Systems-integration of plant metabolism: means, motive and opportunity

The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling

Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.

Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism

The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.

Inheritance patterns in metabolism and growth in diallel crosses of Arabidopsis thaliana from a single growth habitat

Metabolism is a key determinant of plant growth and modulates plant adaptive responses. Increased metabolic variation due to heterozygosity may be beneficial for highly homozygous plants if their progeny is to respond to sudden changes in the habitat. Here, we investigate the extent to which heterozygosity contributes to the variation in metabolism and size of hybrids of Arabidopsis thaliana whose parents are from a single growth habitat. We created full diallel crosses among seven parents, originating from Southern Germany, and analysed the inheritance patterns in primary and secondary metabolism as well as in rosette size in situ. In comparison to primary metabolites, compounds from secondary metabolism were more variable and showed more pronounced non-additive inheritance patterns which could be attributed to epistasis. In addition, we showed that glucosinolates, among other secondary metabolites, were positively correlated with a proxy for plant size. Therefore, our study demonstrates that heterozygosity in local A. thaliana population generates metabolic variation and may impact several tasks directly linked to metabolism.

Oral secretions from Mythimna separata insects specifically induce defence responses in maize as revealed by high-dimensional biological data

Attack from insect herbivores poses a major threat to plant survival, and accordingly, plants have evolved sophisticated defence systems. Maize is cultivated as a staple crop worldwide, and insect feeding causes large production losses. Despite its importance in agriculture, little is known about how maize reacts to insect herbivory. Taking advantage of advances in sequencing and mass spectrometry technology, we studied the response of maize to mechanical wounding and simulated Mythimna separata (a specialist insect) herbivory by applying its oral secretions (OS) to wounds. In comparison to the responses induced by mechanical wounding, OS elicited larger and longer-lasting changes in the maize transcriptome, proteome, metabolome and phytohormones. Specifically, many genes, proteins and metabolites were uniquely induced or repressed by OS. Nearly 290 transcription factor genes from 39 families were involved in OS-induced responses, and among these, more transcription factor genes were specifically regulated by OS than by wounding. This study provides a large-scale omics dataset for understanding maize response to chewing insects and highlights the essential role of OS in plant-insect interactions.

Plants in silico: why, why now and what?--an integrative platform for plant systems biology research

A paradigm shift is needed and timely in moving plant modelling from largely isolated efforts to a connected community endeavour that can take full advantage of advances in computer science and in mechanistic understanding of plant processes. Plants in silico (Psi) envisions a digital representation of layered dynamic modules, linking from gene networks and metabolic pathways through to cellular organization, tissue, organ and whole plant development, together with resource capture and use efficiency in dynamic competitive environments, ultimately allowing a mechanistically rich simulation of the plant or of a community of plants in silico. The concept is to integrate models or modules from different layers of organization spanning from genome to phenome to ecosystem in a modular framework allowing the use of modules of varying mechanistic detail representing the same biological process. Developments in high-performance computing, functional knowledge of plants, the internet and open-source version controlled software make achieving the concept realistic. Open source will enhance collaboration and move towards testing and consensus on quantitative theoretical frameworks. Importantly, Psi provides a quantitative knowledge framework where the implications of a discovery at one level, for example, single gene function or developmental response, can be examined at the whole plant or even crop and natural ecosystem levels.

Integrative Approaches to Enhance Understanding of Plant Metabolic Pathway Structure and Regulation

Huge insight into molecular mechanisms and biological network coordination have been achieved following the application of various profiling technologies. Our knowledge of how the different molecular entities of the cell interact with one another suggests that, nevertheless, integration of data from different techniques could drive a more comprehensive understanding of the data emanating from different techniques. Here, we provide an overview of how such data integration is being used to aid the understanding of metabolic pathway structure and regulation. We choose to focus on the pairwise integration of large-scale metabolite data with that of the transcriptomic, proteomics, whole-genome sequence, growth- and yield-associated phenotypes, and archival functional genomic data sets. In doing so, we attempt to provide an update on approaches that integrate data obtained at different levels to reach a better understanding of either single gene function or metabolic pathway structure and regulation within the context of a broader biological process.

Effects of long-term individual and combined water and temperature stress on the growth of rice, wheat and maize: relationship with morphological and physiological acclimation

This study evaluates the long-term individual and combined effects of high temperature (HT) and water deficit (WD) stress on plant growth, leaf gas-exchange and water use efficiency in cultivars of the three most important crops worldwide, rice, wheat and maize. Total plant biomass (B_t ) accumulation decreased under all treatments, being the combined HT-WD treatment the most detrimental in all three species. Although decreases in B_t correlated with adjustments in biomass allocation patterns (i.e. the leaf area ratio), most of the variation observed in B_t was explained by changes in leaf gas exchange parameters. Thus, integrated values of leaf carbon balance obtained from daily course measurements of photosynthesis and respiration were better predictors of plant growth than the instantaneous measurements of leaf gas exchange. Leaf water use efficiency, assessed both by gas exchange and carbon isotope measurements, was negatively correlated with B_t under WD, but not under the combined WD and HT treatment. A comparative analysis of the negative effects of single and combined stresses on the main parameters showed an additive component for WD and HT in rice and maize, in contrast to wheat. Overall, the results of the specific cultivars included in the study suggest that the species native climate plays a role shaping the species acclimation potential to the applied stresses. In this regard, wheat, originated in a cold climate, was the most affected species, which foretells a higher affectation of this crop due to climate change.

