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

Leaf day respiration: More than just catabolic CO2 production in the light

Day respiration is a net flux resulting from several CO2‐generating and CO2‐fixing reactions, not only related to catabolism but also to anabolism. We review pieces of evidence that decarboxylating reactions are partly fed by carbon sources disconnected from current photosynthesis and how they reflect various metabolic pathways.

Resource allocation modeling for autonomous prediction of plant cell phenotypes

Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO2 and O2, and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.

From leaf to multiscale models of photosynthesis: applications and challenges for crop improvement

To keep up with the growth of human population and to circumvent deleterious effects of global climate change, it is essential to enhance crop yield to achieve higher production. Here we review mathematical models of oxygenic photosynthesis that are extensively used, and discuss in depth a subset that accounts for diverse approaches providing solutions to our objective. These include models (1) to study different ways to enhance photosynthesis, such as fine-tuning antenna size, photoprotection and electron transport; (2) to bioengineer carbon metabolism; and (3) to evaluate the interactions between the process of photosynthesis and the seasonal crop dynamics, or those that have included statistical whole-genome prediction methods to quantify the impact of photosynthesis traits on the improvement of crop yield. We conclude by emphasizing that the results obtained in these studies clearly demonstrate that mathematical modelling is a key tool to examine different approaches to improve photosynthesis for better productivity, while effective multiscale crop models, especially those that also include remote sensing data, are indispensable to verify different strategies to obtain maximized crop yields.

Spectral composition of LED light differentially affects biomass, photosynthesis, nutrient profile, and foliar nitrate accumulation of lettuce grown under various replacement methods of nutrient solution

To enhance crop yield and quality, plant cultivation in controlled-growing systems is an alternative to traditional open-field farming. The use of light-emitting diode (LED) as an adjustable light source represents a promising approach to improve plant growth, metabolism, and function. The objective of this study was to assess the impact of different light spectra (red, red/blue (3:1), blue, and white) with an emission peak of around 656, 656, 450, and 449 nm, respectively, under various replacement methods of nutrient solution (complete replacement (CR), EC-based replacement (ECBR), and replacing based on plant needs (RBPN)), on biomass, physiological traits, and macro- and micronutrient contents of two best-known lettuce varieties, Lollo Rossa (LR) and Lollo Bionda (LB), in the nutrient film technique (NFT) hydroponic system. The results indicated that mix of red and blue LED spectra under RBPN method is the most effective treatment to enhance fresh and dry weights of lettuce plants. In addition, red LED spectrum under RBPN, and red and blue light under ECBR nutrient solution significantly increased leaf stomatal conductance, net photosynthesis and transpiration rate, and intercellular CO2 concentration of LR variety. Phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mn) content in LR variety, and iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn) content in both varieties increased upon exposure to blue and red LED light spectrum with RBPN method. Our results suggest that exposure to combination of red and blue light along with feeding plants using RBPN and ECBR methods can increase absorption of macro- and micronutrient elements and improve photosynthetic properties, and eventually increase lettuce yield with lower nitrate accumulation.

Elevated nocturnal respiratory rates in the mitochondria of CAM plants: current knowledge and unanswered questions

Crassulacean acid metabolism (CAM) is a metabolic adaptation that has evolved convergently in 38 plant families to aid survival in water-limited niches. Whilst primarily considered a photosynthetic adaptation, CAM also has substantial consequences for nocturnal respiratory metabolism. Here, we outline the history, current state and future of nocturnal respiration research in CAM plants, with a particular focus on the energetics of nocturnal respiratory oxygen consumption. Throughout the 20th century, research interest in nocturnal respiration occurred alongside initial discoveries of CAM, although the energetic and mechanistic implications of nocturnal oxygen consumption and links to the operation of the CAM cycle were not fully understood. Recent flux balance analysis (FBA) models have provided new insights into the role that mitochondria play in the CAM cycle. Several FBA models have predicted that CAM requires elevated nocturnal respiratory rates, compared to C3 species, to power vacuolar malic acid accumulation. We provide physiological data, from the genus Clusia, to corroborate these modelling predictions, thereby reinforcing the importance of elevated nocturnal respiratory rates for CAM. Finally, we outline five unanswered questions pertaining to nocturnal respiration which must be addressed if we are to fully understand and utilize CAM plants in a hotter, drier world.

Prospects and perspectives: inferring physiological and regulatory targets for CAM from molecular and modelling approaches

BACKGROUND AND SCOPE

This review summarizes recent advances in our understanding of Crassulacean Acid Metabolism (CAM) by integrating evolutionary, ecological, physiological, metabolic and molecular perspectives. A number of key control loops which moderate the expression of CAM phases, and their metabolic and molecular control, are explored. These include nocturnal stomatal opening, activation of phosphoenolpyruvate carboxylase by a specific protein kinase, interactions with circadian clock control, as well as daytime decarboxylation and activation of Rubisco. The vacuolar storage and release of malic acid and the interplay between the supply and demand for carbohydrate reserves are also key metabolic control points.

FUTURE OPPORTUNITIES

We identify open questions and opportunities, with experimentation informed by top-down molecular modelling approaches allied with bottom-up mechanistic modelling systems. For example, mining transcriptomic datasets using high-speed systems approaches will help to identify targets for future genetic manipulation experiments to define the regulation of CAM (whether circadian or metabolic control). We emphasize that inferences arising from computational approaches or advanced nuclear sequencing techniques can identify potential genes and transcription factors as regulatory targets. However, these outputs then require systematic evaluation, using genetic manipulation in key model organisms over a developmental progression, combining gene silencing and metabolic flux analysis and modelling to define functionality across the CAM day-night cycle. From an evolutionary perspective, the origins and function of CAM succulents and responses to water deficits are set against the mesophyll and hydraulic limitations imposed by cell and tissue succulence in contrasting morphological lineages. We highlight the interplay between traits across shoots (3D vein density, mesophyll conductance and cell shrinkage) and roots (xylem embolism and segmentation). Thus, molecular, biophysical and biochemical processes help to curtail water losses and exploit rapid rehydration during restorative rain events. In the face of a changing climate, we hope such approaches will stimulate opportunities for future research.

