Frontiers in metabolic reconstruction and modeling of plant genomes

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

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.

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.

Streamlining Natural Products Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering

Increasing demands for the supply of biopharmaceuticals have propelled the advancement of metabolic engineering and synthetic biology strategies for biomanufacturing of bioactive natural products. Using metabolically engineered microbes as the bioproduction hosts, a variety of natural products including terpenes, flavonoids, alkaloids, and cannabinoids have been synthesized through the construction and expression of known and newly found biosynthetic genes primarily from model and non-model plants. The employment of omics technology and machine learning (ML) platforms as high throughput analytical tools has been increasingly leveraged in promoting data-guided optimization of targeted biosynthetic pathways and enhancement of the microbial production capacity, thereby representing a critical debottlenecking approach in improving and streamlining natural products biomanufacturing. To this end, this mini review summarizes recent efforts that utilize omics platforms and ML tools in strain optimization and prototyping and discusses the beneficial uses of omics-enabled discovery of plant biosynthetic genes in the production of complex plant-based natural products by bioengineered microbes.

Teleological role of L-2-hydroxyglutarate dehydrogenase in the kidney

L-2-hydroxyglutarate (L-2HG) is an oncometabolite found elevated in renal tumors. However, this molecule might have physiological roles that extend beyond its association with cancer, as L-2HG levels are elevated in response to hypoxia and during Drosophila larval development. L-2HG is known to be metabolized by L-2HG dehydrogenase (L2HGDH), and loss of L2HGDH leads to elevated L-2HG levels. Despite L2HGDH being highly expressed in the kidney, its role in renal metabolism has not been explored. Here, we report our findings utilizing a novel CRISPR/Cas9 murine knockout model, with a specific focus on the role of L2HGDH in the kidney. Histologically, L2hgdh knockout kidneys have no demonstrable histologic abnormalities. However, GC-MS metabolomics demonstrates significantly reduced levels of the TCA cycle intermediate succinate in multiple tissues. Isotope labeling studies with [U-13C] glucose demonstrate that restoration of L2HGDH in renal cancer cells (which lowers L-2HG) leads to enhanced incorporation of label into TCA cycle intermediates. Subsequent biochemical studies demonstrate that L-2HG can inhibit the TCA cycle enzyme α-ketoglutarate dehydrogenase. Bioinformatic analysis of mRNA expression data from renal tumors demonstrates that L2HGDH is co-expressed with genes encoding TCA cycle enzymes as well as the gene encoding the transcription factor PGC-1α, which is known to regulate mitochondrial metabolism. Restoration of PGC-1α in renal tumor cells results in increased L2HGDH expression with a concomitant reduction in L-2HG levels. Collectively, our analyses provide new insight into the physiological role of L2HGDH as well as mechanisms that promote L-2HG accumulation in disease states.

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.

A pathway for every product? Tools to discover and design plant metabolism

The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.

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.

Confronting the catalytic dark matter encoded by sequenced genomes

The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research.

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.

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.

Non-enzymatic molecular damage as a prototypic driver of aging

The chemical potentialities of metabolites far exceed metabolic requirements. The required potentialities are realized mostly through enzymatic catalysis. The rest are realized spontaneously through organic reactions that (i) occur wherever appropriate reactants come together, (ii) are so typical that many have proper names (e.g. Michael addition, Amadori rearrangement, and Pictet-Spengler reaction), and (iii) often have damaging consequences. There are many more causes of non-enzymatic damage to metabolites than reactive oxygen species and free radical processes (the "usual suspects"). Endogenous damage accumulation in non-renewable macromolecules and spontaneously polymerized material is sufficient to account for aging and differentiates aging from wear-and-tear of inanimate objects by deriving it from metabolism, the essential attribute of life.

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.

From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model

Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe's entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe's metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.

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.

Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods

Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.

Human metabolic atlas: an online resource for human metabolism

Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (http://www.metabolicatlas.org). This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: a generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License.

