Central metabolic fluxes in the endosperm of developing maize seeds and their implications for metabolic engineering

Application of metabolic flux analysis to plants

This volume compiles a series of chapters that cover the major aspects of plant metabolic flux analysis, such as but not limited to labeling of plant material, acquisition of labeling data, mathematical modeling of metabolic network at the cell, tissue, and plant level. A short revue, including methodological points and applications of flux analysis to plants, is presented in this introductory chapter.

Modelling metabolic CO₂ evolution--a fresh perspective on respiration

Respiration is a major contributor to net exchange of CO₂ between plants and the atmosphere and thus an important aspect of the vegetation component of global climate change models. However, a mechanistic model of respiration is lacking, and so here we explore the potential for flux balance analysis (FBA) to predict cellular CO₂ evolution rates. Metabolic flux analysis reveals that respiration is not always the dominant source of CO₂, and that metabolic processes such as the oxidative pentose phosphate pathway (OPPP) and lipid synthesis can be quantitatively important. Moreover, there is considerable variation in the metabolic origin of evolved CO₂ between tissues, species and conditions. Comparison of FBA-predicted CO₂ evolution profiles with those determined from flux measurements reveals that FBA is able to predict the metabolic origin of evolved CO₂ in different tissues/species and under different conditions. However, FBA is poor at predicting flux through certain metabolic processes such as the OPPP and we identify the way in which maintenance costs are accounted for as a major area of improvement for future FBA studies. We conclude that FBA, in its standard form, can be used to predict CO₂ evolution in a range of plant tissues and in response to environment.

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.

Reconstitution of EPA and DHA biosynthesis in arabidopsis: iterative metabolic engineering for the synthesis of n-3 LC-PUFAs in transgenic plants

An iterative approach to optimising the accumulation of non-native long chain polyunsaturated fatty acids in transgenic plants was undertaken in Arabidopsis thaliana. The contribution of a number of different transgene enzyme activities was systematically determined, as was the contribution of endogenous fatty acid metabolism. Successive iterations were informed by lipidomic analysis of neutral, polar and acyl-CoA pools. This approach allowed for a four-fold improvement on levels previously reported for the accumulation of eicosapentaenoic acid in Arabidopsis seeds and also facilitated the successful engineering of the high value polyunsaturated fatty acid docosahexaenoic acid to 10-fold higher levels. Our studies identify the minimal gene set required to direct the efficient synthesis of these fatty acids in transgenic seed oil.

Seed architecture shapes embryo metabolism in oilseed rape

Constrained to develop within the seed, the plant embryo must adapt its shape and size to fit the space available. Here, we demonstrate how this adjustment shapes metabolism of photosynthetic embryo. Noninvasive NMR-based imaging of the developing oilseed rape (Brassica napus) seed illustrates that, following embryo bending, gradients in lipid concentration became established. These were correlated with the local photosynthetic electron transport rate and the accumulation of storage products. Experimentally induced changes in embryo morphology and/or light supply altered these gradients and were accompanied by alterations in both proteome and metabolome. Tissue-specific metabolic models predicted that the outer cotyledon and hypocotyl/radicle generate the bulk of plastidic reductant/ATP via photosynthesis, while the inner cotyledon, being enclosed by the outer cotyledon, is forced to grow essentially heterotrophically. Under field-relevant high-light conditions, major contribution of the ribulose-1,5-bisphosphate carboxylase/oxygenase-bypass to seed storage metabolism is predicted for the outer cotyledon and the hypocotyl/radicle only. Differences between in vitro- versus in planta-grown embryos suggest that metabolic heterogeneity of embryo is not observable by in vitro approaches. We conclude that in vivo metabolic fluxes are locally regulated and connected to seed architecture, driving the embryo toward an efficient use of available light and space.

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.

Metabolic network flux analysis for engineering plant systems

Metabolic network flux analysis (NFA) tools have proven themselves to be powerful aids to metabolic engineering of microbes by providing quantitative insights into the flows of material and energy through cellular systems. The development and application of NFA tools to plant systems has advanced in recent years and are yielding significant insights and testable predictions. Plants present substantial opportunities for the practical application of NFA but they also pose serious challenges related to the complexity of plant metabolic networks and to deficiencies in our knowledge of their structure and regulation. By considering the tools available and selected examples, this article attempts to assess where and how NFA is most likely to have a real impact on plant biotechnology.

Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies

Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for (13)C-metabolic flux analysis ((13)C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of (13)C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for (13)C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.

