Responses to light intensity in a genome-scale model of rice metabolism

New insights in metabolism modelling to decipher plant-microbe interactions

Plant disease outbreaks, exacerbated by climate change, threaten food security and environmental sustainability world-wide. Plants interact with a wide range of microorganisms. The quest for resilient agriculture requires a deep insight into the molecular and ecological interplays between plants and their associated microbial communities. Omics methods, by profiling entire molecular sets, have shed light on these complex interactions. Nonetheless, deciphering the relationships among thousands of molecular components remains a formidable challenge, and studies that integrate these components into cohesive biological networks involving plants and associated microbes are still limited. Systems biology has the potential to predict the effects of biotic and abiotic perturbations on these networks. It is therefore a promising framework for addressing the full complexity of plant-microbiome interactions.

Non-canonical plant metabolism

Metabolism is essential for plant growth and has become a major target for crop improvement by enhancing nutrient use efficiency. Metabolic engineering is also the basis for producing high-value plant products such as pharmaceuticals, biofuels and industrial biochemicals. An inherent problem for such engineering endeavours is the tendency to view metabolism as a series of distinct metabolic pathways-glycolysis, the tricarboxylic acid cycle, the Calvin-Benson cycle and so on. While these canonical pathways may represent a dominant or frequently occurring flux mode, systematic analyses of metabolism via computational modelling have emphasized the inherent flexibility of the metabolic network to carry flux distributions that are distinct from the canonical pathways. Recent experimental estimates of metabolic network fluxes using 13C-labelling approaches have revealed numerous instances in which non-canonical pathways occur under different conditions and in different tissues. In this Review, we bring these non-canonical pathways to the fore, summarizing the evidence for their occurrence and the context in which they operate. We also emphasize the importance of non-canonical pathways for metabolic engineering. We argue that the introduction of a high-flux pathway to a desired metabolic product will, by necessity, require non-canonical supporting fluxes in central metabolism to provide the necessary carbon skeletons, energy and reducing power. We illustrate this using the overproduction of isoprenoids and fatty acids as case studies.

A Guide to Metabolic Network Modeling for Plant Biology

Plants produce a diverse array of compounds that play crucial roles in growth, in development, and in responses to abiotic and biotic stresses. Understanding the fluxes within metabolic pathways is essential for guiding strategies aimed at directing metabolism for crop improvement and the plant natural product industry. Over the past decade, metabolic network modeling has emerged as a predominant tool for the integration, quantification, and prediction of the spatial and temporal distribution of metabolic flows. In this review, we present the primary methods for constructing mathematical models of metabolic systems and highlight recent achievements in plant metabolism using metabolic modeling. Furthermore, we discuss current challenges in applying network flux analysis in plants and explore the potential use of machine learning technologies in plant metabolic modeling. The practical application of mathematical modeling is expected to provide significant insights into the structure and regulation of plant metabolic networks.

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.

A diel multi-tissue genome-scale metabolic model of Vitis vinifera

Vitis vinifera, also known as grapevine, is widely cultivated and commercialized, particularly to produce wine. As wine quality is directly linked to fruit quality, studying grapevine metabolism is important to understand the processes underlying grape composition. Genome-scale metabolic models (GSMMs) have been used for the study of plant metabolism and advances have been made, allowing the integration of omics datasets with GSMMs. On the other hand, Machine learning (ML) has been used to analyze and integrate omics data, and while the combination of ML with GSMMs has shown promising results, it is still scarcely used to study plants. Here, the first GSSM of V. vinifera was reconstructed and validated, comprising 7199 genes, 5399 reactions, and 5141 metabolites across 8 compartments. Tissue-specific models for the stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases. The potential of combining ML with GSMMs was explored by using ML to analyze the fluxomics data generated by green and mature grape GSMMs and provide insights regarding the metabolism of grapes at different developmental stages. Therefore, the models developed in this work are useful tools to explore different aspects of grapevine metabolism and understand the factors influencing grape quality.

Contextualized Metabolic Modelling Revealed Factors Affecting Isoflavone Accumulation in Soybean Seeds

Isoflavones, secondary metabolites with numerous health benefits, are predominantly found in legume seeds, especially soybean; however, their contents in domesticated soybean seeds are highly variable. Wild soybeans are known for higher seed isoflavone contents than cultivars. Here we used experimental and modelling approaches on wild soybean (W05) and cultivated soybean (C08) to delineate factors influencing isoflavone accumulation. We found imported nutrients were converted into storage compounds, with isoflavone accumulation in W05 seeds being faster than in C08 ones. The isoflavone accumulation during seed development was simulated using context-specific cotyledon metabolic models of four developmental stages on cultivar C08, and the metabolic burden imposed by increasing biomass was evaluated. Trade-off analyses between biomass and isoflavone suggest that high biomass requirement in cultivars could limit the reallocation of resources for secondary metabolite production. Isoflavone production in mature seeds was also influenced by biomass compositions. Seeds with higher carbohydrate contents favour isoflavone production, while those with highest protein and oil contents had lowest isoflavone contents. Although seeds could synthesize isoflavones on their own, the predicted fluxes from biosynthesis alone were lower than the empirical levels. Shadow price analyses indicated that isoflavone accumulation depended on both intrinsic biosynthesis and direct contribution from the plant.

