Are we ready for genome-scale modeling in plants?

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.

CO2 recycling by phosphoenolpyruvate carboxylase enables cassava leaf metabolism to tolerate low water availability

Cassava is a staple crop that acclimatizes well to dry weather and limited water availability. The drought response mechanism of quick stomatal closure observed in cassava has no explicit link to the metabolism connecting its physiological response and yield. Here, a genome-scale metabolic model of cassava photosynthetic leaves (leaf-MeCBM) was constructed to study on the metabolic response to drought and stomatal closure. As demonstrated by leaf-MeCBM, leaf metabolism reinforced the physiological response by increasing the internal CO₂ and then maintaining the normal operation of photosynthetic carbon fixation. We found that phosphoenolpyruvate carboxylase (PEPC) played a crucial role in the accumulation of the internal CO₂ pool when the CO₂ uptake rate was limited during stomatal closure. Based on the model simulation, PEPC mechanistically enhanced drought tolerance in cassava by providing sufficient CO₂ for carbon fixation by RuBisCO, resulting in high production of sucrose in cassava leaves. The metabolic reprogramming decreased leaf biomass production, which may lead to maintaining intracellular water balance by reducing the overall leaf area. This study indicates the association of metabolic and physiological responses to enhance tolerance, growth, and production of cassava in drought conditions.

A multi-organ metabolic model of tomato predicts plant responses to nutritional and genetic perturbations

Predicting and understanding plant responses to perturbations require integrating the interactions between nutritional sources, genes, cell metabolism, and physiology in the same model. This can be achieved using metabolic modeling calibrated by experimental data. In this study, we developed a multi-organ metabolic model of a tomato (Solanum lycopersicum) plant during vegetative growth, named Virtual Young TOmato Plant (VYTOP) that combines genome-scale metabolic models of leaf, stem and root and integrates experimental data acquired from metabolomics and high-throughput phenotyping of tomato plants. It is composed of 6,689 reactions and 6,326 metabolites. We validated VYTOP predictions on five independent use cases. The model correctly predicted that glutamine is the main organic nutrient of xylem sap. The model estimated quantitatively how stem photosynthetic contribution impacts exchanges between the different organs. The model was also able to predict how nitrogen limitation affects vegetative growth and the metabolic behavior of transgenic tomato lines with altered expression of core metabolic enzymes. The integration of different components, such as a metabolic model, physiological constraints, and experimental data, generates a powerful predictive tool to study plant behavior, which will be useful for several other applications, such as plant metabolic engineering or plant nutrition.

Plant-Microbe Interaction: Aboveground to Belowground, from the Good to the Bad

Soil health and fertility issues are constantly addressed in the agricultural industry. Through the continuous and prolonged use of chemical heavy agricultural systems, most agricultural lands have been impacted, resulting in plateaued or reduced productivity. As such, to invigorate the agricultural industry, we would have to resort to alternative practices that will restore soil health and fertility. Therefore, in recent decades, studies have been directed towards taking a Magellan voyage of the soil rhizosphere region, to identify the diversity, density, and microbial population structure of the soil, and predict possible ways to restore soil health. Microbes that inhabit this region possess niche functions, such as the stimulation or promotion of plant growth, disease suppression, management of toxicity, and the cycling and utilization of nutrients. Therefore, studies should be conducted to identify microbes or groups of organisms that have assigned niche functions. Based on the above, this article reviews the aboveground and below-ground microbiomes, their roles in plant immunity, physiological functions, and challenges and tools available in studying these organisms. The information collected over the years may contribute toward future applications, and in designing sustainable agriculture.

Plant cell cultures as heterologous bio-factories for secondary metabolite production

Synthetic biology has been developing rapidly in the last decade and is attracting increasing attention from many plant biologists. The production of high-value plant-specific secondary metabolites is, however, limited mostly to microbes. This is potentially problematic because of incorrect post-translational modification of proteins and differences in protein micro-compartmentalization, substrate availability, chaperone availability, product toxicity, and cytochrome p450 reductase enzymes. Unlike other heterologous systems, plant cells may be a promising alternative for the production of high-value metabolites. Several commercial plant suspension cell cultures from different plant species have been used successfully to produce valuable metabolites in a safe, low cost, and environmentally friendly manner. However, few metabolites are currently being biosynthesized using plant platforms, with the exception of the natural pigment anthocyanin. Both Arabidopsis thaliana and Nicotiana tabacum cell cultures can be developed by multiple gene transformations and CRISPR-Cas9 genome editing. Given that the introduction of heterologous biosynthetic pathways into Arabidopsis and N. tabacum is not widely used, the biosynthesis of foreign metabolites is currently limited; however, therein lies great potential. Here, we discuss the exemplary use of plant cell cultures and prospects for using A. thaliana and N. tabacum cell cultures to produce valuable plant-specific metabolites.