Taming the perils of photosynthesis by eukaryotes: constraints on endosymbiotic evolution in aquatic ecosystems

An ancestral eukaryote acquired photosynthesis by genetically integrating a cyanobacterial endosymbiont as the chloroplast. The chloroplast was then further integrated into many other eukaryotic lineages through secondary endosymbiotic events of unicellular eukaryotic algae. While photosynthesis enables autotrophy, it also generates reactive oxygen species that can cause oxidative stress. To mitigate the stress, photosynthetic eukaryotes employ various mechanisms, including regulating chloroplast light absorption and repairing or removing damaged chloroplasts by sensing light and photosynthetic status. Recent studies have shown that, besides algae and plants with innate chloroplasts, several lineages of numerous unicellular eukaryotes engage in acquired phototrophy by hosting algal endosymbionts or by transiently utilizing chloroplasts sequestrated from algal prey in aquatic ecosystems. In addition, it has become evident that unicellular organisms engaged in acquired phototrophy, as well as those that feed on algae, have also developed mechanisms to cope with photosynthetic oxidative stress. These mechanisms are limited but similar to those employed by algae and plants. Thus, there appear to be constraints on the evolution of those mechanisms, which likely began by incorporating photosynthetic cells before the establishment of chloroplasts by extending preexisting mechanisms to cope with oxidative stress originating from mitochondrial respiration and acquiring new mechanisms.

Rethinking the potential productivity of crassulacean acid metabolism by integrating metabolic dynamics with shoot architecture, using the example of Agave tequilana

Terrestrial CAM plants typically occur in hot semiarid regions, yet can show high crop productivity under favorable conditions. To achieve a more mechanistic understanding of CAM plant productivity, a biochemical model of diel metabolism was developed and integrated with 3-D shoot morphology to predict the energetics of light interception and photosynthetic carbon assimilation. Using Agave tequilana as an example, this biochemical model faithfully simulated the four diel phases of CO2 and metabolite dynamics during the CAM rhythm. After capturing the 3-D form over an 8-yr production cycle, a ray-tracing method allowed the prediction of the light microclimate across all photosynthetic surfaces. Integration with the biochemical model thereby enabled the simulation of plant and stand carbon uptake over daily and annual courses. The theoretical maximum energy conversion efficiency of Agave spp. is calculated at 0.045-0.049, up to 7% higher than for C3 photosynthesis. Actual light interception, and biochemical and anatomical limitations, reduced this to 0.0069, or 15.6 Mg ha-1  yr-1 dry mass annualized over an 8-yr cropping cycle, consistent with observation. This is comparable to the productivity of many C3 crops, demonstrating the potential of CAM plants in climates where little else may be grown while indicating strategies that could raise their productivity.

The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber

Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.

Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony

Intracellular fluxes represent a joint outcome of cellular transcription and translation and reflect the availability and usage of nutrients from the environment. While approaches from the constraint-based metabolic framework can accurately predict cellular phenotypes, such as growth and exchange rates with the environment, accurate prediction of intracellular fluxes remains a pressing problem. Parsimonious flux balance analysis (pFBA) has become an approach of choice to predict intracellular fluxes by employing the principle of efficient usage of protein resources. Nevertheless, comparative analyses of intracellular flux predictions from pFBA against fluxes estimated from labeling experiments remain scarce. Here, we posited that steady-state flux distributions derived from the principle of maximizing multi-reaction dependencies are of improved accuracy and precision than those resulting from pFBA. To this end, we designed a constraint-based approach, termed complex-balanced FBA (cbFBA), to predict steady-state flux distributions that support the given specific growth rate and exchange fluxes. We showed that the steady-state flux distributions resulting from cbFBA in comparison to pFBA show better agreement with experimentally measured fluxes from 17 Escherichia coli strains and are more precise, due to the smaller space of alternative solutions. We also showed that the same principle holds in eukaryotes by comparing the predictions of pFBA and cbFBA against experimentally derived steady-state flux distributions from 26 knock-out mutants of Saccharomyces cerevisiae. Furthermore, our results showed that intracellular fluxes predicted by cbFBA provide better support for the principle of minimizing metabolic adjustment between mutants and wild types. Together, our findings point that other principles that consider the dynamics and coordination of steady states may govern the distribution of intracellular fluxes.

Shifts in carbon partitioning by photosynthetic activity increase terpenoid synthesis in glandular trichomes

Several commercially important secondary metabolites are produced and accumulated in high amounts by glandular trichomes, giving the prospect of using them as metabolic cell factories. Due to extremely high metabolic fluxes through glandular trichomes, previous research focused on how such flows are achieved. The question regarding their bioenergetics became even more interesting with the discovery of photosynthetic activity in some glandular trichomes. Despite recent advances, how primary metabolism contributes to the high metabolic fluxes in glandular trichomes is still not fully elucidated. Using computational methods and available multi-omics data, we first developed a quantitative framework to investigate the possible role of photosynthetic energy supply in terpenoid production and next tested experimentally the simulation-driven hypothesis. With this work, we provide the first reconstruction of specialised metabolism in Type-VI photosynthetic glandular trichomes of Solanum lycopersicum. Our model predicted that increasing light intensities results in a shift of carbon partitioning from catabolic to anabolic reactions driven by the energy availability of the cell. Moreover, we show the benefit of shifting between isoprenoid pathways under different light regimes, leading to a production of different classes of terpenes. Our computational predictions were confirmed in vivo, demonstrating a significant increase in production of monoterpenoids while the sesquiterpenes remained unchanged under higher light intensities. The outcomes of this research provide quantitative measures to assess the beneficial role of chloroplast in glandular trichomes for enhanced production of secondary metabolites and can guide the design of new experiments that aim at modulating terpenoid production.

Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism

Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83%) of single lethal knock-outs and 75% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism.