Transcriptional Modulation during Photomorphogenesis in Rice Seedlings

Light is one of the most important factors regulating plant gene expression patterns, metabolism, physiology, growth, and development. To explore how light may induce or alter transcript splicing, we conducted RNA-Seq-based transcriptome analyses by comparing the samples harvested as etiolated seedlings grown under continuous dark conditions vs. the light-treated green seedlings. The study aims to reveal differentially regulated protein-coding genes and novel long noncoding RNAs (lncRNAs), their light-induced alternative splicing, and their association with biological pathways. We identified 14,766 differentially expressed genes, of which 4369 genes showed alternative splicing. We observed that genes mapped to the plastid-localized methyl-erythritol-phosphate (MEP) pathway were light-upregulated compared to the cytosolic mevalonate (MVA) pathway genes. Many of these genes also undergo splicing. These pathways provide crucial metabolite precursors for the biosynthesis of secondary metabolic compounds needed for chloroplast biogenesis, the establishment of a successful photosynthetic apparatus, and photomorphogenesis. In the chromosome-wide survey of the light-induced transcriptome, we observed intron retention as the most predominant splicing event. In addition, we identified 1709 novel lncRNA transcripts in our transcriptome data. This study provides insights on light-regulated gene expression and alternative splicing in rice.

Elucidating the impact of in vitro cultivation on Nicotiana tabacum metabolism through combined in silico modeling and multiomics analysis

The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of in-vitro cultivation on the tobacco metabolic network. In-vitro cultivation is a widely used technique for plant cultivation, not only in the field of basic research but also for the rapid propagation of valuable horticultural and pharmaceutical plants. However, the systemic effects of in-vitro cultivation on overall plant metabolism could easily be overlooked and are still poorly understood. We found that in-vitro tobacco showed slower growth, less biomass and suppressed photosynthesis than soil-grown tobacco. Many changes of metabolites and metabolic pathways between in-vitro and soil-grown tobacco plants were identified, which notably revealed a significant increase of the amino acids content under in-vitro condition. The in silico investigation showed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while significantly upregulated the GS/GOGAT cycle, as well as producing more energy and less NADH/NADPH to acclimate in-vitro growth demands. Altogether, the combination of experimental and in silico analyses offers an unprecedented view of tobacco metabolism, with valuable insights into the impact of in-vitro cultivation, enabling more efficient utilization of in-vitro techniques for plant propagation and metabolic engineering.

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.

Genome-scale metabolic model led engineering of Nothapodytes nimmoniana plant cells for high camptothecin production

Camptothecin (CPT) is a vital monoterpene indole alkaloid used in anti-cancer therapeutics. It is primarily derived from Camptotheca acuminata and Nothapodytes nimmoniana plants that are indigenous to Southeast Asia. Plants have intricate metabolic networks and use them to produce secondary metabolites such as CPT, which is a prerequisite for rational metabolic engineering design to optimize their production. By reconstructing metabolic models, we can predict plant metabolic behavior, facilitating the selection of suitable approaches and saving time, cost, and energy, over traditional hit and trial experimental approaches. In this study, we reconstructed a genome-scale metabolic model for N. nimmoniana (NothaGEM iSM1809) and curated it using experimentally obtained biochemical data. We also used in silico tools to identify and rank suitable enzyme targets for overexpression and knockout to maximize camptothecin production. The predicted over-expression targets encompass enzymes involved in the camptothecin biosynthesis pathway, including strictosidine synthase and geraniol 10-hydroxylase, as well as targets related to plant metabolism, such as amino acid biosynthesis and the tricarboxylic acid cycle. The top-ranked knockout targets included reactions responsible for the formation of folates and serine, as well as the conversion of acetyl CoA and oxaloacetate to malate and citrate. One of the top-ranked overexpression targets, strictosidine synthase, was chosen to generate metabolically engineered cell lines of N. nimmoniana using Agrobacterium tumefaciens-mediated transformation. The transformed cell line showed a 5-fold increase in camptothecin production, with a yield of up to 5 µg g-1.