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.

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.

Next-generation metabolic engineering approaches towards development of plant cell suspension cultures as specialized metabolite producing biofactories

Plant cell suspension culture (PCSC) has emerged as a viable technology to produce plant specialized metabolites (PSM). While Taxol® and ginsenoside are two examples of successfully commercialized PCSC-derived PSM, widespread utilization of the PCSC platform has yet to be realized primarily due to a lack of understanding of the molecular genetics of PSM biosynthesis. Recent advances in computational, molecular and synthetic biology tools provide the opportunity to rapidly characterize and harness the specialized metabolic potential of plants. Here, we discuss the prospects of integrating computational modeling, artificial intelligence, and precision genome editing (CRISPR/Cas and its variants) toolboxes to discover the genetic regulators of PSM. We also explore how synthetic biology can be applied to develop metabolically optimized PSM-producing native and heterologous PCSC systems. Taken together, this review provides an interdisciplinary approach to realize and link the potential of next-generation computational and molecular tools to convert PCSC into commercially viable PSM-producing biofactories.

Morphological and metabolic profiling of a tropical-adapted potato association panel subjected to water recovery treatment reveals new insights into plant vigor

Potato (Solanum tuberosum L.) is one of the world's most important crops, but it is facing major challenges due to climatic changes. To investigate the effects of intermittent drought on the natural variability of plant morphology and tuber metabolism in a novel potato association panel comprising 258 varieties we performed an augmented block design field study under normal irrigation and under water-deficit and recovery conditions in Ica, Peru. All potato genotypes were profiled for 45 morphological traits and 42 central metabolites via nuclear magnetic resonance. Statistical tests and norm of reaction analysis revealed that the observed variations were trait specific, that is, genotypic versus environmental. Principal component analysis showed a separation of samples as a result of conditional changes. To explore the relational ties between morphological traits and metabolites, correlation-based network analysis was employed, constructing one network for normal irrigation and one network for water-recovery samples. Community detection and difference network analysis highlighted the differences between the two networks, revealing a significant correlational link between fumarate and plant vigor. A genome-wide association study was performed for each metabolic trait. Eleven single nucleotide polymorphism (SNP) markers were associated with fumarate. Gene Ontology analysis of quantitative trait loci regions associated with fumarate revealed an enrichment of genes regulating metabolic processes. Three of the 11 SNPs were located within genes, coding for a protein of unknown function, a RING domain protein and a zinc finger protein ZAT2. Our findings have important implications for future potato breeding regimes, especially in countries suffering from climate change.

Drought Stress Responses in Context-Specific Genome-Scale Metabolic Models of Arabidopsis thaliana

Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the metabolic pathways of Arabidopsis thaliana which respond to drought stress by omics-based in silico analyses. We proposed an analysis pipeline to understand metabolism under specific conditions based on a genome-scale metabolic model (GEM). Context-specific GEMs under drought and well-watered control conditions were reconstructed using transcriptome data and examined using metabolome data. The metabolic fluxes throughout the metabolic network were estimated by flux balance analysis using the context-specific GEMs. We used in silico methods to identify an important reaction contributing to biomass production and clarified metabolic reaction responses under drought stress by comparative analysis between drought and control conditions. This proposed pipeline can be applied in other studies to understand metabolic changes under specific conditions using Arabidopsis GEM or other available plant GEMs.