Unveiling the dark side of guard cell metabolism

Evidence suggests that guard cells have higher rate of phosphoenolpyruvate carboxylase (PEPc)-mediated dark CO2 assimilation than mesophyll cells. However, it is unknown which metabolic pathways are activated following dark CO2 assimilation in guard cells. Furthermore, it remains unclear how the metabolic fluxes throughout the tricarboxylic acid (TCA) cycle and associated pathways are regulated in illuminated guard cells. Here we carried out a13C-HCO3 labelling experiment in tobacco guard cells harvested under continuous dark or during the dark-to-light transition to elucidate principles of metabolic dynamics downstream of CO2 assimilation. Most metabolic changes were similar between dark-exposed and illuminated guard cells. However, illumination altered the metabolic network structure of guard cells and increased the 13C-enrichment in sugars and metabolites associated to the TCA cycle. Sucrose was labelled in the dark, but light exposure increased the 13C-labelling and leads to more drastic reductions in the content of this metabolite. Fumarate was strongly labelled under both dark and light conditions, while illumination increased the 13C-enrichment in pyruvate, succinate and glutamate. Only one 13C was incorporated into malate and citrate in either dark or light conditions. Our results indicate that several metabolic pathways are redirected following PEPc-mediated CO2 assimilation in the dark, including gluconeogenesis and the TCA cycle. We further showed that the PEPc-mediated CO2 assimilation provides carbons for gluconeogenesis, the TCA cycle and glutamate synthesis and that previously stored malate and citrate are used to underpin the specific metabolic requirements of illuminated guard cells.

Cell-type-specific metabolism in plants

Every plant organ contains tens of different cell types, each with a specialized function. These functions are intrinsically associated with specific metabolic flux distributions that permit the synthesis of the ATP, reducing equivalents and biosynthetic precursors demanded by the cell. Investigating such cell-type-specific metabolism is complicated by the mosaic of different cells within each tissue combined with the relative scarcity of certain types. However, techniques for the isolation of specific cells, their analysis in situ by microscopy, or modeling of their function in silico have permitted insight into cell-type-specific metabolism. In this review we present some of the methods used in the analysis of cell-type-specific metabolism before describing what we know about metabolism in several cell types that have been studied in depth; (i) leaf source and sink cells; (ii) glandular trichomes that are capable of rapid synthesis of specialized metabolites; (iii) guard cells that must accumulate large quantities of the osmolytes needed for stomatal opening; (iv) cells of seeds involved in storage of reserves; and (v) the mesophyll and bundle sheath cells of C4 plants that participate in a CO₂ concentrating cycle. Metabolism is discussed in terms of its principal features, connection to cell function and what factors affect the flux distribution. Demand for precursors and energy, availability of substrates and suppression of deleterious processes are identified as key factors in shaping cell-type-specific metabolism.

The diversity of substrates for plant respiration and how to optimize their use

Plant respiration is a foundational biological process with the potential to be optimized to improve crop yield. To understand and manipulate the outputs of respiration, the inputs of respiration-respiratory substrates-need to be probed in detail. Mitochondria house substrate catabolic pathways and respiratory machinery, so transport into and out of these organelles plays an important role in committing substrates to respiration. The large number of mitochondrial carriers and catabolic pathways that remain unidentified hinder this process and lead to confusion about the identity of direct and indirect respiratory substrates in plants. The sources and usage of respiratory substrates vary and are increasing found to be highly regulated based on cellular processes and environmental factors. This review covers the use of direct respiratory substrates following transport through mitochondrial carriers and catabolism under normal and stressed conditions. We suggest the introduction of enzymes not currently found in plant mitochondria to enable serine and acetate to be direct respiratory substrates in plants. We also compare respiratory substrates by assessing energetic yields, availability in cells, and their full or partial oxidation during cell catabolism. This information can assist in decisions to use synthetic biology approaches to alter the range of respiratory substrates in plants. As a result, respiration could be optimized by introducing, improving, or controlling specific mitochondrial transporters and mitochondrial catabolic pathways.

Clock and riboswitch control of THIC in tandem are essential for appropriate gauging of TDP levels under light/dark cycles in Arabidopsis

Metabolic homeostasis is regulated by enzyme activities, but the importance of regulating their corresponding coenzyme levels is unexplored. The organic coenzyme thiamine diphosphate (TDP) is suggested to be supplied as needed and controlled by a riboswitch-sensing mechanism in plants through the circadian-regulated THIC gene. Riboswitch disruption negatively impacts plant fitness. A comparison of riboswitch-disrupted lines to those engineered for enhanced TDP levels suggests that time-of-day regulation of THIC expression particularly under light/dark cycles is crucial. Altering the phase of THIC expression to be synchronous with TDP transporters disrupts the precision of the riboswitch implying that temporal separation of these processes by the circadian clock is important for gauging its response. All defects are bypassed by growing plants under continuous light conditions, highlighting the need to control levels of this coenzyme under light/dark cycles. Thus, consideration of coenzyme homeostasis within the well-studied domain of metabolic homeostasis is highlighted.

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.

Thioredoxins regulate the metabolic fluxes throughout the tricarboxylic acid cycle and associated pathways in a light-independent manner

The metabolic fluxes throughout the tricarboxylic acid cycle (TCAC) are inhibited in the light by the mitochondrial thioredoxin (TRX) system. However, it is unclear how this system orchestrates the fluxes throughout the TCAC and associated pathways in the dark. Here we carried out a¹³C-HCO₃ labelling experiment in Arabidopsis leaves from wild type (WT) and mutants lacking TRX o1 (trxo1), TRX h2 (trxh2), or both NADPH-dependent TRX reductase A and B (ntra ntrb) exposed to 0, 30 and 60 min of dark or light conditions. No ¹³C-enrichment in TCAC metabolites in illuminated WT leaves was observed. However, increased succinate content was found in parallel to reductions in Ala in the light, suggesting the latter operates as an alternative carbon source for succinate synthesis. By contrast to WT, all mutants showed substantial changes in the content and ¹³C-enrichment in TCAC metabolites under both dark and light conditions. Increased ¹³C-enrichment in glutamine in illuminated trxo1 leaves was also observed, strengthening the idea that TRX o1 restricts in vivo carbon fluxes from glycolysis and the TCAC to glutamine. We further demonstrated that both photosynthetic and gluconeogenic fluxes toward glucose are increased in trxo1 and that the phosphoenolpyruvate carboxylase (PEPc)-mediated ¹³C-incorporation into malate is higher in trxh2 mutants, as compared to WT. Our results collectively provide evidence that TRX h2 and the mitochondrial NTR/TRX system regulate the metabolic fluxes throughout the TCAC and associated pathways, including glycolysis, gluconeogenesis and the synthesis of glutamine in a light-independent manner.