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.

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light-driven metabolism

Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome-scale metabolic models capture species-specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non-photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non-radiative energy dissipation processes, cross-compartment electron shuttling, and non-photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico. Overall, we present a methodology for incorporating a common, informative data type into computational models of light-driven metabolism and show its utilization within the design-build-test-learn cycle for engineering of photosynthetic organisms.

Substrate Utilisation and Energy Metabolism in Non-Growing Campylobacter jejuni M1cam

Campylobacter jejuni, the major cause of bacterial foodborne illness, is also a fastidious organism that requires strict growth requirements in the laboratory. Our aim was to study substrate utilisation and energy metabolism in non-growing C. jejuni to investigate the ability of these bacteria to survive so effectively in the food chain. We integrated phenotypic microarrays and genome-scale metabolic modelling (GSM) to investigate the survival of C. jejuni on 95 substrates. We further investigated the underlying metabolic re-adjustment associated with varying energy demands on each substrate. We identified amino acids, organic acids and H2, as single substrates supporting survival without growth. We identified several different mechanisms, which were used alone or in combination, for ATP production: substrate-level phosphorylation via acetate kinase, the TCA cycle, and oxidative phosphorylation via the electron transport chain that utilised alternative electron donors and acceptors. The benefit of ATP production through each of these mechanisms was associated with the cost of enzyme investment, nutrient availability and/or O2 utilisation. C. jejuni can utilise a wide range of substrates as energy sources, including organic acids commonly used for marination or preservation of ingredients, which might contribute to the success of their survival in changing environments.

A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.

Genome-scale metabolic models (GSMs) provide a tool for unravelling the complex metabolic behaviour of bacteria and how they adapt to changing environments and genetic perturbations, and thus offer invaluable insights for biotechnology applications. For a GSM to be used efficiently for strain development purposes, however, the model must be easily readable and reusable by other researchers, whilst being able to predict metabolic behaviour with a high level of accuracy. In this work, we developed a GSM for Cupriavidus necator H16 that is linked to the BioCyc database, which provides an efficient way of application, model update, integration of experimental data and network visualisation for other researchers. Using our model, we demonstrate how integrating experimental observations, including Transposon-directed Insertion site Sequencing (TraDIS) and omics data, can be used to compensate for the lack of regulatory, kinetic and thermodynamic information in GSMs, and thus improve model accuracy. Importantly, we found that TraDIS in vivo screening and GSM analysis are complementary approaches, which can be used in combination to provide reliable gene essentiality predictions. Overall, our results offer an informed strategy for the deliberate manipulation of C. necator H16 metabolic capabilities, towards its industrial application to convert greenhouse gases into biochemicals and biofuels.

Genome-Scale Metabolic Modelling Approach to Understand the Metabolism of the Opportunistic Human Pathogen Staphylococcus epidermidis RP62A

Staphylococcus epidermidis is a common commensal of collagen-rich regions of the body, such as the skin, but also represents a threat to patients with medical implants (joints and heart), and to preterm babies. Far less studied than Staphylococcus aureus, the mechanisms behind this increasingly recognised pathogenicity are yet to be fully understood. Improving our knowledge of the metabolic processes that allow S. epidermidis to colonise different body sites is key to defining its pathogenic potential. Thus, we have constructed a fully curated, genome-scale metabolic model for S. epidermidis RP62A, and investigated its metabolic properties with a focus on substrate auxotrophies and its utilisation for energy and biomass production. Our results show that, although glucose is available in the medium, only a small portion of it enters the glycolytic pathways, whils most is utilised for the production of biofilm, storage and the structural components of biomass. Amino acids, proline, valine, alanine, glutamate and arginine, are preferred sources of energy and biomass production. In contrast to previous studies, we have shown that this strain has no real substrate auxotrophies, although removal of proline from the media has the highest impact on the model and the experimental growth characteristics. Further study is needed to determine the significance of proline, an abundant amino acid in collagen, in S. epidermidis colonisation.

Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

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.

Engineering improved ethylene production: Leveraging systems biology and adaptive laboratory evolution

Ethylene is a small hydrocarbon gas widely used in the chemical industry. Annual worldwide production currently exceeds 150 million tons, producing considerable amounts of CO₂ contributing to climate change. The need for a sustainable alternative is therefore imperative. Ethylene is natively produced by several different microorganisms, including Pseudomonas syringae pv. phaseolicola via a process catalyzed by the ethylene-forming enzyme (EFE), subsequent heterologous expression of EFE has led to ethylene production in non-native bacterial hosts including Escherichia coli and cyanobacteria. However, solubility of EFE and substrate availability remain rate-limiting steps in biological ethylene production. We employed a combination of genome-scale metabolic modelling, continuous fermentation, and protein evolution to enable the accelerated development of a high efficiency ethylene producing E. coli strain, yielding a 49-fold increase in production, the most significant improvement reported to date. Furthermore, we have clearly demonstrated that this increased yield resulted from metabolic adaptations that were uniquely linked to EFE (wild type versus mutant). Our findings provide a novel solution to deregulate metabolic bottlenecks in key pathways, which can be readily applied to address other engineering challenges.