Extended Utilization of Constraint-Based Metabolic Model in a Long-Growing Crop

The constraint-based rMeCBM-KU50 model of cassava storage root growth was analyzed to evaluate its sensitivity, with respect to reaction flux distribution and storage root growth rate, to changes in model inputted data and constraints, including sucrose uptake rate-related data—photosynthetic rate, total leaf area, total photosynthetic rate, storage root dry weight, and biomass function-related data. These mainly varied within ±90% of the model default values, although exceptions were made for the carbohydrate (−90% to 8%) and starch (−90% to 9%) contents. The results indicated that the predicted storage root growth rate was highly affected by specific sucrose uptake rates through the total photosynthetic rate and storage root dry weight variations; whereas the carbon flux distribution, direction and partitioning inclusive, was more sensitive to the variation in biomass content, particularly the carbohydrate content. This study showed that the specific sucrose uptake rate based on the total photosynthetic rate, storage root dry weight, and carbohydrate content were critical to the constraint-based metabolic modeling and deepened our understanding of the input–output relationship—specifically regarding the rMeCBM-KU50 model—providing a valuable platform for the modeling of plant metabolic systems, especially long-growing crops.

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.

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

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

Metabolic engineering to increase crop yield: From concept to execution

Although the return on investment over the last 20 years for mass screening of individual plant genes to improve crop performance has been low, the investment in these activities was essential to establish the infrastructure and tools of modern plant genomics. Complex traits such as crop yield are likely multigenic, and the exhaustive screening of random gene combinations to achieve yield gains is not realistic. Clearly, smart approaches must be developed. In silico analyses of plant metabolism and gene networks can move a trait discovery program beyond trial-and-error approaches and towards rational design strategies. Metabolic models employing flux-balance analysis are useful to determine the contribution of individual genes to a trait, or to compare, optimize, or even design metabolic pathways. Regulatory association networks provide a transcriptome-based view of the plant and can lead to the identification of transcription factors that control expression of multiple genes affecting a trait. In this review, the use of these models from the perspective of an Ag innovation company's trait discovery and development program will be discussed. Important decisions that can have significant impacts on the cost and timeline to develop a commercial trait will also be presented.

Constraint-Based Modeling Highlights Cell Energy, Redox Status and α-Ketoglutarate Availability as Metabolic Drivers for Anthocyanin Accumulation in Grape Cells Under Nitrogen Limitation

Anthocyanin biosynthesis is regulated by environmental factors (such as light, temperature, and water availability) and nutrient status (such as carbon, nitrogen, and phosphate nutrition). Previous reports show that low nitrogen availability strongly enhances anthocyanin accumulation in non carbon-limited plant organs or cell suspensions. It has been hypothesized that high carbon-to-nitrogen ratio would lead to an energy excess in plant cells, and that an increase in flavonoid pathway metabolic fluxes would act as an "energy escape valve," helping plant cells to cope with energy and carbon excess. However, this hypothesis has never been tested directly. To this end, we used the grapevine Vitis vinifera L. cultivar Gamay Teinturier (syn. Gamay Freaux or Freaux Tintorier, VIVC #4382) cell suspension line as a model system to study the regulation of anthocyanin accumulation in response to nitrogen supply. The cells were sub-cultured in the presence of either control (25 mM) or low (5 mM) nitrate concentration. Targeted metabolomics and enzyme activity determinations were used to parametrize a constraint-based model describing both the central carbon and nitrogen metabolisms and the flavonoid (phenylpropanoid) pathway connected by the energy (ATP) and reducing power equivalents (NADPH and NADH) cofactors. The flux analysis (2 flux maps generated, for control and low nitrogen in culture medium) clearly showed that in low nitrogen-fed cells all the metabolic fluxes of central metabolism were decreased, whereas fluxes that consume energy and reducing power, were either increased (upper part of glycolysis, shikimate, and flavonoid pathway) or maintained (pentose phosphate pathway). Also, fluxes of flavanone 3β-hydroxylase, flavonol synthase, and anthocyanidin synthase were strongly increased, advocating for a regulation of the flavonoid pathway by alpha-ketoglutarate levels. These results strongly support the hypothesis of anthocyanin biosynthesis acting as an energy escape valve in plant cells, and they open new possibilities to manipulate flavonoid production in plant cells. They do not, however, support a role of anthocyanins as an effective mechanism for coping with carbon excess in high carbon to nitrogen ratio situations in grape cells. Instead, constraint-based modeling output and biomass analysis indicate that carbon excess is dealt with by vacuolar storage of soluble sugars.

Integrated omics analysis of specialized metabolism in medicinal plants

Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models.