Using metabolomic approaches to explore chemical diversity in rice

Rice (Oryza sativa) is an excellent resource; it comprises 25% of the total caloric intake of the world's population, and rice plants yield many types of bioactive compounds. To determine the number of metabolites in rice and their chemical diversity, the metabolite composition of cultivated rice has been investigated with analytical techniques such as mass spectrometry (MS) and/or nuclear magnetic resonance spectroscopy and rice metabolite databases have been constructed. This review summarizes current knowledge on metabolites in rice including sugars, amino and organic acids, aromatic compounds, and phytohormones detected by gas chromatography-MS, liquid chromatography-MS, and capillary electrophoresis-MS. The biological properties and the activities of polar and nonpolar metabolites produced by rice plants are also presented. Challenges in the estimation of the structure(s) of unknown metabolites by metabolomic approaches are introduced and discussed. Lastly, examples are presented of the successful application of metabolite profiling of rice to characterize the gene(s) that are potentially critical for improving its quality by combining metabolite quantitative trait loci analysis and to identify potential metabolite biomarkers that play a critical role when rice is grown under abiotic stress conditions.

Metabolic engineering of higher plants and algae for isoprenoid production

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

A systematic simulation of the effect of salicylic acid on sphingolipid metabolism

The phytohormone salicylic acid (SA) affects plant development and defense responses. Recent studies revealed that SA also participates in the regulation of sphingolipid metabolism, but the details of this regulation remain to beexplored. Here, we use in silico Flux Balance Analysis (FBA) with published microarray data to construct a whole-cell simulation model, including 23 pathways, 259 reactions, and 172 metabolites, to predict the alterations in flux of major sphingolipid species after treatment with exogenous SA. This model predicts significant changes in fluxes of certain sphingolipid species after SA treatment, changes that likely trigger downstream physiological and phenotypic effects. To validate the simulation, we used (15)N-labeled metabolic turnover analysis to measure sphingolipid contents and turnover rate in Arabidopsis thaliana seedlings treated with SA or the SA analog benzothiadiazole (BTH). The results show that both SA and BTH affect sphingolipid metabolism, altering the concentrations of certain species and also changing the optimal flux distribution and turnover rate of sphingolipids. Our strategy allows us to estimate sphingolipid fluxes on a short time scale and gives us a systemic view of the effect of SA on sphingolipid homeostasis.

Predictive sulfur metabolism - a field in flux

The key role of sulfur metabolites in response to biotic and abiotic stress in plants, as well as their importance in diet and health has led to a significant interest and effort in trying to understand and manipulate the production of relevant compounds. Metabolic engineering utilizes a set of theoretical tools to help rationally design modifications that enhance the production of a desired metabolite. Such approaches have proven their value in bacterial systems, however, the paucity of success stories to date in plants, suggests that challenges remain. Here, we review the most commonly used methods for understanding metabolic flux, focusing on the sulfur assimilatory pathway. We highlight known issues with both experimental and theoretical approaches, as well as presenting recent methods for integrating different modeling strategies, and progress toward an understanding of flux at the whole plant level.

Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods.

Nitrogen-use efficiency in maize (Zea mays L.): from 'omics' studies to metabolic modelling

In this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when integrated with 'omics' data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed.

The metabolic flux phenotype of heterotrophic Arabidopsis cells reveals a flexible balance between the cytosolic and plastidic contributions to carbohydrate oxidation in response to phosphate limitation

Understanding the mechanisms that allow plants to respond to variable and reduced availability of inorganic phosphate is of increasing agricultural importance because of the continuing depletion of the rock phosphate reserves that are used to combat inadequate phosphate levels in the soil. Changes in gene expression, protein levels, enzyme activities and metabolite levels all point to a reconfiguration of the central metabolic network in response to reduced availability of inorganic phosphate, but the metabolic significance of these changes can only be assessed in terms of the fluxes supported by the network. Steady-state metabolic flux analysis was used to define the metabolic phenotype of a heterotrophic Arabidopsis thaliana cell culture grown on a Murashige and Skoog medium containing 0, 1.25 or 5 mm inorganic phosphate. Fluxes through the central metabolic network were deduced from the redistribution of (13) C into metabolic intermediates and end products when cells were labelled with [1-(13) C], [2-(13) C], or [(13) C6 ]glucose, in combination with (14) C measurements of the rates of biomass accumulation. Analysis of the flux maps showed that reduced levels of phosphate in the growth medium stimulated flux through phosphoenolpyruvate carboxylase and malic enzyme, altered the balance between cytosolic and plastidic carbohydrate oxidation in favour of the plastid, and increased cell maintenance costs. We argue that plant cells respond to phosphate deprivation by reconfiguring the flux distribution through the pathways of carbohydrate oxidation to take advantage of better phosphate homeostasis in the plastid.