Enhancing control systems of higher plant culture chambers via multilevel structural mechanistic modelling

Modelling higher plant growth is of strategic interest for modern agriculture as well as for the development of bioregenerative life support systems for space applications, where crop growth is expected to play an essential role. The capability of constraint-based metabolic models to cope the diel dynamics of plants growth is integrated into a multilevel modelling approach including mass and energy transfer and enzyme kinetics. Lactuca sativa is used as an exemplary crop to validate, with experimental data, the approach presented as well as to design a novel model-based predictive control strategy embedding metabolic information. The proposed modelling strategy predicts with high accuracy the dynamics of gas exchange and the distribution of fluxes in the metabolic network whereas the control architecture presented can be useful to manage higher plants chambers and open new ways of merging metabolome and control algorithms.

Carbon and nitrogen metabolic regulation in freshwater plant Ottelia alismoides in response to carbon limitation: A metabolite perspective

Carbon and nitrogen metabolism are basic, but pivotal metabolic pathways in plants and are tightly coupled. Maintaining the balance of carbon and nitrogen metabolism is critical for plant survival. Comprehensively revealing the metabolic balance of carbon-nitrogen interactions is important and helpful for understanding the adaptation of freshwater plants to CO₂ limited aqueous environment. A comprehensive metabolomics analysis combined with physiological measurement was performed in the freshwater plant Ottelia alismoides acclimated to high and low CO₂, respectively, for a better understanding of how the carbon and nitrogen metabolic adjustment in freshwater plants respond to carbon limitation. The present results showed that low CO₂ acclimated O. alismoides exhibited significant diurnal titratable acidity and malate fluctuations, as well as an opposite diel pattern of starch change and high enzymatic activities required for crassulacean acid metabolism (CAM) photosynthesis, which indicates that CAM was induced under low CO₂. Moreover, the metabolomic analysis showed that most intermediates of glycolysis, pentose phosphate pathway (PPP) and tricarboxylic acid (TCA) cycle, were increased under low CO₂, indicative of active respiration in low-CO₂-treated O. alismoides. Meanwhile, the majority of amino acids involved in pathways of glutamate and arginine metabolism, aspartate metabolism, and the branched-chain amino acids (BCAAs) metabolism were significantly increased under low CO₂. Notably, γ-aminobutyric acid (GABA) level was significantly higher in low CO₂ conditions, indicating a typical response with GABA shunt compensated for energy deprivation at low CO₂. Taken together, we conclude that in low-CO₂-stressed O. alismoides, CAM photosynthesis was induced, leading to higher carbon and nitrogen as well as energy requirements. Correspondingly, the respiration was greatly fueled via numerous starch degradation to ensure CO₂ fixation in dark, while accompanied by linked promoted N metabolism, presumably to produce energy and alternative carbon sources and nitrogenous substances for supporting the operation of CAM and enhancing tolerance for carbon limitation. This study not only helps to elucidate the regulating interaction between C and N metabolism to adapt to different CO₂ but also provides novel insights into the effects of CO₂ variation on the metabolic profiling of O. alismoides.

Genome-scale modeling of the primary-specialized metabolism interface

Environmental challenges and development require plants to reallocate resources between primary and specialized metabolites to survive. Genome-scale metabolic models, which map carbon flux through metabolic pathways, are a valuable tool in the study of tradeoffs that arise at this interface. Due to annotation gaps, models that characterize all the enzymatic steps in individual specialized pathways and their linkages to each other and to central carbon metabolism are difficult to construct. Recent studies have successfully curated subsystems of specialized metabolism and characterized the interfaces where flux is diverted to the precursors of glucosinolates, terpenes, and anthocyanins. Although advances in metabolite profiling can help to constrain models at this interface, quantitative analysis remains challenging because of the different timescales on which specialized metabolites from constitutive and reactive pathways accumulate.

Interaction of Nitrate Assimilation and Photorespiration at Elevated CO2

It has been shown repeatedly that exposure to elevated atmospheric CO₂ causes an increased C/N ratio of plant biomass that could result from either increased carbon or - in relation to C acquisition - reduced nitrogen assimilation. Possible reasons for diminished nitrogen assimilation are controversial, but an impact of reduced photorespiration at elevated CO₂ has frequently been implied. Using a mutant defective in peroxisomal hydroxy-pyruvate reductase (hpr1-1) that is hampered in photorespiratory turnover, we show that indeed, photorespiration stimulates the glutamine-synthetase 2 (GS) / glutamine-oxoglutarate-aminotransferase (GOGAT) cycle, which channels ammonia into amino acid synthesis. However, mathematical flux simulations demonstrated that nitrate assimilation was not reduced at elevated CO₂, pointing to a dilution of nitrogen containing compounds by assimilated carbon at elevated CO₂. The massive growth reduction in the hpr1-1 mutant does not appear to result from nitrogen starvation. Model simulations yield evidence for a loss of cellular energy that is consumed in supporting high flux through the GS/GOGAT cycle that results from inefficient removal of photorespiratory intermediates. This causes a futile cycling of glycolate and hydroxy-pyruvate. In addition to that, accumulation of serine and glycine as well as carboxylates in the mutant creates a metabolic imbalance that could contribute to growth reduction.