First principle-based models in plant suspension cell cultures: a review

In this work, the development and application of published models for describing the behavior of plant cell cultures is reviewed. The structure of each type of model is analyzed and the new tendencies for the modeling of biotechnological processes that can be applied in plant cell cultures are presented. This review is a tool for clarifying the main features that characterize each type of model in the field of plant cell cultures and can be used as a support on the selection of the more suitable model type, taking into account the purpose of specific research.

Systems Biology of Metabolism

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Plant Microbe Interactions in Post Genomic Era: Perspectives and Applications

Deciphering plant-microbe interactions is a promising aspect to understand the benefits and the pathogenic effect of microbes and crop improvement. The advancement in sequencing technologies and various 'omics' tool has impressively accelerated the research in biological sciences in this area. The recent and ongoing developments provide a unique approach to describing these intricate interactions and test hypotheses. In the present review, we discuss the role of plant-pathogen interaction in crop improvement. The plant innate immunity has always been an important aspect of research and leads to some interesting information like the adaptation of unique immune mechanisms of plants against pathogens. The development of new techniques in the post - genomic era has greatly enhanced our understanding of the regulation of plant defense mechanisms against pathogens. The present review also provides an overview of beneficial plant-microbe interactions with special reference to Agrobacterium tumefaciens-plant interactions where plant derived signal molecules and plant immune responses are important in pathogenicity and transformation efficiency. The construction of various Genome-scale metabolic models of microorganisms and plants presented a better understanding of all metabolic interactions activated during the interactions. This review also lists the emerging repertoire of phytopathogens and its impact on plant disease resistance. Outline of different aspects of plant-pathogen interactions is presented in this review to bridge the gap between plant microbial ecology and their immune responses.

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

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

Correlation-Based Network Analysis of Metabolite and Enzyme Profiles Reveals a Role of Citrate Biosynthesis in Modulating N and C Metabolism in Zea mays

To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their variance within the population, consistently with their related enzymes. The overall higher CV values for metabolites as compared to the tested enzymes are indicative for their greater phenotypic plasticity. H(2) tests revealed galactinol (1) and asparagine (0.91) as the highest scorers among metabolites and nitrate reductase (0.73), NAD-glutamate dehydrogenase (0.52), and phosphoglucomutase (0.51) among enzymes. The overall low H(2) scores for metabolites and enzymes are suggestive for a great environmental impact or gene-environment interaction. Correlation-based network generation followed by community detection analysis, partitioned the network into three main communities and one dyad, (i) reflecting the different levels of phenotypic plasticity of the two molecular classes as observed for the CV values and (ii) highlighting the concerted changes between classes of chemically related metabolites. Community 1 is composed mainly of enzymes and specialized metabolites, community 2' is enriched in N-containing compounds and phosphorylated-intermediates. The third community contains mainly organic acids and sugars. Cross-community linkages are supported by aspartate, by the photorespiration amino acids glycine and serine, by the metabolically related GABA and putrescine, and by citrate. The latter displayed the strongest node-betweenness value (185.25) of all nodes highlighting its fundamental structural role in the connectivity of the network by linking between different communities and to the also strongly connected enzyme aldolase.

Mathematical models of plant metabolism

Among various modeling approaches in plant metabolic research, applications of Constraint-Based modeling are fast increasing in recent years, apparently driven by current advances in genomics and genome sequencing. Constraint-Based modeling, the functional analysis of metabolic networks at the whole cell or genome scale, is more difficult to apply to plants than to microbes. Here we discuss recent developments in Constraint-Based modeling in plants with focus on issues of model reconstruction and flux prediction. Another topic is the emerging application of integration of Constraint-Based modeling with omics data to increase predictive power. Furthermore, advances in experimental measurements of cellular fluxes by (13)C-Metabolic Flux Analysis are highlighted, including instationary (13)C-MFA used to probe autotrophic metabolism in photosynthetic tissue in the light.

Omics data input for metabolic modeling

Recent advancements in high-throughput large-scale analytical methods to sequence genomes of organisms, and to quantify gene expression, proteins, lipids and metabolites have changed the paradigm of metabolic modeling. The cost of data generation and analysis has decreased significantly, which has allowed exponential increase in the amount of omics data being generated for an organism in a very short time. Compared to progress made in microbial metabolic modeling, plant metabolic modeling still remains limited due to its complex genomes and compartmentalization of metabolic reactions. Herein, we review and discuss different omics-datasets with potential application in the functional genomics. In particular, this review focuses on the application of omics-datasets towards construction and reconstruction of plant metabolic models.

