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Yu J, Wang X, Yuan Q, Shi J, Cai J, Li Z, Ma H. Elucidating the impact of in vitro cultivation on Nicotiana tabacum metabolism through combined in silico modeling and multiomics analysis. FRONTIERS IN PLANT SCIENCE 2023; 14:1281348. [PMID: 38023876 PMCID: PMC10655011 DOI: 10.3389/fpls.2023.1281348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023]
Abstract
The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of in-vitro cultivation on the tobacco metabolic network. In-vitro cultivation is a widely used technique for plant cultivation, not only in the field of basic research but also for the rapid propagation of valuable horticultural and pharmaceutical plants. However, the systemic effects of in-vitro cultivation on overall plant metabolism could easily be overlooked and are still poorly understood. We found that in-vitro tobacco showed slower growth, less biomass and suppressed photosynthesis than soil-grown tobacco. Many changes of metabolites and metabolic pathways between in-vitro and soil-grown tobacco plants were identified, which notably revealed a significant increase of the amino acids content under in-vitro condition. The in silico investigation showed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while significantly upregulated the GS/GOGAT cycle, as well as producing more energy and less NADH/NADPH to acclimate in-vitro growth demands. Altogether, the combination of experimental and in silico analyses offers an unprecedented view of tobacco metabolism, with valuable insights into the impact of in-vitro cultivation, enabling more efficient utilization of in-vitro techniques for plant propagation and metabolic engineering.
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Affiliation(s)
- Jing Yu
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Xiaowei Wang
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Qianqian Yuan
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Jiaxin Shi
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Jingyi Cai
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Zhichao Li
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Hongwu Ma
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
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2
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Wendering P, Nikoloski Z. Model-driven insights into the effects of temperature on metabolism. Biotechnol Adv 2023; 67:108203. [PMID: 37348662 DOI: 10.1016/j.biotechadv.2023.108203] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/22/2023] [Accepted: 06/18/2023] [Indexed: 06/24/2023]
Abstract
Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.
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Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.
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3
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Abdullah-Zawawi MR, Govender N, Harun S, Muhammad NAN, Zainal Z, Mohamed-Hussein ZA. Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom. PLANTS (BASEL, SWITZERLAND) 2022; 11:2614. [PMID: 36235479 PMCID: PMC9573505 DOI: 10.3390/plants11192614] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.
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Affiliation(s)
- Muhammad-Redha Abdullah-Zawawi
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nisha Govender
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Sarahani Harun
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zamri Zainal
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
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4
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Beilsmith K, Henry CS, Seaver SMD. Genome-scale modeling of the primary-specialized metabolism interface. CURRENT OPINION IN PLANT BIOLOGY 2022; 68:102244. [PMID: 35714443 DOI: 10.1016/j.pbi.2022.102244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 04/21/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
Environmental challenges and development require plants to reallocate resources between primary and specialized metabolites to survive. Genome-scale metabolic models, which map carbon flux through metabolic pathways, are a valuable tool in the study of tradeoffs that arise at this interface. Due to annotation gaps, models that characterize all the enzymatic steps in individual specialized pathways and their linkages to each other and to central carbon metabolism are difficult to construct. Recent studies have successfully curated subsystems of specialized metabolism and characterized the interfaces where flux is diverted to the precursors of glucosinolates, terpenes, and anthocyanins. Although advances in metabolite profiling can help to constrain models at this interface, quantitative analysis remains challenging because of the different timescales on which specialized metabolites from constitutive and reactive pathways accumulate.
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Affiliation(s)
- Kathleen Beilsmith
- Data Science and Learning Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA
| | - Christopher S Henry
- Data Science and Learning Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA
| | - Samuel M D Seaver
- Data Science and Learning Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA.
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5
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Sampaio M, Rocha M, Dias O. Exploring synergies between plant metabolic modelling and machine learning. Comput Struct Biotechnol J 2022; 20:1885-1900. [PMID: 35521559 PMCID: PMC9052043 DOI: 10.1016/j.csbj.2022.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/03/2022] Open
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6
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Bassi R, Dall'Osto L. Dissipation of Light Energy Absorbed in Excess: The Molecular Mechanisms. ANNUAL REVIEW OF PLANT BIOLOGY 2021; 72:47-76. [PMID: 34143647 DOI: 10.1146/annurev-arplant-071720-015522] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Light is essential for photosynthesis. Nevertheless, its intensity widely changes depending on time of day, weather, season, and localization of individual leaves within canopies. This variability means that light collected by the light-harvesting system is often in excess with respect to photon fluence or spectral quality in the context of the capacity of photosynthetic metabolism to use ATP and reductants produced from the light reactions. Absorption of excess light can lead to increased production of excited, highly reactive intermediates, which expose photosynthetic organisms to serious risks of oxidative damage. Prevention and management of such stress are performed by photoprotective mechanisms, which operate by cutting down light absorption, limiting the generation of redox-active molecules, or scavenging reactive oxygen species that are released despite the operation of preventive mechanisms. Here, we describe the major physiological and molecular mechanisms of photoprotection involved in the harmless removal of the excess light energy absorbed by green algae and land plants. In vivo analyses of mutants targeting photosynthetic components and the enhanced resolution of spectroscopic techniques have highlighted specific mechanisms protecting the photosynthetic apparatus from overexcitation. Recent findings unveil a network of multiple interacting elements, the reaction times of which vary from a millisecond to weeks, that continuously maintain photosynthetic organisms within the narrow safety range between efficient light harvesting and photoprotection.
