Quantification of compartmented metabolic fluxes in developing soybean embryos by employing biosynthetically directed fractional (13)C labeling, two-dimensional [(13)C, (1)H] nuclear magnetic resonance, and comprehensive isotopomer balancing

Metabolic flux analysis in plants using dynamic labeling technique: application to tryptophan biosynthesis in cultured rice cells

The concept and methodology of using dynamic labeling for the MFA of plant metabolic pathways are described, based on a case study to develop a method for the MFA of the tryptophan biosynthetic pathway in cultured rice cells. Dynamic labeling traces the change in the labeling level of a metabolite in a metabolic pathway after the application of a stable isotope-labeled compound. In this study, [1-(13)C] l-serine was fed as a labeling precursor and the labeling level of Trp was determined by using the LC-MS/MS. The value of metabolic flux is determined by fitting a model describing the labeling dynamics of the pathway to the observed labeling data. The biosynthetic flux of Trp in rice suspension cultured cell was determined to be 6.0+/-1.1 nmol (gFWh)(-1). It is also demonstrated that an approximately sixfold increase in the biosynthetic flux of Trp in transgenic rice cells expressing the feedback-insensitive version of anthranilate synthase alpha-subunit gene (OASA1D) resulted in a 45-fold increase in the level of Trp. In this article, the basic workflow for the experiment is introduced and the details of the actual experimental procedures are explained. Future perspectives are also discussed by referring recent advances in the dynamic labeling approach.

NMR-based fluxomics: quantitative 2D NMR methods for isotopomers analysis

We have investigated the reliability of 2D-COSY and 2D-TOCSY experiments to provide accurate measurements of (13)C-enrichments in complex mixtures of (13)C-labelled metabolites. This was done from both theoretical considerations and experimental investigations. The results showed that 2D-TOCSY but not 2D-COSY could provide accurate measurements of (13)C-enrichments, provided efficient zero-quantum filters were applied during the mixing period. This approach extends the range of NMR methods applicable in (13)C-labelling experiments and is suitable to investigating the dynamic behaviour of metabolic systems.

Towards dynamic metabolic network measurements by multi-dimensional NMR-based fluxomics

Novel technologies for measuring biological systems and methods for visualizing data have led to a revolution in the life sciences. Nuclear magnetic resonance (NMR) techniques can provide information on metabolite structure and metabolic dynamics at the atomic level. We have been developing a new method for measuring the dynamic metabolic network of crude extracts that combines [(13)C(6)]glucose stable isotope labeling of Arabidopsis thaliana and multi-dimensional heteronuclear NMR analysis, whereas most conventional metabolic flux analyses examine proteinogenic amino acids that are specifically labeled with partially labeled substrates such as [2-(13)C(1)]glucose or 10% [(13)C(6)]glucose. To show the validity of our method, we investigated how to obtain information about biochemical reactions, C-C bond formation, and the cleavage of the main metabolites, such as free amino acids, in crude extracts based on the analysis of the (13)C-(13)C coupling pattern in 2D-NMR spectra. For example, the combination of different extraction solvents allows one to distinguish complicated (13)C-(13)C fine couplings at the C2 position of amino acids. As another approach, f1-f3 projection of the HCACO spectrum also helps in the analysis of (13)C-(13)C connectivities. Using these new methods, we present an example that involves monitoring the incorporation profile of [(13)C(6)]glucose into A. thaliana and its metabolic dynamics, which change in a time-dependent manner with atmospheric (12)CO(2) assimilation.

Experimental and mathematical approaches to modeling plant metabolic networks

To support their sessile and autotrophic lifestyle higher plants have evolved elaborate networks of metabolic pathways. Dynamic changes in these metabolic networks are among the developmental forces underlying the functional differentiation of organs, tissues and specialized cell types. They are also important in the various interactions of a plant with its environment. Further complexity is added by the extensive compartmentation of the various interconnected metabolic pathways in plants. Thus, although being used widely for assessing the control of metabolic flux in microbes, mathematical modeling approaches that require steady-state approximations are of limited utility for understanding complex plant metabolic networks. However, considerable progress has been made when manageable metabolic subsystems were studied. In this article, we will explain in general terms and using simple examples the concepts underlying stoichiometric modeling (metabolic flux analysis and metabolic pathway analysis) and kinetic approaches to modeling (including metabolic control analysis as a special case). Selected studies demonstrating the prospects of these approaches, or combinations of them, for understanding the control of flux through particular plant pathways are discussed. We argue that iterative cycles of (dry) mathematical modeling and (wet) laboratory testing will become increasingly important for simulating the distribution of flux in plant metabolic networks and deriving rational experimental designs for metabolic engineering efforts.