A reductionist approach to model photosynthetic self-regulation in eukaryotes in response to light

Along with the development of several large-scale methods such as mass spectrometry or micro arrays, genome wide models became not only a possibility but an obvious tool for theoretical biologists to integrate and analyse complex biological data. Nevertheless, incorporating the dynamics of photosynthesis remains one of the major challenges while reconstructing metabolic networks of plants and other photosynthetic organisms. In this review, we aim to provide arguments that small-scale models are still a suitable choice when it comes to discovering organisational principles governing the design of biological systems. We give a brief overview of recent modelling efforts in understanding the interplay between rapid, photoprotective mechanisms and the redox balance within the thylakoid membrane, discussing the applicability of a reductionist approach in modelling self-regulation in plants and outline possible directions for further research.

Using Genome-scale Models to Predict Biological Capabilities

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

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

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

The genome-scale metabolic network of Ectocarpus siliculosus (EctoGEM): a resource to study brown algal physiology and beyond

Brown algae (stramenopiles) are key players in intertidal ecosystems, and represent a source of biomass with several industrial applications. Ectocarpus siliculosus is a model to study the biology of these organisms. Its genome has been sequenced and a number of post-genomic tools have been implemented. Based on this knowledge, we report the reconstruction and analysis of a genome-scale metabolic network for E. siliculosus, EctoGEM (http://ectogem.irisa.fr). This atlas of metabolic pathways consists of 1866 reactions and 2020 metabolites, and its construction was performed by means of an integrative computational approach for identifying metabolic pathways, gap filling and manual refinement. The capability of the network to produce biomass was validated by flux balance analysis. EctoGEM enabled the reannotation of 56 genes within the E. siliculosus genome, and shed light on the evolution of metabolic processes. For example, E. siliculosus has the potential to produce phenylalanine and tyrosine from prephenate and arogenate, but does not possess a phenylalanine hydroxylase, as is found in other stramenopiles. It also possesses the complete eukaryote molybdenum co-factor biosynthesis pathway, as well as a second molybdopterin synthase that was most likely acquired via horizontal gene transfer from cyanobacteria by a common ancestor of stramenopiles. EctoGEM represents an evolving community resource to gain deeper understanding of the biology of brown algae and the diversification of physiological processes. The integrative computational method applied for its reconstruction will be valuable to set up similar approaches for other organisms distant from biological benchmark models.

Integration of a constraint-based metabolic model of Brassica napus developing seeds with (13)C-metabolic flux analysis

The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for (13)C-Metabolic Flux Analysis ((13)C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from (13)C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.

Integrated network analysis and effective tools in plant systems biology

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

A workflow for mathematical modeling of subcellular metabolic pathways in leaf metabolism of Arabidopsis thaliana

During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation. In this way, different strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.

Changes in RNA Splicing in Developing Soybean (Glycine max) Embryos

Developing soybean seeds accumulate oils, proteins, and carbohydrates that are used as oxidizable substrates providing metabolic precursors and energy during seed germination. The accumulation of these storage compounds in developing seeds is highly regulated at multiple levels, including at transcriptional and post-transcriptional regulation. RNA sequencing was used to provide comprehensive information about transcriptional and post-transcriptional events that take place in developing soybean embryos. Bioinformatics analyses lead to the identification of different classes of alternatively spliced isoforms and corresponding changes in their levels on a global scale during soybean embryo development. Alternative splicing was associated with transcripts involved in various metabolic and developmental processes, including central carbon and nitrogen metabolism, induction of maturation and dormancy, and splicing itself. Detailed examination of selected RNA isoforms revealed alterations in individual domains that could result in changes in subcellular localization of the resulting proteins, protein-protein and enzyme-substrate interactions, and regulation of protein activities. Different isoforms may play an important role in regulating developmental and metabolic processes occurring at different stages in developing oilseed embryos.

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.

Nematode effector proteins: an emerging paradigm of parasitism

Phytonematodes use a stylet and secreted effectors to modify host cells and ingest nutrients to support their growth and development. The molecular function of nematode effectors is currently the subject of intense investigation. In this review, we summarize our current understanding of nematode effectors, with a particular focus on proteinaceous stylet-secreted effectors of sedentary endoparasitic phytonematodes, for which a wealth of information has surfaced in the past 10 yr. We provide an update on the effector repertoires of several of the most economically important genera of phytonematodes and discuss current approaches to dissecting their function. Lastly, we highlight the latest breakthroughs in effector discovery that promise to shed new light on effector diversity and function across the phylum Nematoda.