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Affiliation(s)
- Roberto Bassi
- Department of Biotechnology, University of Verona, 37134 Verona, Italy;
| | - Luca Dall'Osto
- Department of Biotechnology, University of Verona, 37134 Verona, Italy;
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7
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Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava. Sci Rep 2021; 11:8758. [PMID: 33888810 PMCID: PMC8062692 DOI: 10.1038/s41598-021-88129-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/08/2021] [Indexed: 02/02/2023] Open
Abstract
The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.
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8
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Kleine T, Nägele T, Neuhaus HE, Schmitz-Linneweber C, Fernie AR, Geigenberger P, Grimm B, Kaufmann K, Klipp E, Meurer J, Möhlmann T, Mühlhaus T, Naranjo B, Nickelsen J, Richter A, Ruwe H, Schroda M, Schwenkert S, Trentmann O, Willmund F, Zoschke R, Leister D. Acclimation in plants - the Green Hub consortium. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 106:23-40. [PMID: 33368770 DOI: 10.1111/tpj.15144] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 12/08/2020] [Accepted: 12/14/2020] [Indexed: 05/04/2023]
Abstract
Acclimation is the capacity to adapt to environmental changes within the lifetime of an individual. This ability allows plants to cope with the continuous variation in ambient conditions to which they are exposed as sessile organisms. Because environmental changes and extremes are becoming even more pronounced due to the current period of climate change, enhancing the efficacy of plant acclimation is a promising strategy for mitigating the consequences of global warming on crop yields. At the cellular level, the chloroplast plays a central role in many acclimation responses, acting both as a sensor of environmental change and as a target of cellular acclimation responses. In this Perspective article, we outline the activities of the Green Hub consortium funded by the German Science Foundation. The main aim of this research collaboration is to understand and strategically modify the cellular networks that mediate plant acclimation to adverse environments, employing Arabidopsis, tobacco (Nicotiana tabacum) and Chlamydomonas as model organisms. These efforts will contribute to 'smart breeding' methods designed to create crop plants with improved acclimation properties. To this end, the model oilseed crop Camelina sativa is being used to test modulators of acclimation for their potential to enhance crop yield under adverse environmental conditions. Here we highlight the current state of research on the role of gene expression, metabolism and signalling in acclimation, with a focus on chloroplast-related processes. In addition, further approaches to uncovering acclimation mechanisms derived from systems and computational biology, as well as adaptive laboratory evolution with photosynthetic microbes, are highlighted.
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Affiliation(s)
- Tatjana Kleine
- Plant Molecular Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, 82152, Germany
| | - Thomas Nägele
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Munich, 82152, Germany
| | - H Ekkehard Neuhaus
- Plant Physiology, Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | | | - Alisdair R Fernie
- Central Metabolism, Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, 14476, Germany
| | - Peter Geigenberger
- Plant Metabolism, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Munich, 82152, Germany
| | - Bernhard Grimm
- Plant Physiology, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, 10115, Germany
| | - Kerstin Kaufmann
- Plant Cell and Molecular Biology, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, 10115, Germany
| | - Edda Klipp
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, 10115, Germany
| | - Jörg Meurer
- Plant Molecular Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, 82152, Germany
| | - Torsten Möhlmann
- Plant Physiology, Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Belen Naranjo
- Plant Molecular Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, 82152, Germany
| | - Jörg Nickelsen
- Molecular Plant Science, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Munich, 82152, Germany
| | - Andreas Richter
- Physiology of Plant Organelles, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, 10115, Germany
| | - Hannes Ruwe
- Molecular Genetics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, 10115, Germany
| | - Michael Schroda
- Molecular Biotechnology & Systems Biology, Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Serena Schwenkert
- Plant Biochemistry and Physiology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Munich, 82152, Germany
| | - Oliver Trentmann
- Plant Physiology, Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Felix Willmund
- Molecular Genetics of Eukaryotes, Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Reimo Zoschke
- Translational Regulation in Plants, Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, 14476, Germany
| | - Dario Leister
- Plant Molecular Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, 82152, Germany
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9
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Environment-coupled models of leaf metabolism. Biochem Soc Trans 2021; 49:119-129. [PMID: 33492365 PMCID: PMC7925006 DOI: 10.1042/bst20200059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/30/2020] [Accepted: 12/17/2020] [Indexed: 12/15/2022]
Abstract
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.
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10
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Herrmann HA, Schwartz JM, Johnson GN. Metabolic acclimation-a key to enhancing photosynthesis in changing environments? JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:3043-3056. [PMID: 30997505 DOI: 10.1093/jxb/erz157] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 03/21/2019] [Indexed: 05/18/2023]
Abstract
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.
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Affiliation(s)
- Helena A Herrmann
- School of Earth and Environmental Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, UK
- Division of Evolution & Genomic Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Jean-Marc Schwartz
- Division of Evolution & Genomic Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Giles N Johnson
- School of Earth and Environmental Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, UK
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Integration of Plant Metabolomics Data with Metabolic Networks: Progresses and Challenges. Methods Mol Biol 2019; 1778:297-310. [PMID: 29761447 DOI: 10.1007/978-1-4939-7819-9_21] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
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.
