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

Flux in the field: genome-scale modelling reveals changes in soybean (Glycine max) seed reserve metabolism under drought stress

Soybean (Glyine max) is particularly sensitive to drought stress during reproductive growth, which causes decreased productivity. Rates of reserve accumulation are altered, indicating perturbations in source-sink relations between different organs and in the metabolism of the developing seeds. Here we performed field experiments using controlled irrigation and in silico flux balance analysis to determine how drought alters the mobilisation of reserves in vegetative organs and their accumulation in the seed. Reduced irrigation resulted in stomatal closure and lower rates of photosynthesis and transpiration, together with earlier senescence in leaves and stems, characterized by decreases in starch, nitrogen and fatty acids. On the other hand, seed reserve accumulation, demonstrated by decreasing starch and increasing nitrogen, fatty acids and raffinose series oligosaccharides occurred earlier under stress. This shift in development, with a shortened period of grain filling, ultimately resulted in decreased productivity. The experimental data was used to constrain a genome-scale metabolic model of soybean, and flux balance analysis of seed metabolism predicted that whilst stress affected metabolism the relative distribution of fluxes showed a degree of robustness. Simulations performed with different rates of photon usage support multiple possible roles for seed photosynthesis in reserve accumulation. Overall drought leads to earlier senescence and a shorter window for seed filling that together with alterations in seed metabolism results in reduced productivity.

Multiscale Mathematical Modeling in Systems Biology: A Framework to Boost Plant Synthetic Biology

Global food insecurity and environmental degradation highlight the urgent need for more sustainable agricultural solutions. Plant synthetic biology emerges as a promising yet risky avenue to develop such solutions. While synthetic biology offers the potential for enhanced crop traits, it also entails risks of extensive environmental damage. This review highlights the complexities and risks associated with plant synthetic biology, while presenting the potential of multiscale mathematical modeling to assess and mitigate those risks effectively. Despite its potential, applying multiscale mathematical models in plants remains underutilized. Here, we advocate for integrating technological advancements in agricultural data analysis to develop a comprehensive understanding of crops across biological scales. By reviewing common modeling approaches and methodologies applicable to plants, the paper establishes a foundation for creating and utilizing integrated multiscale mathematical models. Through modeling techniques such as parameter estimation, bifurcation analysis, and sensitivity analysis, researchers can identify mutational targets and anticipate pleiotropic effects, thereby enhancing the safety of genetically engineered species. To demonstrate the potential of this approach, ongoing efforts are highlighted to develop an integrated multiscale mathematical model for maize (Zea mays L.), engineered through synthetic biology to enhance resilience against Striga (Striga spp.) and drought.

Metabolic modelling identifies mitochondrial Pi uptake and pyruvate efflux as key aspects of daytime metabolism and proton homeostasis in crassulacean acid metabolism leaves

Crassulacean acid metabolism (CAM) leaves are characterized by nocturnal acidification and diurnal deacidification processes related with the timed actions of phosphoenolpyruvate carboxylase and Rubisco, respectively. How CAM leaves manage cytosolic proton homeostasis, particularly when facing massive diurnal proton effluxes from the vacuole, remains unclear. A 12-phase flux balance analysis (FBA) model was constructed for a mature malic enzyme-type CAM mesophyll cell in order to predict diel kinetics of intracellular proton fluxes. The charge- and proton-balanced FBA model identified the mitochondrial phosphate carrier (PiC, Pi/H+ symport), which provides Pi to the matrix to sustain ATP biosynthesis, as a major consumer of cytosolic protons during daytime (> 50%). The delivery of Pi to the mitochondrion, co-transported with protons, is required for oxidative phosphorylation and allows sufficient ATP to be synthesized to meet the high energy demand during CAM Phase III. Additionally, the model predicts that mitochondrial pyruvate originating from decarboxylation of malate is exclusively exported to the cytosol, probably via a pyruvate channel mechanism, to fuel gluconeogenesis. In this biochemical cycle, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) acts as another important cytosolic proton consumer. Overall, our findings emphasize the importance of mitochondria in CAM and uncover a hitherto unappreciated role in metabolic proton homeostasis.

Modelling metabolic fluxes of tomato stems reveals that nitrogen shapes central metabolism for defence against Botrytis cinerea

Among plant pathogens, the necrotrophic fungus Botrytis cinerea is one of the most prevalent, leading to severe crop damage. Studies related to its colonization of different plant species have reported variable host metabolic responses to infection. In tomato, high N availability leads to decreased susceptibility. Metabolic flux analysis can be used as an integrated method to better understand which metabolic adaptations lead to effective host defence and resistance. Here, we investigated the metabolic response of tomato infected by B. cinerea in symptomless stem tissues proximal to the lesions for 7 d post-inoculation, using a reconstructed metabolic model constrained by a large and consistent metabolic dataset acquired under four different N supplies. An overall comparison of 48 flux solution vectors of Botrytis- and mock-inoculated plants showed that fluxes were higher in Botrytis-inoculated plants, and the difference increased with a reduction in available N, accompanying an unexpected increase in radial growth. Despite higher fluxes, such as those involved in cell wall synthesis and other pathways, fluxes related to glycolysis, the tricarboxylic acid cycle, and amino acid and protein synthesis were limited under very low N, which might explain the enhanced susceptibility. Limiting starch synthesis and enhancing fluxes towards redox and specialized metabolism also contributed to defence independent of N supply.

