intermediate in the biosynthesis of tryptophan was probably the first strong proof for metabolic channelling (Yanofsky & Rachmeier, 1958) and has since been confirmed by X-ray crystallography (Hyde et al., 1988).
Since their identification in microbial systems, many metabolons have subsequently been defined in plants, including the Calvin cycle (Suss et al., 1993), dhurrin biosynthesis (Moller & Conn, 1980), flavonoid pathways (Winkel-Shirley, 1999), phenylpropanoid metabolism (Achnine et al., 2004) and polyamine biosynthesis (Panicot et al., 2002). Given the diversity of the pathways in this list, it seems likely that the roles of these channels differ considerably. Nevertheless, it can be argued that metabolons have a universal importance both in terms of metabolic regulation and in terms of metabolism per se. Within secondary metabolism, the metabolon structure has been shown to compensate for the surprising lack of substrate specificity for some of the enzymes. In addition, metabolons may also function in the sequestration of toxic intermediates (Mendes et al., 1992).
The above plant metabolons have recently been described in detail (Winkel, 2004; Jorgensen et al., 2005). We will therefore restrict our discussion to one or two specific examples that illustrate the main points. One of the earliest demonstrations of a metabolon in plants came from following the fate of radiolabelled compounds fed to sorghum microsomes. Using this approach, it was possible to demonstrate channelling of the highly toxic and labile intermediates N-hydroxytyrosine and p-hydroxyphenylacetonitrile (Moller & Conn, 1980). The presence of substrate channels has recently been demonstrated to occur in other pathways of secondary metabolism, notably the phenylpropanoid pathway. A wide spectrum of phenylpropanoids – including lignin, flavonols, anthocyanins and isoflavonoids – is produced from phenylalanine, with phenylalanine ammonium lyase (PAL) being the first committed step in these pathways. In most plant species, PAL is encoded by a small multigene family which may confer different specificity to channel flux towards the different classes of phenylpropanoids (Jorgensen et al., 2005). There is growing evidence to support channelling in these pathways. For example, discrete colocalisation of specific PAL isoforms (PAL1 and PAL2) with cinnamate 4-hydroxylase (C4H) has been observed (Achnine et al., 2004). On the basis of accumulated data, current models of phenylpropanoid biosynthesis suggest that the wide product range of these pathways is regulated by differential organisation of metabolons which are composed of different isoforms of the key biosynthetic enzymes and are associated with different downstream enzymes (Jorgensen et al., 2005).
A key feature of metabolon formation within the phenylpropanoid pathway is the association of enzymes with membrane structures. It is therefore interesting that other metabolic pathways have also shown to be associated with membranes. For example, a combination of proteomic, traditional enzymatic, cell biological and stable-isotope feeding experiments was used to provide corroborating evidence that the entire glycolytic pathway is associated with plant mitochondria by attachment to the cytosolic face of the outer mitochondrial membrane (Fig. 3). However, in contrast to the suggested functions of metabolons in secondary metabolism, it is unlikely that the function of this enzyme association with the mitochondrial membrane is product specificity. Perhaps the function of this microcompartmentation of glycolysis is to ensure sufficient pyruvate is provided directly to the mitochondria in the face of competition for glycolytic intermediates from other pathways such as the oxidative pentose phosphate pathway and amino acid biosynthesis (Giegéet al., 2003). As of yet, there is no direct evidence that substrate channelling occurs between the mitochondrially associated glycolytic enzymes. In general, however, this organisation of glycolysis, as well as the organisation of the TCA cycle into a metabolon, suggests that physical interaction between enzymes is prevalent in the respiratory pathway and can therefore be said to be an important feature of both primary and secondary metabolism. Further evidence for the importance of protein–protein interactions in the respiratory pathway comes from the discovery that the respiratory complexes of the electron transport chain themselves interact to form a supercomplex (Eubel et al., 2004).
Figure 3. Possible role of association of glycolysis with the mitochondrion. The pathway of glycolysis is shown with each enzyme represented as a shaded ellipse. In the cytosol, the pathway provides intermediates for a large number of biosynthetic pathways that effectively branch off glycolysis. When attached to the mitochondrion, these branched pathways may be excluded, with the main function of the pathway being the supply of respiratory pyruvate.