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12
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Ma DM, Gandra SVS, Manoharlal R, La Hovary C, Xie DY. Untargeted Metabolomics of Nicotiana tabacum Grown in United States and India Characterizes the Association of Plant Metabolomes With Natural Climate and Geography. FRONTIERS IN PLANT SCIENCE 2019; 10:1370. [PMID: 31737005 PMCID: PMC6831618 DOI: 10.3389/fpls.2019.01370] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Accepted: 10/04/2019] [Indexed: 05/20/2023]
Abstract
Climate change and geography affect all the living organisms. To date, the effects of climate and geographical factors on plant metabolome largely remain open for worldwide and local investigations. In this study, we designed field experiments with tobacco (Nicotiana tabacum) in India versus USA and used untargeted metabolomics to understand the association of two weather factors and two different continental locations with respect to tobacco metabolism. Field research stations in Oxford, North Carolina, USA, and Rajahmundry, Andhra Pradesh India were selected to grow a commercial tobacco genotype (K326) for 2 years. Plant growth, field management, and leaf curing followed protocols standardized for tobacco cultivation. Gas chromatography-mass spectrometry based unbiased profiling annotated 171 non-polar and 225 polar metabolites from cured tobacco leaves. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that two growing years and two field locations played primary and secondary roles affecting metabolite profiles, respectively. PCA and Pearson analysis, which used nicotine, 11 other groups of metabolites, two locations, temperatures, and precipitation, revealed that in North Carolina, temperature changes were positively associated with the profiles of sesquiterpenes, diterpenes, and triterpenes, but negatively associated with the profiles of nicotine, organic acids of tricarboxylic acid, and sugars; in addition, precipitation was positively associated with the profiles of triterpenes. In India, temperature was positively associated with the profiles of benzenes and polycyclic aromatic hydrocarbons, but negatively associated with the profiles of amino acids and sugar. Further comparative analysis revealed that nicotine levels were affected by weather conditions, nevertheless, its trend in leaves was independent of two geographical locations and weather changes. All these findings suggested that climate and geographical variation significantly differentiated the tobacco metabolism.
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Affiliation(s)
- Dong-Ming Ma
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
| | - Saiprasad V. S. Gandra
- ITC Life Sciences and Technology Centre (LSTC), ITC Limited, Karnataka, Bengaluru, India
| | - Raman Manoharlal
- ITC Life Sciences and Technology Centre (LSTC), ITC Limited, Karnataka, Bengaluru, India
| | - Christophe La Hovary
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
| | - De-Yu Xie
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
- *Correspondence: De-Yu Xie,
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13
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Botero K, Restrepo S, Pinzón A. A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism. BMC Genomics 2018; 19:863. [PMID: 30537923 PMCID: PMC6288859 DOI: 10.1186/s12864-018-5192-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Phytophthora infestans is a plant pathogen that causes an important plant disease known as late blight in potato plants (Solanum tuberosum) and several other solanaceous hosts. This disease is the main factor affecting potato crop production worldwide. In spite of the importance of the disease, the molecular mechanisms underlying the compatibility between the pathogen and its hosts are still unknown. RESULTS To explain the metabolic response of late blight, specifically photosynthesis inhibition in infected plants, we reconstructed a genome-scale metabolic network of the S. tuberosum leaf, PstM1. This metabolic network simulates the effect of this disease in the leaf metabolism. PstM1 accounts for 2751 genes, 1113 metabolic functions, 1773 gene-protein-reaction associations and 1938 metabolites involved in 2072 reactions. The optimization of the model for biomass synthesis maximization in three infection time points suggested a suppression of the photosynthetic capacity related to the decrease of metabolic flux in light reactions and carbon fixation reactions. In addition, a variation pattern in the flux of carboxylation to oxygenation reactions catalyzed by RuBisCO was also identified, likely to be associated to a defense response in the compatible interaction between P. infestans and S. tuberosum. CONCLUSIONS In this work, we introduced simultaneously the first metabolic network of S. tuberosum and the first genome-scale metabolic model of the compatible interaction of a plant with P. infestans.
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Affiliation(s)
- Kelly Botero
- Grupo de Bioinformática y Biología de Sistemas, Universidad Nacional del Colombia - Instituto de Genética, Calle 53- Carrera 32, Edificio 426, Bogotá, Colombia.,Centro de Bioinformática y Biología Computacional, Manizales, Colombia
| | - Silvia Restrepo
- Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia
| | - Andres Pinzón
- Grupo de Bioinformática y Biología de Sistemas, Universidad Nacional del Colombia - Instituto de Genética, Calle 53- Carrera 32, Edificio 426, Bogotá, Colombia.
