Metabolic modelling revealed source-sink interactions between four segments of Setaria viridis leaves.

As in most plants, during their growth from immature to mature stages, the leaves of Setaria viridis, a model C4 bioenergy plant, have differential growth rates from the base (immature or growing) to the tip (most mature). In this study, we constructed a multi-segment C4 leaf metabolic model of S. viridis with two cell types (bundle sheath and mesophyll cells) across four leaf segments (base to tip). We incorporated differential growth rates for each leaf segment as constraints and integrated transcriptomic data as the objective function for our model simulation using flux balance analysis. The model was able to predict the exchanges of metabolites between immature and mature segments of the leaf and the distribution of the activities of biomass synthesis across those segments. Our model demonstrated the use of a modelling approach in studying the source-sink relationship within an organ and provided insights into the metabolic interactions across different parts of a leaf.

Spatial resolution of an integrated C4+CAM photosynthetic metabolism.

C4 and CAM photosynthesis have repeatedly evolved in plants over the past 30 million years. Because both repurpose the same set of enzymes but differ in their spatial and temporal deployment, they have long been considered as distinct and incompatible adaptations. Portulaca contains multiple C4 species that perform CAM when droughted. Spatially explicit analyses of gene expression reveal that C4 and CAM systems are completely integrated in Portulaca oleracea, with CAM and C4 carbon fixation occurring in the same cells and CAM-generated metabolites likely incorporated directly into the C4 cycle. Flux balance analysis corroborates the gene expression findings and predicts an integrated C4+CAM system under drought. This first spatially explicit description of a C4+CAM photosynthetic metabolism presents a potential new blueprint for crop improvement.

A multiphase flux balance model reveals flexibility of central carbon metabolism in guard cells of C3 plants.

Experimental research into guard cell metabolism has revealed the roles of the accumulation of various metabolites in guard cell function, but a comprehensive understanding of their metabolism over the diel cycle is still incomplete due to the limitations of current experimental methods. In this study we constructed a four-phase flux balance model of guard cell metabolism to investigate the changes in guard cell metabolism over the diel cycle, including the day and night and stomatal opening and closing. Our model predicted metabolic flexibility in guard cells of C3 plants, showing that multiple metabolic processes can contribute to the synthesis and metabolism of malate and sucrose as osmolytes during stomatal opening and closing. Our model showed the possibility of guard cells adapting to varying light availability and sucrose uptake from the apoplast during the day by operating in a mixotrophic mode with a switch between sucrose synthesis via the Calvin-Benson cycle and sucrose degradation via the oxidative pentose phosphate pathway. During stomatal opening, our model predicted an alternative flux mode of the Calvin-Benson cycle with all dephosphorylating steps diverted to diphosphate-fructose-6-phosphate 1-phosphotransferase to produce inorganic pyrophosphate, which is used to pump protons across the tonoplast for the accumulation of osmolytes. An analysis of the energetics of the use of different osmolytes in guard cells showed that malate and Cl- are similarly efficient as the counterion of K+ during stomatal opening.

Metabolic Modeling of the C3-CAM Continuum Revealed the Establishment of a Starch/Sugar-Malate Cycle in CAM Evolution.

The evolution of Crassulacean acid metabolism (CAM) is thought to be along a C3-CAM continuum including multiple variations of CAM such as CAM cycling and CAM idling. Here, we applied large-scale constraint-based modeling to investigate the metabolism and energetics of plants operating in C3, CAM, CAM cycling, and CAM idling. Our modeling results suggested that CAM cycling and CAM idling could be potential evolutionary intermediates in CAM evolution by establishing a starch/sugar-malate cycle. Our model analysis showed that by varying CO2 exchange during the light period, as a proxy of stomatal conductance, there exists a C3-CAM continuum with gradual metabolic changes, supporting the notion that evolution of CAM from C3 could occur solely through incremental changes in metabolic fluxes. Along the C3-CAM continuum, our model predicted changes in metabolic fluxes not only through the starch/sugar-malate cycle that is involved in CAM photosynthetic CO2 fixation but also other metabolic processes including the mitochondrial electron transport chain and the tricarboxylate acid cycle at night. These predictions could guide engineering efforts in introducing CAM into C3 crops for improved water use efficiency.

