Subcellular flux analysis of central metabolism in a heterotrophic Arabidopsis cell suspension using steady-state stable isotope labeling

Understanding and manipulating sucrose phloem loading, unloading, metabolism, and signalling to enhance crop yield and food security

Sucrose is produced in, and translocated from, photosynthetically active leaves (sources) to support non-photosynthetic tissues (sinks), such as developing seeds, fruits, and tubers. Different plants can utilize distinct mechanisms to transport sucrose into the phloem sieve tubes in source leaves. While phloem loading mechanisms have been extensively studied in dicot plants, there is less information about phloem loading in monocots. Maize and rice are major dietary staples, which have previously been proposed to use different cellular routes to transport sucrose from photosynthetic cells into the translocation stream. The anatomical, physiological, and genetic evidence supporting these conflicting hypotheses is examined. Upon entering sink cells, sucrose often is degraded into hexoses for a wide range of metabolic and storage processes, including biosynthesis of starch, protein, and cellulose, which are all major constituents for food, fibre, and fuel. Sucrose, glucose, fructose, and their derivate, trehalose-6-phosphate, also serve as signalling molecules to regulate gene expression either directly or through cross-talk with other signalling pathways. As such, sugar transport and metabolism play pivotal roles in plant development and realization of crop yield that needs to be increased substantially to meet the projected population demand in the foreseeable future. This review will discuss the current understanding of the control of carbon partitioning from the cellular to whole-plant levels, focusing on (i) the pathways employed for phloem loading in source leaves, particularly in grasses, and the routes used in sink organs for phloem unloading; (ii) the transporter proteins responsible for sugar efflux and influx across plasma membranes; and (iii) the key enzymes regulating sucrose metabolism, signalling, and utilization. Examples of how sugar transport and metabolism can be manipulated to improve crop productivity and stress tolerance are discussed.

Systems analysis of metabolic phenotypes: what have we learnt?

Flux is one of the most informative measures of metabolic behavior. Its estimation requires integration of experimental and modeling approaches and, thus, is at the heart of metabolic systems biology. In this review, we argue that flux analysis and modeling of a range of plant systems points to the importance of the supply of metabolic inputs and demand for metabolic end-products as key drivers of metabolic behavior. This has implications for metabolic engineering, and the use of in silico models will be important to help design more effective engineering strategies. We also consider the importance of cell type-specific metabolism and the challenges of characterizing metabolism at this resolution. A combination of new measurement technologies and modeling approaches is bringing us closer to integrating metabolic behavior with whole-plant physiology and growth.

Flux and reflux: metabolite reflux in plant suspension cells and its implications for isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (MFA) is a powerful methodology to quantify intracellular fluxes via isotope labeling experiments (ILEs). In batch cultures, which are often convenient, inexpensive or inevitable especially for eukaryotic systems, MFA is complicated by the presence of the initially present biomass. This unlabeled biomass may either mix with the newly synthesized labeled biomass or reflux into the metabolic network, thus masking the true labeling patterns in the newly synthesized biomass. Here, we report a detailed investigation of such metabolite reflux in cell suspensions of the tree poplar. In ILEs supplying 28% or 98% U-(13)C glucose as the sole organic carbon source, biomass components exhibited lower (13)C enrichments than the supplied glucose as well as anomalous isotopomers not explainable by simple mixing of the initial and newly synthesized biomass. These anomalous labeling patterns were most prominent in a 98% U-(13)C glucose ILE. By comparing the performance of light- and dark-grown cells as well as by analyzing the isotope labeling patterns in aspartic and glutamic acids, we eliminated photosynthetic or anaplerotic fixation of extracellular (12)CO2 as explanations for the anomalous labeling patterns. We further investigated four different metabolic models for interpreting the labeling patterns and evaluating fluxes: (i) a carbon source (glucose) dilution model, (ii) an isotopomer correction model with uniform dilution for all amino acids, (iii) an isotopomer correction model with variable dilution for different amino acids, and (iv) a comprehensive metabolite reflux model. Of these, the metabolite reflux model provided a substantially better fit for the observed labeling patterns (sum of squared residues: 538) than the other three models whose sum of squared residues were (i) 4626, (ii) 4983, and (iii) 1748, respectively. We compared fluxes determined using the metabolite reflux model to those determined using an independent methodology involving an excessively long ILE to wash out initial biomass and a minimal reflux model. This comparison showed identical or similar distributions for a majority of fluxes, thus validating our comprehensive reflux model. In summary, we have demonstrated the need for quantifying interactions between initially present biomass and newly synthesized biomass in batch ILEs, especially through the use of ≈100% U-(13)C carbon sources. Our ILEs reveal a high amount of metabolite reflux in poplar cell suspensions, which is well explained by a comprehensive metabolite reflux model.