Genome-scale models of plant metabolism

A genome-scale model (GSM) is an in silico metabolic model comprising hundreds or thousands of chemical reactions that constitute the metabolic inventory of a cell, tissue, or organism. A complete, accurate GSM, in conjunction with a simulation technique such as flux balance analysis (FBA), can be used to comprehensively predict cellular metabolic flux distributions for a given genotype and given environmental conditions. Apart from enabling a user to quantitatively visualize carbon flow through metabolic pathways, these flux predictions also facilitate the hypothesis of new network properties. By simulating the impacts of environmental stresses or genetic interventions on metabolism, GSMs can aid the formulation of nontrivial metabolic engineering strategies. GSMs for plants and other eukaryotes are significantly more complicated than those for prokaryotes due to their extensive compartmentalization and size. The reconstruction of a GSM involves creating an initial model, curating the model, and then rendering the model ready for FBA. Model reconstruction involves obtaining organism-specific reactions from the annotated genome sequence or organism-specific databases. Model curation involves determining metabolite protonation status or charge, ensuring that reactions are stoichiometrically balanced, assigning reactions to appropriate subcellular compartments, deleting generic reactions or creating specific versions of them, linking dead-end metabolites, and filling of pathway gaps to complete the model. Subsequently, the model requires the addition of transport, exchange, and biomass synthesis reactions to make it FBA-ready. This cycle of editing, refining, and curation has to be performed iteratively to obtain an accurate model. This chapter outlines the reconstruction and curation of GSMs with a focus on models of plant metabolism.

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.

Strategies and future trends to identify the mode of action of phytotoxic compounds

Small molecules affecting plant processes have been widely used as probes to study basic physiology. In agricultural practices some of these molecules have served as herbicides or plant growth regulators. Historically, most of the compounds were identified in large screens by the agrochemical industry, but also as phytoactive natural products. More recently, novel phytoactive compounds originated from academic research by chemical screens performed to induce specific phenotypes of interest. In the present review different approaches were evaluated for the identification of the mode of action (MoA) of phytoactive compounds. Based on the methodologies used for MoA identification, three approaches are differentiated: a phenotyping approach, an approach based on a genetic screen and a biochemical screening approach. Target sites of compounds targeting primary or secondary metabolism were identified most successfully with a phenotyping approach. Target sites for compounds that influence cell structure, such as cell wall biosynthesis or the cytoskeleton, or compounds that interact with the hormone system, were in most cases discovered by using a genetic approach. Examples showing the strengths and weaknesses of the different approaches are discussed in detail. Additionally, new techniques that could contribute to future MoA identification projects are reviewed. In particular, next-generation sequencing techniques may be used for the fast-forward mapping of mutants identified in genetic screens. Finally, a revised three-tiered approach for the MoA identification of phytoactive compounds is proposed. The approach consists of a 1st tier, which addresses compound stability, uniformity of effects in different species, general cytotoxicity and the effect on common processes such as transcription and translation. Advanced studies based on these findings initiate the 2nd tier MoA characterization, either with further phenotypic characterization, starting a genetic screen or establishing a biochemical screen. At the 3rd tier, enzyme assays or protein affinity studies should show the activity of the compound on the hypothesized target and should associate the in vitro effects with the in vivo profile of the compound.

Computational approaches for understanding energy metabolism

There has been a surge of interest in understanding the regulation of metabolic networks involved in disease in recent years. Quantitative models are increasingly being used to interrogate the metabolic pathways that are contained within this complex disease biology. At the core of this effort is the mathematical modeling of central carbon metabolism involving glycolysis and the citric acid cycle (referred to as energy metabolism). Here, we discuss several approaches used to quantitatively model metabolic pathways relating to energy metabolism and discuss their formalisms, successes, and limitations.

