Simulating plant metabolic pathways with enzyme-kinetic models

Kinetic modelling of the cellular metabolic responses underpinning in vitro glycolysis assays

This study aims to demonstrate the benefits of augmenting commercially available, real-time, in vitro glycolysis assays with phenomenological rate equation-based kinetic models, describing the contributions of the underpinning metabolic pathways. To this end, a commercially available glycolysis assay, sensitive to changes in extracellular acidification (extracellular pH), was used to derive the glycolysis pathway kinetics. The pathway was numerically modelled using a series of ordinary differential rate equations, to simulate the obtained experimental results. The sensitivity of the model to the key equation parameters was also explored. The cellular glycolysis pathway kinetics were determined for three different cell-lines, under nonmodulated and modulated conditions. Over the timescale studied, the assay demonstrated a two-phase metabolic response, representing the differential kinetics of glycolysis pathway rate as a function of time, and this behaviour was faithfully reproduced by the model simulations. The model enabled quantitative comparison of the pathway kinetics of three cell lines, and also the modulating effect of two known drugs. Moreover, the modelling tool allows the subtle differences between different cell lines to be better elucidated and also allows augmentation of the assay sensitivity. A simplistic numerical model can faithfully reproduce the differential pathway kinetics for three different cell lines, with and without pathway-modulating drugs, and furthermore provides insights into the cellular metabolism by elucidating the underlying mechanisms leading to the pathway end-product. This study demonstrates that augmenting a relatively simple, real-time, in vitro assay with a model of the underpinning metabolic pathway provides considerable insights into the observed differences in cellular systems.

Enzyme-based kinetic modelling of ASC-GSH cycle during tomato fruit development reveals the importance of reducing power and ROS availability

The ascorbate-glutathione (ASC-GSH) cycle is at the heart of redox metabolism, linking the major redox buffers with central metabolism through the processing of reactive oxygen species (ROS) and pyridine nucleotide metabolism. Tomato fruit development is underpinned by changes in redox buffer contents and their associated enzyme capacities, but interactions between them remain unclear. Based on quantitative data obtained for the core redox metabolism, we built an enzyme-based kinetic model to calculate redox metabolite concentrations with their corresponding fluxes and control coefficients. Dynamic and associated regulations of the ASC-GSH cycle throughout the whole fruit development were analysed and pointed to a sequential metabolic control of redox fluxes by ASC synthesis, NAD(P)H and ROS availability depending on the developmental phase. Furthermore, we highlighted that monodehydroascorbate reductase and the availability of reducing power were found to be the main regulators of the redox state of ASC and GSH during fruit growth under optimal conditions. Our kinetic modelling approach indicated that tomato fruit development displayed growth phase-dependent redox metabolism linked with central metabolism via pyridine nucleotides and H2 O2 availability, while providing a new tool to the scientific community to investigate redox metabolism in fruits.

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

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

Modeling to Understand Plant Protein Structure-Function Relationships-Implications for Seed Storage Proteins

Proteins are among the most important molecules on Earth. Their structure and aggregation behavior are key to their functionality in living organisms and in protein-rich products. Innovations, such as increased computer size and power, together with novel simulation tools have improved our understanding of protein structure-function relationships. This review focuses on various proteins present in plants and modeling tools that can be applied to better understand protein structures and their relationship to functionality, with particular emphasis on plant storage proteins. Modeling of plant proteins is increasing, but less than 9% of deposits in the Research Collaboratory for Structural Bioinformatics Protein Data Bank come from plant proteins. Although, similar tools are applied as in other proteins, modeling of plant proteins is lagging behind and innovative methods are rarely used. Molecular dynamics and molecular docking are commonly used to evaluate differences in forms or mutants, and the impact on functionality. Modeling tools have also been used to describe the photosynthetic machinery and its electron transfer reactions. Storage proteins, especially in large and intrinsically disordered prolamins and glutelins, have been significantly less well-described using modeling. These proteins aggregate during processing and form large polymers that correlate with functionality. The resulting structure-function relationships are important for processed storage proteins, so modeling and simulation studies, using up-to-date models, algorithms, and computer tools are essential for obtaining a better understanding of these relationships.

Metabolic acclimation-a key to enhancing photosynthesis in changing environments?