Metabolic flux phenotype of tobacco hairy roots engineered for increased geraniol production

The goal of this study was to characterise the metabolic flux phenotype of transgenic tobacco (Nicotiana tabacum) hairy roots engineered for increased biosynthesis of geraniol, an intermediate of the terpenoid indole alkaloid pathway. Steady state, stable isotope labelling was used to determine flux maps of central carbon metabolism for transgenic lines over-expressing (i) plastid-targeted geraniol synthase (pGES) from Valeriana officinalis, and (ii) pGES in combination with plastid-targeted geranyl pyrophosphate synthase from Arabidopsis thaliana (pGES+pGPPS), as well as for wild type and control-vector-transformed roots. Fluxes were constrained by the redistribution of label from [1-¹³C]-, [2-¹³C]- or [¹³C6]glucose into amino acids, sugars and organic acids at isotopic steady state, and by biomass output fluxes determined from the fractionation of [U-¹⁴C]glucose into insoluble polymers. No significant differences in growth and biomass composition were observed between the lines. The pGES line accumulated significant amounts of geraniol/geraniol glycosides (151±24 ng/mg dry weight) and the de novo synthesis of geraniol in pGES was confirmed by ¹³C labelling analysis. The pGES+pGPPS also accumulated geraniol and geraniol glycosides, but to lower levels than the pGES line. Although there was a distinct impact of the transgenes at the level of geraniol synthesis, other network fluxes were unaffected, reflecting the capacity of central metabolism to meet the relatively modest demand for increased precursors in the transgenic lines. It is concluded that re-engineering of the terpenoid indole alkaloid pathway will only require simultaneous manipulation of the steps producing the pathway precursors that originate in central metabolism in tissues engineered to produce at least an order of magnitude more geraniol than has been achieved so far.

Genome-scale models of plant metabolism

A genome-scale model (GSM) is an in silico metabolic model comprising hundreds or thousands of chemical reactions that constitute the metabolic inventory of a cell, tissue, or organism. A complete, accurate GSM, in conjunction with a simulation technique such as flux balance analysis (FBA), can be used to comprehensively predict cellular metabolic flux distributions for a given genotype and given environmental conditions. Apart from enabling a user to quantitatively visualize carbon flow through metabolic pathways, these flux predictions also facilitate the hypothesis of new network properties. By simulating the impacts of environmental stresses or genetic interventions on metabolism, GSMs can aid the formulation of nontrivial metabolic engineering strategies. GSMs for plants and other eukaryotes are significantly more complicated than those for prokaryotes due to their extensive compartmentalization and size. The reconstruction of a GSM involves creating an initial model, curating the model, and then rendering the model ready for FBA. Model reconstruction involves obtaining organism-specific reactions from the annotated genome sequence or organism-specific databases. Model curation involves determining metabolite protonation status or charge, ensuring that reactions are stoichiometrically balanced, assigning reactions to appropriate subcellular compartments, deleting generic reactions or creating specific versions of them, linking dead-end metabolites, and filling of pathway gaps to complete the model. Subsequently, the model requires the addition of transport, exchange, and biomass synthesis reactions to make it FBA-ready. This cycle of editing, refining, and curation has to be performed iteratively to obtain an accurate model. This chapter outlines the reconstruction and curation of GSMs with a focus on models of plant metabolism.

High-throughput data pipelines for metabolic flux analysis in plants

In this chapter we illustrate the methodology for high-throughput metabolic flux analysis. Central to this is developing an end to end data pipeline, crucial for integrating the wet lab experiments and analytics, combining hardware and software automation, and standardizing data representation providing importers and exporters to support third party tools. The use of existing software at the start, data extraction from the chromatogram, and the end, MFA analysis, allows for the most flexibility in this workflow. Developing iMS2Flux provided a standard, extensible, platform independent tool to act as the "glue" between these end points. Most importantly this tool can be easily adapted to support different data formats, data verification and data correction steps allowing it to be central to managing the data necessary for high-throughput MFA. An additional tool was needed to automate the MFA software and in particular to take advantage of the course grained parallel nature of high-throughput analysis and available high performance computing facilities.In combination these methods show the development of high-throughput pipelines that allow metabolic flux analysis to join as a full member of the omics family.