A role for fermentation in aerobic conditions as revealed by computational analysis of maize root metabolism during growth by cell elongation

The root is a well-studied example of cell specialisation, yet little is known about the metabolism that supports the transport functions and growth of different root cell types. To address this, we used computational modelling to study metabolism in the elongation zone of a maize lateral root. A functional-structural model captured the cell-anatomical features of the root and modelled how they changed as the root elongated. From these data, we derived constraints for a flux balance analysis model that predicted metabolic fluxes of the 11 concentric rings of cells in the root. We discovered a distinct metabolic flux pattern in the cortical cell rings, endodermis and pericycle (but absent in the epidermis) that involved a high rate of glycolysis and production of the fermentation end-products lactate and ethanol. This aerobic fermentation was confirmed experimentally by metabolite analysis. The use of fermentation in the model was not obligatory but was the most efficient way to meet the specific demands for energy, reducing power and carbon skeletons of expanding cells. Cytosolic acidification was avoided in the fermentative mode due to the substantial consumption of protons by lipid synthesis. These results expand our understanding of fermentative metabolism beyond that of hypoxic niches and suggest that fermentation could play an important role in the metabolism of aerobic tissues.

Model-driven insights into the effects of temperature on metabolism

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.

Proteomics and Lysine Acetylation Modification Reveal the Responses of Pakchoi (Brassica rapa L. ssp. chinensis) to Oxybenzone Stress

The broad-spectrum UV filter oxybenzone is toxic to plants at environmentally relevant concentrations. Lysine acetylation (LysAc) is one of the essential post-translational modifications (PTMs) in plant signaling responses. The goal of this study was to uncover the LysAc regulatory mechanism in response to toxic exposures to oxybenzone as a first step in elucidating xenobiotic acclimatory reactions by using the model Brassica rapa L. ssp. chinensis. A total of 6124 sites on 2497 proteins were acetylated, 63 proteins were differentially abundant, and 162 proteins were differentially acetylated under oxybenzone treatment. Bioinformatics analysis showed that a large number of antioxidant proteins were significantly acetylated under oxybenzone treatment, implying that LysAc alleviated the adverse effects of reactive oxygen species (ROS) by inducing antioxidant systems and stress-related proteins; the significant changes in acetylation modification of enzymes involved in different branches of carbon metabolism in plants under oxybenzone treatment mean that plants can change the direction of carbon flow allocation by regulating the activities of carbon metabolism-related enzymes. Our results profile the protein LysAc under oxybenzone treatment and propose an adaptive mechanism at the post-translational level of vascular plants in response to pollutants, providing a dataset reference for future related research.

Antioxidant Green Factories: Toward Sustainable Production of Vitamin E in Plant In Vitro Cultures

Vitamin E is a dietary supplement synthesized only by photosynthetic organisms and, hence, is an essential vitamin for human well-being. Because of the ever-increasing demand for natural vitamin E and limitations in existing synthesis modes, attempts to improve its yield using plant in vitro cultures have gained traction in recent years. With inflating industrial production costs, integrative approaches to conventional bioprocess optimization is the need of the hour for multifold vitamin E productivity enhancement. In this review, we briefly discuss the structure, isomers, and important metabolic routes of biosynthesis for vitamin E in plants. We then emphasize its vital role in human health and its industrial applications and highlight the market demand and supply. We illustrate the advantages of in vitro plant cell/tissue culture cultivation as an alternative to current commercial production platforms for natural vitamin E. We touch upon the conventional vitamin E metabolic pathway engineering strategies, such as single/multigene overexpression and chloroplast engineering. We highlight the recent progress in plant systems biology to rationally identify metabolic bottlenecks and knockout targets in the vitamin E biosynthetic pathway. We then discuss bioprocess optimization strategies for sustainable vitamin E production, including media/process optimization, precursor/elicitor addition, and scale-up to bioreactors. We culminate the review with a short discussion on kinetic modeling to predict vitamin E production in plant cell cultures and suggestions on sustainable green extraction methods of vitamin E for reduced environmental impact. This review will be of interest to a wider research fraternity, including those from industry and academia working in the field of plant cell biology, plant biotechnology, and bioprocess engineering for phytochemical enhancement.

Isotopically non-stationary metabolic flux analysis of heterotrophic Arabidopsis thaliana cell cultures

Fluxes are the ultimate phenotype of metabolism and their accurate quantification is fundamental to any understanding of metabolic networks. Steady state metabolic flux analysis has been the method of choice for quantifying fluxes in heterotrophic cells, but it is unable to measure fluxes during short-lived metabolic states, such as a transient oxidative load. Isotopically non-stationary metabolic flux analysis (INST-MFA) can be performed over shorter timescales (minutes - hours) and might overcome this limitation. INST-MFA has recently been applied to photosynthesising leaves, but agriculturally important tissues such as roots and storage organs, or plants during the night are heterotrophic. Here we outline the application of INST-MFA to heterotrophic plant cells. Using INST-MFA we were able to identify changes in the fluxes supported by phosphoenolpyruvate carboxylase and malic enzyme under oxidative load, highlighting the potential of INST-MFA to measure fluxes during short-lived metabolic states. We discuss the challenges in applying INST-MFA, and highlight further development required before it can be routinely used to quantify fluxes in heterotrophic plant cells.

Dissection of Paenibacillus polymyxa NSY50-Induced Defense in Cucumber Roots against Fusarium oxysporum f. sp. cucumerinum by Target Metabolite Profiling

Simple Summary

Plant growth-promoting rhizobacteria (PGPR) have significant potential to enhance the tolerance of biotic and abiotic stresses and the productivity of crops. However, the mechanism of PGPR in improving plant resistance to pathogens is unclear. Recently, the newly isolated Paenibacillus polymyxa strain NSY50 was shown to considerably suppress the Fusarium wilt of cucumber plants. This study was carried out to explore the underlying mechanism of NSY50 in improving plant resistance to pathogen invasion via target metabolite profiling, and the results indicated that strain NSY50 was able to alleviate Fusarium wilt stress by activating GSH metabolism and improving redox balance. Our research findings enable a deeper understanding of P. polymyxa NSY50-induced enhanced defense against F. oxysporum in cucumber.