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.

Boosting Biomass Quantity and Quality by Improved Mixotrophic Culture of the Diatom Phaeodactylum tricornutum

Diatoms are photoautotrophic unicellular algae and are among the most abundant, adaptable, and diverse marine phytoplankton. They are extremely interesting not only for their ecological role but also as potential feedstocks for sustainable biofuels and high-value commodities such as omega fatty acids, because of their capacity to accumulate lipids. However, the cultivation of microalgae on an industrial scale requires higher cell densities and lipid accumulation than those found in nature to make the process economically viable. One of the known ways to induce lipid accumulation in Phaeodactylum tricornutum is nitrogen deprivation, which comes at the expense of growth inhibition and lower cell density. Thus, alternative ways need to be explored to enhance the lipid production as well as biomass density to make them sustainable at industrial scale. In this study, we have used experimental and metabolic modeling approaches to optimize the media composition, in terms of elemental composition, organic and inorganic carbon sources, and light intensity, that boost both biomass quality and quantity of P. tricornutum. Eventually, the optimized conditions were scaled-up to 2 L photobioreactors, where a better system control (temperature, pH, light, aeration/mixing) allowed a further improvement of the biomass capacity of P. tricornutum to 12 g/L.

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.

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.

Deciphering rice metabolic flux reprograming under salinity stress via in silico metabolic modeling

Rice is one of the most economically important commodities globally. However, rice plants are salt susceptible species in which high salinity can significantly constrain its productivity. Several physiological parameters in adaptation to salt stress have been observed, though changes in metabolic aspects remain to be elucidated. In this study, rice metabolic activities of salt-stressed flag leaf were systematically characterized. Transcriptomics and metabolomics data were combined to identify disturbed pathways, altered metabolites and metabolic hotspots within the rice metabolic network under salt stress condition. Besides, the feasible flux solutions in different context-specific metabolic networks were estimated and compared. Our findings highlighted metabolic reprogramming in primary metabolic pathways, cellular respiration, antioxidant biosynthetic pathways, and phytohormone biosynthetic pathways. Photosynthesis and hexose utilization were among the major disturbed pathways in the stressed flag leaf. Notably, the increased flux distribution of the photorespiratory pathway could contribute to cellular redox control. Predicted flux statuses in several pathways were consistent with the results from transcriptomics, end-point metabolomics, and physiological studies. Our study illustrated that the contextualized genome-scale model together with multi-omics analysis is a powerful approach to unravel the metabolic responses of rice to salinity stress.

A Genome-Scale Metabolic Model of Thalassiosira pseudonana CCMP 1335 for a Systems-Level Understanding of Its Metabolism and Biotechnological Potential

Thalassiosira pseudonana is a transformable and biotechnologically promising model diatom with an ability to synthesise nutraceuticals such as fucoxanthin and store a significant amount of polyglucans and lipids including omega-3 fatty acids. While it was the first diatom to be sequenced, a systems-level analysis of its metabolism has not been done yet. This work presents first comprehensive, compartmentalized, and functional genome-scale metabolic model of the marine diatom Thalassiosira pseudonana CCMP 1335, which we have termed iThaps987. The model includes 987 genes, 2477 reactions, and 2456 metabolites. Comparison with the model of another diatom Phaeodactylum tricornutum revealed presence of 183 unique enzymes (belonging primarily to amino acid, carbohydrate, and lipid metabolism) in iThaps987. Model simulations showed a typical C3-type photosynthetic carbon fixation and suggested a preference of violaxanthin-diadinoxanthin pathway over violaxanthin-neoxanthin pathway for the production of fucoxanthin. Linear electron flow was found be active and cyclic electron flow was inactive under normal phototrophic conditions (unlike green algae and plants), validating the model predictions with previous reports. Investigation of the model for the potential of Thalassiosira pseudonana CCMP 1335 to produce other industrially useful compounds suggest iso-butanol as a foreign compound that can be synthesized by a single-gene addition. This work provides novel insights about the metabolism and potential of the organism and will be helpful to further investigate its metabolism and devise metabolic engineering strategies for the production of various compounds.

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.