Optimization of steady-state ¹³C-labeling experiments for metabolic flux analysis

While steady-state (13)C metabolic flux analysis is a powerful method for deducing multiple fluxes in the central metabolic network of heterotrophic and mixotrophic plant tissues, it is also time-consuming and technically challenging. Key steps in the design and interpretation of steady-state (13)C labeling experiments are illustrated with a generic protocol based on applications to plant cell suspension cultures.

Quantification of ¹³C enrichments and isotopomer abundances for metabolic flux analysis using 1D NMR spectroscopy

The analysis of stable isotope incorporation following feeding of (13)C-labeled precursors to plant tissues provides the constraints necessary for metabolic flux analysis. This protocol describes the use of one-dimensional (1)H and (13)C nuclear magnetic resonance spectroscopy for the quantification of (13)C enrichments and isotopomer abundances in mixtures of metabolites or hydrolyzed biomass components.

Analysis of proteinogenic amino acid and starch labeling by 2D NMR

Comprehensive analysis of isotopic labeling patterns of metabolites in proteinogenic amino acids and starch for plant systems lay in the powerful tool of 2-Dimensional [(1)H, (13)C] Nuclear Magnetic Resonance (2D NMR) spectroscopy. From (13)C-labeling experiments, 2D NMR provides information on the labeling of particular carbon positions, which contributes to the quantification of positional isotope isomers (isotopomer). 2D Heteronuclear Single Quantum Correlation (HSQC) NMR distinguishes particularly between the labeling patterns of adjacent carbon atoms, and leads to a characteristic enrichment of each carbon atom of amino acids and glucosyl and mannosyl units present in hydrolysates of glycosylated protein. Furthermore, this technique can quantitatively classify differences in glucosyl units of starch hydrolysate and of protein hydrolysate of plant biomass. Therefore, the 2D HSQC NMR method uses proteinogenic amino acids and starch to provide an understanding of carbon distribution of compartmentalization in the plant system. NMR has the advantage of minimal sample handle without separate individual compounds prior to analysis, for example multiple isotopomers can be detected, and their distribution extracted quantitatively from a single 2D HSQC NMR spectrum. The peak structure obtained from the HSQC experiment show multiplet patterns, which are directly related to isotopomer balancing. These abundances can be translated to maximum information on the metabolic flux analysis. Detailed methods for the extractions of protein, oil, soluble sugars, and starch, hydrolysis of proteinogenic amino acid and starch, and NMR preparation using soybean embryos cultured in vitro as a model plant systems are reported in this text. In addition, this chapter includes procedures to obtain the relative intensity of 16 amino acids and glucosyl units from protein hydrolysate and the glucosyl units of starch hydrolysate of soybean embryos in 2D HSQC NMR spectra.

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.

Analysis of metabolic flux using dynamic labelling and metabolic modelling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.

Chloroplast-localized 6-phosphogluconate dehydrogenase is critical for maize endosperm starch accumulation

Plants have duplicate versions of the oxidative pentose phosphate pathway (oxPPP) enzymes with a subset localized to the chloroplast. The chloroplast oxPPP provides NADPH and pentose sugars for multiple metabolic pathways. This study identified two loss-of-function alleles of the Zea mays (maize) chloroplast-localized oxPPP enzyme 6-phosphogluconate dehydrogenase (6PGDH). These mutations caused a rough endosperm seed phenotype with reduced embryo oil and endosperm starch. Genetic translocation experiments showed that pgd3 has separate, essential roles in both endosperm and embryo development. Endosperm metabolite profiling experiments indicated that pgd3 shifts redox-related metabolites and increases reducing sugars similar to starch-biosynthetis mutants. Heavy isotope-labelling experiments indicates that carbon flux into starch is altered in pgd3 mutants. Labelling experiments with a loss of cytosolic 6PGDH did not affect flux into starch. These results support the known role for plastid-localized oxPPP in oil synthesis and argue that amyloplast-localized oxPPP reactions are integral to endosperm starch accumulation in maize kernels.

The metabolic flux phenotype of heterotrophic Arabidopsis cells reveals a complex response to changes in nitrogen supply