Abstract

To gain insights into the roles of beneficial PGPR in controlling soil-borne disease, we adopted a metabolomics approach to investigate the beneficial impacts of P. polymyxa NSY50 on cucumber seedling roots under the pathogen of Fusarium oxysporum f. sp. cucumerinum (FOC). We found that NSY50 pretreatment (NSY50 + FOC) obviously reduced the production of reactive oxygen species (ROS). Untargeted metabolomic analysis revealed that 106 metabolites responded to NSY50 and/or FOC inoculation. Under FOC stress, the contents of root osmotic adjustment substances, such as proline and betaine were significantly increased, and dehydroascorbic acid and oxidized glutathione (GSH) considerably accumulated. Furthermore, the contents of free amino acids such as tryptophan, phenylalanine, and glutamic acid were also significantly accumulated under FOC stress. Similarly, FOC stress adversely affected glycolysis and the tricarboxylic acid cycles and transferred to the pentose phosphate pathway. Conversely, NSY50 + FOC better promoted the accumulation of α-ketoglutaric acid, ribulose-5-phosphate, and 7-phosphosodiheptanone compared to FOC alone. Furthermore, NSY50 + FOC activated GSH metabolism and increased GSH synthesis and metabolism-related enzyme activity and their encoding gene expressions, which may have improved redox homoeostasis, energy flow, and defense ability. Our results provide a novel perspective to understanding the function of P. polymyxa NSY50, accelerating the application of this beneficial PGPR in sustainable agricultural practices.

Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

Genome-scale metabolic models (GEMs) have enabled researchers to perform systems-level studies of living organisms. Flux balance analysis (FBA), as a constraint-based technique, enables computation of reaction fluxes and prediction of the metabolic phenotypes of a cell under a set of specified conditions. The quality of a GEM is important for obtaining accurate predictions. In this study, we evaluated the quality of five available GEMs for Arabidopsis thaliana from various points of views. To do this, we inspected some of their important features, including the number of reactions with well-defined gene-protein-reaction rules, number of blocked reactions, mass-unbalanced reactions, prediction accuracy in the simulation of key metabolic functions and existence of erroneous energy generating cycles (EGCs). All of the models were found to include some mass-unbalanced reactions. Moreover, four out of five models were found to include EGCs. However, Aracell includes the maximum number of blocked reactions, which suggests the presence of several incomplete pathways. These results clearly show that simulation by using these models may result in erroneous predictions and all of the publicly available GEMs for A. thaliana require extensive curations before being applied in practice.

Assessing Protein Synthesis and Degradation Rates in Arabidopsis thaliana Using Amino Acid Analysis

Plants continually synthesize and degrade proteins, for example, to adjust protein content during development or during adaptation to new environments. In order to estimate global protein synthesis and degradation rates in plants, we developed a relatively simple and inexpensive method using a combination of ¹³ CO₂ labeling and mass spectrometry-based analyses. Arabidopsis thaliana plants are subjected to a 24-hr ¹³ CO₂ pulse followed by a 4-day ¹² CO₂ chase. Soluble alanine and serine from total protein and glucose from cell wall material are analyzed by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) and their ¹³ C enrichment (%) is estimated. The rate of protein synthesis during the ¹³ CO₂ pulse experiment is defined as the rate of incorporation of labeled amino acids into proteins normalized by a correction factor for incomplete enrichment in free amino acid pools. The rate of protein degradation is estimated as the difference between the rate of protein synthesis and the relative growth rate calculated using the ¹³ C enrichment of glucose from cell wall material. Degradation rates are also estimated from the ¹² CO₂ pulse experiment. The following method description includes setting up and performing labeling experiments, preparation and measurement of samples, and calculation steps. In addition, an R script is provided for the calculations. 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Setting up the ¹³ CO₂ labeling system and stable isotope labeling of Arabidopsis thaliana rosette leaves Basic Protocol 2: Extraction of soluble amino acids for GC-TOF-MS analysis Basic Protocol 3: Preparation of amino acids from total protein for GC-TOF-MS analysis Basic Protocol 4: Preparation of sugars from cell wall material for GC-TOF-MS analysis Basis Protocol 5: GC-TOF-MS analysis of ¹³ C-labeled samples and estimation of ¹³ C enrichment (%) Basis Protocol 6: Estimation of protein synthesis and degradation rates.

iTRAQ-based quantitative proteomic analysis of heat stress-induced mechanisms in pepper seedlings

Background

As one of the most important vegetable crops, pepper has rich nutritional value and high economic value. Increasing heat stress due to the global warming has a negative impact on the growth and yield of pepper.

Methods

To understand the heat stress response mechanism of pepper, an iTRAQ-based quantitative proteomic analysis was employed to identify possible heat-responsive proteins and metabolic pathways in 17CL30 and 05S180 pepper seedlings under heat stress.

Result

In the present study, we investigated the changes of phenotype, physiology, and proteome in heat-tolerant (17CL30) and heat-sensitive (05S180) pepper cultivars in response to heat stress. Phenotypic and physiological changes showed that 17CL30 had a stronger ability to resist heat stress compared with 05S180. In proteomic analysis, a total of 3,874 proteins were identified, and 1,591 proteins were considered to participate in the process of heat stress response. According to bioinformatic analysis of heat-responsive proteins, the heat tolerance of 17CL30 might be related to a higher ROS scavenging, photosynthesis, signal transduction, carbohydrate metabolism, and stress defense, compared with 05S180.

Multiscale plant modeling: from genome to phenome and beyond

Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.

Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava

The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and ¹³C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between ¹³C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.

Biotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

Biotechnological strategies for enhancing heavy metal tolerance in neglected and underutilized legume crops: A comprehensive review

Contamination of agricultural land and water by heavy metals due to rapid industrialization and urbanization including various natural processes have become one of the major constraints to crop growth and productivity. Several studies have reported that to counteract heavy metal stress, plants should be able to maneuver various physiological, biochemical and molecular processes to improve their growth and development under heavy metal stress. With the advent of modern biotechnological tools and techniques it is now possible to tailor legume and other plants overexpressing stress-induced genes, transcription factors, proteins, and metabolites that are directly involved in heavy metal stress tolerance. This review provides an in-depth overview of various biotechnological approaches and/or strategies that can be used for enhancing detoxification of the heavy metals by stimulating phytoremediation processes. Synthetic biology tools involved in the engineering of legume and other crop plants against heavy metal stress tolerance are also discussed herewith some pioneering examples where synthetic biology tools that have been used to modify plants for specific traits. Also, CRISPR based genetic engineering of plants, including their role in modulating the expression of several genes/ transcription factors in the improvement of abiotic stress tolerance and phytoremediation ability using knockdown and knockout strategies has also been critically discussed.

Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways

Efficient approaches to increase plant lipid production are necessary to meet current industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires an understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom-up metabolic reconstruction of J. curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint-based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping stone for future rational optimization of other agronomically relevant traits in J. curcas.

Flux balance analysis of metabolism during growth by osmotic cell expansion and its application to tomato fruits

Cell expansion is a significant contributor to organ growth and is driven by the accumulation of osmolytes to increase cell turgor pressure. Metabolic modelling has the potential to provide insights into the processes that underpin osmolyte synthesis and transport, but the main computational approach for predicting metabolic network fluxes, flux balance analysis, often uses biomass composition as the main output constraint and ignores potential changes in cell volume. Here we present growth-by-osmotic-expansion flux balance analysis (GrOE-FBA), a framework that accounts for both the metabolic and ionic contributions to the osmotica that drive cell expansion, as well as the synthesis of protein, cell wall and cell membrane components required for cell enlargement. Using GrOE-FBA, the metabolic fluxes in dividing and expanding cells were analysed, and the energetic costs for metabolite biosynthesis and accumulation in the two scenarios were found to be surprisingly similar. The expansion phase of tomato fruit growth was also modelled using a multiphase single-optimization GrOE-FBA model and this approach gave accurate predictions of the major metabolite levels throughout fruit development, as well as revealing a role for transitory starch accumulation in ensuring optimal fruit development.

Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications

The polyextremotolerant black yeast Exophiala dermatitidis is a tractable model system for investigation of adaptations that support growth under extreme conditions. Foremost among these adaptations are melanogenesis and carotenogenesis. A particularly important question is their metabolic production cost. However, investigation of this issue has been hindered by a relatively poor systems-level understanding of E. dermatitidis metabolism. To address this challenge, a genome-scale model (iEde2091) was developed. Using iEde2091, carotenoids were found to be more expensive to produce than melanins. Given their overlapping protective functions, this suggests that carotenoids have an underexplored yet important role in photo-protection. Furthermore, multiple defensive pigments with overlapping functions might allow E. dermatitidis to minimize cost. Because iEde2091 revealed that E. dermatitidis synthesizes the same melanins as humans and the active sites of the key tyrosinase enzyme are highly conserved this model may enable a broader understanding of melanin production across kingdoms.

Computational systems biology of cellular processes in Arabidopsis thaliana: an overview

Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation and computational modeling. While there is already a large quantity of systems biology studies describing human, animal and especially microbial cell biological systems, plant biology has been lagging behind for many years. However, in the case of the model plant Arabidopsis thaliana, the steadily increasing amount of information on the levels of its genome, proteome and on a variety of its metabolic and signalling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated, showing also for plant sciences how fruitful systems biology research can be. In this review, we provide an overview over some of these recent studies which use different systems biological approaches to get a better understanding of the cell biology of A. thaliana. The approaches used in these are genome-scale metabolic modeling, as well as kinetic modeling of metabolic and signalling pathways. Furthermore, we selected several cases to exemplify how the modeling approaches have led to significant advances or new perspectives in the field.

All roads lead to growth: imaging-based and biochemical methods to measure plant growth

Plant growth is a highly complex biological process that involves innumerable interconnected biochemical and signalling pathways. Many different techniques have been developed to measure growth, unravel the various processes that contribute to plant growth, and understand how a complex interaction between genotype and environment determines the growth phenotype. Despite this complexity, the term 'growth' is often simplified by researchers; depending on the method used for quantification, growth is viewed as an increase in plant or organ size, a change in cell architecture, or an increase in structural biomass. In this review, we summarise the cellular and molecular mechanisms underlying plant growth, highlight state-of-the-art imaging and non-imaging-based techniques to quantitatively measure growth, including a discussion of their advantages and drawbacks, and suggest a terminology for growth rates depending on the type of technique used.

Jasmonates-Mediated Rewiring of Central Metabolism Regulates Adaptive Responses

The lipid-derived hormones jasmonates (JAs) play key functions in a wide range of physiological and developmental processes that regulate growth, secondary metabolism and defense against biotic and abiotic stresses. In this connection, biosynthesis, tissue-specific distribution, metabolism, perception, signaling of JAs have been the target of extensive studies. In recent years, the involvement of JAs signaling pathway in the regulation of growth and adaptive responses to environmental challenges has been further examined. However, JAs-mediated mechanisms underlying the transition from 'growth mode' to 'adaptive mode' remain ambiguous. Combined analysis of transgenic lines deficient in JAs signaling in conjunction with the data from JAs-treated plants revealed the function of these hormones in rewiring of central metabolism. The collective data illustrate JAs-mediated decrease in the levels of metabolites associated with active growth such as sucrose, raffinose, orotate, citrate, malate, and an increase in phosphorylated hexoses, responsible for the suppression of growth and photosynthesis, concurrent with the induction of protective metabolites, such as aromatic and branched-chain amino acids, and aspartate family of metabolites. This finding provides an insight into the function of JAs in shifting the central metabolism from the production of growth-promoting metabolites to protective compounds and expands our understanding of the role of JAs in resource allocation in response to environmental challenges.