Modelling metabolic CO₂ evolution--a fresh perspective on respiration

Respiration is a major contributor to net exchange of CO₂ between plants and the atmosphere and thus an important aspect of the vegetation component of global climate change models. However, a mechanistic model of respiration is lacking, and so here we explore the potential for flux balance analysis (FBA) to predict cellular CO₂ evolution rates. Metabolic flux analysis reveals that respiration is not always the dominant source of CO₂, and that metabolic processes such as the oxidative pentose phosphate pathway (OPPP) and lipid synthesis can be quantitatively important. Moreover, there is considerable variation in the metabolic origin of evolved CO₂ between tissues, species and conditions. Comparison of FBA-predicted CO₂ evolution profiles with those determined from flux measurements reveals that FBA is able to predict the metabolic origin of evolved CO₂ in different tissues/species and under different conditions. However, FBA is poor at predicting flux through certain metabolic processes such as the OPPP and we identify the way in which maintenance costs are accounted for as a major area of improvement for future FBA studies. We conclude that FBA, in its standard form, can be used to predict CO₂ evolution in a range of plant tissues and in response to environment.

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.

The carotenoid biosynthetic pathway: thinking in all dimensions

The carotenoid biosynthetic pathway serves manifold roles in plants related to photosynthesis, photoprotection, development, stress hormones, and various volatiles and signaling apocarotenoids. The pathway also produces compounds that impact human nutrition and metabolic products that contribute to fragrance and flavor of food and non-food crops. It is no surprise that the pathway has been a target of metabolic engineering, most prominently in the case of Golden Rice. The future success and predictability of metabolic engineering of carotenoids rests in the ability to target carotenoids for specific physiological purposes as well as to simultaneously modify carotenoids along with other desired traits. Here, we ask whether predictive metabolic engineering of the carotenoid pathway is indeed possible. Despite a long history of research on the pathway, at this point in time we can only describe the pathway as a parts list and have almost no knowledge of the location of the complete pathway, how it is assembled, and whether there exists any trafficking of the enzymes or the carotenoids themselves. We discuss the current state of knowledge regarding the "complete" pathway and make the argument that predictive metabolic engineering of the carotenoid pathway (and other pathways) will require investigation of the three dimensional state of the pathway as it may exist in plastids of different ultrastructures. Along with this message we point out the need to develop new types of visualization tools and resources that better reflect the dynamic nature of biosynthetic pathways.

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

We describe the construction and analysis of a genome-scale metabolic model representing a developing leaf cell of rice (Oryza sativa) primarily derived from the annotations in the RiceCyc database. We used flux balance analysis to determine that the model represents a network capable of producing biomass precursors (amino acids, nucleotides, lipid, starch, cellulose, and lignin) in experimentally reported proportions, using carbon dioxide as the sole carbon source. We then repeated the analysis over a range of photon flux values to examine responses in the solutions. The resulting flux distributions show that (1) redox shuttles between the chloroplast, cytosol, and mitochondrion may play a significant role at low light levels, (2) photorespiration can act to dissipate excess energy at high light levels, and (3) the role of mitochondrial metabolism is likely to vary considerably according to the balance between energy demand and availability. It is notable that these organelle interactions, consistent with many experimental observations, arise solely as a result of the need for mass and energy balancing without any explicit assumptions concerning kinetic or other regulatory mechanisms.

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.

Metabolite damage and its repair or pre-emption

It is increasingly evident that metabolites suffer various kinds of damage, that such damage happens in all organisms and that cells have dedicated systems for damage repair and containment. First, chemical biology is demonstrating that diverse metabolites are damaged by side reactions of 'promiscuous' enzymes or by spontaneous chemical reactions, that the products are useless or toxic and that the unchecked buildup of these products can be devastating. Second, genetic and genomic evidence from prokaryotes and eukaryotes is implicating a network of new, conserved enzymes that repair damaged metabolites or somehow pre-empt damage. Metabolite (that is, small-molecule) repair is analogous to macromolecule (DNA and protein) repair and seems from comparative genomic evidence to be equally widespread. Comparative genomics also implies that metabolite repair could be the function of many conserved protein families lacking known activities. How--and how well--cells deal with metabolite damage affects fields ranging from medical genetics to metabolic engineering.