Multi-tissue to whole plant metabolic modelling.

Genome-scale metabolic models have been successfully applied to study the metabolism of multiple plant species in the past decade. While most existing genome-scale modelling studies have focussed on studying the metabolic behaviour of individual plant metabolic systems, there is an increasing focus on combining models of multiple tissues or organs to produce multi-tissue models that allow the investigation of metabolic interactions between tissues and organs. Multi-tissue metabolic models were constructed for multiple plants including Arabidopsis, barley, soybean and Setaria. These models were applied to study various aspects of plant physiology including the division of labour between organs, source and sink tissue relationship, growth of different tissues and organs and charge and proton balancing. In this review, we outline the process of constructing multi-tissue genome-scale metabolic models, discuss the strengths and challenges in using multi-tissue models, review the current status of plant multi-tissue and whole plant metabolic models and explore the approaches for integrating genome-scale metabolic models into multi-scale plant models.

A mass and charge balanced metabolic model of Setaria viridis revealed mechanisms of proton balancing in C4 plants.

BACKGROUND: C4 photosynthesis is a key domain of plant research with outcomes ranging from crop quality improvement, biofuel production and efficient use of water and nutrients. A metabolic network model of C4 "lab organism" Setaria viridis with extensive gene-reaction associations can accelerate target identification for desired metabolic manipulations and thereafter in vivo validation. Moreover, metabolic reconstructions have also been shown to be a significant tool to investigate fundamental metabolic traits. RESULTS: A mass and charge balance genome-scale metabolic model of Setaria viridis was constructed, which was tested to be able to produce all major biomass components in phototrophic and heterotrophic conditions. Our model predicted an important role of the utilization of NH[Formula: see text] and NO[Formula: see text] ratio in balancing charges in plants. A multi-tissue extension of the model representing C4 photosynthesis was able to utilize NADP-ME subtype of C4 carbon fixation for the production of lignocellulosic biomass in stem, providing a tool for identifying gene associations for cellulose, hemi-cellulose and lignin biosynthesis that could be potential target for improved lignocellulosic biomass production. Besides metabolic engineering, our modeling results uncovered a previously unrecognized role of the 3-PGA/triosephosphate shuttle in proton balancing. CONCLUSIONS: A mass and charge balance model of Setaria viridis, a model C4 plant, provides the possibility of system-level investigation to identify metabolic characteristics based on stoichiometric constraints. This study demonstrated the use of metabolic modeling in identifying genes associated with the synthesis of particular biomass components, and elucidating new role of previously known metabolic processes.

A Dynamic Multi-Tissue Flux Balance Model Captures Carbon and Nitrogen Metabolism and Optimal Resource Partitioning During Arabidopsis Growth.

Plant metabolism is highly adapted in response to its surrounding for acquiring limiting resources. In this study, a dynamic flux balance modeling framework with a multi-tissue (leaf and root) diel genome-scale metabolic model of Arabidopsis thaliana was developed and applied to investigate the reprogramming of plant metabolism through multiple growth stages under different nutrient availability. The framework allowed the modeling of optimal partitioning of resources and biomass in leaf and root over diel phases. A qualitative flux map of carbon and nitrogen metabolism was identified which was consistent across growth phases under both nitrogen rich and limiting conditions. Results from the model simulations suggested distinct metabolic roles in nitrogen metabolism played by enzymes with different cofactor specificities. Moreover, the dynamic model was used to predict the effect of physiological or environmental perturbation on the growth of Arabidopsis leaves and roots.

Computational analysis of the productivity potential of CAM.

There is considerable interest in transferring crassulacean acid metabolism (CAM) to C3 crops to improve their water-use efficiency. However, because the CAM biochemical cycle is energetically costly, it is unclear what impact this would have on yield. Using diel flux balance analysis of the CAM and C3 leaf metabolic networks, we show that energy consumption is three-fold higher in CAM at night. However, this additional cost of CAM can be entirely offset by the carbon-concentrating effect of malate decarboxylation behind closed stomata during the day. Depending on the resultant rates of the carboxylase and oxygenase activities of rubisco, the productivity of the PEPCK-CAM subtype is 74-100% of the C3 network. We conclude that CAM does not impose a significant productivity penalty and that engineering CAM into C3 crops is likely to lead to a major increase in water-use efficiency without substantially affecting yield.