A Genome-Scale Metabolic Model of Soybean (Glycine max) Highlights Metabolic Fluxes in Seedlings

Until they become photoautotrophic juvenile plants, seedlings depend upon the reserves stored in seed tissues. These reserves must be mobilized and metabolized, and their breakdown products must be distributed to the different organs of the growing seedling. Here, we investigated the mobilization of soybean (Glycine max) seed reserves during seedling growth by initially constructing a genome-scale stoichiometric model for this important crop plant and then adapting the model to reflect metabolism in the cotyledons and hypocotyl/root axis (HRA). A detailed analysis of seedling growth and alterations in biomass composition was performed over 4 d of postgerminative growth and used to constrain the stoichiometric model. Flux balance analysis revealed marked differences in metabolism between the two organs, together with shifts in primary metabolism occurring during different periods postgermination. In particular, from 48 h onward, cotyledons were characterized by the oxidation of fatty acids to supply carbon for the tricarboxylic acid cycle as well as production of sucrose and glutamate for export to the HRA, while the HRA was characterized by the use of a range of imported amino acids in protein synthesis and catabolic processes. Overall, the use of flux balance modeling provided new insight into well-characterized metabolic processes in an important crop plant due to their analysis within the context of a metabolic network and reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.

Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light

Key aspects of leaf mitochondrial metabolism in the light remain unresolved. For example, there is debate about the relative importance of exporting reducing equivalents from mitochondria for the peroxisomal steps of photorespiration versus oxidation of NADH to generate ATP by oxidative phosphorylation. Here, we address this and explore energetic coupling between organelles in the light using a diel flux balance analysis model. The model included more than 600 reactions of central metabolism with full stoichiometric accounting of energy production and consumption. Different scenarios of energy availability (light intensity) and demand (source leaf versus a growing leaf) were considered, and the model was constrained by the nonlinear relationship between light and CO2 assimilation rate. The analysis demonstrated that the chloroplast can theoretically generate sufficient ATP to satisfy the energy requirements of the rest of the cell in addition to its own. However, this requires unrealistic high light use efficiency and, in practice, the availability of chloroplast-derived ATP is limited by chloroplast energy dissipation systems, such as nonphotochemical quenching, and the capacity of the chloroplast ATP export shuttles. Given these limitations, substantial mitochondrial ATP synthesis is required to fulfill cytosolic ATP requirements, with only minimal, or zero, export of mitochondrial reducing equivalents. The analysis also revealed the importance of exporting reducing equivalents from chloroplasts to sustain photorespiration. Hence, the chloroplast malate valve and triose phosphate-3-phosphoglycerate shuttle are predicted to have important metabolic roles, in addition to their more commonly discussed contribution to the avoidance of photooxidative stress.

Shoot tip culture: a step towards 13C metabolite flux analysis of sink leaf metabolism

BACKGROUND

Better understanding of the physiological and metabolic status of plants can only be obtained when metabolic fluxes are accurately assessed in a growing plant. Steady state ¹³C-MFA has been established as a routine method for analysis of fluxes in plant primary metabolism. However, the experimental system needs to be improved for continuous carbon enrichment from labelled sugars into metabolites for longer periods until complex secondary metabolism reaches steady state.

RESULTS

We developed an in vitro plant culture strategy by using peppermint as a model plant with minimizing unlabelled carbon fixation where growing shoot tip was strongly dependent on labelled glucose for their carbon necessity. We optimized the light condition and detected the satisfactory phenotypical growth under the lower light intensity. Total volatile terpenes were also highest at the same light. Analysis of label incorporation into pulegone monoterpene after continuous U-¹³C₆ glucose feeding revealed nearly 100% ¹³C, even at 15 days after first leaf visibility (DALV). Label enrichment was gradually scrambled with increasing light intensity and leaf age. This study was validated by showing similar levels of label enrichment in proteinogenic amino acids. The efficiency of this method was also verified in oregano.

CONCLUSIONS

Our shoot tip culture depicted a method in achieving long term, stable and a high percentage of label accumulation in secondary metabolites within a fully functional growing plant system. It recommends the potential application for the investigations of various facets of plant metabolism by steady state ¹³C-MFA. The system also provides a greater potential to study sink leaf metabolism. Overall, we propose a system to accurately describe complex metabolic phenotypes in a growing plant.

PlantSEED enables automated annotation and reconstruction of plant primary metabolism with improved compartmentalization and comparative consistency

Genome-scale metabolic reconstructions help us to understand and engineer metabolism. Next-generation sequencing technologies are delivering genomes and transcriptomes for an ever-widening range of plants. While such omic data can, in principle, be used to compare metabolic reconstructions in different species, organs and environmental conditions, these comparisons require a standardized framework for the reconstruction of metabolic networks from transcript data. We previously introduced PlantSEED as a framework covering primary metabolism for 10 species. We have now expanded PlantSEED to include 39 species and provide tools that enable automated annotation and metabolic reconstruction from transcriptome data. The algorithm for automated annotation in PlantSEED propagates annotations using a set of signature k-mers (short amino acid sequences characteristic of particular proteins) that identify metabolic enzymes with an accuracy of about 97%. PlantSEED reconstructions are built from a curated template that includes consistent compartmentalization for more than 100 primary metabolic subsystems. Together, the annotation and reconstruction algorithms produce reconstructions without gaps and with more accurate compartmentalization than existing resources. These tools are available via the PlantSEED web interface at http://modelseed.org, which enables users to upload, annotate and reconstruct from private transcript data and simulate metabolic activity under various conditions using flux balance analysis. We demonstrate the ability to compare these metabolic reconstructions with a case study involving growth on several nitrogen sources in roots of four species.