CAM Models: Lessons and Implications for CAM Evolution

The evolution of Crassulacean acid metabolism (CAM) by plants has been one of the most successful strategies in response to aridity. On the onset of climate change, expanding the use of water efficient crops and engineering higher water use efficiency into C3 and C4 crops constitute a plausible solution for the problems of agriculture in hotter and drier environments. A firm understanding of CAM is thus crucial for the development of agricultural responses to climate change. Computational models on CAM can contribute significantly to this understanding. Two types of models have been used so far. Early CAM models based on ordinary differential equations (ODE) reproduced the typical diel CAM features with a minimal set of components and investigated endogenous day/night rhythmicity. This line of research brought to light the preponderant role of vacuolar malate accumulation in diel rhythms. A second wave of CAM models used flux balance analysis (FBA) to better understand the role of CO₂ uptake in flux distribution. They showed that flux distributions resembling CAM metabolism emerge upon constraining CO₂ uptake by the system. We discuss the evolutionary implications of this and also how CAM components from unrelated pathways could have integrated along evolution.

Responses of butter leaf lettuce to mixed red and blue light with extended light/dark cycle period

To investigate the effects of extended light/dark (L/D) cycle period (relative to the diurnal L/D cycle) on lettuce and explore potential advantages of abnormal L/D cycles, butter leaf lettuce were grown in a plant factory with artificial light (PFAL) and exposed to mixed red (R) and blue (B) LED light with different L/D cycles that were respectively 16 h light/8 h dark (L16/D8, as control), L24/D12, L48/D24, L96/D48 and L120/D60. The results showed that, all the abnormal L/D cycles increased shoot dry weight (DW) of lettuce (by 34-83%) compared with the control, and lettuce DW increased with the L/D cycle period prolonged. The contents of soluble sugar and crude fiber in lettuce showed an overall upward trend with the length of L/D cycle extended, and the highest vitamin C content as well as low nitrate content were both detected in lettuce treated with L120/D60. The light use efficiency (LUE) and electric use efficiency (EUE) of lettuce reached the maximum (respectively 5.37% and 1.76%) under L120/D60 treatment and so were DW, Assimilation rate (A), RC/CS, ABS/CS, TRo/CS and DIo/CS, indicating that longer L/D cycle period was beneficial for the assimilation efficiency and dry matter accumulation in lettuce leaves. The highest shoot fresh weight (FW) and nitrate content detected in lettuce subjected to L24/D12 may be related to the vigorous growth of root, specific L/D cycle seemed to strengthen root growth and water absorption of lettuce. The openness level of RC in PSII (Ψo), ETo/CS, and PIabs were all the highest in lettuce treated with L24/D12, implying that slightly extending the L/D cycle period might promote the energy flowing to the final electron transfer chain. In general, irradiation modes with extended L/D cycle period had the potential to improve energy use efficiency and biomass of lettuce in PFAL. No obvious stress or injury was detected in lettuce subjected to prolonged L/D cycles in terms of plant growth and production. From the perspective of shoot FW, the optimal treatment in this study was L24/D12, while L120/D60 was the recommended treatment as regards of the energy use efficiency and nutritional quality.

Elucidation of Triacylglycerol Overproduction in the C4 Bioenergy Crop Sorghum bicolor by Constraint-Based Analysis

Upregulation of triacylglycerols (TAGs) in vegetative plant tissues such as leaves has the potential to drastically increase the energy density and biomass yield of bioenergy crops. In this context, constraint-based analysis has the promise to improve metabolic engineering strategies. Here we present a core metabolism model for the C4 biomass crop Sorghum bicolor (iTJC1414) along with a minimal model for photosynthetic CO2 assimilation, sucrose and TAG biosynthesis in C3 plants. Extending iTJC1414 to a four-cell diel model we simulate C4 photosynthesis in mature leaves with the principal photo-assimilatory product being replaced by TAG produced at different levels. Independent of specific pathways and per unit carbon assimilated, energy content and biosynthetic demands in reducing equivalents are about 1.3 to 1.4 times higher for TAG than for sucrose. For plant generic pathways, ATP- and NADPH-demands per CO2 assimilated are higher by 1.3- and 1.5-fold, respectively. If the photosynthetic supply in ATP and NADPH in iTJC1414 is adjusted to be balanced for sucrose as the sole photo-assimilatory product, overproduction of TAG is predicted to cause a substantial surplus in photosynthetic ATP. This means that if TAG synthesis was the sole photo-assimilatory process, there could be an energy imbalance that might impede the process. Adjusting iTJC1414 to a photo-assimilatory rate that approximates field conditions, we predict possible daily rates of TAG accumulation, dependent on varying ratios of carbon partitioning between exported assimilates and accumulated oil droplets (TAG, oleosin) and in dependence of activation of futile cycles of TAG synthesis and degradation. We find that, based on the capacity of leaves for photosynthetic synthesis of exported assimilates, mature leaves should be able to reach a 20% level of TAG per dry weight within one month if only 5% of the photosynthetic net assimilation can be allocated into oil droplets. From this we conclude that high TAG levels should be achievable if TAG synthesis is induced only during a final phase of the plant life cycle.

A hybrid kinetic and constraint-based model of leaf metabolism allows predictions of metabolic fluxes in different environments

While flux balance analysis (FBA) provides a framework for predicting steady-state leaf metabolic network fluxes, it does not readily capture the response to environmental variables without being coupled to other modelling formulations. To address this, we coupled an FBA model of 903 reactions of soybean (Glycine max) leaf metabolism with e-photosynthesis, a dynamic model that captures the kinetics of 126 reactions of photosynthesis and associated chloroplast carbon metabolism. Successful coupling was achieved in an iterative formulation in which fluxes from e-photosynthesis were used to constrain the FBA model and then, in turn, fluxes computed from the FBA model used to update parameters in e-photosynthesis. This process was repeated until common fluxes in the two models converged. Coupling did not hamper the ability of the kinetic module to accurately predict the carbon assimilation rate, photosystem II electron flux, and starch accumulation of field-grown soybean at two CO2 concentrations. The coupled model also allowed accurate predictions of additional parameters such as nocturnal respiration, as well as analysis of the effect of light intensity and elevated CO2 on leaf metabolism. Predictions included an unexpected decrease in the rate of export of sucrose from the leaf at high light, due to altered starch-sucrose partitioning, and altered daytime flux modes in the tricarboxylic acid cycle at elevated CO2 . Mitochondrial fluxes were notably different between growing and mature leaves, with greater anaplerotic, tricarboxylic acid cycle and mitochondrial ATP synthase fluxes predicted in the former, primarily to provide carbon skeletons and energy for protein synthesis.