The importance of thiamine (vitamin B1) in plant health: From crop yield to biofortification

Ensuring that people have access to sufficient and nutritious food is necessary for a healthy life and the core tenet of food security. With the global population set to reach 9.8 billion by 2050, and the compounding effects of climate change, the planet is facing challenges that necessitate significant and rapid changes in agricultural practices. In the effort to provide food in terms of calories, the essential contribution of micronutrients (vitamins and minerals) to nutrition is often overlooked. Here, we focus on the importance of thiamine (vitamin B1) in plant health and discuss its impact on human health. Vitamin B1 is an essential dietary component, and deficiencies in this micronutrient underlie several diseases, notably nervous system disorders. The predominant source of dietary vitamin B1 is plant-based foods. Moreover, vitamin B1 is also vital for plants themselves, and its benefits in plant health have received less attention than in the human health sphere. In general, vitamin B1 is well-characterized for its role as a coenzyme in metabolic pathways, particularly those involved in energy production and central metabolism, including carbon assimilation and respiration. Vitamin B1 is also emerging as an important component of plant stress responses, and several noncoenzyme roles of this vitamin are being characterized. We summarize the importance of vitamin B1 in plants from the perspective of food security, including its roles in plant disease resistance, stress tolerance, and crop yield, and review the potential benefits of biofortification of crops with increased vitamin B1 content to improve human health.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Genome-Scale Metabolic Model Driven Design of a Defined Medium for Campylobacter jejuni M1cam

Campylobacter jejuni, the most frequent cause of food-borne bacterial gastroenteritis, is a fastidious organism when grown in the laboratory. Oxygen is required for growth, despite the presence of the metabolic mechanism for anaerobic respiration. Amino acid auxotrophies are variably reported and energy metabolism can occur through several electron donor/acceptor combinations. Overall, the picture is one of a flexible, but vulnerable metabolism. To understand Campylobacter metabolism, we have constructed a fully curated, metabolic model for the reference organism M1 (our variant is M1cam) and validated it through laboratory experiments. Our results show that M1cam is auxotrophic for methionine, niacinamide, and pantothenate. There are complete biosynthesis pathways for all amino acids except methionine and it can produce energy, but not biomass, in the absence of oxygen. M1cam will grow in DMEM/F-12 defined media but not in the previously published Campylobacter specific defined media tested. Using the model, we identified potential auxotrophies and substrates that may improve growth. With this information, we designed simple defined media containing inorganic salts, the auxotrophic substrates, L-methionine, niacinamide, and pantothenate, pyruvate and additional amino acids L-cysteine, L-serine, and L-glutamine for growth enhancement. Our defined media supports a 1.75-fold higher growth rate than Brucella broth after 48 h at 37°C and sustains the growth of other Campylobacter jejuni strains. This media can be used to design reproducible assays that can help in better understanding the adaptation, stress resistance, and the virulence mechanisms of this pathogen. We have shown that with a well-curated metabolic model it is possible to design a media to grow this fastidious organism. This has implications for the investigation of new Campylobacter species defined through metagenomics, such as C. infans.

Introducing an Optimization- and explicit Runge-Kutta- based Approach to Perform Dynamic Flux Balance Analysis

In this work we introduce the generalized Optimization- and explicit Runge-Kutta-based Approach (ORKA) to perform dynamic Flux Balance Analysis (dFBA), which is numerically more accurate and computationally tractable than existing approaches. ORKA is applied to a four-tissue (leaf, root, seed, and stem) model of Arabidopsis thaliana, p-ath773, uniquely capturing the core-metabolism of several stages of growth from seedling to senescence at hourly intervals. Model p-ath773 has been designed to show broad agreement with published plant-scale properties such as mass, maintenance, and senescence, yet leaving reaction-level behavior unconstrainted. Hence, it serves as a framework to study the reaction-level behavior necessary for observed plant-scale behavior. Two such case studies of reaction-level behavior include the lifecycle progression of sulfur metabolism and the diurnal flow of water throughout the plant. Specifically, p-ath773 shows how transpiration drives water flow through the plant and how water produced by leaf tissue metabolism may contribute significantly to transpired water. Investigation of sulfur metabolism elucidates frequent cross-compartment exchange of a standing pool of amino acids which is used to regulate the proton flow. Overall, p-ath773 and ORKA serve as scaffolds for dFBA-based lifecycle modeling of plants and other systems to further broaden the scope of in silico metabolic investigation.