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Mullineaux PM, Exposito-Rodriguez M, Laissue PP, Smirnoff N. ROS-dependent signalling pathways in plants and algae exposed to high light: Comparisons with other eukaryotes. Free Radic Biol Med 2018; 122:52-64. [PMID: 29410363 DOI: 10.1016/j.freeradbiomed.2018.01.033] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/27/2018] [Accepted: 01/31/2018] [Indexed: 01/09/2023]
Abstract
Like all aerobic organisms, plants and algae co-opt reactive oxygen species (ROS) as signalling molecules to drive cellular responses to changes in their environment. In this respect, there is considerable commonality between all eukaryotes imposed by the constraints of ROS chemistry, similar metabolism in many subcellular compartments, the requirement for a high degree of signal specificity and the deployment of thiol peroxidases as transducers of oxidising equivalents to regulatory proteins. Nevertheless, plants and algae carry out specialised signalling arising from oxygenic photosynthesis in chloroplasts and photoautotropism, which often induce an imbalance between absorption of light energy and the capacity to use it productively. A key means of responding to this imbalance is through communication of chloroplasts with the nucleus to adjust cellular metabolism. Two ROS, singlet oxygen (1O2) and hydrogen peroxide (H2O2), initiate distinct signalling pathways when photosynthesis is perturbed. 1O2, because of its potent reactivity means that it initiates but does not transduce signalling. In contrast, the lower reactivity of H2O2 means that it can also be a mobile messenger in a spatially-defined signalling pathway. How plants translate a H2O2 message to bring about changes in gene expression is unknown and therefore, we draw on information from other eukaryotes to propose a working hypothesis. The role of these ROS generated in other subcellular compartments of plant cells in response to HL is critically considered alongside other eukaryotes. Finally, the responses of animal cells to oxidative stress upon high irradiance exposure is considered for new comparisons between plant and animal cells.
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Affiliation(s)
- Philip M Mullineaux
- School of Biological Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK.
| | | | | | - Nicholas Smirnoff
- Biosciences, College of Life and Environmental Sciences, University of Exeter, Geoffrey Pope Building, Stocker Road, Exeter EX4 4QD, UK
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15
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Sweetlove LJ, Nielsen J, Fernie AR. Engineering central metabolism - a grand challenge for plant biologists. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2017; 90:749-763. [PMID: 28004455 DOI: 10.1111/tpj.13464] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Revised: 12/14/2016] [Accepted: 12/15/2016] [Indexed: 06/06/2023]
Abstract
The goal of increasing crop productivity and nutrient-use efficiency is being addressed by a number of ambitious research projects seeking to re-engineer photosynthetic biochemistry. Many of these projects will require the engineering of substantial changes in fluxes of central metabolism. However, as has been amply demonstrated in simpler systems such as microbes, central metabolism is extremely difficult to rationally engineer. This is because of multiple layers of regulation that operate to maintain metabolic steady state and because of the highly connected nature of central metabolism. In this review we discuss new approaches for metabolic engineering that have the potential to address these problems and dramatically improve the success with which we can rationally engineer central metabolism in plants. In particular, we advocate the adoption of an iterative 'design-build-test-learn' cycle using fast-to-transform model plants as test beds. This approach can be realised by coupling new molecular tools to incorporate multiple transgenes in nuclear and plastid genomes with computational modelling to design the engineering strategy and to understand the metabolic phenotype of the engineered organism. We also envisage that mutagenesis could be used to fine-tune the balance between the endogenous metabolic network and the introduced enzymes. Finally, we emphasise the importance of considering the plant as a whole system and not isolated organs: the greatest increase in crop productivity will be achieved if both source and sink metabolism are engineered.
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Affiliation(s)
- Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41128, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark
- Science for Life Laboratory, Royal Institute of Technology, SE17121, Stockholm, Sweden
| | - Alisdair R Fernie
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany
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16
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A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation. mSystems 2017; 2:mSystems00165-16. [PMID: 28382331 PMCID: PMC5371395 DOI: 10.1128/msystems.00165-16] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 03/04/2017] [Indexed: 01/10/2023] Open
Abstract
The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms. Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.
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17
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Jablonsky J, Papacek S, Hagemann M. Different strategies of metabolic regulation in cyanobacteria: from transcriptional to biochemical control. Sci Rep 2016; 6:33024. [PMID: 27611502 PMCID: PMC5017163 DOI: 10.1038/srep33024] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 08/17/2016] [Indexed: 12/23/2022] Open
Abstract
Cyanobacteria Synechococcus sp. PCC 7942 and Synechocystis sp. PCC 6803 show similar changes in the metabolic response to changed CO2 conditions but exhibit significant differences at the transcriptomic level. This study employs a systems biology approach to investigate the difference in metabolic regulation of Synechococcus sp. PCC 7942 and Synechocystis sp. PCC 6803. Presented multi-level kinetic model for Synechocystis sp. PCC 6803 is a new approach integrating and analysing metabolomic, transcriptomic and fluxomics data obtained under high and ambient CO2 levels. Modelling analysis revealed that higher number of different isozymes in Synechocystis 6803 improves homeostatic stability of several metabolites, especially 3PGA by 275%, against changes in gene expression, compared to Synechococcus sp. PCC 7942. Furthermore, both cyanobacteria have the same amount of phosphoglycerate mutases but Synechocystis 6803 exhibits only ~20% differences in their mRNA levels after shifts from high to ambient CO2 level, in comparison to ~500% differences in the case of Synechococcus sp. PCC 7942. These and other data imply that the biochemical control dominates over transcriptional regulation in Synechocystis 6803 to acclimate central carbon metabolism in the environment of variable inorganic carbon availability without extra cost carried by large changes in the proteome.