Validation of carbon isotopologue distribution measurements by GC-MS and application to 13C-metabolic flux analysis of the tricarboxylic acid cycle in Brassica napus leaves

The estimation of metabolic fluxes in photosynthetic organisms represents an important challenge that has gained interest over the last decade with the development of ¹³C-Metabolic Flux Analysis at isotopically non-stationary steady-state. This approach requires a high level of accuracy for the measurement of Carbon Isotopologue Distribution in plant metabolites. But this accuracy has still not been evaluated at the isotopologue level for GC-MS, leading to uncertainties for the metabolic fluxes calculated based on these fragments. Here, we developed a workflow to validate the measurements of CIDs from plant metabolites with GC-MS by producing tailor-made E. coli standard extracts harboring a predictable binomial CID for some organic and amino acids. Overall, most of our TMS-derivatives mass fragments were validated with these standards and at natural isotope abundance in plant matrices. Then, we applied this validated MS method to investigate the light/dark regulation of plant TCA cycle by incorporating U-¹³C-pyruvate to Brassica napus leaf discs. We took advantage of pathway-specific isotopologues/isotopomers observed between two and six hours of labeling to show that the TCA cycle can operate in a cyclic manner under both light and dark conditions. Interestingly, this forward cyclic flux mode has a nearly four-fold higher contribution for pyruvate-to-citrate and pyruvate-to-malate fluxes than the phosphoenolpyruvate carboxylase (PEPc) flux reassimilating carbon derived from some mitochondrial enzymes. The contribution of stored citrate to the mitochondrial TCA cycle activity was also questioned based on dynamics of ¹³C-enrichment in citrate, glutamate and succinate and variations of citrate total amounts under light and dark conditions. Interestingly, there was a light-dependent ¹³C-incorporation into glycine and serine showing that decarboxylations from pyruvate dehydrogenase complex and TCA cycle enzymes were actively reassimilated and could represent up to 5% to net photosynthesis.

CAM photosynthesis: the acid test

There is currently considerable interest in the prospects for bioengineering crassulacean acid metabolism (CAM) photosynthesis - or key elements associated with it, such as increased water-use efficiency - into C₃ plants. Resolving how CAM photosynthesis evolved from the ancestral C₃ pathway could provide valuable insights into the targets for such bioengineering efforts. It has been proposed that the ability to accumulate organic acids at night may be common among C₃ plants, and that the transition to CAM might simply require enhancement of pre-existing fluxes, without the need for changes in circadian or diurnal regulation. We show, in a survey encompassing 40 families of vascular plants, that nocturnal acidification is a feature entirely restricted to CAM species. Although many C₃ species can synthesize malate during the light period, we argue that the switch to night-time malic acid accumulation requires a fundamental metabolic reprogramming that couples glycolytic breakdown of storage carbohydrate to the process of net dark CO₂ fixation. This central element of the CAM pathway, even when expressed at a low level, represents a biochemical capability not seen in C₃ plants, and so is better regarded as a discrete evolutionary innovation than as part of a metabolic continuum between C₃ and CAM.

Recent Advances in Plant Nanoscience

Plants have complex internal signaling pathways to quickly adjust to environmental changes and harvest energy from the environment. Facing the growing population, there is an urgent need for plant transformation and precise monitoring of plant growth to improve crop yields. Nanotechnology, an interdisciplinary research field, has recently been boosting plant yields and meeting global energy needs. In this context, a new field, "plant nanoscience," which describes the interaction between plants and nanotechnology, emerges as the times require. Nanosensors, nanofertilizers, nanopesticides, and nano-plant genetic engineering are of great help in increasing crop yields. Nanogenerators are helping to develop the potential of plants in the field of energy harvesting. Furthermore, the uptake and internalization of nanomaterials in plants and the possible effects are also worthy of attention. In this review, a forward-looking perspective on the plant nanoscience is presented and feasible solutions for future food shortages and energy crises are provided.

The versatility of plant organic acid metabolism in leaves is underpinned by mitochondrial malate-citrate exchange

Malate and citrate underpin the characteristic flexibility of central plant metabolism by linking mitochondrial respiratory metabolism with cytosolic biosynthetic pathways. However, the identity of mitochondrial carrier proteins that influence both processes has remained elusive. Here we show by a systems approach that DICARBOXYLATE CARRIER 2 (DIC2) facilitates mitochondrial malate-citrate exchange in vivo in Arabidopsis thaliana. DIC2 knockout (dic2-1) retards growth of vegetative tissues. In vitro and in organello analyses demonstrate that DIC2 preferentially imports malate against citrate export, which is consistent with altered malate and citrate utilization in response to prolonged darkness of dic2-1 plants or a sudden shift to darkness of dic2-1 leaves. Furthermore, isotopic glucose tracing reveals a reduced flux towards citrate in dic2-1, which results in a metabolic diversion towards amino acid synthesis. These observations reveal the physiological function of DIC2 in mediating the flow of malate and citrate between the mitochondrial matrix and other cell compartments.

Potential metabolic mechanisms for inhibited chloroplast nitrogen assimilation under high CO2

Improving photosynthesis is considered a major and feasible option to dramatically increase crop yield potential. Increased atmospheric CO2 concentration often stimulates both photosynthesis and crop yield, but decreases protein content in the main C3 cereal crops. This decreased protein content in crops constrains the benefits of elevated CO2 on crop yield and affects their nutritional value for humans. To support studies of photosynthetic nitrogen assimilation and its complex interaction with photosynthetic carbon metabolism for crop improvement, we developed a dynamic systems model of plant primary metabolism, which includes the Calvin-Benson cycle, the photorespiration pathway, starch synthesis, glycolysis-gluconeogenesis, the tricarboxylic acid cycle, and chloroplastic nitrogen assimilation. This model successfully captures responses of net photosynthetic CO2 uptake rate (A), respiration rate, and nitrogen assimilation rate to different irradiance and CO2 levels. We then used this model to predict inhibition of nitrogen assimilation under elevated CO2. The potential mechanisms underlying inhibited nitrogen assimilation under elevated CO2 were further explored with this model. Simulations suggest that enhancing the supply of α-ketoglutarate is a potential strategy to maintain high rates of nitrogen assimilation under elevated CO2. This model can be used as a heuristic tool to support research on interactions between photosynthesis, respiration, and nitrogen assimilation. It also provides a basic framework to support the design and engineering of C3 plant primary metabolism for enhanced photosynthetic efficiency and nitrogen assimilation in the coming high-CO2 world.