The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling

Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.

From reconstruction to C4 metabolic engineering: A case study for overproduction of polyhydroxybutyrate in bioenergy grasses

The compartmentalization of C₄ plants increases photosynthetic efficiency, while constraining how material and energy must flow in leaf tissues. To capture this metabolic phenomenon, a generic plant metabolic reconstruction was replicated into four connected spatiotemporal compartments, namely bundle sheath (B) and mesophyll (M) across the day and night cycle. The C₄ leaf model was used to explore how amenable polyhydroxybutyrate (PHB) production is with these four compartments working cooperatively. A strategic pattern of metabolite conversion and exchange emerged from a systems-level network that has very few constraints imposed; mainly the sequential two-step carbon capture in mesophyll, then bundle sheath and photosynthesis during the day only. The building of starch reserves during the day and their mobilization during the night connects day and night metabolism. Flux simulations revealed that PHB production did not require rerouting of metabolic pathways beyond what is already utilised for growth. PHB yield was sensitive to photoassimilation capacity, availability of carbon reserves, ATP maintenance, relative photosynthetic activity of B and M, and type of metabolites exchanged in the plasmodesmata, but not sensitive towards compartmentalization. Hence, the compartmentalization issues currently encountered are likely to be kinetic or thermodynamic limitations rather than stoichiometric.

Assessing Flux Distribution Associated with Metabolic Specialization of Glandular Trichomes

Many aromatic plants accumulate mixtures of secondary (or specialized) metabolites in anatomical structures called glandular trichomes (GTs). Different GT types may also synthesize different mixtures of secreted metabolites, and this contributes to the enormous chemical diversity reported to occur across species. Over the past two decades, significant progress has been made in characterizing the genes and enzymes that are responsible for the unique metabolic capabilities of GTs in different lineages of flowering plants. Less is known about the processes that regulate flux distribution through precursor pathways toward metabolic end-products. We discuss here the results from a meta-analysis of genome-scale models that were developed to capture the unique metabolic capabilities of different GT types.

Gas Chromatography-Mass Spectrometry-Based 13C-Labeling Studies in Plant Metabolomics

Stable-isotope labeling analysis has been used to discover new metabolic pathways and their key regulatory points in a wide range of organisms. Given the complexity of the plant metabolic network, this analysis provides information complementary to that obtained from metabolite profiling that can be used to understand how plants cope with adverse conditions, and how metabolism varies between different cells, tissues, and organs. Here we describe the experimental procedures from sample harvesting and extraction to mass spectral analysis and interpretation that allow the researcher to perform ¹³C-labeling experiments. A wide range of plant material, from single cells to whole plants, can be used to investigate the metabolic fate of the ¹³C from a predefined tracer. Thus, a key point of this analysis is to choose the correct biological system, the substrate and the condition to be investigated; all of which implicitly relies on the biological question to be investigated. Rapid sample quenching and a careful data analysis are also critical points in such studies. By contrast to other metabolomic approaches, stable-isotope labeling can provide information concerning the fluxes through metabolic networks, which is essential for understanding and manipulating metabolic phenotypes and therefore of pivotal importance for both systems biology and plant metabolic engineering.

Cell-Type Specific Metabolic Flux Analysis: A Challenge for Metabolic Phenotyping and a Potential Solution in Plants

Stable isotope labelling experiments are used routinely in metabolic flux analysis (MFA) to determine the metabolic phenotype of cells and tissues. A complication arises in multicellular systems because single cell measurements of transcriptomes, proteomes and metabolomes in multicellular organisms suggest that the metabolic phenotype will differ between cell types. In silico analysis of simulated metabolite isotopomer datasets shows that cellular heterogeneity confounds conventional MFA because labelling data averaged over multiple cell types does not necessarily yield averaged flux values. A potential solution to this problem—the use of cell-type specific reporter proteins as a source of cell-type specific labelling data—is proposed and the practicality of implementing this strategy in the roots of Arabidopsis thaliana seedlings is explored. A protocol for the immunopurification of ectopically expressed green fluorescent protein (GFP) from Arabidopsis thaliana seedlings using a GFP-binding nanobody is developed, and through GC-MS analysis of protein hydrolysates it is established that constitutively expressed GFP reports accurately on the labelling of total protein in root tissues. It is also demonstrated that the constitutive expression of GFP does not perturb metabolism. The principal obstacle to the implementation of the method in tissues with cell-type specific GFP expression is the sensitivity of the GC-MS system.

Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses

In this review, we present the latest developments in plant systems biology with particular emphasis on plant genome-scale reconstructions and multi-omics analyses. Understanding multicellular metabolism is far from trivial and 'omics' data are difficult to interpret in the absence of a systems framework. 'Omics' data appropriately integrated with genome-scale reconstructions and modelling facilitates our understanding of how individual components interact and influence overall cell, tissue or organisms function. Here we present examples of how plant metabolic reconstructions and modelling are used as a systems-based framework for improving our understanding of the plant metabolic processes in single cells and multiple tissues.

Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii

Despite the wealth of knowledge available for C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, ¹³C-metabolic flux analysis (¹³C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments (cytosol, mitochondria and plastid) included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with ¹³C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.