Metabolic and Transcriptional Reprogramming in Developing Soybean (Glycine max) Embryos

Soybean (Glycine max) seeds are an important source of seed storage compounds, including protein, oil, and sugar used for food, feed, chemical, and biofuel production. We assessed detailed temporal transcriptional and metabolic changes in developing soybean embryos to gain a systems biology view of developmental and metabolic changes and to identify potential targets for metabolic engineering. Two major developmental and metabolic transitions were captured enabling identification of potential metabolic engineering targets specific to seed filling and to desiccation. The first transition involved a switch between different types of metabolism in dividing and elongating cells. The second transition involved the onset of maturation and desiccation tolerance during seed filling and a switch from photoheterotrophic to heterotrophic metabolism. Clustering analyses of metabolite and transcript data revealed clusters of functionally related metabolites and transcripts active in these different developmental and metabolic programs. The gene clusters provide a resource to generate predictions about the associations and interactions of unknown regulators with their targets based on “guilt-by-association” relationships. The inferred regulators also represent potential targets for future metabolic engineering of relevant pathways and steps in central carbon and nitrogen metabolism in soybean embryos and drought and desiccation tolerance in plants.

SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks

Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.

Post-translational events and modifications regulating plant enzymes involved in isoprenoid precursor biosynthesis

Identification of regulatory enzymes is fundamental for engineering metabolic pathways such as the isoprenoid one. All too often, investigation of gene expression remains the major trend in unraveling regulation mechanisms of the isoprenoid cytosolic mevalonate and the plastid-localized methylerythritol phosphate metabolic pathways. But such metabolic regulatory enzymes are frequently multilevel-regulated, especially at a post-translational level. A prominent example is the endoplasmic reticulum-bound 3-hydroxy-3-methylglutaryl coenzyme A reductase catalyzing the synthesis of mevalonic acid. Despite the discovery and the intense efforts made to understand regulation of the methylerythritol phosphate pathway, this enzyme remains a leading player in the regulation of the whole isoprenoid pathway. Strict correlation between this enzyme's gene expression, protein level and enzyme activity is not observed, thus confirming multilevel-regulation. In this context, besides post-translational modifications of proteins, we have to consider feedback of metabolic flow and allosteric regulation, alternative protein structures, targeted proteolysis and/or redox regulation. Such multilevel-regulation processes deliver a range of benefits including rapid response to environmental and physiological challenges or metabolic fluctuations. This review specially emphasizes essential functions of these post-translational events that permit the close regulation of key enzymes involved in plant isoprenoid precursor biosynthesis.

Metabolic engineering with plants for a sustainable biobased economy

Plants are bona fide sustainable organisms because they accumulate carbon and synthesize beneficial metabolites from photosynthesis. To meet the challenges to food security and health threatened by increasing population growth and depletion of nonrenewable natural resources, recent metabolic engineering efforts have shifted from single pathways to holistic approaches with multiple genes owing to integration of omics technologies. Successful engineering of plants results in the high yield of biomass components for primary food sources and biofuel feedstocks, pharmaceuticals, and platform chemicals through synthetic biology and systems biology strategies. Further discovery of undefined biosynthesis pathways in plants, integrative analysis of discrete omics data, and diversified process developments for production of platform chemicals are essential to overcome the hurdles for sustainable production of value-added biomolecules from plants.

Metabolic network flux analysis for engineering plant systems

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

Recent progress in the development of metabolome databases for plant systems biology

Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology.

Plant genome-scale metabolic reconstruction and modelling

Genome-scale metabolic reconstructions are used extensively in the study of microbial metabolism and have proven powerful tools to guide rational pathway design of industrial strains. Generation and curation of plant genome-scale metabolic models has proven far more challenging, not the least of which is our incomplete knowledge of compartmentation and organelle transporters in plants. Conversely, the potential value of modelling is far greater when exploring a complex, multi-organelle and multi-tissue metabolism. The first generation of plant genome-scale metabolic reconstructions have proven surprisingly functional and robust as well as capable of predicting many observed complex phenotypes. With further refinement, the application of these models promises to make important contributions to plant biology and metabolic engineering.