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It is likely that the use of postgenomic tools will allow the identification of far more functional complexes in the near future and given the influence such complexes exert on cellular metabolism it is vital that such information is incorporated into network models. While we can expect advances in our cataloging of protein-protein and protein–DNA interactions, it is worth noting that technologies capable of identifying for protein–metabolite interactions on a systematic scale are currently not yet available.
4. Case studies of regulatory hierarchies within metabolic pathways
As Section III.3 implies, it is maybe too early to attempt large-scale network analyses in plants. That said, there are several pathways for which the relative importance of various levels of metabolic regulation has been defined. These pathways include the central metabolic pathways of glycolysis, the sucrose-to-starch transition, carbon–nitrogen interactions and amino acid metabolism. Given that the regulation of the sucrose-to-starch transition and carbon–nitrogen interactions have been comprehensively reviewed elsewhere (Stitt & Fernie, 2003; Geigenberger et al., 2004), we will concentrate here on detailing the regulatory hierarchy of glycolysis and amino acid metabolism.
The study of the metabolic regulation of glycolysis in plants has made dramatic progress since the initial isolation of plant aldolase in 1948. Plant glycolysis shares many features in common with that of animals and yeasts; however, it also contains many peculiarities. One feature of plant glycolysis is the presence of a complete or near-complete duplication of the pathway in the plastid. The two pathways are independently controlled both at transcriptional (Urbanczyk-Wochniak et al., 2003b) and kinetic (Givan, 1999) levels, suggesting that they are differentially regulated. Cytosolic glycolysis in plants also differs from that in animals by the presence of additional enzymes including pyrophosphate-dependent phosphofructokinase, nonphosphorylating glyceraldehyde 3-phosphate dehydrogenase and phosphoenolpyruvate phosphatase, all of which may be of particular importance under conditions of metabolic stress (Givan, 1999). Despite a wide number of reverse-genetic investigations of glycolysis, it has not yet been possible to pinpoint where the majority of the metabolic control lies in this pathway. Moreover, little is currently known concerning the transcriptional control of glycolysis, although several important observations have recently been made (Fernie et al., 2004a). Firstly, studies on transgenic plants exhibiting elevated sucrose cycling revealed an up-regulation of the entire cytosolic glycolytic pathway that is most probably mediated at the translational level – this coordinated change is very similar to that observed in several recent transciptomic studies (Wang et al., 2003; Wasaki et al., 2003). The fact that enhanced sucrose cycling places a large additional ATP demand on the cell makes it tempting to suggest that glycolysis in plants is demand driven as it is in E. coli (Koebmann et al. 2002), despite the fact that plant glycolysis is not allosterically regulated by ATP (Plaxton, 1996). The kinetic properties of most of the enzymes of glycolysis have long been established and are readily accessible through the BRENDA database (http://www.brenda.uni-koeln.de). Nevertheless, the recent discovery of post-translational modifications of the cytosolic pyruvate kinase (Tang et al., 2003) and the identification of glycolytic complexes suggest that our understanding of the regulation of even this cardinal pathway is far from complete.