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Affiliation(s)
- Jiri Jablonsky
- Institute of Complex Systems, FFPW, University of South Bohemia, Cenakva, Czech Republic
| | - Stepan Papacek
- Institute of Complex Systems, FFPW, University of South Bohemia, Cenakva, Czech Republic
| | - Martin Hagemann
- Department of Plant Physiology, University of Rostock, Einsteinstr. 3, D-18059 Rostock, Germany
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18
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Lakshmanan M, Cheung CYM, Mohanty B, Lee DY. Modeling Rice Metabolism: From Elucidating Environmental Effects on Cellular Phenotype to Guiding Crop Improvement. FRONTIERS IN PLANT SCIENCE 2016; 7:1795. [PMID: 27965696 PMCID: PMC5126141 DOI: 10.3389/fpls.2016.01795] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Accepted: 11/15/2016] [Indexed: 05/20/2023]
Abstract
Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently six such networks are available, where five are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.
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Affiliation(s)
- Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and ResearchSingapore, Singapore
| | - C. Y. Maurice Cheung
- Department of Chemical and Biomolecular Engineering, National University of SingaporeSingapore, Singapore
| | - Bijayalaxmi Mohanty
- Department of Chemical and Biomolecular Engineering, National University of SingaporeSingapore, Singapore
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and ResearchSingapore, Singapore
- Department of Chemical and Biomolecular Engineering, National University of SingaporeSingapore, Singapore
- Synthetic Biology for Clinical and Technological Innovation, Life Sciences Institute, National University of SingaporeSingapore, Singapore
- *Correspondence: Dong-Yup Lee,
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19
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Sun CX, Li MQ, Gao XX, Liu LN, Wu XF, Zhou JH. Metabolic response of maize plants to multi-factorial abiotic stresses. PLANT BIOLOGY (STUTTGART, GERMANY) 2016; 18 Suppl 1:120-9. [PMID: 25622534 DOI: 10.1111/plb.12305] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 01/08/2015] [Indexed: 05/09/2023]
Abstract
Clarification of the metabolic mechanisms underlying multi-stress responses in plants will allow further optimisation of crop breeding and cultivation to obtain high yields in an increasingly variable environment. Using NMR metabolomic techniques, we examined the metabolic responses of maize plants grown under different conditions: soil drought, soil salinity, heat and multiple concurrent stresses. A detailed time-course metabolic profile was also performed on maize plants sampled 1, 3 and 7 days after initiation of soil drought and heat stress. The metabolic profile of maize plants subjected to soil drought was more similar to plants exposed to salt stress than to heat-stressed plants. Drought-stressed maize plants subjected to salt or heat stress showed distinct integrated metabolic profiles compared with those exposed to either stressor individually. These differences show the considerable metabolic plasticity of maize in response to different growth conditions. Moreover, glucose, fructose, malate, citrate, proline, alanine, aspartate, asparagine, threonine and one unknown compound fluctuated obviously between maize plants grown in controlled growth cabinet and a natural regime. These changes were associated with the TCA cycle and core nitrogen metabolism, and could be related to their multiple functions during plant growth. The evident stress-induced trajectory of metabolic changes in maize indicated that the primary metabolic responses to soil drought, heat and combined drought and heat stresses occurred in a time-dependent manner. Plasticity at the metabolic level may allow maize plants to acclimatise their metabolic ranges in response to changing environmental conditions.
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Affiliation(s)
- C X Sun
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - M Q Li
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - X X Gao
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - L N Liu
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - X F Wu
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - J H Zhou
- College of Life and Health Sciences, Northeastern University, Shenyang, China
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20
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Shi H, Schwender J. Mathematical models of plant metabolism. Curr Opin Biotechnol 2015; 37:143-152. [PMID: 26723012 DOI: 10.1016/j.copbio.2015.10.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 10/16/2015] [Accepted: 10/26/2015] [Indexed: 11/24/2022]
Abstract
Among various modeling approaches in plant metabolic research, applications of Constraint-Based modeling are fast increasing in recent years, apparently driven by current advances in genomics and genome sequencing. Constraint-Based modeling, the functional analysis of metabolic networks at the whole cell or genome scale, is more difficult to apply to plants than to microbes. Here we discuss recent developments in Constraint-Based modeling in plants with focus on issues of model reconstruction and flux prediction. Another topic is the emerging application of integration of Constraint-Based modeling with omics data to increase predictive power. Furthermore, advances in experimental measurements of cellular fluxes by (13)C-Metabolic Flux Analysis are highlighted, including instationary (13)C-MFA used to probe autotrophic metabolism in photosynthetic tissue in the light.
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Affiliation(s)
- Hai Shi
- Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Jörg Schwender
- Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, United States.
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21
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Rai A, Saito K. Omics data input for metabolic modeling. Curr Opin Biotechnol 2015; 37:127-134. [PMID: 26723010 DOI: 10.1016/j.copbio.2015.10.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 10/09/2015] [Accepted: 10/26/2015] [Indexed: 12/20/2022]
Abstract
Recent advancements in high-throughput large-scale analytical methods to sequence genomes of organisms, and to quantify gene expression, proteins, lipids and metabolites have changed the paradigm of metabolic modeling. The cost of data generation and analysis has decreased significantly, which has allowed exponential increase in the amount of omics data being generated for an organism in a very short time. Compared to progress made in microbial metabolic modeling, plant metabolic modeling still remains limited due to its complex genomes and compartmentalization of metabolic reactions. Herein, we review and discuss different omics-datasets with potential application in the functional genomics. In particular, this review focuses on the application of omics-datasets towards construction and reconstruction of plant metabolic models.