A transient isotopic labeling methodology for 13C metabolic flux analysis of photoautotrophic microorganisms

Metabolic flux analysis is increasingly recognized as an integral component of systems biology. However, techniques for experimental measurement of system-wide metabolic fluxes in purely photoautotrophic systems (growing on CO(2) as the sole carbon source) have not yet been developed due to the unique problems posed by such systems. In this paper, we demonstrate that an approach that balances positional isotopic distributions transiently is the only route to obtaining system-wide metabolic flux maps for purely autotrophic metabolism. The outlined transient (13)C-MFA methodology enables measurement of fluxes at a metabolic steady-state, while following changes in (13)C-labeling patterns of metabolic intermediates as a function of time, in response to a step-change in (13)C-label input. We use mathematical modeling of the transient isotopic labeling patterns of central intermediates to assess various experimental requirements for photoautotrophic MFA. This includes the need for intracellular metabolite concentration measurements and isotopic labeling measurements as a function of time. We also discuss photobioreactor design and operation in order to measure fluxes under precise environmental conditions. The transient MFA technique can be used to measure and compare fluxes under different conditions of light intensity, nitrogen sources or compare strains with various mutations or gene deletions and additions.

Vacuolar compartmentation complicates the steady-state analysis of glucose metabolism and forces reappraisal of sucrose cycling in plants

Steady-state stable isotope labelling provides a method for generating flux maps of the compartmented network of central metabolism in heterotrophic plant tissues. Theoretical analysis of the contribution of the vacuole to the regeneration of glucose by endogenous processes shows that numerical fitting of isotopomeric data will only generate an accurate map of the fluxes involving intracellular glucose if information is available on the labelling of both the cytosolic and vacuolar glucose pools. In the absence of this information many of the calculated fluxes are at best unreliable or at worst indeterminate. This result suggests that the anomalously high rates of sucrose cycling and glucose resynthesis that have been reported in earlier steady-state analyses of tissues labelled with (13)C-glucose precursors may be an artefact of assuming that the labelling pattern of extracted glucose reflected the labelling of the cytosolic pool. The analysis emphasises that although subcellular information can sometimes be deduced from a steady-state analysis without recourse to subcellular fractionation, the success of this procedure depends critically on the structure of the metabolic network. It is concluded that methods need to be implemented that will allow measurement of the subcellular labelling pattern of glucose and other metabolites, as part of the routine analysis of the redistribution of label in steady-state stable isotope labelling experiments, if the true potential of network flux analysis for generating metabolic phenotypes is to be realized.

Compartment-specific labeling information in 13C metabolic flux analysis of plants

Metabolic engineering of plants has great potential for the low cost production of chemical feedstocks and novel compounds, but to take full advantage of this potential a better understanding of plant central carbon metabolism is needed. Flux studies define the cellular phenotype of living systems and can facilitate rational metabolic engineering. However the measurements usually made in these analyses are often not sufficient to reliably determine many fluxes that are distributed between different subcellular compartments of eukaryotic cells. We have begun to address this shortcoming by increasing the number and quality of measurements that provide (13)C labeling information from specific compartments within the plant cell. The analysis of fatty acid groups, cell wall components, protein glycans, and starch, using both gas chromatography/mass spectrometry and nuclear magnetic resonance spectroscopy are presented here. Fatty acid labeling determinations are sometimes highly convoluted. Derivatization to butyl amides reduces the errors in isotopomer resolution and quantification, resulting in better determination of fluxes into seed lipid reserves, including both plastidic and cytosolic reactions. While cell walls can account for a third or more of biomass in many seeds, no quantitative cell wall labeling measurements have been reported for plant flux analysis. Hydrolyzing cell wall and derivatizing sugars to the alditol acetates, provides novel labeling information and thereby can improve identification of flux through upper glycolytic intermediates of the cytosol. These strategies improve the quantification of key carbon fluxes in the compartmentalized flux network of plant cells.

