Proteome reorganization and amino acid metabolism during germination and seedling establishment in Lupinus albus.

During germination plants rely entirely on their seed storage compounds to provide energy and precursors for the synthesis of macromolecular structures until the seedling has emerged from the soil and photosynthesis can be established. Lupin seeds use proteins as their major storage compounds, accounting for up to 40% of the seed dry weight. Lupins are therefore a valuable complement to soy as a source of plant protein for human and animal nutrition. The aim of this study was to elucidate how storage protein metabolism is coordinated with other metabolic processes to meet the requirements of the growing seedling. In a quantitative approach, we analyzed seedling growth, as well as alterations in biomass composition, the proteome, and metabolite profiles during germination and seedling establishment in Lupinus albus. The reallocation of nitrogen resources from seed storage proteins to functional seed proteins was mapped based on a manually curated functional protein annotation database. Although classified as a protein crop, Lupinus albus does not use amino acids as a primary substrate for energy metabolism during germination. However, fatty acid and amino acid metabolism may be integrated at the level of malate synthase to combine stored carbon from lipids and proteins into gluconeogenesis.

CobraMod: a pathway-centric curation tool for constraint-based metabolic models.

SUMMARY: COnstraint-Based Reconstruction and Analysis of genome-scale metabolic models has become a widely used tool to understand metabolic network behavior at a large scale. However, existing reconstruction tools lack functionalities to address modellers' common objective to study metabolic networks on the pathway level. Thus, we developed CobraMod-a Python package for pathway-centric modification and extension of genome-scale metabolic networks. CobraMod can integrate data from various metabolic pathway databases as well as user-curated information. Our tool tests newly added metabolites, reactions and pathways against multiple curation criteria, suggests manual curation steps and provides the user with records of changes to ensure high quality metabolic reconstructions. CobraMod uses the visualization tool Escher for pathway representation and offers simple customization options for comparison of pathways and flux distributions. Our package enables coherent and reproducible workflows as it can be seamlessly integrated with COBRApy and Escher. AVAILABILITY AND IMPLEMENTATION: The source code can be found at https://github.com/Toepfer-Lab/cobramod/ and can be installed with pip. The documentation including tutorials is available at https://cobramod.readthedocs.io/.

Advances in flux balance analysis by integrating machine learning and mechanism-based models.

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Environment-coupled models of leaf metabolism.

The plant leaf is the main site of photosynthesis. This process converts light energy and inorganic nutrients into chemical energy and organic building blocks for the biosynthesis and maintenance of cellular components and to support the growth of the rest of the plant. The leaf is also the site of gas-water exchange and due to its large surface, it is particularly vulnerable to pathogen attacks. Therefore, the leaf's performance and metabolic modes are inherently determined by its interaction with the environment. Mathematical models of plant metabolism have been successfully applied to study various aspects of photosynthesis, carbon and nitrogen assimilation and metabolism, aided suggesting metabolic intervention strategies for optimized leaf performance, and gave us insights into evolutionary drivers of plant metabolism in various environments. With the increasing pressure to improve agricultural performance in current and future climates, these models have become important tools to improve our understanding of plant-environment interactions and to propel plant breeders efforts. This overview article reviews applications of large-scale metabolic models of leaf metabolism to study plant-environment interactions by means of flux-balance analysis. The presented studies are organized in two ways - by the way the environment interactions are modelled - via external constraints or data-integration and by the studied environmental interactions - abiotic or biotic.

Alternative Crassulacean Acid Metabolism Modes Provide Environment-Specific Water-Saving Benefits in a Leaf Metabolic Model.

Crassulacean acid metabolism (CAM) evolved in arid environments as a water-saving alternative to C3 photosynthesis. There is great interest in engineering more drought-resistant crops by introducing CAM into C3 plants. However, it is unknown whether full CAM or alternative water-saving modes would be more productive in the environments typically experienced by C3 crops. To study the effect of temperature and relative humidity on plant metabolism in the context of water saving, we coupled a time-resolved diel (based on a 24-h day-night cycle) model of leaf metabolism to an environment-dependent gas-exchange model. This combined model allowed us to study the emergence of CAM as a trade-off between leaf productivity and water saving. We show that vacuolar storage capacity in the leaf is a major determinant of the extent of CAM. Moreover, our model identified an alternative CAM cycle involving mitochondrial isocitrate dehydrogenase as a potential contributor to initial carbon fixation at night. Simulations across a range of environmental conditions show that the water-saving potential of CAM strongly depends on the daytime weather conditions and that the additional water-saving effect of carbon fixation by isocitrate dehydrogenase can reach 11% total water saving for the conditions tested.

Integration of Plant Metabolomics Data with Metabolic Networks: Progresses and Challenges.