Plants adjust their photosynthetic capacity in response to their environment in a way that optimizes their yield and fitness. There is growing evidence that this acclimation is a response to changes in the leaf metabolome, but the extent to which these are linked and how this is optimized remain poorly understood. Using as an example the metabolic perturbations occurring in response to cold, we define the different stages required for acclimation, discuss the evidence for a metabolic temperature sensor, and suggest further work towards designing climate-smart crops. In particular, we discuss how constraint-based and kinetic metabolic modelling approaches can be used to generate targeted hypotheses about relevant pathways, and argue that a stronger integration of experimental and in silico studies will help us to understand the tightly regulated interplay of carbon partitioning and resource allocation required for photosynthetic acclimation to different environmental conditions.

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.

Putting primary metabolism into perspective to obtain better fruits

Background

One of the key goals of fruit biology is to understand the factors that influence fruit growth and quality, ultimately with a view to manipulating them for improvement of fruit traits.

Scope

Primary metabolism, which is not only essential for growth but is also a major component of fruit quality, is an obvious target for improvement. However, metabolism is a moving target that undergoes marked changes throughout fruit growth and ripening.

Conclusions

Agricultural practice and breeding have successfully improved fruit metabolic traits, but both face the complexity of the interplay between development, metabolism and the environment. Thus, more fundamental knowledge is needed to identify further strategies for the manipulation of fruit metabolism. Nearly two decades of post-genomics approaches involving transcriptomics, proteomics and/or metabolomics have generated a lot of information about the behaviour of fruit metabolic networks. Today, the emergence of modelling tools is providing the opportunity to turn this information into a mechanistic understanding of fruits, and ultimately to design better fruits. Since high-quality data are a key requirement in modelling, a range of must-have parameters and variables is proposed.

Assessing the impact of the 4CL enzyme complex on the robustness of monolignol biosynthesis using metabolic pathway analysis

Lignin is a polymer present in the secondary cell walls of all vascular plants. It is a known barrier to pulping and the extraction of high-energy sugars from cellulosic biomass. The challenge faced with predicting outcomes of transgenic plants with reduced lignin is due in part to the presence of unique protein-protein interactions that influence the regulation and metabolic flux in the pathway. Yet, it is unclear why certain plants have evolved to create these protein complexes. In this study, we use mathematical models to investigate the role that the protein complex, formed specifically between Ptr4CL3 and Ptr4CL5 enzymes, have on the monolignol biosynthesis pathway. The role of this Ptr4CL3-Ptr4CL5 enzyme complex on the steady state flux distribution was quantified by performing Monte Carlo simulations. The effect of this complex on the robustness and the homeostatic properties of the pathway were identified by performing sensitivity and stability analyses, respectively. Results from these robustness and stability analyses suggest that the monolignol biosynthetic pathway is resilient to mild perturbations in the presence of the Ptr4CL3-Ptr4CL5 complex. Specifically, the presence of Ptr4CL3-Ptr4CL5 complex increased the stability of the pathway by 22%. The robustness in the pathway is maintained due to the presence of multiple enzyme isoforms as well as the presence of alternative pathways resulting from the presence of the Ptr4CL3-Ptr4CL5 complex.

Overcoming antibiotic resistance: Is siderophore Trojan horse conjugation an answer to evolving resistance in microbial pathogens?

Comparative study of siderophore biosynthesis pathway in pathogens provides potential targets for antibiotics and host drug delivery as a part of computationally feasible microbial therapy. Iron acquisition using siderophore models is an essential and well established model in all microorganisms and microbial infections a known to cause great havoc to both plant and animal. Rapid development of antibiotic resistance in bacterial as well as fungal pathogens has drawn us at a verge where one has to get rid of the traditional way of obstructing pathogen using single or multiple antibiotic/chemical inhibitors or drugs. 'Trojan horse' strategy is an answer to this imperative call where antibiotic are by far sneaked into the pathogenic cell via the siderophore receptors at cell and outer membrane. This antibiotic once gets inside, generates a 'black hole' scenario within the opportunistic pathogens via iron scarcity. For pathogens whose siderophore are not compatible to smuggle drug due to their complex conformation and stiff valence bonds, there is another approach. By means of the siderophore biosynthesis pathways, potential targets for inhibition of these siderophores in pathogenic bacteria could be achieved and thus control pathogenic virulence. Method to design artificial exogenous siderophores for pathogens that would compete and succeed the battle of intake is also covered with this review. These manipulated siderophore would enter pathogenic cell like any other siderophore but will not disperse iron due to which iron inadequacy and hence pathogens control be accomplished. The aim of this review is to offer strategies to overcome the microbial infections/pathogens using siderophore.