Establishment of a GC-MS-based 13 C-positional isotopomer approach suitable for investigating metabolic fluxes in plant primary metabolism

¹³ C-Metabolic flux analysis (¹³ C-MFA) has greatly contributed to our understanding of plant metabolic regulation. However, the generation of detailed in vivo flux maps remains a major challenge. Flux investigations based on nuclear magnetic resonance have resolved small networks with high accuracy. Mass spectrometry (MS) approaches have broader potential, but have hitherto been limited in their power to deduce flux information due to lack of atomic level position information. Herein we established a gas chromatography (GC) coupled to MS-based approach that provides ¹³ C-positional labelling information in glucose, malate and glutamate (Glu). A map of electron impact (EI)-mediated MS fragmentation was created and validated by ¹³ C-positionally labelled references via GC-EI-MS and GC-atmospheric pressure chemical ionization-MS technologies. The power of the approach was revealed by analysing previous ¹³ C-MFA data from leaves and guard cells, and ¹³ C-HCO₃ labelling of guard cells harvested in the dark and after the dark-to-light transition. We demonstrated that the approach is applicable to established GC-EI-MS-based ¹³ C-MFA without the need for experimental adjustment, but will benefit in the future from paired analyses by the two GC-MS platforms. We identified specific glucose carbon atoms that are preferentially labelled by photosynthesis and gluconeogenesis, and provide an approach to investigate the phosphoenolpyruvate carboxylase (PEPc)-derived ¹³ C-incorporation into malate and Glu. Our results suggest that gluconeogenesis and the PEPc-mediated CO₂ assimilation into malate are activated in a light-independent manner in guard cells. We further highlight that the fluxes from glycolysis and PEPc toward Glu are restricted by the mitochondrial thioredoxin system in illuminated leaves.

Data Management and Modeling in Plant Biology

The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.

Thioredoxin-mediated regulation of (photo)respiration and central metabolism

Thioredoxins (TRXs) are ubiquitous proteins engaged in the redox regulation of plant metabolism. Whilst the light-dependent TRX-mediated activation of Calvin-Benson cycle enzymes is well documented, the role of extraplastidial TRXs in the control of the mitochondrial (photo)respiratory metabolism has been revealed relatively recently. Mitochondrially located TRX o1 has been identified as a regulator of alternative oxidase, enzymes of, or associated with, the tricarboxylic acid (TCA) cycle, and the mitochondrial dihydrolipoamide dehydrogenase (mtLPD) involved in photorespiration, the TCA cycle, and the degradation of branched chain amino acids. TRXs are seemingly a major point of metabolic regulation responsible for activating photosynthesis and adjusting mitochondrial photorespiratory metabolism according to the prevailing cellular redox status. Furthermore, TRX-mediated (de)activation of TCA cycle enzymes contributes to explain the non-cyclic flux mode of operation of this cycle in illuminated leaves. Here we provide an overview on the decisive role of TRXs in the coordination of mitochondrial metabolism in the light and provide in silico evidence for other redox-regulated photorespiratory enzymes. We further discuss the consequences of mtLPD regulation beyond photorespiration and provide outstanding questions that should be addressed in future studies to improve our understanding of the role of TRXs in the regulation of central metabolism.

Gluconeogenesis in Plants: A Key Interface between Organic Acid/Amino Acid/Lipid and Sugar Metabolism

Gluconeogenesis is a key interface between organic acid/amino acid/lipid and sugar metabolism. The aims of this article are four-fold. First, to provide a concise overview of plant gluconeogenesis. Second, to emphasise the widespread occurrence of gluconeogenesis and its utilisation in diverse processes. Third, to stress the importance of the vacuolar storage and release of Krebs cycle acids/nitrogenous compounds, and of the role of gluconeogenesis and malic enzyme in this process. Fourth, to outline the contribution of fine control of enzyme activity to the coordinate-regulation of gluconeogenesis and malate metabolism, and the importance of cytosolic pH in this.

Advances in flux balance analysis by integrating machine learning and mechanism-based models

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Engineering of Crassulacean Acid Metabolism

Crassulacean acid metabolism (CAM) has evolved from a C₃ ground state to increase water use efficiency of photosynthesis. During CAM evolution, selective pressures altered the abundance and expression patterns of C₃ genes and their regulators to enable the trait. The circadian pattern of CO₂ fixation and the stomatal opening pattern observed in CAM can be explained largely with a regulatory architecture already present in C₃ plants. The metabolic CAM cycle relies on enzymes and transporters that exist in C₃ plants and requires tight regulatory control to avoid futile cycles between carboxylation and decarboxylation. Ecological observations and modeling point to mesophyll conductance as a major factor during CAM evolution. The present state of knowledge enables suggestions for genes for a minimal CAM cycle for proof-of-concept engineering, assuming altered regulation of starch synthesis and degradation are not critical elements of CAM photosynthesis and sufficient malic acid export from the vacuole is possible.

Low-level CAM photosynthesis in a succulent-leaved member of the Urticaceae, Pilea peperomioides

Pilea peperomioides Diels (Urticaceae) is a semi-succulent herbaceous species native to south-western China that has become popular in cultivation as an ornamental plant. To investigate whether this species possesses the capacity for CAM photosynthesis, measurements were made of CO2 gas exchange and titratable acidity in plants under both well-watered and water-deficit conditions. Plants were found to assimilate CO2 almost exclusively in the light via C3 photosynthesis. However, distinct transient reductions in the rate of net nocturnal CO2 release were consistently observed during the course of the dark period, and under water-deficit conditions one plant exhibited a brief period of net nocturnal CO2 uptake, providing unequivocal evidence of CAM activity. Furthermore, nocturnal increases in titratable acidity in both leaf laminas and petioles were observed in all plants exposed to wet-dry-wet cycles. This is the first report of CAM in the family Urticaceae. The results are discussed in relation to the phylogenetic position of Pilea and the partially shaded montane habitats in which this species is typically found. An updated list of all plant families currently known to contain species with CAM is presented.