The extent to which individual plants utilise nitrate and ammonium, the two principal nitrogen sources in the rhizosphere, is variable and many species require a balance between the two forms for optimal growth. The effects of nitrate and ammonium on gene expression, enzyme activity and metabolite composition have been documented extensively with the aim of understanding the way in which plant cells respond to the different forms of nitrogen, but ultimately the impact of these changes on the organisation and operation of the central metabolic network can only be addressed by analysing the fluxes supported by the network. Accordingly steady-state metabolic flux analysis was used to define the metabolic phenotype of a heterotrophic Arabidopsis thaliana cell culture grown in Murashige and Skoog and ammonium-free media, treatments that influenced growth and biomass composition. Fluxes through the central metabolic network were deduced from the redistribution of label into metabolic intermediates and end products observed when cells were labelled with [1-(13) C]-, [2-(13) C]- or [(13) C6 ]glucose, in tandem with (14) C-measurements of the net accumulation of biomass. Analysis of the flux maps showed that: (i) flux through the oxidative pentose phosphate pathway varied independently of the reductant demand for biosynthesis, (ii) non-plastidic processes made a significant and variable contribution to the provision of reducing power for the plastid, and (iii) the inclusion of ammonium in the growth medium increased cell maintenance costs, in agreement with the futile cycling model of ammonium toxicity. These conclusions highlight the complexity of the metabolic response to a change in nitrogen nutrition.

Approximating subcellular organisation of carbohydrate metabolism during cold acclimation in different natural accessions of Arabidopsis thaliana

Accessions of Arabidopsis thaliana originating from climatically different habitats show different levels of cold acclimation when exposed to low temperatures. The central carbohydrate metabolism plays a crucial role during this acclimation. Subcellular distribution of carbohydrates over the compartments cytosol, vacuole and plastids, and putative interactions of the compartments, are analyzed in three differentially cold-tolerant accessions of Arabidopsis thaliana, originating from the Iberian Peninsula (C24), Russia (Rschew) and Scandinavia (Tenela), respectively. Subcellular carbohydrate concentrations were determined by applying the nonaqueous fractionation technique. Mathematical modeling and steady-state simulation was used to analyse the metabolic homeostasis during cold exposure. In all accessions, the initial response to cold exposure was a significant increase of plastidial and cytosolic sucrose concentrations. Raffinose accumulated in all cellular compartments of cold-tolerant accessions with a delay of 3 d, indicating that raffinose accumulation is a long-term component of cold acclimation. Minimal rates of metabolite transport permitting steady-state simulations of metabolite concentrations correlated with cold tolerance, indicating an important role of subcellular re-distribution of metabolites during cold acclimation. A highly regulated interplay of enzymatic reactions and intracellular transport processes appears to be a prerequisite for maintaining carbohydrate homeostasis during cold exposure and allowing cold acclimation in Arabidopsis thaliana.

Carbon and nitrogen provisions alter the metabolic flux in developing soybean embryos

Soybean (Glycine max) seeds store significant amounts of their biomass as protein, levels of which reflect the carbon and nitrogen received by the developing embryo. The relationship between carbon and nitrogen supply during filling and seed composition was examined through a series of embryo-culturing experiments. Three distinct ratios of carbon to nitrogen supply were further explored through metabolic flux analysis. Labeling experiments utilizing [U-(13)C5]glutamine, [U-(13)C4]asparagine, and [1,2-(13)C2]glucose were performed to assess embryo metabolism under altered feeding conditions and to create corresponding flux maps. Additionally, [U-(14)C12]sucrose, [U-(14)C6]glucose, [U-(14)C5]glutamine, and [U-(14)C4]asparagine were used to monitor differences in carbon allocation. The analyses revealed that: (1) protein concentration as a percentage of total soybean embryo biomass coincided with the carbon-to-nitrogen ratio; (2) altered nitrogen supply did not dramatically impact relative amino acid or storage protein subunit profiles; and (3) glutamine supply contributed 10% to 23% of the carbon for biomass production, including 9% to 19% of carbon to fatty acid biosynthesis and 32% to 46% of carbon to amino acids. Seed metabolism accommodated different levels of protein biosynthesis while maintaining a consistent rate of dry weight accumulation. Flux through ATP-citrate lyase, combined with malic enzyme activity, contributed significantly to acetyl-coenzyme A production. These fluxes changed with plastidic pyruvate kinase to maintain a supply of pyruvate for amino and fatty acids. The flux maps were independently validated by nitrogen balancing and highlight the robustness of primary metabolism.