Metabolic model of central carbon and energy metabolisms of growing Arabidopsis thaliana in relation to sucrose translocation

BACKGROUND

Sucrose translocation between plant tissues is crucial for growth, development and reproduction of plants. Systemic analysis of these metabolic and underlying regulatory processes allow a detailed understanding of carbon distribution within the plant and the formation of associated phenotypic traits. Sucrose translocation from 'source' tissues (e.g. mesophyll) to 'sink' tissues (e.g. root) is tightly bound to the proton gradient across the membranes. The plant sucrose transporters are grouped into efflux exporters (SWEET family) and proton-symport importers (SUC, STP families). To better understand regulation of sucrose export from source tissues and sucrose import into sink tissues, there is a need for a metabolic model that takes in account the tissue organisation of Arabidopsis thaliana with corresponding metabolic specificities of respective tissues in terms of sucrose and proton production/utilization. An ability of the model to operate under different light modes ('light' and 'dark') and correspondingly in different energy producing modes is particularly important in understanding regulatory modules.

RESULTS

Here, we describe a multi-compartmental model consisting of a mesophyll cell with plastid and mitochondrion, a phloem cell, as well as a root cell with mitochondrion. In this model, the phloem was considered as a non-growing transport compartment, the mesophyll compartment was considered as both autotrophic (growing on CO₂ under light) and heterotrophic (growing on starch in darkness), and the root was always considered as heterotrophic tissue dependent on sucrose supply from the mesophyll compartment. In total, the model includes 413 balanced compounds interconnected by 400 transformers. The structured metabolic model accounts for central carbon metabolism, photosynthesis, photorespiration, carbohydrate metabolism, energy and redox metabolisms, proton metabolism, biomass growth, nutrients uptake, proton gradient generation and sucrose translocation between tissues. Biochemical processes in the model were associated with gene-products (742 ORFs). Flux Balance Analysis (FBA) of the model resulted in balanced carbon, nitrogen, proton, energy and redox states under both light and dark conditions. The main H⁺-fluxes were reconstructed and their directions matched with proton-dependent sucrose translocation from 'source' to 'sink' under any light condition.

CONCLUSIONS

The model quantified the translocation of sucrose between plant tissues in association with an integral balance of protons, which in turn is defined by operational modes of the energy metabolism.

Parameter estimation in tree graph metabolic networks

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

Transcriptional modulation of squalene synthase genes in barley treated with PGPR

Phytosterol contents and food quality of plant produce is directly associated with transcription of gene squalene synthase (SS). In current study, barley plants were treated with different rhizobacterial strains under semi controlled (27 ± 3°C) greenhouse conditions in order to modulate expression of SS gene. Plant samples were analyzed through semi-quantitative PCR to evaluate effect of rhizobacterial application on transcriptional status of SS. Results revealed that among four SS genes (i.e., SSA, SS1, SS2, and SS3), the most expressive gene was SSA; while, SS2 was screened out as the second best induced gene due to Acetobacter aceti. The most efficient bacterial strain which recorded maximum gene expression was A. aceti AC8. Moreover, AC7 was reported as the least efficient bacterial species for inducing SS gene expression. AC8 enhanced the share of SSA and SS2 up to 43 and 31%, respectively. The study also described ribosomal sequence of the most efficient bacterial strain AC8, which was used to determine its phylogenetic relationships with other microbial strains. The study would be helpful to improve quality of plant produce by modulating transcription of SS genes.

Alcohol Selectivity in a Synthetic Thermophilic n-Butanol Pathway Is Driven by Biocatalytic and Thermostability Characteristics of Constituent Enzymes