Fluxomics: mass spectrometry versus quantitative imaging

The recent development of analytic high-throughput technologies enables us to take a bird's view of how metabolism is regulated in real time. We have known for a long time that metabolism is highly regulated at all levels, including transcriptional, posttranslational and allosteric controls. Flux through a metabolic or signaling pathway is determined by the activity of its individual components. Fluxomics aims to define the genes involved in regulation by following the flux. Two technologies are used to monitor fluxes. Pulse labeling of the organism or cell with a tracer, such as 13C, followed by mass spectrometric analysis of the partitioning of label into different compounds provides an efficient tool to study flux and to compare the effect of mutations on flux. The second approach is based on the use of flux sensors, proteins that respond with a conformational change to ligand binding. Fluorescence resonance energy transfer (FRET) detects the conformational change and serves as a proxy for ligand concentration. In contrast to the mass spectrometry assays, FRET nanosensors monitor only a single compound. Both methods provide high time resolution. The major advantages of FRET nanosensors are that they yield data with cellular and subcellular resolution and the method is minimally invasive.

Top-down phenomics of Arabidopsis thaliana: metabolic profiling by one- and two-dimensional nuclear magnetic resonance spectroscopy and transcriptome analysis of albino mutants

Elucidating the function of each gene in a genome is important for understanding the whole organism. We previously constructed 4000 disruptant mutants of Arabidopsis by insertion of Ds transposons. Here, we describe a top-down phenomics approach based on metabolic profiling that uses one-dimensional 1H and two-dimensional 1H,13C NMR analyses and transcriptome analysis of albino mutant lines of Arabidopsis. One-dimensional 1H NMR metabolic fingerprinting revealed global metabolic changes in the albino mutants, notably a decrease in aromatic metabolites and changes in aliphatic metabolites. NMR measurements of plants fed with 13C6-glucose showed that the albino lines had dramatically different 13C-labeling patterns and increased levels of several amino acids, especially Asn and Gln. Microarray analysis of one of the albino lines revealed a unique expression profile and showed that changes in the expression of genes encoding metabolic enzymes did not correspond with changes in the levels of metabolites. Collectively, these results suggest that albino mutants lose the normal carbon/nitrogen balance, presumably mainly through lack of photosynthesis. Our study offers an idea of how much the metabolite network is affected by chloroplast function in plants and shows the effectiveness of NMR-based metabolic analysis for metabolite profiling. On the basis of these findings, we propose that future investigations of plant systems biology combine transcriptomic, metabolomic, and phenomic analyses of gene disruptant lines.

Metabolic networks in motion: 13C-based flux analysis

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism.

Mitochondrial metabolism in developing embryos of Brassica napus

The metabolism of developing plant seeds is directed toward transforming primary assimilatory products (sugars and amino acids) into seed storage compounds. To understand the role of mitochondria in this metabolism, metabolic fluxes were determined in developing embryos of Brassica napus. After labeling with [1,2-(13)C2]glucose + [U-(13)C6]glucose, [U-(13)C3]alanine, [U-(13)C5]glutamine, [(15)N]alanine, (amino)-[(15)N]glutamine, or (amide)-[(15)N]glutamine, the resulting labeling patterns in protein amino acids and in fatty acids were analyzed by gas chromatography-mass spectrometry. Fluxes through mitochondrial metabolism were quantified using a steady state flux model. Labeling information from experiments using different labeled substrates was essential for model validation and reliable flux estimation. The resulting flux map shows that mitochondrial metabolism in these developing seeds is very different from that in either heterotrophic or autotrophic plant tissues or in most other organisms: (i) flux around the tricarboxylic acid cycle is absent and the small fluxes through oxidative reactions in the mitochondrion can generate (via oxidative phosphorylation) at most 22% of the ATP needed for biosynthesis; (ii) isocitrate dehydrogenase is reversible in vivo; (iii) about 40% of mitochondrial pyruvate is produced by malic enzyme rather than being imported from the cytosol; (iv) mitochondrial flux is largely devoted to providing precursors for cytosolic fatty acid elongation; and (v) the uptake of amino acids rather than anaplerosis via PEP carboxylase determines carbon flow into storage proteins.