Global responses to oxytetracycline treatment in tetracycline-resistant Escherichia coli

We characterized the global transcriptome of Escherichia coli MG1655:: tetA grown in the presence of ½ MIC (14 mg/L) of OTC, and for comparison WT MG1655 strain grown with 1//2 MIC of OTC (0.25 mg/L OTC). 1646 genes changed expression significantly (FDR > 0.05) in the resistant strain, the majority of which (1246) were also regulated in WT strain. Genes involved in purine synthesis and ribosome structure and function were top-enriched among up-regulated genes, and anaerobic respiration, nitrate metabolism and aromatic amino acid biosynthesis genes among down-regulated genes. Blocking of the purine-synthesis- did not affect resistance phenotypes (MIC and growth rate with OTC), while blocking of protein synthesis using low concentrations of chloramphenicol or gentamicin, lowered MIC towards OTC. Metabolic-modeling, using a novel model for MG1655 and continuous weighing factor that reflected the degree of up or down regulation of genes encoding a reaction, identified 102 metabolic reactions with significant change in flux in MG1655:: tetA when grown in the presence of OTC compared to growth without OTC. These pathways could not have been predicted by simply analyzing functions of the up and down regulated genes, and thus this work has provided a novel method for identification of reactions which are essential in the adaptation to growth in the presence of antimicrobials.

Multi-tissue to whole plant metabolic modelling

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

Rewiring of Metabolic Network in Mycobacterium tuberculosis During Adaptation to Different Stresses

Metabolic adaptation of Mycobacterium tuberculosis (M. tuberculosis) to microbicidal intracellular environment of host macrophages is fundamental to its pathogenicity. However, an in-depth understanding of metabolic adjustments through key reaction pathways and networks is limited. To understand how such changes occur, we measured the cellular metabolome of M. tuberculosis subjected to four microbicidal stresses using liquid chromatography-mass spectrometric multiple reactions monitoring (LC-MRM/MS). Overall, 87 metabolites were identified. The metabolites best describing the separation between stresses were identified through multivariate analysis. The coupling of the metabolite measurements with existing genome-scale metabolic model, and using constraint-based simulation led to several new concepts and unreported observations in M. tuberculosis; such as (i) the high levels of released ammonia as an adaptive response to acidic stress was due to increased flux through L-asparaginase rather than urease activity; (ii) nutrient starvation-induced anaplerotic pathway for generation of TCA intermediates from phosphoenolpyruvate using phosphoenolpyruvate kinase; (iii) quenching of protons through GABA shunt pathway or sugar alcohols as possible mechanisms of early adaptation to acidic and oxidative stresses; and (iv) usage of alternate cofactors by the same enzyme as a possible mechanism of rewiring metabolic pathways to overcome stresses. Besides providing new leads and important nodes that can be used for designing intervention strategies, the study advocates the strength of applying flux balance analyses coupled with metabolomics to get a global picture of complex metabolic adjustments.

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.

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

Background

C4 photosynthesis is a key domain of plant research with outcomes ranging from crop quality improvement, biofuel production and efficient use of water and nutrients. A metabolic network model of C4 “lab organism” Setaria viridis with extensive gene-reaction associations can accelerate target identification for desired metabolic manipulations and thereafter in vivo validation. Moreover, metabolic reconstructions have also been shown to be a significant tool to investigate fundamental metabolic traits.

Results

A mass and charge balance genome-scale metabolic model of Setaria viridis was constructed, which was tested to be able to produce all major biomass components in phototrophic and heterotrophic conditions. Our model predicted an important role of the utilization of NH\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$_{4}^{+}$\end{document}4+ and NO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$_{3}^{-}$\end{document}3− ratio in balancing charges in plants. A multi-tissue extension of the model representing C4 photosynthesis was able to utilize NADP-ME subtype of C4 carbon fixation for the production of lignocellulosic biomass in stem, providing a tool for identifying gene associations for cellulose, hemi-cellulose and lignin biosynthesis that could be potential target for improved lignocellulosic biomass production. Besides metabolic engineering, our modeling results uncovered a previously unrecognized role of the 3-PGA/triosephosphate shuttle in proton balancing.

Conclusions

A mass and charge balance model of Setaria viridis, a model C4 plant, provides the possibility of system-level investigation to identify metabolic characteristics based on stoichiometric constraints. This study demonstrated the use of metabolic modeling in identifying genes associated with the synthesis of particular biomass components, and elucidating new role of previously known metabolic processes.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2941-z) contains supplementary material, which is available to authorized users.