Highlighting the tricarboxylic acid cycle: liquid and gas chromatography-mass spectrometry analyses of (13)C-labeled organic acids

The tricarboxylic acid (TCA) cycle is involved in the complete oxidation of organic acids to carbon dioxide in aerobic cells. It not only uses the acetyl-CoA derived from glycolysis but also uses breakdown products of proteins, fatty acids, and nucleic acids. Therefore, the TCA cycle involves numerous carbon fluxes through central metabolism to produce reductant power and transfer the generated electrons to the aerobic electron transport system where energy is formed by oxidative phosphorylation. Although the TCA cycle plays a crucial role in aerobic organisms and tissues, the lack of direct isotopic labeling information in its intermediates (organic acids) makes the quantification of its metabolic fluxes rather approximate. This is the major technical gap that this study intended to fill. In this work, we established and validated liquid and gas chromatography-mass spectrometry methods to determine (13)C labeling in organic acids involved in the TCA cycle using scheduled multiple reaction monitoring and single ion monitoring modes, respectively. Labeled samples were generated using maize embryos cultured with [(13)C]glucose or [(13)C]glutamine. Once steady-state labeling was reached, (13)C-labeled organic acids were extracted and purified. When applying our mass spectrometric methods to those extracts, mass isotopomer abundances of seven major organic acids were successfully determined.

Parallel labeling experiments with [1,2-(13)C]glucose and [U-(13)C]glutamine provide new insights into CHO cell metabolism

We applied a parallel labeling strategy using two isotopic tracers, [1,2-(13)C]glucose and [U-(13)C]glutamine, to determine metabolic fluxes in Chinese hamster ovary (CHO) cells. CHO cells were grown in parallel cultures over a period of six days with glucose and glutamine feeding. On days 2 and 5, isotopic tracers were introduced and (13)C-labeling of intracellular metabolites was measured by gas chromatography-mass spectrometry (GC-MS). Metabolites in glycolysis pathway reached isotopic steady state for [1,2-(13)C]glucose within 1.5h, and metabolites in the TCA cycle reached isotopic steady state for [U-(13)C]glutamine within 3h. Combined analysis of multiple data sets produced detailed flux maps at two key metabolic phases, exponential growth phase (day 2) and early stationary phase (day 5). Flux results revealed significant rewiring of intracellular metabolism in the transition from growth to non-growth, including changes in oxidative pentose phosphate pathway, anaplerosis, amino acid metabolism, and fatty acid biosynthesis. At the growth phase, de novo fatty acid biosynthesis correlated well with the lipid requirements for cell growth. However, surprisingly, at the non-growth phase the fatty acid biosynthesis flux remained high even though no new lipids were needed for cell growth. Additionally, we identified a discrepancy in the estimated TCA cycle flux obtained using traditional stoichiometric flux balancing and (13)C-metabolic flux analysis. Our results suggested that CHO cells produced additional metabolites from glucose that were not captured in previous metabolic models. Follow-up experiments with [U-(13)C]glucose confirmed that additional metabolites were accumulating in the medium that became M+3 and M+6 labeled.

Isotope labelling of Rubisco subunits provides in vivo information on subcellular biosynthesis and exchange of amino acids between compartments

The architecture of plant metabolism includes substantial duplication of metabolite pools and enzyme catalyzed reactions in different subcellular compartments. This poses challenges for understanding the regulation of metabolism particularly in primary metabolism and amino acid biosynthesis. To explore the extent to which amino acids are made in single compartments and to gain insight into the metabolic precursors from which they derive, we used steady state (13) C labelling and analysed labelling in protein amino acids from plastid and cytosol. Ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) is a major component of green tissues and its large and small subunits are synthesized from different pools of amino acids in the plastid and cytosol, respectively. Developing Brassica napus embryos were cultured in the presence of [U-(13) C]-sucrose, [U-(13) C]-glucose, [U-(13) C]-glutamine or [U-(13) C]-alanine to generate proteins. The large subunits (LSU) and small subunits (SSU) of Rubisco were isolated and the labelling in their constituent amino acids was analysed by gas chromatography-mass spectrometry. Amino acids including alanine, glycine and serine exhibited different (13) C enrichment in the LSU and SSU, demonstrating that these pools have different metabolic origins and are not isotopically equilibrated between the plastid and cytosol on the time scale of cellular growth. Potential extensions of this novel approach to other macromolecules, organelles and cell types of eukaryotes are discussed.