Robustness of central carbohydrate metabolism in developing maize kernels

The central carbohydrate metabolism provides the precursors for the syntheses of various storage products in seeds. While the underlying biochemical map is well established, little is known about the organization and flexibility of carbohydrate metabolic fluxes in the face of changing biosynthetic demands or other perturbations. This question was addressed in developing kernels of maize (Zea mays L.), a model system for the study of starch and sugar metabolism. (13)C-labeling experiments were carried out with inbred lines, heterotic hybrids, and starch-deficient mutants that were selected to cover a wide range of performances and kernel phenotypes. In total, 46 labeling experiments were carried out using either [U-(13)C(6)]glucose or [U-(13)C(12)]sucrose and up to three stages of kernel development. Carbohydrate flux distributions were estimated based on glucose isotopologue abundances, which were determined in hydrolysates of starch by using quantitative (13)C-NMR and GC-MS. Similar labeling patterns in all samples indicated robustness of carbohydrate fluxes in maize endosperm, and fluxes were rather stable in response to glucose or sucrose feeding and during development. A lack of ADP-glucose pyrophosphorylase in the bt2 and sh2 mutants triggered significantly increased hexose cycling. In contrast, other mutations with similar kernel phenotypes had no effect. Thus, the distribution of carbohydrate fluxes is stable and not determined by sink strength in maize kernels.

Integrative analysis of metabolic networks: from peaks to flux models?

Recent developments in genomic and post-genomic technologies have led to the amassment of data describing genome sequences, transcript, protein and metabolite abundances, protein modifications, and protein-protein and protein-DNA interactions. Such technologies have vastly expanded the inventory of detectable molecular species and can be used to describe their interdependence, but they have yet to fulfill their promise in enhancing our knowledge of how flux through metabolic pathways is regulated. A convergence of traditional reductionistic and novel holistic experimental approaches could aid in elucidating flux control.

Measuring multiple fluxes through plant metabolic networks

Fluxes through metabolic networks are crucial for cell function, and a knowledge of these fluxes is essential for understanding and manipulating metabolic phenotypes. Labeling provides the key to flux measurement, and in network flux analysis the measurement of multiple fluxes allows a flux map to be superimposed on the metabolic network. The principles and practice of two complementary methods, dynamic and steady-state labeling, are described, emphasizing best practice and illustrating their contribution to network flux analysis with examples taken from the plant and microbial literature. The principal analytical methods for the detection of stable isotopes are also described, as well as the procedures for obtaining flux maps from labeling data. A series of boxes summarizing the key concepts of network flux analysis is provided for convenience.

Bioinformatics and its applications in plant biology

Bioinformatics plays an essential role in today's plant science. As the amount of data grows exponentially, there is a parallel growth in the demand for tools and methods in data management, visualization, integration, analysis, modeling, and prediction. At the same time, many researchers in biology are unfamiliar with available bioinformatics methods, tools, and databases, which could lead to missed opportunities or misinterpretation of the information. In this review, we describe some of the key concepts, methods, software packages, and databases used in bioinformatics, with an emphasis on those relevant to plant science. We also cover some fundamental issues related to biological sequence analyses, transcriptome analyses, computational proteomics, computational metabolomics, bio-ontologies, and biological databases. Finally, we explore a few emerging research topics in bioinformatics.