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Affiliation(s)
- Amit Rai
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan.
| | - Kazuki Saito
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan; RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan.
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22
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Lakshmanan M, Lim SH, Mohanty B, Kim JK, Ha SH, Lee DY. Unraveling the Light-Specific Metabolic and Regulatory Signatures of Rice through Combined in Silico Modeling and Multiomics Analysis. PLANT PHYSIOLOGY 2015; 169:3002-20. [PMID: 26453433 PMCID: PMC4677915 DOI: 10.1104/pp.15.01379] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Accepted: 10/07/2015] [Indexed: 05/21/2023]
Abstract
Light quality is an important signaling component upon which plants orchestrate various morphological processes, including seed germination and seedling photomorphogenesis. However, it is still unclear how plants, especially food crops, sense various light qualities and modulate their cellular growth and other developmental processes. Therefore, in this work, we initially profiled the transcripts of a model crop, rice (Oryza sativa), under four different light treatments (blue, green, red, and white) as well as in the dark. Concurrently, we reconstructed a fully compartmentalized genome-scale metabolic model of rice cells, iOS2164, containing 2,164 unique genes, 2,283 reactions, and 1,999 metabolites. We then combined the model with transcriptome profiles to elucidate the light-specific transcriptional signatures of rice metabolism. Clearly, light signals mediated rice gene expressions, differentially regulating numerous metabolic pathways: photosynthesis and secondary metabolism were up-regulated in blue light, whereas reserve carbohydrates degradation was pronounced in the dark. The topological analysis of gene expression data with the rice genome-scale metabolic model further uncovered that phytohormones, such as abscisate, ethylene, gibberellin, and jasmonate, are the key biomarkers of light-mediated regulation, and subsequent analysis of the associated genes' promoter regions identified several light-specific transcription factors. Finally, the transcriptional control of rice metabolism by red and blue light signals was assessed by integrating the transcriptome and metabolome data with constraint-based modeling. The biological insights gained from this integrative systems biology approach offer several potential applications, such as improving the agronomic traits of food crops and designing light-specific synthetic gene circuits in microbial and mammalian systems.
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Affiliation(s)
- Meiyappan Lakshmanan
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Sun-Hyung Lim
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Bijayalaxmi Mohanty
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Jae Kwang Kim
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Sun-Hwa Ha
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
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23
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Nikoloski Z, Perez-Storey R, Sweetlove LJ. Inference and Prediction of Metabolic Network Fluxes. PLANT PHYSIOLOGY 2015; 169:1443-55. [PMID: 26392262 PMCID: PMC4634083 DOI: 10.1104/pp.15.01082] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 09/06/2015] [Indexed: 05/18/2023]
Abstract
In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.
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Affiliation(s)
- Zoran Nikoloski
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (Z.N.); andDepartment of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom (R.P.-S., L.J.S.)
| | - Richard Perez-Storey
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (Z.N.); andDepartment of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom (R.P.-S., L.J.S.)
| | - Lee J Sweetlove
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (Z.N.); andDepartment of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom (R.P.-S., L.J.S.)
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24
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Tohge T, Scossa F, Fernie AR. Integrative Approaches to Enhance Understanding of Plant Metabolic Pathway Structure and Regulation. PLANT PHYSIOLOGY 2015; 169:1499-511. [PMID: 26371234 PMCID: PMC4634077 DOI: 10.1104/pp.15.01006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 09/10/2015] [Indexed: 05/05/2023]
Abstract
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.
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Affiliation(s)
- Takayuki Tohge
- Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany (T.T., A.R.F.); andConsiglio per la Ricerca e Analisi dell'Economia Agraria, Centro di Ricerca per la Frutticoltura, 00134 Rome, Italy (F.S.)
| | - Federico Scossa
- Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany (T.T., A.R.F.); andConsiglio per la Ricerca e Analisi dell'Economia Agraria, Centro di Ricerca per la Frutticoltura, 00134 Rome, Italy (F.S.)
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany (T.T., A.R.F.); andConsiglio per la Ricerca e Analisi dell'Economia Agraria, Centro di Ricerca per la Frutticoltura, 00134 Rome, Italy (F.S.)
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25
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Hill CB, Czauderna T, Klapperstück M, Roessner U, Schreiber F. Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants. Front Bioeng Biotechnol 2015; 3:167. [PMID: 26557642 PMCID: PMC4617106 DOI: 10.3389/fbioe.2015.00167] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 10/05/2015] [Indexed: 12/23/2022] Open
Abstract
Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions.