Molecular insights into how a deficiency of amylose affects carbon allocation--carbohydrate and oil analyses and gene expression profiling in the seeds of a rice waxy mutant

BACKGROUND

Understanding carbon partitioning in cereal seeds is of critical importance to develop cereal crops with enhanced starch yields for food security and for producing specified end-products high in amylose, β-glucan, or fructan, such as functional foods or oils for biofuel applications. Waxy mutants of cereals have a high content of amylopectin and have been well characterized. However, the allocation of carbon to other components, such as β-glucan and oils, and the regulation of the altered carbon distribution to amylopectin in a waxy mutant are poorly understood. In this study, we used a rice mutant, GM077, with a low content of amylose to gain molecular insight into how a deficiency of amylose affects carbon allocation to other end products and to amylopectin. We used carbohydrate analysis, subtractive cDNA libraries, and qPCR to identify candidate genes potentially responsible for the changes in carbon allocation in GM077 seeds.

RESULTS

Carbohydrate analysis indicated that the content of amylose in GM077 seeds was significantly reduced, while that of amylopectin significantly rose as compared to the wild type BP034. The content of glucose, sucrose, total starch, cell-wall polysaccharides and oil were only slightly affected in the mutant as compared to the wild type. Suppression subtractive hybridization (SSH) experiments generated 116 unigenes in the mutant on the wild-type background. Among the 116 unigenes, three, AGP, ISA1 and SUSIBA2-like, were found to be directly involved in amylopectin synthesis, indicating their possible roles in redirecting carbon flux from amylose to amylopectin. A bioinformatics analysis of the putative SUSIBA2-like binding elements in the promoter regions of the upregulated genes indicated that the SUSIBA2-like transcription factor may be instrumental in promoting the carbon reallocation from amylose to amylopectin.

CONCLUSION

Analyses of carbohydrate and oil fractions and gene expression profiling on a global scale in the rice waxy mutant GM077 revealed several candidate genes implicated in the carbon reallocation response to an amylose deficiency, including genes encoding AGPase and SUSIBA2-like. We believe that AGP and SUSIBA2 are two promising targets for classical breeding and/or transgenic plant improvement to control the carbon flux between starch and other components in cereal seeds.

Progress in understanding and engineering primary plant metabolism

The maximum yield of crop plants depends on the efficiency of conversion of sunlight into biomass. This review summarises recent models that estimate energy conversion efficiency for successive steps in photosynthesis and metabolism. Photorespiration was identified as a major reason for energy loss during photosynthesis and strategies to modify or suppress photorespiration are presented. Energy loss during the conversion of photosynthate to biomass is also large but cannot be modelled as precisely due to incomplete knowledge about pathways and turnover and maintenance costs. Recent research on pathways involved in metabolite transport and interconversion in different organs, and recent insights into energy requirements linked to the production, maintenance and turnover of the apparatus for cellular growth and repair processes are discussed.

iMS2Flux--a high-throughput processing tool for stable isotope labeled mass spectrometric data used for metabolic flux analysis

Background

Metabolic flux analysis has become an established method in systems biology and functional genomics. The most common approach for determining intracellular metabolic fluxes is to utilize mass spectrometry in combination with stable isotope labeling experiments. However, before the mass spectrometric data can be used it has to be corrected for biases caused by naturally occurring stable isotopes, by the analytical technique(s) employed, or by the biological sample itself. Finally the MS data and the labeling information it contains have to be assembled into a data format usable by flux analysis software (of which several dedicated packages exist). Currently the processing of mass spectrometric data is time-consuming and error-prone requiring peak by peak cut-and-paste analysis and manual curation. In order to facilitate high-throughput metabolic flux analysis, the automation of multiple steps in the analytical workflow is necessary.

Results

Here we describe iMS2Flux, software developed to automate, standardize and connect the data flow between mass spectrometric measurements and flux analysis programs. This tool streamlines the transfer of data from extraction via correction tools to ¹³C-Flux software by processing MS data from stable isotope labeling experiments. It allows the correction of large and heterogeneous MS datasets for the presence of naturally occurring stable isotopes, initial biomass and several mass spectrometry effects. Before and after data correction, several checks can be performed to ensure accurate data. The corrected data may be returned in a variety of formats including those used by metabolic flux analysis software such as 13CFLUX, OpenFLUX and 13CFLUX2.

Conclusion

iMS2Flux is a versatile, easy to use tool for the automated processing of mass spectrometric data containing isotope labeling information. It represents the core framework for a standardized workflow and data processing. Due to its flexibility it facilitates the inclusion of different experimental datasets and thus can contribute to the expansion of flux analysis applications.