n-Butanol is generated as a natural product of metabolism by several microorganisms, but almost all grow at mesophilic temperatures. A synthetic pathway for n-butanol production from acetyl coenzyme A (acetyl-CoA) that functioned at 70°C was assembled in vitro from enzymes recruited from thermophilic bacteria to inform efforts for engineering butanol production into thermophilic hosts. Recombinant versions of eight thermophilic enzymes (β-ketothiolase [Thl], 3-hydroxybutyryl-CoA dehydrogenase [Hbd], and 3-hydroxybutyryl-CoA dehydratase [Crt] from Caldanaerobacter subterraneus subsp. tengcongensis; trans-2-enoyl-CoA reductase [Ter] from Spirochaeta thermophila; bifunctional acetaldehyde dehydrogenase/alcohol dehydrogenase [AdhE] from Clostridium thermocellum; and AdhE, aldehyde dehydrogenase [Bad], and butanol dehydrogenase [Bdh] from Thermoanaerobacter sp. strain X514) were utilized to examine three possible pathways for n-butanol. These pathways differed in the two steps required to convert butyryl-CoA to n-butanol: Thl-Hbd-Crt-Ter-AdhE (C. thermocellum), Thl-Hbd-Crt-Ter-AdhE (Thermoanaerobacter X514), and Thl-Hbd-Crt-Ter-Bad-Bdh. n-Butanol was produced at 70°C, but with different amounts of ethanol as a coproduct, because of the broad substrate specificities of AdhE, Bad, and Bdh. A reaction kinetics model, validated via comparison to in vitro experiments, was used to determine relative enzyme ratios needed to maximize n-butanol production. By using large relative amounts of Thl and Hbd and small amounts of Bad and Bdh, >70% conversion to n-butanol was observed in vitro, but with a 60% decrease in the predicted pathway flux. With more-selective hypothetical versions of Bad and Bdh, >70% conversion to n-butanol is predicted, with a 19% increase in pathway flux. Thus, more-selective thermophilic versions of Bad, Bdh, and AdhE are needed to fully exploit biocatalytic n-butanol production at elevated temperatures.

A Process-Based Model of TCA Cycle Functioning to Analyze Citrate Accumulation in Pre- and Post-Harvest Fruits

Citrate is one of the most important organic acids in many fruits and its concentration plays a critical role in organoleptic properties. The regulation of citrate accumulation throughout fruit development, and the origins of the phenotypic variability of the citrate concentration within fruit species remain to be clarified. In the present study, we developed a process-based model of citrate accumulation based on a simplified representation of the TCA cycle to predict citrate concentration in fruit pulp during the pre- and post-harvest stages. Banana fruit was taken as a reference because it has the particularity of having post-harvest ripening, during which citrate concentration undergoes substantial changes. The model was calibrated and validated on the two stages, using data sets from three contrasting cultivars in terms of citrate accumulation, and incorporated different fruit load, potassium supply, and harvest dates. The model predicted the pre and post-harvest dynamics of citrate concentration with fairly good accuracy for the three cultivars. The model suggested major differences in TCA cycle functioning among cultivars during post-harvest ripening of banana, and pointed to a potential role for NAD-malic enzyme and mitochondrial malate carriers in the genotypic variability of citrate concentration. The sensitivity of citrate accumulation to growth parameters and temperature differed among cultivars during post-harvest ripening. Finally, the model can be used as a conceptual basis to study citrate accumulation in fleshy fruits and may be a powerful tool to improve our understanding of fruit acidity.

Plant stress biomarkers from biosimulations: the Transcriptome-To-Metabolome (TTM) technology - effects of drought stress on rice

Measuring biomarkers from plant tissue samples is challenging and expensive when the desire is to integrate transcriptomics, fluxomics, metabolomics, lipidomics, proteomics, physiomics and phenomics. We present a computational biology method where only the transcriptome needs to be measured and is used to derive a set of parameters for deterministic kinetic models of metabolic pathways. The technology is called Transcriptome-To-Metabolome (TTM) biosimulations, currently under commercial development, but available for non-commercial use by researchers. The simulated results on metabolites of 30 primary and secondary metabolic pathways in rice (Oryza sativa) were used as the biomarkers to predict whether the transcriptome was from a plant that had been under drought conditions. The rice transcriptomes were accessed from public archives and each individual plant was simulated. This unique quality of the TTM technology allows standard analyses on biomarker assessments, i.e. sensitivity, specificity, positive and negative predictive values, accuracy, receiver operator characteristics (ROC) curve and area under the ROC curve (AUC). Two validation methods were also used, the holdout and 10-fold cross validations. Initially 17 metabolites were identified as candidate biomarkers based on either statistical significance on binary phenotype when compared with control samples or recognition from the literature. The top three biomarkers based on AUC were gibberellic acid 12 (0.89), trehalose (0.80) and sn1-palmitate-sn2-oleic-phosphatidylglycerol (0.70). Neither heat map analyses of transcriptomes nor all 300 metabolites clustered the stressed and control groups effectively. The TTM technology allows the emergent properties of the integrated system to generate unique and useful 'Omics' information.

Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.

Model-assisted analysis of sugar metabolism throughout tomato fruit development reveals enzyme and carrier properties in relation to vacuole expansion

A kinetic model combining enzyme activity measurements and subcellular compartmentation was parameterized to fit the sucrose, hexose, and glucose-6-P contents of pericarp throughout tomato (Solanum lycopersicum) fruit development. The model was further validated using independent data obtained from domesticated and wild tomato species and on transgenic lines. A hierarchical clustering analysis of the calculated fluxes and enzyme capacities together revealed stage-dependent features. Cell division was characterized by a high sucrolytic activity of the vacuole, whereas sucrose cleavage during expansion was sustained by both sucrose synthase and neutral invertase, associated with minimal futile cycling. Most importantly, a tight correlation between flux rate and enzyme capacity was found for fructokinase and PPi-dependent phosphofructokinase during cell division and for sucrose synthase, UDP-glucopyrophosphorylase, and phosphoglucomutase during expansion, thus suggesting an adaptation of enzyme abundance to metabolic needs. In contrast, for most enzymes, flux rates varied irrespectively of enzyme capacities, and most enzymes functioned at <5% of their maximal catalytic capacity. One of the major findings with the model was the high accumulation of soluble sugars within the vacuole together with organic acids, thus enabling the osmotic-driven vacuole expansion that was found during cell division.

Kinetic modeling of plant metabolism and its predictive power: peppermint essential oil biosynthesis as an example

The integration of mathematical modeling with analytical experimentation in an iterative fashion is a powerful approach to advance our understanding of the architecture and regulation of metabolic networks. Ultimately, such knowledge is highly valuable to support efforts aimed at modulating flux through target pathways by molecular breeding and/or metabolic engineering. In this article we describe a kinetic mathematical model of peppermint essential oil biosynthesis, a pathway that has been studied extensively for more than two decades. Modeling assumptions and approximations are described in detail. We provide step-by-step instructions on how to run simulations of dynamic changes in pathway metabolites concentrations.

Solving the differential biochemical Jacobian from metabolomics covariance data

High-throughput molecular analysis has become an integral part in organismal systems biology. In contrast, due to a missing systematic linkage of the data with functional and predictive theoretical models of the underlying metabolic network the understanding of the resulting complex data sets is lacking far behind. Here, we present a biomathematical method addressing this problem by using metabolomics data for the inverse calculation of a biochemical Jacobian matrix, thereby linking computer-based genome-scale metabolic reconstruction and in vivo metabolic dynamics. The incongruity of metabolome coverage by typical metabolite profiling approaches and genome-scale metabolic reconstruction was solved by the design of superpathways to define a metabolic interaction matrix. A differential biochemical Jacobian was calculated using an approach which links this metabolic interaction matrix and the covariance of metabolomics data satisfying a Lyapunov equation. The predictions of the differential Jacobian from real metabolomic data were found to be correct by testing the corresponding enzymatic activities. Moreover it is demonstrated that the predictions of the biochemical Jacobian matrix allow for the design of parameter optimization strategies for ODE-based kinetic models of the system. The presented concept combines dynamic modelling strategies with large-scale steady state profiling approaches without the explicit knowledge of individual kinetic parameters. In summary, the presented strategy allows for the identification of regulatory key processes in the biochemical network directly from metabolomics data and is a fundamental achievement for the functional interpretation of metabolomics data.

Complete proteomic-based enzyme reaction and inhibition kinetics reveal how monolignol biosynthetic enzyme families affect metabolic flux and lignin in Populus trichocarpa

We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants.

Systems biology of lignin biosynthesis in Populus trichocarpa: heteromeric 4-coumaric acid:coenzyme A ligase protein complex formation, regulation, and numerical modeling

As a step toward predictive modeling of flux through the pathway of monolignol biosynthesis in stem differentiating xylem of Populus trichocarpa, we discovered that the two 4-coumaric acid:CoA ligase (4CL) isoforms, 4CL3 and 4CL5, interact in vivo and in vitro to form a heterotetrameric protein complex. This conclusion is based on laser microdissection, coimmunoprecipitation, chemical cross-linking, bimolecular fluorescence complementation, and mass spectrometry. The tetramer is composed of three subunits of 4CL3 and one of 4CL5. 4CL5 appears to have a regulatory role. This protein-protein interaction affects the direction and rate of metabolic flux for monolignol biosynthesis in P. trichocarpa. A mathematical model was developed for the behavior of 4CL3 and 4CL5 individually and in mixtures that form the enzyme complex. The model incorporates effects of mixtures of multiple hydroxycinnamic acid substrates, competitive inhibition, uncompetitive inhibition, and self-inhibition, along with characteristic of the substrates, the enzyme isoforms, and the tetrameric complex. Kinetic analysis of different ratios of the enzyme isoforms shows both inhibition and activation components, which are explained by the mathematical model and provide insight into the regulation of metabolic flux for monolignol biosynthesis by protein complex formation.