Intracellular and cell-to-apoplast compartmentation of carbohydrate metabolism

In most plants, carbohydrates represent the major energy store as well as providing the building blocks for essential structural polymers. Although the major pathways for carbohydrate biosynthesis, degradation, and transport are well characterized, several key steps have only recently been discovered. In addition, several novel minor metabolic routes have been uncovered in the past few years. Here we review current studies of plant carbohydrate metabolism detailing the expanding compendium of functionally characterized transport proteins as well as our deeper comprehension of more minor and conditionally activated metabolic pathways. We additionally explore the pertinent questions that will allow us to enhance our understanding of the response of both major and minor carbohydrate fluxes to changing cellular circumstances.

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

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

A network perspective on nitrogen metabolism from model to crop plants using integrated 'omics' approaches

Nitrogen (N), as an essential element in amino acids, nucleotides, and proteins, is a key factor in plant growth and development. Omics approaches such as metabolomics and transcriptomics have become a promising way to inspect complex network interactions in N metabolism and can be used for monitoring the uptake and regulation, translocation, and remobilization of N. In this review, the authors highlight recent progress in omics approaches, including transcript profiling using microarrays and deep sequencing, and show recent technical developments in metabolite profiling for N studies. Further, network analysis studies including network inference methods with correlations, information-theoretic measures, and a network concept to examine gene expression clusters in relation to N regulatory systems in plants are introduced, and integrating network inference methods and integrated networks using multiple omics data are discussed. Finally, this review summarizes recent omics application examples using metabolite and/or transcript profiling analysis to elucidate the regulation of N metabolism and signalling and the coordination of N and carbon metabolism in model plants (Arabidopsis and rice), crops (tomato, maize, and legumes), and trees (Populus).

Transcriptional evidence for inferred pattern of pollen tube-stigma metabolic coupling during pollination

It is difficult to derive all qualitative proteomic and metabolomic experimental data in male (pollen tube) and female (pistil) reproductive tissues during pollination because of the limited sensitivity of current technology. In this study, genome-scale enzyme correlation network models for plants (Arabidopsis/maize) were constructed by analyzing the enzymes and metabolic routes from a global perspective. Then, we developed a data-driven computational pipeline using the "guilt by association" principle to analyze the transcriptional coexpression profiles of enzymatic genes in the consecutive steps for metabolic routes in the fast-growing pollen tube and stigma during pollination. The analysis identified an inferred pattern of pollen tube-stigma ethanol coupling. When the pollen tube elongates in the transmitting tissue (TT) of the pistil, this elongation triggers the mobilization of energy from glycolysis in the TT cells of the pistil. Energy-rich metabolites (ethanol) are secreted that can be taken up by the pollen tube, where these metabolites are incorporated into the pollen tube's tricarboxylic acid (TCA) cycle, which leads to enhanced ATP production for facilitating pollen tube growth. In addition, our analysis also provided evidence for the cooperation of kaempferol, dTDP-alpha-L-rhamnose and cell-wall-related proteins; phosphatidic-acid-mediated Ca2+ oscillations and cytoskeleton; and glutamate degradation IV for γ-aminobutyric acid (GABA) signaling activation in Arabidopsis and maize stigmas to provide the signals and materials required for pollen tube tip growth. In particular, the "guilt by association" computational pipeline and the genome-scale enzyme correlation network models (GECN) developed in this study was initiated with experimental "omics" data, followed by data analysis and data integration to determine correlations, and could provide a new platform to assist inachieving a deeper understanding of the co-regulation and inter-regulation model in plant research.

Why measure enzyme activities in the era of systems biology?

Information about the abundance and biological activities of proteins is essential to reveal how genes affect phenotypes. Over the past decade, mass spectrometry (MS)-based proteomics has revolutionized the identification and quantification of proteins, and the detection of post-translational modifications. Interpretation of proteomics data depends on information about the biological activities of proteins, which has created a bottleneck in research. This review focuses on enzymes in central metabolism. We examine the methods used for measuring enzyme activities, and discuss how these methods provide information about the kinetic and regulatory properties of enzymes, their turnover, and how this information can be integrated into metabolic models. We also discuss how robotized assays could enable the genetic networks that control enzyme abundance to be analyzed.

Systems analysis of metabolic phenotypes: what have we learnt?