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Affiliation(s)
- Camilla Beate Hill
- School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - Tobias Czauderna
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | | | - Ute Roessner
- School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - Falk Schreiber
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany
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Medeiros DB, Daloso DM, Fernie AR, Nikoloski Z, Araújo WL. Utilizing systems biology to unravel stomatal function and the hierarchies underpinning its control. PLANT, CELL & ENVIRONMENT 2015; 38:1457-70. [PMID: 25689387 DOI: 10.1111/pce.12517] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2014] [Revised: 01/20/2015] [Accepted: 01/27/2015] [Indexed: 05/08/2023]
Abstract
Stomata control the concomitant exchange of CO2 and transpiration in land plants. While a constant supply of CO2 is need to maintain the rate of photosynthesis, the accompanying water losses must be tightly regulated to prevent dehydration and undesired metabolic changes. The factors affecting stomatal movement are directly coupled with the cellular networks of guard cells. Although the guard cell has been used as a model for characterization of signaling pathways, several important questions about its functioning remain elusive. Current modeling approaches describe the stomatal conductance in terms of relatively few easy-to-measure variables being unsuitable for in silico design of genetic manipulation strategies. Here, we argue that a system biology approach, combining modeling and high-throughput experiments, may be used to elucidate the mechanisms underlying stomata control and to determine targets for modulation of stomatal responses to environment. In support of our opinion, we review studies demonstrating how high-throughput approaches have provided a systems-view of guard cells. Finally, we emphasize the opportunities and challenges of genome-scale modeling and large-scale data integration for in silico manipulation of guard cell functions to improve crop yields, particularly under stress conditions which are of pertinence both to climate change and water use efficiency.
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Affiliation(s)
- David B Medeiros
- Max-Planck Partner Group, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Danilo M Daloso
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Wagner L Araújo
- Max-Planck Partner Group, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
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27
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Fluxes through plant metabolic networks: measurements, predictions, insights and challenges. Biochem J 2015; 465:27-38. [PMID: 25631681 DOI: 10.1042/bj20140984] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
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.
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Seaver SMD, Bradbury LMT, Frelin O, Zarecki R, Ruppin E, Hanson AD, Henry CS. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm. FRONTIERS IN PLANT SCIENCE 2015; 6:142. [PMID: 25806041 PMCID: PMC4354304 DOI: 10.3389/fpls.2015.00142] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 02/22/2015] [Indexed: 05/08/2023]
Abstract
There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.
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Affiliation(s)
- Samuel M. D. Seaver
- Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA
- Computation Institute, The University of ChicagoChicago, IL, USA
| | - Louis M. T. Bradbury
- Horticultural Sciences Department, University of FloridaGainesville, FL, USA
- Department of Biology, York College, City University of New YorkNew York, NY, USA
| | - Océane Frelin
- Horticultural Sciences Department, University of FloridaGainesville, FL, USA
| | - Raphy Zarecki
- Sackler Faculty of Medicine, Tel Aviv UniversityTel Aviv, Israel
| | - Eytan Ruppin
- Sackler Faculty of Medicine, Tel Aviv UniversityTel Aviv, Israel
| | - Andrew D. Hanson
- Horticultural Sciences Department, University of FloridaGainesville, FL, USA
| | - Christopher S. Henry
- Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA
- Computation Institute, The University of ChicagoChicago, IL, USA
- *Correspondence: Christopher S. Henry, Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, USA
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Töpfer N, Kleessen S, Nikoloski Z. Integration of metabolomics data into metabolic networks. FRONTIERS IN PLANT SCIENCE 2015; 6:49. [PMID: 25741348 PMCID: PMC4330704 DOI: 10.3389/fpls.2015.00049] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 01/19/2015] [Indexed: 05/08/2023]
Abstract
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.
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Affiliation(s)
- Nadine Töpfer
- Systems Biology and Mathematical Modeling Group, Department Willmitzer, Max-Planck Institute of Molecular Plant PhysiologyPotsdam, Germany
- Department of Plant Sciences, Weizmann Institute of ScienceRehovot, Israel
| | - Sabrina Kleessen
- Systems Biology and Mathematical Modeling Group, Department Willmitzer, Max-Planck Institute of Molecular Plant PhysiologyPotsdam, Germany
- Targenomix GmbHPotsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Department Willmitzer, Max-Planck Institute of Molecular Plant PhysiologyPotsdam, Germany
- *Correspondence: Zoran Nikoloski, Systems Biology and Mathematical Modeling Group, Department Willmitzer, Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany e-mail:
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Calderwood A, Morris RJ, Kopriva S. Predictive sulfur metabolism - a field in flux. FRONTIERS IN PLANT SCIENCE 2014; 5:646. [PMID: 25477892 PMCID: PMC4235266 DOI: 10.3389/fpls.2014.00646] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 11/02/2014] [Indexed: 05/08/2023]
Abstract
The key role of sulfur metabolites in response to biotic and abiotic stress in plants, as well as their importance in diet and health has led to a significant interest and effort in trying to understand and manipulate the production of relevant compounds. Metabolic engineering utilizes a set of theoretical tools to help rationally design modifications that enhance the production of a desired metabolite. Such approaches have proven their value in bacterial systems, however, the paucity of success stories to date in plants, suggests that challenges remain. Here, we review the most commonly used methods for understanding metabolic flux, focusing on the sulfur assimilatory pathway. We highlight known issues with both experimental and theoretical approaches, as well as presenting recent methods for integrating different modeling strategies, and progress toward an understanding of flux at the whole plant level.
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Affiliation(s)
| | - Richard J. Morris
- Department of Computational and Systems Biology, John Innes CentreNorwich, UK
| | - Stanislav Kopriva
- Botanical Institute and Cluster of Excellence on Plant Sciences, University of Cologne, Cologne BiocenterCologne, Germany
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31
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Simons M, Saha R, Guillard L, Clément G, Armengaud P, Cañas R, Maranas CD, Lea PJ, Hirel B. Nitrogen-use efficiency in maize (Zea mays L.): from 'omics' studies to metabolic modelling. JOURNAL OF EXPERIMENTAL BOTANY 2014; 65:5657-71. [PMID: 24863438 DOI: 10.1093/jxb/eru227] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
In this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when integrated with 'omics' data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.