The pathways of amino acid biosynthesis are considerably more complex than the unbranched pathway of glycolysis. However, the research of Gad Galili and coworkers has led to a considerable degree of understanding of the interregulation of lysine, glutamate and aspartate metabolism (Galili et al., 2001; Galili, 2002; Zhu et al., 2002). Lysine metabolism in plants is regulated both by the rate of its synthesis and its catabolism, with the latter operating via the alpha-amino adipic acid pathway, which is largely regulated by the first two enzymes of the pathway, namely lysine-ketoglutarate dehydrogenase (LKD) and saccharophine dehydrogenase (SDH) (Galili et al., 2001). These enzymes are encoded as a bifunctional protein. In Arabidopsis, the LKD/SDH gene encodes an additional monofunctional SDH enzyme and the two forms of SDH are only partially coordinately regulated in response to hormonal and metabolic stimuli, in keeping with the hypothesis that the monofunctional enzyme functions mainly to enhance the flux of lysine catabolism (Stepansky et al., 2005). In a series of elegant studies, it was shown that increasing lysine levels leads to increased levels of methionine, glutamine and asparagine, and a corresponding elevation in the conversions of glutamine to glutamate and asparagine to aspartate (see Fig. 4). Furthermore, via action on the key enzyme of lysine biosynthesis, dihydrodipicolinate synthase (DHPS), high lysine levels feedback-inhibit its synthesis. Moreover, the accumulation of glutamate leads to elevations in asparagine and glutamine content and also enhances the conversions of glutamine to glutamate and asparagine to aspartate. This regulatory loop is further complicated by the fact that the majority of the enzymes involved in the interconversion of these amino acids are under strict transcriptional regulation, and it is with some justification that Galili has termed lysine catabolism a stress- and developmentally super-regulated metabolic pathway (Galili et al., 2000). Different fluxes of lysine catabolism can apparently be achieved under different developmental and physiological programs via complex transcriptional and post-transcriptional regulation of the composite LKR/SDH locus which encodes three different proteins with different variants of SDH exhibiting different pH optima. The linker region between LKR and SDH, which plays a significant role in the regulation of the bifunctional LKR/SDH enzymes of plants (Zhu et al., 2002), exists neither in the bifunctional LKR/SDH genes of animals nor in the separate fungal LKR and SDH genes suggesting, that it evolved specifically in plants to regulate plant-specific processes.
Figure 4. Model depicting Glu, Asp and Lys at the core of an important regulatory loop in plant amino acid metabolism. The Glu–Asp–Lys loop is illustrated as the central pathway, whilst a different branch of the Asp family pathway synthesises Thr and Met. The pathway of Lys catabolism produces acetyl CoA. Two of the most important regulators of this network are represented in the core of the circle, namely the extreme sensitivity of DHPS activity to feedback inhibition by Lys and the stimulation of LKR/SDH activity by excess lysine levels (for a discussion, see Galili et al., 2001; Galili, 2002). Impairment of these important regulatory elements causes extensive conversion of Glu and Asp to Lys, thereby activating several other regulatory circuits, for example, reduction of Glu triggers increased synthesis of Gln and Asn and their subsequent conversion to Glu and Asp. Redrawn from Zhu and Galili (2003).
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The two case studies on glycolysis and amino acid biosynthesis discussed here demonstrate both the complexity inherent within plant metabolism and our fragmented current understanding of it. However, they also provide strong examples of how pathway regulation can be assessed in the context of the wider metabolic networks to which these pathways belong.
5. The impact of global profiling technologies on the analysis of plant metabolism
From the above examples, it is apparent that network thinking has long pervaded plant metabolic research, but tools that afford adequate coverage to allow the assessment of pathway function in a system context have been lacking. The development of rapid molecular profiling platforms would be expected to facilitate such an approach. In this section, we will discuss the impact that the relatively new technologies of transcriptomics, proteomics, metabolomics and comprehensive flux analyses have had on our understanding of plant metabolic networks.
Of the four technologies, transcriptomics is by far and away the most mature and offers genuinely comprehensive coverage of the majority of expressed transcripts. The technique is now a standard part of the molecular biologist's experimental arsenal and an ever-increasing data mountain has accumulated, much of which is accessible in public data warehouses. Although only a small number of published transcriptomic studies has been directed at metabolism, the results of such studies do allow some general conclusions to be drawn (Buckhout & Thimm, 2003). Generally, transcriptional regulation of gene expression is seen as the least important of the regulatory mechanisms that impinge upon metabolism. Nevertheless, it is clear that during development and in response to environmental stress there is coordinated regulation of genes that encode enzymes in the same pathways. The same is true during sugar starvation, in which coordinated repression of genes involved in carbohydrate metabolism was observed (Thimm et al., 2004). It is also apparent that during sugar starvation metabolism can be re-organised to allow flexible use of carbon skeletons from alternative sources. Coordinated regulation of respiratory pathways is also seen following genetic intervention in tomato (Baxter et al., 2005). One can take two views of such coordinated gene expression. First, that it is an active process to drive pathway fluxes at different rates or in different directions. Second, that it is a homeostatic process designed to bring metabolites back to optimal levels following a metabolic disturbance. Given the rapidity of metabolic flux changes driven by altered substrate supply and the relatively slow process of changing enzyme abundance through altered gene exression, the latter is perhaps more likely. Thus, the fact that there is evidence that the expression of enzymes through a pathway actually changes in a sequential manner (the first enzyme changing first and by the greatest amount) should be seen not as a mechanism to optimise response time (Zaslaver et al., 2004) but rather as the most efficient way to bring metabolites concentrations back into line following metabolic disturbance.