Protein-protein interactions and metabolite channelling in the plant tricarboxylic acid cycle

Protein complexes of sequential metabolic enzymes, often termed metabolons, may permit direct channelling of metabolites between the enzymes, providing increased control over metabolic pathway fluxes. Experimental evidence supporting their existence in vivo remains fragmentary. In the present study, we test binary interactions of the proteins constituting the plant tricarboxylic acid (TCA) cycle. We integrate (semi-)quantitative results from affinity purification-mass spectrometry, split-luciferase and yeast-two-hybrid assays to generate a single reliability score for assessing protein–protein interactions. By this approach, we identify 158 interactions including those between catalytic subunits of sequential enzymes and between subunits of enzymes mediating non-adjacent reactions. We reveal channelling of citrate and fumarate in isolated potato mitochondria by isotope dilution experiments. These results provide evidence for a functional TCA cycle metabolon in plants, which we discuss in the context of contemporary understanding of this pathway in other kingdoms.

A metabolon is a complex of sequential metabolic enzymes that channels substrates directly between enzymes, thus optimizing metabolic flux. Here Zhang et al. provide protein interaction and isotope dilution data that support the existence of a metabolon that channels both citrate and fumarate in the plant TCA cycle.

Engineering central metabolism - a grand challenge for plant biologists

The goal of increasing crop productivity and nutrient-use efficiency is being addressed by a number of ambitious research projects seeking to re-engineer photosynthetic biochemistry. Many of these projects will require the engineering of substantial changes in fluxes of central metabolism. However, as has been amply demonstrated in simpler systems such as microbes, central metabolism is extremely difficult to rationally engineer. This is because of multiple layers of regulation that operate to maintain metabolic steady state and because of the highly connected nature of central metabolism. In this review we discuss new approaches for metabolic engineering that have the potential to address these problems and dramatically improve the success with which we can rationally engineer central metabolism in plants. In particular, we advocate the adoption of an iterative 'design-build-test-learn' cycle using fast-to-transform model plants as test beds. This approach can be realised by coupling new molecular tools to incorporate multiple transgenes in nuclear and plastid genomes with computational modelling to design the engineering strategy and to understand the metabolic phenotype of the engineered organism. We also envisage that mutagenesis could be used to fine-tune the balance between the endogenous metabolic network and the introduced enzymes. Finally, we emphasise the importance of considering the plant as a whole system and not isolated organs: the greatest increase in crop productivity will be achieved if both source and sink metabolism are engineered.

GABA (γ-Aminobutyric Acid) Uptake Via the GABA Permease GabP Represses Virulence Gene Expression in Pseudomonas syringae pv. tomato DC3000

The nonprotein amino acid γ-aminobutyric acid (GABA) is the most abundant amino acid in the tomato (Solanum lycopersicum) leaf apoplast and is synthesized by Arabidopsis thaliana in response to infection by the bacterial pathogen Pseudomonas syringae pv. tomato DC3000 (hereafter called DC3000). High levels of exogenous GABA have previously been shown to repress the expression of the type III secretion system (T3SS) in DC3000, resulting in reduced elicitation of the hypersensitive response (HR) in the nonhost plant tobacco (Nicotiana tabacum). This study demonstrates that the GABA permease GabP provides the primary mechanism for GABA uptake by DC3000 and that the gabP deletion mutant ΔgabP is insensitive to GABA-mediated repression of T3SS expression. ΔgabP displayed an enhanced ability to elicit the HR in young tobacco leaves and in tobacco plants engineered to produce increased levels of GABA, which supports the hypothesis that GABA uptake via GabP acts to regulate T3SS expression in planta. The observation that P. syringae can be rendered insensitive to GABA through loss of gabP but that gabP is retained by this bacterium suggests that GabP is important for DC3000 in a natural setting, either for nutrition or as a mechanism for regulating gene expression. [Formula: see text] Copyright © 2016 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Pseudomonas aeruginosa is a metabolically versatile wide-ranging opportunistic pathogen. In humans P. aeruginosa causes infections of the skin, urinary tract, blood, and the lungs of Cystic Fibrosis patients. In addition, P. aeruginosa's broad environmental distribution, relatedness to biotechnologically useful species, and ability to form biofilms have made it the focus of considerable interest. We used ¹³C metabolic flux analysis (MFA) and flux balance analysis to understand energy and redox production and consumption and to explore the metabolic phenotypes of one reference strain and five strains isolated from the lungs of cystic fibrosis patients. Our results highlight the importance of the oxidative pentose phosphate and Entner-Doudoroff pathways in P. aeruginosa growth. Among clinical strains we report two divergent metabolic strategies and identify changes between genetically related strains that have emerged during a chronic infection of the same patient. MFA revealed that the magnitude of fluxes through the glyoxylate cycle correlates with growth rates.

Perspectives for a better understanding of the metabolic integration of photorespiration within a complex plant primary metabolism network

Photorespiration is an essential high flux metabolic pathway that is found in all oxygen-producing photosynthetic organisms. It is often viewed as a closed metabolic repair pathway that serves to detoxify 2-phosphoglycolic acid and to recycle carbon to fuel the Calvin-Benson cycle. However, this view is too simplistic since the photorespiratory cycle is known to interact with several primary metabolic pathways, including photosynthesis, nitrate assimilation, amino acid metabolism, C1 metabolism and the Krebs (TCA) cycle. Here we will review recent advances in photorespiration research and discuss future priorities to better understand (i) the metabolic integration of the photorespiratory cycle within the complex network of plant primary metabolism and (ii) the importance of photorespiration in response to abiotic and biotic stresses.

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.

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.

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.

Using Genome-scale Models to Predict Biological Capabilities

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.