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Affiliation(s)
- Margaret Simons
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rajib Saha
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Lenaïg Guillard
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Gilles Clément
- Plateau Technique Spécifique de Chimie du Végétal, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Route de St Cyr, F-78026 Versailles Cedex, France
| | - Patrick Armengaud
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Rafael Cañas
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Peter J Lea
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
| | - Bertrand Hirel
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
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32
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Baghalian K, Hajirezaei MR, Schreiber F. Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering. THE PLANT CELL 2014; 26:3847-66. [PMID: 25344492 PMCID: PMC4247579 DOI: 10.1105/tpc.114.130328] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
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.
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Affiliation(s)
- Kambiz Baghalian
- Leibniz Institute of Plant Genetics and Crop Plant Research, D-06466 Gatersleben, Germany Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle, Germany College of Agriculture and Natural Resources, Islamic Azad University-Karaj Branch, Karaj 31485-313, Iran
| | | | - Falk Schreiber
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle, Germany Faculty of IT, Monash University, Clayton, VIC 3800, Australia
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33
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Heise R, Arrivault S, Szecowka M, Tohge T, Nunes-Nesi A, Stitt M, Nikoloski Z, Fernie AR. Flux profiling of photosynthetic carbon metabolism in intact plants. Nat Protoc 2014; 9:1803-24. [DOI: 10.1038/nprot.2014.115] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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34
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Töpfer N, Scossa F, Fernie A, Nikoloski Z. Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses. PLoS Comput Biol 2014; 10:e1003656. [PMID: 24946036 PMCID: PMC4063599 DOI: 10.1371/journal.pcbi.1003656] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2014] [Accepted: 04/16/2014] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Nadine Töpfer
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Federico Scossa
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Centro di ricerca per l'Orticoltura, Pontecagnano (Salerno), Italy
| | - Alisdair Fernie
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- * E-mail:
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35
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Sweetlove LJ, Obata T, Fernie AR. Systems analysis of metabolic phenotypes: what have we learnt? TRENDS IN PLANT SCIENCE 2014; 19:222-30. [PMID: 24139444 DOI: 10.1016/j.tplants.2013.09.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Revised: 09/12/2013] [Accepted: 09/18/2013] [Indexed: 05/26/2023]
Abstract
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.
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Affiliation(s)
- Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK.
| | - Toshihiro Obata
- Max-Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max-Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany.
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36
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Hay JO, Shi H, Heinzel N, Hebbelmann I, Rolletschek H, Schwender J. Integration of a constraint-based metabolic model of Brassica napus developing seeds with (13)C-metabolic flux analysis. FRONTIERS IN PLANT SCIENCE 2014; 5:724. [PMID: 25566296 PMCID: PMC4271587 DOI: 10.3389/fpls.2014.00724] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 12/01/2014] [Indexed: 05/19/2023]
Abstract
The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for (13)C-Metabolic Flux Analysis ((13)C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from (13)C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.
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Affiliation(s)
- Jordan O. Hay
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Hai Shi
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Nicolas Heinzel
- Department of Molecular Genetics, Leibniz-Institut für Pflanzengenetik und KulturpflanzenforschungGatersleben, Germany
| | - Inga Hebbelmann
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Hardy Rolletschek
- Department of Molecular Genetics, Leibniz-Institut für Pflanzengenetik und KulturpflanzenforschungGatersleben, Germany
| | - Jorg Schwender
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
- *Correspondence: Jorg Schwender, Brookhaven National Laboratory, Biological, Environment and Climate Sciences Department, Building 463, Upton, NY 11973, USA e-mail:
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Fukushima A, Kanaya S, Nishida K. Integrated network analysis and effective tools in plant systems biology. FRONTIERS IN PLANT SCIENCE 2014; 5:598. [PMID: 25408696 PMCID: PMC4219401 DOI: 10.3389/fpls.2014.00598] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 10/14/2014] [Indexed: 05/18/2023]
Abstract
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.
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Affiliation(s)
- Atsushi Fukushima
- RIKEN Center for Sustainable Resource ScienceTsurumi, Yokohama, Japan
- Japan Science and Technology Agency, National Bioscience Database CenterTokyo, Japan
- *Correspondence: Atsushi Fukushima, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehirocho, Tsurumi, Yokohama 230-0045, Japan e-mail:
| | - Shigehiko Kanaya
- Graduate School of Information Science, Nara Institute of Science and TechnologyNara, Japan
| | - Kozo Nishida
- Japan Science and Technology Agency, National Bioscience Database CenterTokyo, Japan
- Laboratory for Biochemical Simulation, RIKEN Quantitative Biology CenterOsaka, Japan
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Töpfer N, Niokoloski Z. Large-scale modeling provides insights into Arabidopsis's acclimation to changing light and temperature conditions. PLANT SIGNALING & BEHAVIOR 2013; 8:25480. [PMID: 23838962 PMCID: PMC4002592 DOI: 10.4161/psb.25480] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Accepted: 06/20/2013] [Indexed: 05/24/2023]
Abstract
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.
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