Computational Approaches to Design and Test Plant Synthetic Metabolic Pathways

Successfully designed and implemented plant-specific synthetic metabolic pathways hold promise to increase crop yield and nutritional value. Advances in synthetic biology have already demonstrated the capacity to design artificial biological pathways whose behavior can be predicted and controlled in microbial systems. However, the transfer of these advances to model plants and crops faces the lack of characterization of plant cellular pathways and increased complexity due to compartmentalization and multicellularity. Modern computational developments provide the means to test the feasibility of plant synthetic metabolic pathways despite gaps in the accumulated knowledge of plant metabolism. Here, we provide a succinct systematic review of optimization-based and retrobiosynthesis approaches that can be used to design and in silico test synthetic metabolic pathways in large-scale plant context-specific metabolic models. In addition, by surveying the existing case studies, we highlight the challenges that these approaches face when applied to plants. Emphasis is placed on understanding the effect that metabolic designs can have on native metabolism, particularly with respect to metabolite concentrations and thermodynamics of biochemical reactions. In addition, we discuss the computational developments that may help to transform the identified challenges into opportunities for plant synthetic biology.

Understanding carbon utilization routes between high and low starch-producing cultivars of cassava through Flux Balance Analysis

Analysis of metabolic flux was used for system level assessment of carbon partitioning in Kasetsart 50 (KU50) and Hanatee (HN) cassava cultivars to understand the metabolic routes for their distinct phenotypes. First, the constraint-based metabolic model of cassava storage roots, rMeCBM, was developed based on the carbon assimilation pathway of cassava. Following the subcellular compartmentalization and curation to ensure full network connectivity and reflect the complexity of eukaryotic cells, cultivar specific data on sucrose uptake and biomass synthesis were input, and rMeCBM model was used to simulate storage root growth in KU50 and HN. Results showed that rMeCBM-KU50 and rMeCBM-HN models well imitated the storage root growth. The flux-sum analysis revealed that both cultivars utilized different metabolic precursors to produce energy in plastid. More carbon flux was invested in the syntheses of carbohydrates and amino acids in KU50 than in HN. Also, KU50 utilized less flux for respiration and less energy to synthesize one gram of dry storage root. These results may disclose metabolic potential of KU50 underlying its higher storage root and starch yield over HN. Moreover, sensitivity analysis indicated the robustness of rMeCBM model. The knowledge gained might be useful for identifying engineering targets for cassava yield improvement.

A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism

BACKGROUND

Phytophthora infestans is a plant pathogen that causes an important plant disease known as late blight in potato plants (Solanum tuberosum) and several other solanaceous hosts. This disease is the main factor affecting potato crop production worldwide. In spite of the importance of the disease, the molecular mechanisms underlying the compatibility between the pathogen and its hosts are still unknown.

RESULTS

To explain the metabolic response of late blight, specifically photosynthesis inhibition in infected plants, we reconstructed a genome-scale metabolic network of the S. tuberosum leaf, PstM1. This metabolic network simulates the effect of this disease in the leaf metabolism. PstM1 accounts for 2751 genes, 1113 metabolic functions, 1773 gene-protein-reaction associations and 1938 metabolites involved in 2072 reactions. The optimization of the model for biomass synthesis maximization in three infection time points suggested a suppression of the photosynthetic capacity related to the decrease of metabolic flux in light reactions and carbon fixation reactions. In addition, a variation pattern in the flux of carboxylation to oxygenation reactions catalyzed by RuBisCO was also identified, likely to be associated to a defense response in the compatible interaction between P. infestans and S. tuberosum.

CONCLUSIONS

In this work, we introduced simultaneously the first metabolic network of S. tuberosum and the first genome-scale metabolic model of the compatible interaction of a plant with P. infestans.

Stoichiometric analysis of the energetics and metabolic impact of photorespiration in C3 plants

Analysis of the impact of photorespiration on plant metabolism is usually based on manual inspection of small network diagrams. Here we create a structural metabolic model that contains the reactions that participate in photorespiration in the plastid, peroxisome, mitochondrion and cytosol, and the metabolite exchanges between them. This model was subjected to elementary flux modes analysis, a technique that enumerates all the component, minimal pathways of a network. Any feasible photorespiratory metabolism in the plant will be some combination of the elementary flux modes (EFMs) that contain the Rubisco oxygenase reaction. Amongst the EFMs we obtained was the classic photorespiratory cycle, but there were also modes that involve photorespiration coupled with mitochondrial metabolism and ATP production, the glutathione-ascorbate cycle and nitrate reduction to ammonia. The modes analysis demonstrated the underlying basis of the metabolic linkages with photorespiration that have been inferred experimentally. The set of reactions common to all the elementary modes showed good agreement with the gene products of mutants that have been reported to have a defective phenotype in photorespiratory conditions. Finally, the set of modes provided a formal demonstration that photorespiration itself does not impact on the CO₂ :O₂ ratio (assimilation quotient), except in those modes associated with concomitant nitrate reduction.

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.

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.