Applications of kinetic modeling to plant metabolism

The importance of kinetic modeling for understanding the control and regulation of complex metabolic networks is increasingly being recognized. Kinetic models encapsulate the available kinetic information of all the enzymes in a pathway, and then calculate the complex behavior that emerges from the interactions between these network components. Kinetic models are particularly useful because they can simulate untested scenarios and thus explore pathway behavior beyond the realm of what is experimentally available or currently feasible. Models can also suggest new experiments in a directed approach.This chapter provides a brief introduction to kinetic modeling and its application to plant metabolic pathways. A two-pronged strategy is followed: first, a method is presented for further analysis of existing published models, with references to the relevant databases housing such models and instructions on how to load the models into simulation software. Next, the requirements for and processes of constructing and validating a kinetic model from scratch are outlined. To conclude, potential applications of models are summarized.

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.

An optimization rule for in silico identification of targeted overproduction in metabolic pathways

In an extension of previous work, here we introduce a second-order optimization method for determining optimal paths from the substrate to a target product of a metabolic network, through which the amount of the target is maximum. An objective function for the said purpose, along with certain linear constraints, is considered and minimized. The basis vectors spanning the null space of the stoichiometric matrix, depicting the metabolic network, are computed, and their convex combinations satisfying the constraints are considered as flux vectors. A set of other constraints, incorporating weighting coefficients corresponding to the enzymes in the pathway, are considered. These weighting coefficients appear in the objective function to be minimized. During minimization, the values of these weighting coefficients are estimated and learned. These values, on minimization, represent an optimal pathway, depicting optimal enzyme concentrations, leading to overproduction of the target. The results on various networks demonstrate the usefulness of the methodology in the domain of metabolic engineering. A comparison with the standard gradient descent and the extreme pathway analysis technique is also performed. Unlike the gradient descent method, the present method, being independent of the learning parameter, exhibits improved results.

Monolignol pathway 4-coumaric acid:coenzyme A ligases in Populus trichocarpa: novel specificity, metabolic regulation, and simulation of coenzyme A ligation fluxes

4-Coumaric acid:coenzyme A ligase (4CL) is involved in monolignol biosynthesis for lignification in plant cell walls. It ligates coenzyme A (CoA) with hydroxycinnamic acids, such as 4-coumaric and caffeic acids, into hydroxycinnamoyl-CoA thioesters. The ligation ensures the activated state of the acid for reduction into monolignols. In Populus spp., it has long been thought that one monolignol-specific 4CL is involved. Here, we present evidence of two monolignol 4CLs, Ptr4CL3 and Ptr4CL5, in Populus trichocarpa. Ptr4CL3 is the ortholog of the monolignol 4CL reported for many other species. Ptr4CL5 is novel. The two Ptr4CLs exhibited distinct Michaelis-Menten kinetic properties. Inhibition kinetics demonstrated that hydroxycinnamic acid substrates are also inhibitors of 4CL and suggested that Ptr4CL5 is an allosteric enzyme. Experimentally validated flux simulation, incorporating reaction/inhibition kinetics, suggested two CoA ligation paths in vivo: one through 4-coumaric acid and the other through caffeic acid. We previously showed that a membrane protein complex mediated the 3-hydroxylation of 4-coumaric acid to caffeic acid. The demonstration here of two ligation paths requiring these acids supports this 3-hydroxylation function. Ptr4CL3 regulates both CoA ligation paths with similar efficiencies, whereas Ptr4CL5 regulates primarily the caffeic acid path. Both paths can be inhibited by caffeic acid. The Ptr4CL5-catalyzed caffeic acid metabolism, therefore, may also act to mitigate the inhibition by caffeic acid to maintain a proper ligation flux. A high level of caffeic acid was detected in stem-differentiating xylem of P. trichocarpa. Our results suggest that Ptr4CL5 and caffeic acid coordinately modulate the CoA ligation flux for monolignol biosynthesis.