Substrate cycles in Penicillium chrysogenum quantified by isotopic non-stationary flux analysis

Background

Penicillium chrysogenum, the main production strain for penicillin-G, has a high content of intracellular carbohydrates, especially reduced sugars such as mannitol, arabitol, erythritol, as well as trehalose and glycogen. In previous steady state ¹³C wash-in experiments a delay of labeling enrichments in glycolytic intermediates was observed, which suggests turnover of storage carbohydrates. The turnover of storage pools consumes ATP which is expected to reduce the product yield for energy demanding production pathways like penicillin-G.

Results

In this study, a ¹³C labeling wash-in experiment of 1 hour was performed to systematically quantify the intracellular flux distribution including eight substrate cycles. The experiments were performed using a mixed carbon source of 85% Cmol_Glc/Cmol_Glc+EtOH labeled glucose (mixture of 90% [1-¹³C₁] and 10% [U-¹³C₆]) and 15% ethanol [U-¹³C₂]. It was found, that (1) also several extracellular pools are enriched with ¹³C labeling rapidly (trehalose, mannitol, and others), (2) the intra- to extracellular metabolite concentration ratios were comparable for a large set of metabolites while for some carbohydrates (mannitol, trehalose, and glucose) the measured ratios were much higher.

Conclusions

The fast enrichment of several extracellular carbohydrates and a concentration ratio higher than the ratio expected from cell lysis (2%) indicate active (e.g. ATP consuming) transport cycles over the cellular membrane. The flux estimation indicates, that substrate cycles account for about 52% of the gap in the ATP balance based on metabolic flux analysis.

Are we ready for genome-scale modeling in plants?

As it is becoming easier and faster to generate various types of high-throughput data, one would expect that by now we should have a comprehensive systems-level understanding of biology, biochemistry, and physiology at least in major prokaryotic and eukaryotic model systems. Despite the wealth of available data, we only get a glimpse of what is going on at the molecular level from the global perspective. The major reason is the high level of cellular complexity and our limited ability to identify all (or at least important) components and their interactions in virtually infinite number of internal and external conditions. Metabolism can be modeled mathematically by the use of genome-scale models (GEMs). GEMs are in silico metabolic flux models derived from available genome annotation. These models predict the combination of flux values of a defined metabolic network given the influence of internal and external signals. GEMs have been successfully implemented to model bacterial metabolism for over a decade. However, it was not until 2009 when the first GEM for Arabidopsis thaliana cell-suspension cultures was generated. Genome-scale modeling ("GEMing") in plants brings new challenges primarily due to the missing components and complexity of plant cells represented by the existence of: (i) photosynthesis; (ii) compartmentation; (iii) variety of cell and tissue types; and (iv) diverse metabolic responses to environmental and developmental cues as well as pathogens, insects, and competing weeds. This review presents a critical discussion of the advantages of existing plant GEMs, while identifies key targets for future improvements. Plant GEMs tend to be accurate in predicting qualitative changes in selected aspects of central carbon metabolism, while secondary metabolism is largely neglected mainly due to the missing (unknown) genes and metabolites. As such, these models are suitable for exploring metabolism in plants grown in favorable conditions, but not in field-grown plants that have to cope with environmental changes in complex ecosystems. AraGEM is the first GEM describing a photosynthetic and photorespiring plant cell (Arabidopsis thaliana). We demonstrate the use of AraGEM given the current (limited) knowledge of plant metabolism and reveal the unexpected robustness of AraGEM by a series of in silico simulations. The major focus of these simulations is on the assessment of the: (i) network connectivity; (ii) influence of CO₂ and photon uptake rates on cellular growth rates and production of individual biomass components; and (iii) stability of plant central carbon metabolism with internal pH changes.

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.

Deep EST profiling of developing fenugreek endosperm to investigate galactomannan biosynthesis and its regulation

Galactomannans are hemicellulosic polysaccharides composed of a (1 → 4)-linked β-D-mannan backbone substituted with single-unit (1 → 6)-α-linked D-galactosyl residues. Developing fenugreek (Trigonella foenum-graecum) seeds are known to accumulate large quantities of galactomannans in the endosperm, and were thus used here as a model system to better understand galactomannan biosynthesis and its regulation. We first verified the specific deposition of galactomannans in developing endosperms and determined that active accumulation occurred from 25 to 38 days post anthesis (DPA) under our growth conditions. We then examined the expression levels during seed development of ManS and GMGT, two genes encoding backbone and side chain synthetic enzymes. Based on transcript accumulation dynamics for ManS and GMGT, cDNA libraries were constructed using RNA isolated from endosperms at four ages corresponding to before, at the beginning of, and during active galactomannan deposition. DNA from these libraries was sequenced using the 454 sequencing technology to yield a total of 1.5 million expressed sequence tags (ESTs). Through analysis of the EST profiling data, we identified genes known to be involved in galactomannan biosynthesis, as well as new genes that may be involved in this process, and proposed a model for the flow of carbon from sucrose to galactomannans. Measurement of in vitro ManS and GMGT activities and analysis of sugar phosphate and nucleotide sugar levels in the endosperms of developing fenugreek seeds provided data consistent with this model. In vitro enzymatic assays also revealed that the ManS enzyme from fenugreek endosperm preferentially used GDP-mannose as the substrate for the backbone synthesis.