Metabolism control over growth: a case for trehalose-6-phosphate in plants

How plants relate their requirements for energy with the reducing power necessary to fuel growth is not understood. The activated glucose forms and NADPH are key precursors in pathways yielding, respectively, energy and reducing power for anabolic metabolism. Moreover, they are substrates or allosteric regulators of trehalose-phosphate synthase (TPS1) in fungi and probably also in plants. TPS1 synthesizes the signalling metabolite trehalose-6-phosphate (T6P) and, therefore, has the potential to relate reducing power with energy metabolism to fuel growth. A working model is discussed where trehalose-6-phosphate (T6P) inhibition of SnRK1 is part of a growth-regulating loop in young and metabolically active heterotrophic plant tissues. SnRK1 is the Snf1 Related Kinase 1 and the plant homologue of the AMP-dependent protein kinase of animals, a central energy gauge. T6P accumulation in response to high sucrose levels in a cell inhibits SnRK1 activity, thus promoting anabolic processes and growth. When T6P levels drop due to low glucose-6-phosphate, uridine-diphosphoglucose, and altered NADPH or due to restricted TPS1 activity, active SnRK1 promotes catabolic processes required to respond to energy and carbon deprivation. The model explains why too little or too much T6P has been found to be growth inhibitory: Arabidopsis thaliana embryos and seedlings without TPS1 are growth arrested and Arabidopsis seedlings accumulating T6P on a trehalose medium are growth arrested. Finally, the insight gained with respect to the possible role of T6P metabolism, where it is known to alter developmental and environmental responses of plants, is discussed.

Gas chromatography-mass spectrometry analysis of 13C labeling in sugars for metabolic flux analysis

Metabolic flux analysis, using 13C labeled substrates, has become a powerful methodology for quantifying intracellular fluxes. Most often, analysis is restricted to nuclear magnetic resonance or mass spectrometry measurement of 13C label incorporation into protein amino acids. However, amino acid isotopomer distribution insufficiently covers the entire network of central metabolism, especially in plant cells with highly compartmented metabolism, and analysis of other metabolites is required. Analysis of label in saccharides provides complementary data to better define fluxes around hexose, pentose, and triose phosphate pools. Here, we propose a gas chromatography-mass spectrometry (GC-MS) method to analyze 13C labeling in glucose and fructose moieties of sucrose, free glucose, fructose, maltose, inositol, and starch. Our results show that saccharide labeling for isotopomer quantification is better analyzed by chemical ionization than by electron ionization. The structure of the generated fragments was simulated and validated using labeled standards. The method is illustrated by analysis of saccharides extracted from developing rapeseed (Brassica napus L.) embryos. It is shown that glucose 6-phosphate isomerase and plastidial glucose 6-phosphate transport reactions are not at equilibrium, and light is shed on the pathways leading to fructose, maltose, and inositol synthesis.

Insights into metabolic efficiency from flux analysis

The efficiency of carbon and energy flows throughout metabolism defines the potential for growth and reproductive success of plants. Understanding the basis for metabolic efficiency requires relevant definitions of efficiency as well as measurements of biochemical functions through metabolism. Here insights into the basis of efficiency provided by (13)C-based metabolic flux analysis (MFA) as well as the uses and limitations of efficiency in predictive flux balance analysis (FBA) are highlighted. (13)C-MFA studies have revealed unusual features of central metabolism in developing green seeds for the efficient use of light to conserve carbon and identified metabolic inefficiencies in plant metabolism due to dissipation of ATP by substrate cycling. Constraints-based FBA has used efficiency to guide the prediction of the growth and actual internal flux distribution of plant systems. Comparisons in a few cases have been made between flux maps measured by (13)C-based MFA and those predicted by FBA assuming one or more maximal efficiency parameters. These studies suggest that developing plant seeds and photoautotrophic microorganisms may indeed have patterns of metabolic flux that maximize efficiency. MFA and FBA are synergistic toolsets for uncovering and explaining the metabolic basis of efficiencies and inefficiencies in plant systems.

Strategies for investigating the plant metabolic network with steady-state metabolic flux analysis: lessons from an Arabidopsis cell culture and other systems

Steady-state (13)C metabolic flux analysis (MFA) is currently the experimental method of choice for generating flux maps of the compartmented network of primary metabolism in heterotrophic and mixotrophic plant tissues. While statistically robust protocols for the application of steady-state MFA to plant tissues have been developed by several research groups, the implementation of the method is still far from routine. The effort required to produce a flux map is more than justified by the information that it contains about the metabolic phenotype of the system, but it remains the case that steady-state MFA is both analytically and computationally demanding. This article provides an overview of principles that underpin the implementation of steady-state MFA, focusing on the definition of the metabolic network responsible for redistribution of the label, experimental considerations relating to data collection, the modelling process that allows a set of metabolic fluxes to be deduced from the labelling data, and the interpretation of flux maps. The article draws on published studies of Arabidopsis cell cultures and other systems, including developing oilseeds, with the aim of providing practical guidance and strategies for handling the issues that arise when applying steady-state MFA to the complex metabolic networks encountered in plants.