Characterization of effects of genetic variants via genome-scale metabolic modelling

Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.

A Holistic Approach to Study Photosynthetic Acclimation Responses of Plants to Fluctuating Light

Plants in natural environments receive light through sunflecks, the duration and distribution of these being highly variable across the day. Consequently, plants need to adjust their photosynthetic processes to avoid photoinhibition and maximize yield. Changes in the composition of the photosynthetic apparatus in response to sustained changes in the environment are referred to as photosynthetic acclimation, a process that involves changes in protein content and composition. Considering this definition, acclimation differs from regulation, which involves processes that alter the activity of individual proteins over short-time periods, without changing the abundance of those proteins. The interconnection and overlapping of the short- and long-term photosynthetic responses, which can occur simultaneously or/and sequentially over time, make the study of long-term acclimation to fluctuating light in plants challenging. In this review we identify short-term responses of plants to fluctuating light that could act as sensors and signals for acclimation responses, with the aim of understanding how plants integrate environmental fluctuations over time and tailor their responses accordingly. Mathematical modeling has the potential to integrate physiological processes over different timescales and to help disentangle short-term regulatory responses from long-term acclimation responses. We review existing mathematical modeling techniques for studying photosynthetic responses to fluctuating light and propose new methods for addressing the topic from a holistic point of view.

How to resolve the enigma of diurnal malate remobilisation from the vacuole in plants with crassulacean acid metabolism?

Opening of stomata in plants with crassulacean acid metabolism (CAM) is mainly shifted to the night period when atmospheric CO₂ is fixed by phosphoenolpyruvate carboxylase and stored as malic acid in the vacuole. As such, CAM plants ameliorate transpirational water losses and display substantially higher water-use efficiency compared with C₃ and C₄ plants. In the past decade significant technical advances have allowed an unprecedented exploration of genomes, transcriptomes, proteomes and metabolomes of CAM plants and efforts are ongoing to engineer the CAM pathway in C₃ plants. Whilst research efforts have traditionally focused on nocturnal carboxylation, less information is known regarding the drivers behind diurnal malate remobilisation from the vacuole that liberates CO₂ to be fixed by RuBisCo behind closed stomata. To shed more light on this process, we provide a stoichiometric analysis to identify potentially rate-limiting steps underpinning diurnal malate mobilisation and help direct future research efforts. Within this remit we address three key questions: Q1 Does light-dependent assimilation of CO₂ via RuBisCo dictate the rate of malate mobilisation? Q2: Do the enzymes responsible for malate decarboxylation limit daytime mobilisation from the vacuole? Q3: Does malate efflux from the vacuole set the pace of decarboxylation?

Environment-coupled models of leaf metabolism

The plant leaf is the main site of photosynthesis. This process converts light energy and inorganic nutrients into chemical energy and organic building blocks for the biosynthesis and maintenance of cellular components and to support the growth of the rest of the plant. The leaf is also the site of gas–water exchange and due to its large surface, it is particularly vulnerable to pathogen attacks. Therefore, the leaf's performance and metabolic modes are inherently determined by its interaction with the environment. Mathematical models of plant metabolism have been successfully applied to study various aspects of photosynthesis, carbon and nitrogen assimilation and metabolism, aided suggesting metabolic intervention strategies for optimized leaf performance, and gave us insights into evolutionary drivers of plant metabolism in various environments. With the increasing pressure to improve agricultural performance in current and future climates, these models have become important tools to improve our understanding of plant–environment interactions and to propel plant breeders efforts. This overview article reviews applications of large-scale metabolic models of leaf metabolism to study plant–environment interactions by means of flux-balance analysis. The presented studies are organized in two ways — by the way the environment interactions are modelled — via external constraints or data-integration and by the studied environmental interactions — abiotic or biotic.

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.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

What is quantitative plant biology?

Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathematics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric or mechanical, are quantified, statistically assessed and integrated at multiple scales and across fields. They feed testable predictions that, in turn, guide further experimental tests. Quantitative features such as variability, noise, robustness, delays or feedback loops are included to account for the inner dynamics of plants and their interactions with the environment. Here, we present the main features of this ongoing revolution, through new questions around signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science.

Biochemical and functional characterization of a mitochondrial citrate carrier in Arabidopsis thaliana

A homolog of the mitochondrial succinate/fumarate carrier from yeast (Sfc1p) has been found in the Arabidopsis genome, named AtSFC1. The AtSFC1 gene was expressed in Escherichia coli, and the gene product was purified and reconstituted in liposomes. Its transport properties and kinetic parameters demonstrated that AtSFC1 transports citrate, isocitrate and aconitate and, to a lesser extent, succinate and fumarate. This carrier catalyzes a fast counter-exchange transport as well as a low uniport of substrates, exhibits a higher transport affinity for tricarboxylates than dicarboxylates, and is inhibited by pyridoxal 5'-phosphate and other inhibitors of mitochondrial carriers to various degrees. Gene expression analysis indicated that the AtSFC1 transcript is mainly present in heterotrophic tissues, and fusion with a green-fluorescent protein localized AtSFC1 to the mitochondria. Furthermore, 35S-AtSFC1 antisense lines were generated and characterized at metabolic and physiological levels in different organs and at various developmental stages. Lower expression of AtSFC1 reduced seed germination and impaired radicle growth, a phenotype that was related to reduced respiration rate. These findings demonstrate that AtSFC1 might be involved in storage oil mobilization at the early stages of seedling growth and in nitrogen assimilation in root tissue by catalyzing citrate/isocitrate or citrate/succinate exchanges.

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.