Quantifying protein synthesis and degradation in Arabidopsis by dynamic 13CO2 labeling and analysis of enrichment in individual amino acids in their free pools and in protein

Protein synthesis and degradation represent substantial costs during plant growth. To obtain a quantitative measure of the rate of protein synthesis and degradation, we supplied (13)CO2 to intact Arabidopsis (Arabidopsis thaliana) Columbia-0 plants and analyzed enrichment in free amino acids and in amino acid residues in protein during a 24-h pulse and 4-d chase. While many free amino acids labeled slowly and incompletely, alanine showed a rapid rise in enrichment in the pulse and a decrease in the chase. Enrichment in free alanine was used to correct enrichment in alanine residues in protein and calculate the rate of protein synthesis. The latter was compared with the relative growth rate to estimate the rate of protein degradation. The relative growth rate was estimated from sequential determination of fresh weight, sequential images of rosette area, and labeling of glucose in the cell wall. In an 8-h photoperiod, protein synthesis and cell wall synthesis were 3-fold faster in the day than at night, protein degradation was slow (3%-4% d(-1)), and flux to growth and degradation resulted in a protein half-life of 3.5 d. In the starchless phosphoglucomutase mutant at night, protein synthesis was further decreased and protein degradation increased, while cell wall synthesis was totally inhibited, quantitatively accounting for the inhibition of growth in this mutant. We also investigated the rates of protein synthesis and degradation during leaf development, during growth at high temperature, and compared synthesis rates of Rubisco large and small subunits of in the light and dark.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

Metabolic engineering of higher plants and algae for isoprenoid production

Isoprenoids are a class of compounds derived from the five carbon precursors, dimethylallyl diphosphate, and isopentenyl diphosphate. These molecules present incredible natural chemical diversity, which can be valuable for humans in many aspects such as cosmetics, agriculture, and medicine. However, many terpenoids are only produced in small quantities by their natural hosts and can be difficult to generate synthetically. Therefore, much interest and effort has been directed toward capturing the genetic blueprint for their biochemistry and engineering it into alternative hosts such as plants and algae. These autotrophic organisms are attractive when compared to traditional microbial platforms because of their ability to utilize atmospheric CO2 as a carbon substrate instead of supplied carbon sources like glucose. This chapter will summarize important techniques and strategies for engineering the accumulation of isoprenoid metabolites into higher plants and algae by choosing the correct host, avoiding endogenous regulatory mechanisms, and optimizing potential flux into the target compound. Future endeavors will build on these efforts by fine-tuning product accumulation levels via the vast amount of available "-omic" data and devising metabolic engineering schemes that integrate this into a whole-organism approach. With the development of high-throughput transformation protocols and synthetic biology molecular tools, we have only begun to harness the power and utility of plant and algae metabolic engineering.

Phenylalanine and tyrosine levels are rate-limiting factors in production of health promoting metabolites in Vitis vinifera cv. Gamay Red cell suspension

Environmental stresses such as high light intensity and temperature cause induction of the shikimate pathway, aromatic amino acids (AAA) pathways, and of pathways downstream from AAAs. The induction leads to production of specialized metabolites that protect the cells from oxidative damage. The regulation of the diverse AAA derived pathways is still not well understood. To gain insight on that regulation, we increased AAA production in red grape Vitis vinifera cv. Gamay Red cell suspension, without inducing external stress on the cells, and characterized the metabolic effect of this induction. Increased AAA production was achieved by expressing a feedback-insensitive bacterial form of 3-deoxy- D-arabino-heptulosonate 7-phosphate synthase enzyme (AroG (*)) of the shikimate pathway under a constitutive promoter. The presence of AroG(*) protein led to elevated levels of primary metabolites in the shikimate and AAA pathways including phenylalanine and tyrosine, and to a dramatic increase in phenylpropanoids. The AroG (*) transformed lines accumulated up to 20 and 150 fold higher levels of resveratrol and dihydroquercetin, respectively. Quercetin, formed from dihydroquercetin, and resveratrol, are health promoting metabolites that are induced due to environmental stresses. Testing the expression level of key genes along the stilbenoids, benzenoids, and phenylpropanoid pathways showed that transcription was not affected by AroG (*). This suggests that concentrations of AAAs, and of phenylalanine in particular, are rate-limiting in production of these metabolites. In contrast, increased phenylalanine production did not lead to elevated concentrations of anthocyanins, even though they are also phenylpropanoid metabolites. This suggests a control mechanism of this pathway that is independent of AAA concentration. Interestingly, total anthocyanin concentrations were slightly lower in AroG(*) cells, and the relative frequencies of the different anthocyanins changed as well.

Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit

Modelling of metabolic networks is a powerful tool to analyse the behaviour of developing plant organs, including fruits. Guided by our current understanding of heterotrophic metabolism of plant cells, a medium-scale stoichiometric model, including the balance of co-factors and energy, was constructed in order to describe metabolic shifts that occur through the nine sequential stages of Solanum lycopersicum (tomato) fruit development. The measured concentrations of the main biomass components and the accumulated metabolites in the pericarp, determined at each stage, were fitted in order to calculate, by derivation, the corresponding external fluxes. They were used as constraints to solve the model by minimizing the internal fluxes. The distribution of the calculated fluxes of central metabolism were then analysed and compared with known metabolic behaviours. For instance, the partition of the main metabolic pathways (glycolysis, pentose phosphate pathway, etc.) was relevant throughout fruit development. We also predicted a valid import of carbon and nitrogen by the fruit, as well as a consistent CO2 release. Interestingly, the energetic balance indicates that excess ATP is dissipated just before the onset of ripening, supporting the concept of the climacteric crisis. Finally, the apparent contradiction between calculated fluxes with low values compared with measured enzyme capacities suggest a complex reprogramming of the metabolic machinery during fruit development. With a powerful set of experimental data and an accurate definition of the metabolic system, this work provides important insight into the metabolic and physiological requirements of the developing tomato fruits.