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.

The spatial organization of metabolism within the plant cell

Identifying the correct subcellular locations for all enzymes and metabolites in plant metabolic networks is a major challenge, but is critically important for the success of the new generation of large-scale metabolic models that are driving a network-level appreciation of metabolic behavior. Even though the subcellular compartmentation of many central metabolic processes is thought to be well understood, recent gene-by-gene studies have revealed several unexpected enzyme localizations. Metabolite transport between subcellular compartments is crucial because it fundamentally affects the metabolic network structure. Although new metabolite transporters are being steadily identified, modeling work suggests that we have barely scratched the surface of the catalog of intracellular metabolite transporter proteins. In addition to compartmentation among organelles, it is increasingly apparent that microcompartment formation via the interactions of enzyme groups with intracellular membranes, the cytoskeleton, or other proteins is an important regulatory mechanism. In particular, this mechanism can promote metabolite channeling within the metabolic microcompartment, which can help control reaction specificity as well as dictate flux routes through the network. This has clear relevance for both synthetic biology in general and the engineering of plant metabolic networks in particular.

Identification and characterization of the missing pyrimidine reductase in the plant riboflavin biosynthesis pathway

Riboflavin (vitamin B₂) is the precursor of the flavin coenzymes flavin mononucleotide and flavin adenine dinucleotide. In Escherichia coli and other bacteria, sequential deamination and reduction steps in riboflavin biosynthesis are catalyzed by RibD, a bifunctional protein with distinct pyrimidine deaminase and reductase domains. Plants have two diverged RibD homologs, PyrD and PyrR; PyrR proteins have an extra carboxyl-terminal domain (COG3236) of unknown function. Arabidopsis (Arabidopsis thaliana) PyrD (encoded by At4g20960) is known to be a monofunctional pyrimidine deaminase, but no pyrimidine reductase has been identified. Bioinformatic analyses indicated that plant PyrR proteins have a catalytically competent reductase domain but lack essential zinc-binding residues in the deaminase domain, and that the Arabidopsis PyrR gene (At3g47390) is coexpressed with riboflavin synthesis genes. These observations imply that PyrR is a pyrimidine reductase without deaminase activity. Consistent with this inference, Arabidopsis or maize (Zea mays) PyrR (At3g47390 or GRMZM2G090068) restored riboflavin prototrophy to an E. coli ribD deletant strain when coexpressed with the corresponding PyrD protein (At4g20960 or GRMZM2G320099) but not when expressed alone; the COG3236 domain was unnecessary for complementing activity. Furthermore, recombinant maize PyrR mediated NAD(P)H-dependent pyrimidine reduction in vitro. Import assays with pea (Pisum sativum) chloroplasts showed that PyrR and PyrD are taken up and proteolytically processed. Ablation of the maize PyrR gene caused early seed lethality. These data argue that PyrR is the missing plant pyrimidine reductase, that it is plastid localized, and that it is essential. The role of the COG3236 domain remains mysterious; no evidence was obtained for the possibility that it catalyzes the dephosphorylation that follows pyrimidine reduction.

The challenges of cellular compartmentalization in plant metabolic engineering

The complex metabolic networks in plants are highly compartmentalized and biochemical steps of a single pathway can take place in multiple subcellular locations. Our knowledge regarding reactions and precursor compounds in the various cellular compartments has increased in recent years due to innovations in tracking the spatial distribution of proteins and metabolites. Nevertheless, to date only few studies have integrated subcellular localization criteria in metabolic engineering attempts. Here, we highlight the crucial factors for subcellular-localization-based strategies in plant metabolic engineering including substrate availability, enzyme targeting, the role of transporters, and multigene transfer approaches. The availability of compartmentalized metabolic network models for plants in the near future will greatly advance the integration of localization constraints in metabolic engineering experiments and aid in predicting their outcomes.