Modeling Rice Metabolism: From Elucidating Environmental Effects on Cellular Phenotype to Guiding Crop Improvement.

Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently six such networks are available, where five are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.

Flux Balance Analysis of Plant Metabolism: The Effect of Biomass Composition and Model Structure on Model Predictions.

The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.

A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses.

Identification of candidate network hubs involved in metabolic adjustments of rice under drought stress by integrating transcriptome data and genome-scale metabolic network.

In this study, we have integrated a rice genome-scale metabolic network and the transcriptome of a drought-tolerant rice line, DK151, to identify the major transcriptional regulators involved in metabolic adjustments necessary for adaptation to drought. This was achieved by examining the differential expressions of transcription factors and metabolic genes in leaf, root and young panicle of rice plants subjected to drought stress during tillering, booting and panicle elongation stages. Critical transcription factors such as AP2/ERF, bZIP, MYB and NAC that control the important nodes in the gene regulatory pathway were identified through correlative analysis of the patterns of spatio-temporal expression and cis-element enrichment. We showed that many of the candidate transcription factors involved in metabolic adjustments were previously linked to phenotypic variation for drought tolerance. This approach represents the first attempt to integrate models of transcriptional regulation and metabolic pathways for the identification of candidate regulatory genes for targeted selection in rice breeding.

A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf.

Flux balance analysis of plant metabolism is an established method for predicting metabolic flux phenotypes and for exploring the way in which the plant metabolic network delivers specific outcomes in different cell types, tissues, and temporal phases. A recurring theme is the need to explore the flexibility of the network in meeting its objectives and, in particular, to establish the extent to which alternative pathways can contribute to achieving specific outcomes. Unfortunately, predictions from conventional flux balance analysis minimize the simultaneous operation of alternative pathways, but by introducing flux-weighting factors to allow for the variable intrinsic cost of supporting each flux, it is possible to activate different pathways in individual simulations and, thus, to explore alternative pathways by averaging thousands of simulations. This new method has been applied to a diel genome-scale model of Arabidopsis (Arabidopsis thaliana) leaf metabolism to explore the flexibility of the network in meeting the metabolic requirements of the leaf in the light. This identified alternative flux modes in the Calvin-Benson cycle revealed the potential for alternative transitory carbon stores in leaves and led to predictions about the light-dependent contribution of alternative electron flow pathways and futile cycles in energy rebalancing. Notable features of the analysis include the light-dependent tradeoff between the use of carbohydrates and four-carbon organic acids as transitory storage forms and the way in which multiple pathways for the consumption of ATP and NADPH can contribute to the balancing of the requirements of photosynthetic metabolism with the energy available from photon capture.

A Diel Flux Balance Model Captures Interactions between Light and Dark Metabolism during Day-Night Cycles in C3 and Crassulacean Acid Metabolism Leaves.

Although leaves have to accommodate markedly different metabolic flux patterns in the light and the dark, models of leaf metabolism based on flux-balance analysis (FBA) have so far been confined to consideration of the network under continuous light. An FBA framework is presented that solves the two phases of the diel cycle as a single optimization problem and, thus, provides a more representative model of leaf metabolism. The requirement to support continued export of sugar and amino acids from the leaf during the night and to meet overnight cellular maintenance costs forces the model to set aside stores of both carbon and nitrogen during the day. With only minimal constraints, the model successfully captures many of the known features of C3 leaf metabolism, including the recently discovered role of citrate synthesis and accumulation in the night as a precursor for the provision of carbon skeletons for amino acid synthesis during the day. The diel FBA model can be applied to other temporal separations, such as that which occurs in Crassulacean acid metabolism (CAM) photosynthesis, allowing a system-level analysis of the energetics of CAM. The diel model predicts that there is no overall energetic advantage to CAM, despite the potential for suppression of photorespiration through CO2 concentration. Moreover, any savings in enzyme machinery costs through suppression of photorespiration are likely to be offset by the higher flux demand of the CAM cycle. It is concluded that energetic or nitrogen use considerations are unlikely to be evolutionary drivers for CAM photosynthesis.

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.

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.