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.

Computational tools for metabolic engineering

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.

Kinetic modelling of plant metabolic pathways

This paper provides a review of kinetic modelling of plant metabolic pathways as a tool for analysing their control and regulation. An overview of different modelling strategies is presented, starting with those approaches that only require a knowledge of the network stoichiometry; these are referred to as structural. Flux-balance analysis, metabolic flux analysis using isotope labelling, and elementary mode analysis are briefly mentioned as three representative examples. The main focus of this paper, however, is a discussion of kinetic modelling, which requires, in addition to the stoichiometry, a knowledge of the kinetic properties of the constituent pathway enzymes. The different types of kinetic modelling analysis, namely time-course simulation, steady-state analysis, and metabolic control analysis, are explained in some detail. An overview is presented of strategies for obtaining model parameters, as well as software tools available for simulation of such models. The kinetic modelling approach is exemplified with discussion of three models from the general plant physiology literature. With the aid of kinetic modelling it is possible to perform a control analysis of a plant metabolic system, to identify potential targets for biotechnological manipulation, as well as to ascertain the regulatory importance of different enzymes (including isoforms of the same enzyme) in a pathway. Finally, a framework is presented for extending metabolic models to the whole-plant scale by linking biochemical reactions with diffusion and advective flow through the phloem. Future challenges include explicit modelling of subcellular compartments, as well as the integration of kinetic models on the different levels of the cellular and organizational hierarchy.

Functional genomics tools applied to plant metabolism: a survey on plant respiration, its connections and the annotation of complex gene functions

The application of post-genomic techniques in plant respiration studies has greatly improved our ability to assign functions to gene products. In addition it has also revealed previously unappreciated interactions between distal elements of metabolism. Such results have reinforced the need to consider plant respiratory metabolism as part of a complex network and making sense of such interactions will ultimately require the construction of predictive and mechanistic models. Transcriptomics, proteomics, metabolomics, and the quantification of metabolic flux will be of great value in creating such models both by facilitating the annotation of complex gene function, determining their structure and by furnishing the quantitative data required to test them. In this review, we highlight how these experimental approaches have contributed to our current understanding of plant respiratory metabolism and its interplay with associated process (e.g., photosynthesis, photorespiration, and nitrogen metabolism). We also discuss how data from these techniques may be integrated, with the ultimate aim of identifying mechanisms that control and regulate plant respiration and discovering novel gene functions with potential biotechnological implications.

Flux-balance modeling of plant metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.

Modelling the size and composition of fruit, grain and seed by process-based simulation models

Understanding what determines the size and composition of fruit, grain and seed in response to the environment and genotype is challenging, as these traits result from several linked processes controlled at different levels of organization, from the subcellular to the crop level, with subtle interactions occurring at or between the levels of organization. Process-based simulation models (PBSMs) implement algorithms to simulate metabolic and biophysical aspects of cell, tissue and organ behaviour. In this review, fruit, grain and seed PBSMs describing the main phases of growth, development and storage metabolism are discussed. From this concurrent work, it is possible to identify generic storage organ processes which can be modelled similarly for fruit, grain and seed. Spatial heterogeneity at the tissue and whole-plant level is found to be a key consideration in modelling the effects of the environment and genotype on fruit, grain and seed end-use value. In the future, PBSMs may well become the main link between studies at the molecular and whole-plant levels. To bridge this phenotype-to-genotype gap, future models need to remain plastic without becoming overparameterized.

Metabolomic approaches toward understanding nitrogen metabolism in plants

Plants can assimilate inorganic nitrogen (N) sources to organic N such as amino acids. N is the most important of the mineral nutrients required by plants and its metabolism is tightly coordinated with carbon (C) metabolism in the fundamental processes that permit plant growth. Increased understanding of N regulation may provide important insights for plant growth and improvement of quality of crops and vegetables because N as well as C metabolism are fundamental components of plant life. Metabolomics is a global biochemical approach useful to study N metabolism because metabolites not only reflect the ultimate phenotypes (traits), but can mediate transcript levels as well as protein levels directly and/or indirectly under different N conditions. This review outlines analytical and bioinformatic techniques particularly used to perform metabolomics for studying N metabolism in higher plants. Examples are used to illustrate the application of metabolomic techniques to the model plants Arabidopsis and rice, as well as other crops and vegetables.