Of course, one has to be extremely cautious in interpreting the results of microarray experiments, as it has been frequently observed that transcript abundance does not translate into a correlated change in protein amount. A more direct and systematic analysis of protein abundance is required. Mark Stitt's group has used a high-throughput enzyme assay platform as a proxy for the metabolic proteome and was able to characterise changes in the activity (abundance) of proteins of central carbohydrate metabolism during the diurnal cycle and during sugar starvation (Gibon et al., 2004). Interestingly, when compared with the equivalent transcriptomic changes, protein changes were highly damped and often out of phase. More sophisticated proteomic platforms (based on mass spectrometry in combination with electrophoretic or chromatographic fractionation) have the potential to interrogate a much greater proportion of the proteome. However, the experimental and technical demands of quantitation within this approach (Aebersold & Mann, 2003) have meant that relatively few quantitative proteomic studies have been attempted to date. The most rigourous study of this nature in plants used greening maize as a system to investigate plastid biogenesis (Lonosky et al., 2004). Again, evidence of coordinated regulation of protein abundance within metabolic pathways was observed. However, a more complex picture emerges as one goes through the developmental sequence. For example, coordinated regulation of the enzymes of photosynthetic carbon assimilation is observed during early development, but the pattern of the same proteins diverges later in development.
One of the major problems in accurately defining the plant metabolic networks is the fact that the plant cell is extensively compartmented. Proteomics is making a major impact upon our characterisation of the organellar localisation of pathways, providing essential evidence that justifies placing a particular enzyme isoform in a given subcellular compartment (Millar et al., 2004; van Wijk, 2004). Proteomic studies have also highlighted the extent to which proteins can be targeted simultaneously to more than one compartment (Chew et al., 2003). Perhaps the area in which proteomics has the greatest potential to unlock aspects of metabolic control is in the systematic analysis of post-translational modifications of proteins (Mann & Jensen, 2003). From a technological point of view, the phosphoproteome has proved the most accessible and already some systematic characterisations of phosphorylation of plant proteins have been undertaken (Bykova et al., 2003; Nuhse et al., 2004). With the appropriate technology, it is even possible to capture the dynamics of protein phosphorylation (Blagoev et al., 2004). The other main post-translational modification that has been characterised using a proteomic approach is redox modification of protein thiols by thioredoxin (Motohashi et al., 2001; Yano et al., 2001; Balmer et al., 2003; Balmer et al., 2004; Marchand et al., 2004; Wong et al., 2004; Rey et al., 2005). Although such proteomic analyses will undoubtedly improve our understanding of post-translational regulation of enzymes across the metabolic network, it is important to realise that the proteomic studies provide only a list of proteins that are post-translationally modified. It remains to be established whether those modifications actually have regulatory significance for protein function or not.