In the last decade, plant genome-scale modeling has developed rapidly and modeling efforts have advanced from representing metabolic behavior of plant heterotrophic cell suspensions to studying the complex interplay of cell types, tissues, and organs. A crucial driving force for such developments is the availability and integration of "omics" data (e.g., transcriptomics, proteomics, and metabolomics) which enable the reconstruction, extraction, and application of context-specific metabolic networks. In this chapter, we demonstrate a workflow to integrate gas chromatography coupled to mass spectrometry (GC-MS)-based metabolomics data of tomato fruit pericarp (flesh) tissue, at five developmental stages, with a genome-scale reconstruction of tomato metabolism. This method allows for the extraction of context-specific networks reflecting changing activities of metabolic pathways throughout fruit development and maturation.

The PhytoClust tool for metabolic gene clusters discovery in plant genomes.

The existence of Metabolic Gene Clusters (MGCs) in plant genomes has recently raised increased interest. Thus far, MGCs were commonly identified for pathways of specialized metabolism, mostly those associated with terpene type products. For efficient identification of novel MGCs, computational approaches are essential. Here, we present PhytoClust; a tool for the detection of candidate MGCs in plant genomes. The algorithm employs a collection of enzyme families related to plant specialized metabolism, translated into hidden Markov models, to mine given genome sequences for physically co-localized metabolic enzymes. Our tool accurately identifies previously characterized plant MGCs. An exhaustive search of 31 plant genomes detected 1232 and 5531 putative gene cluster types and candidates, respectively. Clustering analysis of putative MGCs types by species reflected plant taxonomy. Furthermore, enrichment analysis revealed taxa- and species-specific enrichment of certain enzyme families in MGCs. When operating through our web-interface, PhytoClust users can mine a genome either based on a list of known cluster types or by defining new cluster rules. Moreover, for selected plant species, the output can be complemented by co-expression analysis. Altogether, we envisage PhytoClust to enhance novel MGCs discovery which will in turn impact the exploration of plant metabolism.

Label-free deep shotgun proteomics reveals protein dynamics during tomato fruit tissues development.

Current innovations in mass-spectrometry-based technologies allow deep coverage of protein expression. Despite its immense value and in contrast to transcriptomics, only a handful of studies in crop plants engaged with global proteome assays. Here, we present large-scale shotgun proteomics profiling of tomato fruit across two key tissues and five developmental stages. A total of 7738 individual protein groups were identified and reliably measured at least in one of the analyzed tissues or stages. The depth of our assay enabled identification of 61 differentially expressed transcription factors, including renowned ripening-related regulators and elements of ethylene signaling. Significantly, we measured proteins involved in 83% of all predicted enzymatic reactions in the tomato metabolic network. Hence, proteins representing almost the complete set of reactions in major metabolic pathways were identified, including the cytosolic and plastidic isoprenoid and the phenylpropanoid pathways. Furthermore, the data allowed us to discern between protein isoforms according to expression patterns, which is most significant in light of the weak transcript-protein expression correspondence. Finally, visualization of changes in protein abundance associated with a particular process provided us with a unique view of skin and flesh tissues in developing fruit. This study adds a new dimension to the existing genomic, transcriptomic and metabolomic resources. It is therefore likely to promote translational and post-translational research in tomato and additional species, which is presently focused on transcription.

iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models.

MOTIVATION: Understanding the rerouting of metabolic reaction fluxes upon perturbations has the potential to link changes in molecular state of a cellular system to alteration of growth. Yet, differential flux profiling on a genome-scale level remains one of the biggest challenges in systems biology. This is particularly relevant in plants, for which fluxes in autotrophic growth necessitate time-consuming instationary labeling experiments and costly computations, feasible for small-scale networks. RESULTS: Here we present a computationally and experimentally facile approach, termed iReMet-Flux, which integrates relative metabolomics data in a metabolic model to predict differential fluxes at a genome-scale level. Our approach and its variants complement the flux estimation methods based on radioactive tracer labeling. We employ iReMet-Flux with publically available metabolic profiles to predict reactions and pathways with altered fluxes in photo-autotrophically grown Arabidopsis and four photorespiratory mutants undergoing high-to-low CO2 acclimation. We also provide predictions about reactions and pathways which are most strongly regulated in the investigated experiments. The robustness and variability analyses, tailored to the formulation of iReMet-Flux, demonstrate that the findings provide biologically relevant information that is validated with external measurements of net CO2 exchange and biomass production. Therefore, iReMet-Flux paves the wave for mechanistic dissection of the interplay between pathways of primary and secondary metabolisms at a genome-scale. AVAILABILITY AND IMPLEMENTATION: The source code is available from the authors upon request. CONTACT: nikoloski@mpimp-golm.mpg.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integration of metabolomics data into metabolic networks.

Metabolite levels together with their corresponding metabolic fluxes are integrative outcomes of biochemical transformations and regulatory processes and they can be used to characterize the response of biological systems to genetic and/or environmental changes. However, while changes in transcript or to some extent protein levels can usually be traced back to one or several responsible genes, changes in fluxes and particularly changes in metabolite levels do not follow such rationale and are often the outcome of complex interactions of several components. The increasing quality and coverage of metabolomics technologies have fostered the development of computational approaches for integrating metabolic read-outs with large-scale models to predict the physiological state of a system. Constraint-based approaches, relying on the stoichiometry of the considered reactions, provide a modeling framework amenable to analyses of large-scale systems and to the integration of high-throughput data. Here we review the existing approaches that integrate metabolomics data in variants of constrained-based approaches to refine model reconstructions, to constrain flux predictions in metabolic models, and to relate network structural properties to metabolite levels. Finally, we discuss the challenges and perspectives in the developments of constraint-based modeling approaches driven by metabolomics data.