Flux is one of the most informative measures of metabolic behavior. Its estimation requires integration of experimental and modeling approaches and, thus, is at the heart of metabolic systems biology. In this review, we argue that flux analysis and modeling of a range of plant systems points to the importance of the supply of metabolic inputs and demand for metabolic end-products as key drivers of metabolic behavior. This has implications for metabolic engineering, and the use of in silico models will be important to help design more effective engineering strategies. We also consider the importance of cell type-specific metabolism and the challenges of characterizing metabolism at this resolution. A combination of new measurement technologies and modeling approaches is bringing us closer to integrating metabolic behavior with whole-plant physiology and growth.

Lignin, mitochondrial family, and photorespiratory transporter classification as case studies in using co-expression, co-response, and protein locations to aid in identifying transport functions

Whole genome sequencing and the relative ease of transcript profiling have facilitated the collection and data warehousing of immense quantities of expression data. However, a substantial proportion of genes are not yet functionally annotated a problem which is particularly acute for transport proteins. In Arabidopsis, for example, only a minor fraction of the estimated 700 intracellular transporters have been identified at the molecular genetic level. Furthermore it is only within the last couple of years that critical genes such as those encoding the final transport step required for the long distance transport of sucrose and the first transporter of the core photorespiratory pathway have been identified. Here we will describe how transcriptional coordination between genes of known function and non-annotated genes allows the identification of putative transporters on the premise that such co-expressed genes tend to be functionally related. We will additionally extend this to include the expansion of this approach to include phenotypic information from other levels of cellular organization such as proteomic and metabolomic data and provide case studies wherein this approach has successfully been used to fill knowledge gaps in important metabolic pathways and physiological processes.

Analysis of isotopic labeling in peptide fragments by tandem mass spectrometry

Phenotype in multicellular organisms is the consequence of dynamic metabolic events that occur in a spatially dependent fashion. This spatial and temporal complexity presents challenges for investigating metabolism; creating a need for improved methods that effectively probe biochemical events such as amino acid biosynthesis. Isotopic labeling can provide a temporal-spatial recording of metabolic events through, for example, the description of enriched amino acids in the protein pool. Proteins are therefore an important readout of metabolism and can be assessed with modern mass spectrometers. We compared the measurement of isotopic labeling in MS2 spectra obtained from tandem mass spectrometry under either higher energy collision dissociation (HCD) or collision induced dissociation (CID) at varied energy levels. Developing soybean embryos cultured with or without 13C-labeled substrates, and Escherichia coli MG1655 enriched by feeding 7% uniformly labeled glucose served as a source of biological material for protein evaluation. CID with low energies resulted in a disproportionate amount of heavier isotopologues remaining in the precursor isotopic distribution. HCD resulted in fewer quantifiable products; however deviation from predicted distributions were small relative to the CID-based comparisons. Fragment ions have the potential to provide information on the labeling of amino acids in peptides, but our results indicate that without further development the use of this readout in quantitative methods such as metabolic flux analysis is limited.

Flux analysis in plant metabolic networks: increasing throughput and coverage

Quantitative information about metabolic networks has been mainly obtained at the level of metabolite contents, transcript abundance, and enzyme activities. However, the active process of metabolism is represented by the flow of matter through the pathways. These metabolic fluxes can be predicted by Flux Balance Analysis or determined experimentally by (13)C-Metabolic Flux Analysis. These relatively complicated and time-consuming methods have recently seen significant improvements at the level of coverage and throughput. Metabolic models have developed from single cell models into whole-organism dynamic models. Advances in lab automation and data handling have significantly increased the throughput of flux measurements. This review summarizes advances to increase coverage and throughput of metabolic flux analysis in plants.

Quantification of peptide m/z distributions from 13C-labeled cultures with high-resolution mass spectrometry

Isotopic labeling studies of primary metabolism frequently utilize GC/MS to quantify (13)C in protein-hydrolyzed amino acids. During processing some amino acids are degraded, which reduces the size of the measurement set. The advent of high-resolution mass spectrometers provides a tool to assess molecular masses of peptides with great precision and accuracy and computationally infer information about labeling in amino acids. Amino acids that are isotopically labeled during metabolism result in labeled peptides that contain spatial and temporal information that is associated with the biosynthetic origin of the protein. The quantification of isotopic labeling in peptides can therefore provide an assessment of amino acid metabolism that is specific to subcellular, cellular, or temporal conditions. A high-resolution orbital trap was used to quantify isotope labeling in peptides that were obtained from unlabeled and isotopically labeled soybean embryos and Escherichia coli cultures. Standard deviations were determined by estimating the multinomial variance associated with each element of the m/z distribution. Using the estimated variance, quantification of the m/z distribution across multiple scans was achieved by a nonlinear fitting approach. Observed m/z distributions of uniformly labeled E. coli peptides indicated no significant differences between observed and simulated m/z distributions. Alternatively, amino acid m/z distributions obtained from GC/MS were convolved to simulate peptide m/z distributions but resulted in distinct profiles due to the production of protein prior to isotopic labeling. The results indicate that peptide mass isotopologue measurements faithfully represent mass distributions, are suitable for quantification of isotope-labeling-based studies, and provide additional information over existing methods.

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.