Metabolomic approaches are currently trailing somewhat behind transcriptomic and proteomic approaches with respect to network studies, largely due to their compromised coverage (Weckwerth & Fiehn, 2002; Fernie et al., 2004b). That said, several important observations have been made on the basis of correlative behaviour between metabolites. In an important case study, Arkin and coworkers demonstrated that metabolic pathways can be determined kinetically by the monitoring of correlative behaviour between only a handful of metabolites (Arkin et al., 1997). However, despite the convincing case made by the authors, and proof of functionality in the identification of signal transduction cascades (Sontag et al., 2004), it is perhaps telling that such approaches have as yet not facilitated the identification of multiple metabolic pathways. That is not to say that this approach is of limited usefulness; the use of metabolite–metabolite correlations, in a manner analogous to co-response analysis (Hofmeyer et al., 1996), has revealed several important aspects of pathway and network behaviour (Steuer et al., 2003; Camacho et al., 2005). The analysis of the intracellular concentrations of metabolites in yeast revealed phenotypes for mutations of proteins active in metabolic regulation (Raamsdoonk et al., 2001). Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite was demonstrated to reveal the site of action, in the metabolic network, of silent genes. In addition, early metabolite profiling studies identified hyperbolically related metabolite–metabolite pairs that were consistent with known feedforward and feedback mechanisms of regulation (Roessner et al., 2001). However, it is important to note that, to date, no novel regulatory mechanisms have been kinetically confirmed following such analyses. The analysis of the global data sets accrued by metabolite profiling have additionally been used to define the influence of specific proteins on metabolism during development (see, for example, Roessner-Tunali et al., 2003) and to discriminate the apparently silent phenotype of potato plants deficient in an isoform of sucrose synthase (Weckwerth, 2004).
In contrast to steady-state metabolite analysis, the contribution of dynamic flux analyses to network thinking has been visible for many years. In fact, arguably, flux is the ultimate expression of the metabolic network (Koffas et al., 1999). Until recently, flux analysis was severely restricted in coverage. However, recent technical advances have allowed a broadening of the information accessible via high-throughput flux analysis (Roessner-Tunali et al., 2004; Sauer, 2004; Sriram et al., 2004). As mentioned earlier (Section I), flux measurements have been used in systems biology in the framework of metabolic control analysis for many years. Furthermore, by analogy to experimentation carried out in the microbial field (Hellerstein & Nesse, 1999), network analyses have also been carried out within this context in plant systems. Whilst several such analyses have recently been reported, two are of particular note. In the first, a comprehensive analysis of fluxes during three different stages in the growth cycle of tomato cells was made (Rontein et al., 2002), whilst the second was concerned with analysing the compartmentation of the major pathways of carbohydrate oxidation in B. napus embryos (Schwender et al., 2003). The tomato cells study utilised nuclear magnetic resonance (NMR) spectroscopy to derive cellular fluxes for central metabolism as well as measuring the accumulation of polymeric components of the cell. This study revealed that the fluxes through the central pathways of carbon oxidation were remarkably constant, whereas effluxes into pathways of polymer synthesis were highly variable (Rontein et al., 2002). In a similar study, Schwender and coworkers tackled the thorny issue of compartmentation by culturing B. napus embryos on variously labelled stable isotopes of glucose and measuring the label composition of amino acids, lipids, sucrose and starch (Schwender et al., 2003). The cumulative data from this study were then used to verify the reaction network that was distributed between the cytosol and plastid and via overdetermination of key parameters, the relative fluxes of the compartmented pathways were reliably computed. The above examples indicate that tracer labelling experiments have great utility not only in confirming network structure but also in the analysis of metabolic regulation in their own right.
The preceding sections have concentrated on the use of genomic tools in isolation. However, it is becoming increasingly clear that integrated analysis will be necessary in order to maximise our understanding of metabolic networks (Sweetlove et al., 2003; Ratcliffe & Shachar-Hill, 2004; Oksmann Kaldentey & Saito, 2005). Such analyses have been carried out at high frequency in microbial systems (Even et al., 2003; Hellerstein, 2003; Sauer, 2004; Stephanopoulos et al., 2004) and are beginning to be attempted in plants (Suzuki et al., 2002; Urbanczyk-Wochniak et al., 2003a; Hirai et al., 2004; Fridman & Pichersky, 2005). To date, the major utility of these approaches has been the analysis of gene function, important examples of this being the identification of genes of the flavonoid biosynthesis pathway (Tohge et al., 2005) and of triterpene biosynthesis (Suzuki et al., 2002). Whilst such targeted analyses are of great importance, the global nature of the profiling technologies clearly also allow the unbiased analysis of correlations between genes, proteins and metabolites. Exactly such an approach has been used in the identification of candidate genes for metabolic engineering both in bacterial (Askenazi et al., 2003) and plant systems (Goossens et al., 2003; Urbanczyk-Wochniak et al., 2003a). Since integrative analysis is in its infancy it remains likely that further advancements will be made in the near future.