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

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

Investigating mixotrophic metabolism in the model diatom Phaeodactylum tricornutum

Diatoms are prominent marine microalgae, interesting not only from an ecological point of view, but also for their possible use in biotechnology applications. They can be cultivated in phototrophic conditions, using sunlight as the sole energy source. Some diatoms, however, can also grow in a mixotrophic mode, wherein both light and external reduced carbon contribute to biomass accumulation. In this study, we investigated the consequences of mixotrophy on the growth and metabolism of the pennate diatom Phaeodactylum tricornutum, using glycerol as the source of reduced carbon. Transcriptomics, metabolomics, metabolic modelling and physiological data combine to indicate that glycerol affects the central-carbon, carbon-storage and lipid metabolism of the diatom. In particular, provision of glycerol mimics typical responses of nitrogen limitation on lipid metabolism at the level of triacylglycerol accumulation and fatty acid composition. The presence of glycerol, despite provoking features reminiscent of nutrient limitation, neither diminishes photosynthetic activity nor cell growth, revealing essential aspects of the metabolic flexibility of these microalgae and suggesting possible biotechnological applications of mixotrophy.This article is part of the themed issue 'The peculiar carbon metabolism in diatoms'.

Computational Approaches on Stoichiometric and Kinetic Modeling for Efficient Strain Design

Engineering biological systems that are capable of overproducing products of interest is the ultimate goal of any biotechnology application. To this end, stoichiometric (or steady state) and kinetic models are increasingly becoming available for a variety of organisms including prokaryotes, eukaryotes, and microbial communities. This ever-accelerating pace of such model reconstructions has also spurred the development of optimization-based strain design techniques. This chapter highlights a number of such frameworks developed in recent years in order to generate testable hypotheses (in terms of genetic interventions), thus addressing the challenges in metabolic engineering. In particular, three major methods are covered in detail including two methods for designing strains (i.e., one stoichiometric model-based and the other by integrating kinetic information into a stoichiometric model) and one method for analyzing microbial communities.

Reconstruction of Oryza sativa indica Genome Scale Metabolic Model and Its Responses to Varying RuBisCO Activity, Light Intensity, and Enzymatic Cost Conditions

To combat decrease in rice productivity under different stresses, an understanding of rice metabolism is needed. Though there are different genome scale metabolic models (GSMs) of Oryza sativa japonica, no GSM with gene-protein-reaction association exist for Oryza sativa indica. Here, we report a GSM, OSI1136 of O.s. indica, which includes 3602 genes and 1136 metabolic reactions and transporters distributed across the cytosol, mitochondrion, peroxisome, and chloroplast compartments. Flux balance analysis of the model showed that for varying RuBisCO activity (Vc/Vo) (i) the activity of the chloroplastic malate valve increases to transport reducing equivalents out of the chloroplast under increased photorespiratory conditions and (ii) glyceraldehyde-3-phosphate dehydrogenase and phosphoglycerate kinase can act as source of cytosolic ATP under decreased photorespiration. Under increasing light conditions we observed metabolic flexibility, involving photorespiration, chloroplastic triose phosphate and the dicarboxylate transporters of the chloroplast and mitochondrion for redox and ATP exchanges across the intracellular compartments. Simulations under different enzymatic cost conditions revealed (i) participation of peroxisomal glutathione-ascorbate cycle in photorespiratory H2O2 metabolism (ii) different modes of the chloroplastic triose phosphate transporters and malate valve, and (iii) two possible modes of chloroplastic Glu-Gln transporter which were related with the activity of chloroplastic and cytosolic isoforms of glutamine synthetase. Altogether, our results provide new insights into plant metabolism.

Bioenergetics of Monoterpenoid Essential Oil Biosynthesis in Nonphotosynthetic Glandular Trichomes

The commercially important essential oils of peppermint (Mentha × piperita) and its relatives in the mint family (Lamiaceae) are accumulated in specialized anatomical structures called glandular trichomes (GTs). A genome-scale stoichiometric model of secretory phase metabolism in peppermint GTs was constructed based on current biochemical and physiological knowledge. Fluxes through the network were predicted based on metabolomic and transcriptomic data. Using simulated reaction deletions, this model predicted that two processes, the regeneration of ATP and ferredoxin (in its reduced form), exert substantial control over flux toward monoterpenes. Follow-up biochemical assays with isolated GTs indicated that oxidative phosphorylation and ethanolic fermentation were active and that cooperation to provide ATP depended on the concentration of the carbon source. We also report that GTs with high flux toward monoterpenes express, at very high levels, genes coding for a unique pair of ferredoxin and ferredoxin-NADP+ reductase isoforms. This study provides, to our knowledge, the first evidence of how bioenergetic processes determine flux through monoterpene biosynthesis in GTs.

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.