Changes in flux pattern of the central carbohydrate metabolism during kernel development in maize

Developing kernels of the inbred maize line W22 were grown in sterile culture and supplied with a mixture of [U-13C6]glucose and unlabeled glucose during three consecutive intervals (11-18, 18-25, or 25-32 days after pollination) within the linear phase of starch formation. At the end of each labeling period, glucose was prepared from starch and analyzed by 13C isotope ratio mass spectrometry and high-resolution (13)C NMR spectroscopy. The abundances of individual glucose isotopologs were calculated by computational deconvolution of the NMR data. [1,2-(13)C2]-, [5,6-(13)C2]-, [2,3-(13)C2]-, [4,5-(13)C2]-, [1,2,3-(13)C3]-, [4,5,6-(13)C3]-, [3,4,5,6-(13)C4]-, and [U-(13)C6]-isotopologs were detected as the major multiple-labeled glucose species, albeit at different normalized abundances in the three intervals. Relative flux contributions by five different pathways in the primary carbohydrate metabolism were determined by computational simulation of the isotopolog space of glucose. The relative fractions of some of these processes in the overall glucose cycling changed significantly during maize kernel development. The simulation showed that cycling via the non-oxidative pentose phosphate pathway was lowest during the middle interval of the experiment. The observed flux pattern could by explained by a low demand for amino acid precursors recruited from the pentose phosphate pathway during the middle interval of kernel development.

Regulation of metabolic networks: understanding metabolic complexity in the systems biology era

Metabolism is one of the best recognised networks within biological systems, but our understanding of metabolic regulation has been limited by a failure to consider regulation within the context of the whole network. With recent advances in theoretical aspects of network thinking and a postgenomic landscape in which our ability to quantify molecular changes at a systems level is unsurpassed, the time is ripe for the development of a new level of understanding of the regulation of plant metabolic networks. Theoretical advances such as the formal description of 'scale-free' networks have provided explanations for network behaviour (such as robustness). In parallel, the appreciation of the importance of new levels of the metabolic regulatory hierarchy (such as protein-protein interaction) and the continuing development of global profiling technologies is generating a system-wide molecular data set of increasing resolution. In this review we will argue that the integration of these different aspects of metabolic research will bring about a step change in our understanding of the regulation of metabolic networks in plants.

Evidence of a key role for photosynthetic oxygen release in oil storage in developing soybean seeds

Based on the topographical analysis of photosynthesis and oil storage, we propose in a companion paper that photosynthetic oxygen release plays a key role in the local energy state, storage metabolism and flux toward lipid biosynthesis in developing soybean seeds. To test this hypothesis, we combined topographical analysis of ATP gradients across tissues, microsensor quantifications of internal O2 levels, assays of energy balance, metabolite profiles and isotope-labelling studies. Seeds show a marked degree of oxygen starvation in vivo (minimum O2 levels 0.1 kPa, approximately 1.3 microm), affecting ATP gradients, overall energy state, metabolite pools and storage activity. Despite the low light availability, photosynthesis supplies significant amounts of oxygen to the hypoxic seed tissue. This is followed by an increase in local ATP levels, most prominently within the lipid-synthesizing (inner) regions of the embryo. Concomitantly, partitioning of 14C-sucrose to lipids is increased, suggesting higher rates of lipid biosynthesis. It is concluded that both respiratory and biosynthetic fluxes are dynamically adjusted to photosynthetic oxygen supply.

Gradients of lipid storage, photosynthesis and plastid differentiation in developing soybean seeds

This study establishes a topographical framework for functional investigations on the regulation of lipid biosynthesis and its interaction with embryo photosynthesis in developing soybean seed. Structural observations, combined with molecular and functional parameters, revealed the gradual transformation of chloroplasts into storage organelles, starting from inner regions going outwards. This is evidenced by electron microscopy, confocal laser scanning microscopy, in situ hybridization and histochemical/biochemical data. As a consequence of plastid differentiation, photosynthesis becomes distributed along a gradient within the developing embryo. Electron transport rate, effective quantum yield and O2 production rate are maximal in the embryo periphery, as documented by imaging pulse-amplitude-modulated fluorescence and O2 release via microsensors. The gradual loss of photosynthetic capacity was accompanied by a similarly gradual accumulation of starch and lipids. Noninvasive nuclear magnetic resonance spectroscopy of mature seeds revealed steep gradients in lipid deposition, with the highest concentrations in inner regions. The inverse relationship between photosynthesis and lipid biosynthesis argues against a direct metabolic involvement of photosynthesis in lipid biosynthesis during the late storage stage, but points to a role for photosynthetic oxygen release. This hypothesis is verified in a companion paper.