Progress in understanding and engineering primary plant metabolism

The maximum yield of crop plants depends on the efficiency of conversion of sunlight into biomass. This review summarises recent models that estimate energy conversion efficiency for successive steps in photosynthesis and metabolism. Photorespiration was identified as a major reason for energy loss during photosynthesis and strategies to modify or suppress photorespiration are presented. Energy loss during the conversion of photosynthate to biomass is also large but cannot be modelled as precisely due to incomplete knowledge about pathways and turnover and maintenance costs. Recent research on pathways involved in metabolite transport and interconversion in different organs, and recent insights into energy requirements linked to the production, maintenance and turnover of the apparatus for cellular growth and repair processes are discussed.

Mathematical modeling of plant metabolism-from reconstruction to prediction

Due to their sessile lifestyle, plants are exposed to a large set of environmental cues. In order to cope with changes in environmental conditions a multitude of complex strategies to regulate metabolism has evolved. The complexity is mainly attributed to interlaced regulatory circuits between genes, proteins and metabolites and a high degree of cellular compartmentalization. The genetic model plant Arabidopsis thaliana was intensely studied to characterize adaptive traits to a changing environment. The availability of genetically distinct natural populations has made it an attractive system to study plant-environment interactions. The impact on metabolism caused by changing environmental conditions can be estimated by mathematical approaches and deepens the understanding of complex biological systems. In combination with experimental high-throughput technologies this provides a promising platform to develop in silico models which are not only able to reproduce but also to predict metabolic phenotypes and to allow for the interpretation of plant physiological mechanisms leading to successful adaptation to a changing environment. Here, we provide an overview of mathematical approaches to analyze plant metabolism, with experimental procedures being used to validate their output, and we discuss them in the context of establishing a comprehensive understanding of plant-environment interactions.

Plant B vitamin pathways and their compartmentation: a guide for the perplexed

The B vitamins and the cofactors derived from them are essential for life. B vitamin synthesis in plants is consequently as crucial to plants themselves as it is to humans and animals, whose B vitamin nutrition depends largely on plants. The synthesis and salvage pathways for the seven plant B vitamins are now broadly known, but certain enzymes and many transporters have yet to be identified, and the subcellular locations of various reactions are unclear. Although very substantial, what is not known about plant B vitamin pathways is regrettably difficult to discern from the literature or from biochemical pathway databases. Nor do databases accurately represent all that is known about B vitamin pathways-above all their compartmentation-because the facts are scattered throughout the literature, and thus hard to piece together. These problems (i) deter discoveries because newcomers to B vitamins cannot see which mysteries still need solving; and (ii) impede metabolic reconstruction and modelling of B vitamin pathways because genes for reactions or transport steps are missing. This review therefore takes a fresh approach to capture current knowledge of B vitamin pathways in plants. The synthesis pathways, key salvage routes, and their subcellular compartmentation are surveyed in depth, and encoded in the SEED database (http://pubseed.theseed.org/seedviewer.cgi?page=PlantGateway) for Arabidopsis and maize. The review itself and the encoded pathways specifically identify enigmatic or missing reactions, enzymes, and transporters. The SEED-encoded B vitamin pathway collection is a publicly available, expertly curated, one-stop resource for metabolic reconstruction and modeling.

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.

Pathways and fluxes: exploring the plant metabolic network

The transition from a pathway-centred view of plant metabolism to a network-wide perspective is still incomplete. Further progress in this direction requires tools to facilitate the structural description of the network on the basis of fully annotated genomes, techniques for modelling the properties of the network, and experimental methods for constraining the models and verifying their outputs. It also requires a focus on metabolic flux as the key to understanding the regulation of metabolic activity and the relationship between the inputs and outputs of the network. Progress is being made on several fronts and this Special Issue on 'Pathways and fluxes: exploring the plant metabolic network' describes current developments in the genomic reconstruction of metabolic networks, the application of flux-balance analysis to such networks, kinetic modelling, and both steady-state-and non-steady state isotope-based measurements of multiple fluxes in the network of central carbon metabolism. The papers also highlight insights that can be obtained from pathway analysis, particularly in relation to the thermodynamic and kinetic efficiency of the predicted and observed flux distributions.