Metabolic cartography: experimental quantification of metabolic fluxes from isotopic labelling studies

For the past decade, flux maps have provided researchers with an in-depth perspective on plant metabolism. As a rapidly developing field, significant headway has been made recently in computation, experimentation, and overall understanding of metabolic flux analysis. These advances are particularly applicable to the study of plant metabolism. New dynamic computational methods such as non-stationary metabolic flux analysis are finding their place in the toolbox of metabolic engineering, allowing more organisms to be studied and decreasing the time necessary for experimentation, thereby opening new avenues by which to explore the vast diversity of plant metabolism. Also, improved methods of metabolite detection and measurement have been developed, enabling increasingly greater resolution of flux measurements and the analysis of a greater number of the multitude of plant metabolic pathways. Methods to deconvolute organelle-specific metabolism are employed with increasing effectiveness, elucidating the compartmental specificity inherent in plant metabolism. Advances in metabolite measurements have also enabled new types of experiments, such as the calculation of metabolic fluxes based on (13)CO(2) dynamic labelling data, and will continue to direct plant metabolic engineering. Newly calculated metabolic flux maps reveal surprising and useful information about plant metabolism, guiding future genetic engineering of crops to higher yields. Due to the significant level of complexity in plants, these methods in combination with other systems biology measurements are necessary to guide plant metabolic engineering in the future.

Experimental flux measurements on a network scale

Metabolic flux is a fundamental property of living organisms. In recent years, methods for measuring metabolic flux in plants on a network scale have evolved further. One major challenge in studying flux in plants is the complexity of the plant's metabolism. In particular, in the presence of parallel pathways in multiple cellular compartments, the core of plant central metabolism constitutes a complex network. Hence, a common problem with the reliability of the contemporary results of (13)C-Metabolic Flux Analysis in plants is the substantial reduction in complexity that must be included in the simulated networks; this omission partly is due to limitations in computational simulations. Here, I discuss recent emerging strategies that will better address these shortcomings.

Eukaryotic metabolism: measuring compartment fluxes

Metabolic compartmentation represents a major characteristic of eukaryotic cells. The analysis of compartmented metabolic networks is complicated by separation and parallelization of pathways, intracellular transport, and the need for regulatory systems to mediate communication between interdependent compartments. Metabolic flux analysis (MFA) has the potential to reveal compartmented metabolic events, although it is a challenging task requiring demanding experimental techniques and sophisticated modeling. At present no ready-made solution can be provided to cope with the complexity of compartmented metabolic networks, but new powerful tools are emerging. This review gives an overview of different strategies to approach this issue, focusing on different MFA methods and highlighting the additional information that should be included to improve the outcome of an experiment and associate estimation procedures.

Flux-balance modeling of plant metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.

Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos

Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganic nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.

Synergy between (13)C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli

Genome-based Flux Balance Analysis (FBA) and steady-state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here, genome-derived models of Escherichia coli (E. coli) metabolism were used for FBA and ¹³C-MFA analyses of aerobic and anaerobic growths of wild-type E. coli (K-12 MG1655) cells. Validated MFA flux maps reveal that the fraction of maintenance ATP consumption in total ATP production is about 14% higher under anaerobic (51.1%) than aerobic conditions (37.2%). FBA revealed that an increased ATP utilization is consumed by ATP synthase to secrete protons from fermentation. The TCA cycle is shown to be incomplete in aerobically growing cells and submaximal growth is due to limited oxidative phosphorylation. An FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but the most-frequently predicted values of internal fluxes yielded from sampling the feasible space differ substantially from MFA-derived fluxes.

Modelling dynamic plant cells

A major challenge in plant biology is to understand how functions in plant cells emerge from interactions between molecular components. Computational and mathematical modelling can encapsulate the relationships between molecular components and reveal how biological functions emerge. We review recent progress in modelling in metabolism, growth, signalling and circadian rhythms in plant cells. We discuss challenges and opportunities for future directions.

Metabolic systems analysis to advance algal biotechnology

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Network analysis can direct microbial development efforts towards successful strategies and enable quantitative fine-tuning of the network for optimal product yields while maintaining the robustness of the production microbe. Metabolic modeling yields insights into microbial function, guides experiments by generating testable hypotheses, and enables the refinement of knowledge on the specific organism. While the application of such analytical approaches to algal systems is limited to date, metabolic network analysis can improve understanding of algal metabolic systems and play an important role in expediting the adoption of new biofuel technologies.