Metabolite profiling and integrative modeling reveal metabolic constraints for carbon partitioning under nitrogen starvation in the green algae Haematococcus pluvialis.

The green alga Hematococcus pluvialis accumulates large amounts of the antioxidant astaxanthin under inductive stress conditions, such as nitrogen starvation. The response to nitrogen starvation and high light leads to the accumulation of carbohydrates and fatty acids as well as increased activity of the tricarboxylic acid cycle. Although the behavior of individual pathways has been well investigated, little is known about the systemic effects of the stress response mechanism. Here we present time-resolved metabolite, enzyme activity, and physiological data that capture the metabolic response of H. pluvialis under nitrogen starvation and high light. The data were integrated into a putative genome-scale model of the green alga to in silico test hypotheses of underlying carbon partitioning. The model-based hypothesis testing reinforces the involvement of starch degradation to support fatty acid synthesis in the later stages of the stress response. In addition, our findings support a possible mechanism for the involvement of the increased activity of the tricarboxylic acid cycle in carbon repartitioning. Finally, the in vitro experiments and the in silico modeling presented here emphasize the predictive power of large scale integrative approaches to pinpoint metabolic adjustment to changing environments.

Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses.

Constraint-based approaches have been used for integrating data in large-scale metabolic networks to obtain insights into metabolism of various organisms. Due to the underlying steady-state assumption, these approaches are usually not suited for making predictions about metabolite levels. Here, we ask whether we can make inferences about the variability of metabolite levels from a constraint-based analysis based on the integration of transcriptomics data. To this end, we analyze time-resolved transcriptomics and metabolomics data from Arabidopsis thaliana under a set of eight different light and temperature conditions. In a previous study, the gene expression data have already been integrated in a genome-scale metabolic network to predict pathways, termed modulators and sustainers, which are differentially regulated with respect to a biochemically meaningful data-driven null model. Here, we present a follow-up analysis which bridges the gap between flux- and metabolite-centric methods. One of our main findings demonstrates that under certain environmental conditions, the levels of metabolites acting as substrates in modulators or sustainers show significantly lower temporal variations with respect to the remaining measured metabolites. This observation is discussed within the context of a systems-view of plasticity and robustness of metabolite contents and pathway fluxes. Our study paves the way for investigating the existence of similar principles in other species for which both genome-scale networks and high-throughput metabolomics data of high quality are becoming increasingly available.

Large-scale modeling provides insights into Arabidopsis's acclimation to changing light and temperature conditions.

Classical flux balance analysis predicts steady-state flux distributions that maximize a given objective function. A recent study, Schuetz et al., (1) demonstrated that competing objectives constrain the metabolic fluxes in E. coli. For plants, with multiple cell types, fulfilling different functions, the objectives remain elusive and, therefore, hinder the prediction of actual fluxes, particularly for changing environments. In our study, we presented a novel approach to predict flux capacities for a large collection of metabolic pathways under eight different temperature and light conditions. (2) By integrating time-series transcriptomics data to constrain the flux boundaries of the metabolic model, we captured the time- and condition-specific state of the network. Although based on a single time-series experiment, the comparison of these capacities to a novel null model for transcript distribution allowed us to define a measure for differential behavior that accounts for the underlying network structure and the complex interplay of metabolic pathways.

Integration of genome-scale modeling and transcript profiling reveals metabolic pathways underlying light and temperature acclimation in Arabidopsis.

Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism.

Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli.

BACKGROUND: Changes in environmental conditions require temporal effectuation of different metabolic pathways in order to maintain the organisms' viability but also to enable the settling into newly arising conditions. While analyses of robustness in biological systems have resulted in the characterization of reactions that facilitate homeostasis, temporal adaptation-related processes and the role of cellular pathways in the metabolic response to changing conditions remain elusive. RESULTS: Here we develop a flux-based approach that allows the integration of time-resolved transcriptomics data with genome-scale metabolic networks. Our framework uses bilevel optimization to extract temporal minimal operating networks from a given large-scale metabolic model. The minimality of the extracted networks enables the computation of elementary flux modes for each time point, which are in turn used to characterize the transitional behavior of the network as well as of individual reactions. Application of the approach to the metabolic network of Escherichia coli in conjunction with time-series gene expression data from cold and heat stress results in two distinct time-resolved modes for reaction utilization-constantly active and temporally (de)activated reactions. These patterns contrast the processes for the maintenance of basic cellular functioning and those required for adaptation. They also allow the prediction of reactions involved in time- and stress-specific metabolic response and are verified with respect to existing experimental studies. CONCLUSIONS: Altogether, our findings pinpoint the inherent relation between the systemic properties of robustness and adaptability arising from the interplay of metabolic network structure and changing environment.