Controlling lipid accumulation in cereal grains

Plant oils have so far been mostly directed toward food and feed production. Nowadays however, these oils are more and more used as competitive alternatives to mineral hydrocarbon-based products. This increasing demand for vegetable oils has led to a renewed interest in elucidating the metabolism of storage lipids and its regulation in various plant systems. Cereal grains store carbon in the form of starch in a large endosperm and as oil in an embryo of limited size. Complementary studies on kernel development and metabolism have paved the way for breeding or engineering new varieties with higher grain oil content. This could be achieved either by increasing the relative proportion of the oil-rich embryo within the grain, or by enhancing oil synthesis and accumulation in embryonic structures. For instance, diacylglycerol acyltransferase (DGAT) that catalyses the ultimate reaction in the biosynthesis of triacylglycerol appears to be a promising target for increasing oil content in maize embryos. Similarly, over-expression of the maize transcriptional regulators ZmLEAFY COTYLEDON1 and ZmWRINKLED1 efficiently stimulates oil accumulation in the kernels of transgenic lines. Redirecting carbon from starch to oil in the endosperm, though not yet realized, is discussed.

Surveying the plant's world by magnetic resonance imaging

Understanding the way in which plants develop, grow and interact with their environment requires tools capable of a high degree of both spatial and temporal resolution. Magnetic resonance imaging (MRI), a technique which is able to visualize internal structures and metabolites, has the great virtue that it is non-invasive and therefore has the potential to monitor physiological processes occurring in vivo. The major aim of this review is to attract plant biologists to MRI by explaining its advantages and wide range of possible applications for solving outstanding issues in plant science. We discuss the challenges and opportunities of MRI in the study of plant physiology and development, plant-environment interactions, biodiversity, gene functions and metabolism. Overall, it is our view that the potential benefit of harnessing MRI for plant research purposes is hard to overrate.

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.

Cofactome analyses reveal enhanced flux of carbon into oil for potential biofuel production

To identify the underlying molecular basis of carbon partitioning between starch and oil we conducted 454 pyrosequencing, followed by custom microarrays to profile gene expression throughout endosperm development, of two closely related oat cultivars that differ in oil content at the expense of starch as determined by several approaches including non-invasive magnetic resonance imaging. Comparative transcriptome analysis in conjunction with metabolic profiling displays a close coordination between energy metabolism and carbon partitioning pathways, with increased demands for energy and reducing equivalents in kernels with a higher oil content. These studies further expand the repertoire of networks regulating carbon partitioning to those involved in metabolism of cofactors, suggesting that an elevated supply of cofactors, here called cofactomes, contribute to the allocation of higher carbon pools for production of oils and storage proteins. These data highlight a close association between cofactomes and carbon partitioning, thereby providing a biotechnological target for conversion of starch to oil.

Combined noninvasive imaging and modeling approaches reveal metabolic compartmentation in the barley endosperm

The starchy endosperm of cereals is a priori taken as a metabolically uniform tissue. By applying a noninvasive assay based on (13)C/(1)H-magnetic resonance imaging (MRI) to barley (Hordeum vulgare) grains, we uncovered metabolic compartmentation in the endosperm. (13)C-Suc feeding during grain filling showed that the primary site of Ala synthesis was the central region of the endosperm, the part of the caryopsis experiencing the highest level of hypoxia. Region-specific metabolism in the endosperm was characterized by flux balance analysis (FBA) and metabolite profiling. FBA predicts that in the central region of the endosperm, the tricarboxylic acid cycle shifts to a noncyclic mode, accompanied by elevated glycolytic flux and the accumulation of Ala. The metabolic compartmentation within the endosperm is advantageous for the grain's carbon and energy economy, with a prominent role being played by Ala aminotransferase. An investigation of caryopses with a genetically perturbed tissue pattern demonstrated that Ala accumulation is a consequence of oxygen status, rather than being either tissue specific or dependent on the supply of Suc. Hence the (13)C-Ala gradient can be used as an in vivo marker for hypoxia. The combination of MRI and metabolic modeling offers opportunities for the noninvasive analysis of metabolic compartmentation in plants.