13C metabolic flux analysis in complex systems

Experimental determination of in vivo metabolic rates by methods of (13)C metabolic flux analysis is a pivotal approach to unravel structure and regulation of metabolic networks, in particular with microorganisms grown in minimal media. However, the study of real-life and eukaryotic systems calls for the quantification of fluxes also in cellular compartments, rich media, cell-wide metabolic networks, dynamic systems or single cells. These scenarios drastically increase the complexity of the task, which is only partly dealt by existing approaches that rely on rigorous simulations of label propagation through metabolic networks and require multiple labeling experiments or a priori information on pathway inactivity to simplify the problem. Albeit qualitative and largely driven by human interpretation, statistical analysis of measured (13)C-patterns remains the exclusive alternative to comprehensively handle such complex systems. In the future, this practice will be complemented by novel modeling frameworks to assay particular fluxes within a network by stable isotopic tracer for targeted validation of well-defined hypotheses.

A genome-scale metabolic model accurately predicts fluxes in central carbon metabolism under stress conditions

Flux is a key measure of the metabolic phenotype. Recently, complete (genome-scale) metabolic network models have been established for Arabidopsis (Arabidopsis thaliana), and flux distributions have been predicted using constraints-based modeling and optimization algorithms such as linear programming. While these models are useful for investigating possible flux states under different metabolic scenarios, it is not clear how close the predicted flux distributions are to those occurring in vivo. To address this, fluxes were predicted for heterotrophic Arabidopsis cells and compared with fluxes estimated in parallel by (13)C-metabolic flux analysis (MFA). Reactions of the central carbon metabolic network (glycolysis, the oxidative pentose phosphate pathway, and the tricarboxylic acid [TCA] cycle) were independently analyzed by the two approaches. Net fluxes in glycolysis and the TCA cycle were predicted accurately from the genome-scale model, whereas the oxidative pentose phosphate pathway was poorly predicted. MFA showed that increased temperature and hyperosmotic stress, which altered cell growth, also affected the intracellular flux distribution. Under both conditions, the genome-scale model was able to predict both the direction and magnitude of the changes in flux: namely, increased TCA cycle and decreased phosphoenolpyruvate carboxylase flux at high temperature and a general decrease in fluxes under hyperosmotic stress. MFA also revealed a 3-fold reduction in carbon-use efficiency at the higher temperature. It is concluded that constraints-based genome-scale modeling can be used to predict flux changes in central carbon metabolism under stress conditions.

Cloning, biochemical characterisation, tissue localisation and possible post-translational regulatory mechanism of the cytosolic phosphoglucose isomerase from developing sunflower seeds

Lipid biosynthesis in developing sunflower (Helianthus annuus L.) seeds requires reducing power. One of the main sources of cellular NADPH is the oxidative pentose phosphate pathway (OPPP), generated from the oxidation of glucose-6-phosphate. This glycolytic intermediate, which can be imported to the plastid and enter in the OPPP, is the substrate and product of cytosolic phosphoglucose isomerase (cPGI, EC 5.3.1.9). In this report, we describe the cloning of a full-length cDNA encoding cPGI from developing sunflower seeds. The sequence was predicted to code for a protein of 566 residues characterised by the presence of two sugar isomerase domains. This cDNA was heterologously expressed in Escherichia coli as a His-tagged protein. The recombinant protein was purified using immobilised metal ion affinity chromatography and biochemically characterised. The enzyme had a specific activity of 1,436 micromol min(-1) mg(-1) and 1,011 micromol min(-1) mg(-1) protein when the reaction was initiated with glucose-6-phosphate and fructose-6-phosphate, respectively. Activity was not affected by erythrose-4-phosphate, but was inhibited by 6-P gluconate and glyceraldehyde-3-phosphate. A polyclonal immune serum was raised against the purified enzyme, allowing the study of protein levels during the period of active lipid synthesis in seeds. These results were compared with PGI activity profiles and mRNA expression levels obtained from Q-PCR studies. Our results point to the existence of a possible post-translational regulatory mechanism during seed development. Immunolocalisation of the protein in seed tissues further indicated that cPGI is highly expressed in the procambial ring.

Not just a circle: flux modes in the plant TCA cycle

The tricarboxylic acid (TCA) cycle is one of the iconic pathways in metabolism. The cycle is commonly thought of in terms of energy metabolism, being responsible for the oxidation of respiratory substrates to drive ATP synthesis. However, the reactions of carboxylic acid metabolism are embedded in a larger metabolic network and the conventional TCA cycle is only one way in which the component reactions can be organised. Recent evidence from labelling studies and metabolic network models suggest that the organisation of carboxylic acid metabolism in plants is highly dependent on the metabolic and physiological demands of the cell. Thus, alternative, non-cyclic flux modes occur in leaves in the light, in some developing oilseeds, and under specific physiological circumstances such as anoxia.