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Wu L, Shao H, Li J, Chen C, Hu N, Yang B, Weng H, Xiang L, Ye D. Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0180. [PMID: 38779576 PMCID: PMC11109595 DOI: 10.34133/plantphenomics.0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/29/2024] [Indexed: 05/25/2024]
Abstract
The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.
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Affiliation(s)
- Libin Wu
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Han Shao
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Center for Artificial Intelligence in Agriculture, School of Future Technology,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jiayi Li
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Chen Chen
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Nana Hu
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Center for Artificial Intelligence in Agriculture, School of Future Technology,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Biyun Yang
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Haiyong Weng
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Lirong Xiang
- Department of Biological and Agricultural Engineering,
North Carolina State University, Raleigh, NC 27606, USA
| | - Dapeng Ye
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
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2
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Angarita-Rodríguez A, González-Giraldo Y, Rubio-Mesa JJ, Aristizábal AF, Pinzón A, González J. Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets. Int J Mol Sci 2023; 25:365. [PMID: 38203536 PMCID: PMC10778851 DOI: 10.3390/ijms25010365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.
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Affiliation(s)
- Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Yeimy González-Giraldo
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Juan J. Rubio-Mesa
- Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Andrés Felipe Aristizábal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
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Saadat NP, van Aalst M, Brand A, Ebenhöh O, Tissier A, Matuszyńska AB. Shifts in carbon partitioning by photosynthetic activity increase terpenoid synthesis in glandular trichomes. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 115:1716-1728. [PMID: 37337787 DOI: 10.1111/tpj.16352] [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: 12/16/2022] [Accepted: 06/08/2023] [Indexed: 06/21/2023]
Abstract
Several commercially important secondary metabolites are produced and accumulated in high amounts by glandular trichomes, giving the prospect of using them as metabolic cell factories. Due to extremely high metabolic fluxes through glandular trichomes, previous research focused on how such flows are achieved. The question regarding their bioenergetics became even more interesting with the discovery of photosynthetic activity in some glandular trichomes. Despite recent advances, how primary metabolism contributes to the high metabolic fluxes in glandular trichomes is still not fully elucidated. Using computational methods and available multi-omics data, we first developed a quantitative framework to investigate the possible role of photosynthetic energy supply in terpenoid production and next tested experimentally the simulation-driven hypothesis. With this work, we provide the first reconstruction of specialised metabolism in Type-VI photosynthetic glandular trichomes of Solanum lycopersicum. Our model predicted that increasing light intensities results in a shift of carbon partitioning from catabolic to anabolic reactions driven by the energy availability of the cell. Moreover, we show the benefit of shifting between isoprenoid pathways under different light regimes, leading to a production of different classes of terpenes. Our computational predictions were confirmed in vivo, demonstrating a significant increase in production of monoterpenoids while the sesquiterpenes remained unchanged under higher light intensities. The outcomes of this research provide quantitative measures to assess the beneficial role of chloroplast in glandular trichomes for enhanced production of secondary metabolites and can guide the design of new experiments that aim at modulating terpenoid production.
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Affiliation(s)
- Nima P Saadat
- Institute of Theoretical and Quantitative Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
- Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Marvin van Aalst
- Institute of Theoretical and Quantitative Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Alejandro Brand
- Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany
| | - Oliver Ebenhöh
- Institute of Theoretical and Quantitative Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
- Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Alain Tissier
- Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Anna B Matuszyńska
- Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
- Computational Life Science, Department of Biology, RWTH Aachen University, Worringerweg 1, 52074, Aachen, Germany
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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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Gerlin L, Cottret L, Escourrou A, Genin S, Baroukh C. A multi-organ metabolic model of tomato predicts plant responses to nutritional and genetic perturbations. PLANT PHYSIOLOGY 2022; 188:1709-1723. [PMID: 34907432 PMCID: PMC8896645 DOI: 10.1093/plphys/kiab548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/27/2021] [Indexed: 06/14/2023]
Abstract
Predicting and understanding plant responses to perturbations require integrating the interactions between nutritional sources, genes, cell metabolism, and physiology in the same model. This can be achieved using metabolic modeling calibrated by experimental data. In this study, we developed a multi-organ metabolic model of a tomato (Solanum lycopersicum) plant during vegetative growth, named Virtual Young TOmato Plant (VYTOP) that combines genome-scale metabolic models of leaf, stem and root and integrates experimental data acquired from metabolomics and high-throughput phenotyping of tomato plants. It is composed of 6,689 reactions and 6,326 metabolites. We validated VYTOP predictions on five independent use cases. The model correctly predicted that glutamine is the main organic nutrient of xylem sap. The model estimated quantitatively how stem photosynthetic contribution impacts exchanges between the different organs. The model was also able to predict how nitrogen limitation affects vegetative growth and the metabolic behavior of transgenic tomato lines with altered expression of core metabolic enzymes. The integration of different components, such as a metabolic model, physiological constraints, and experimental data, generates a powerful predictive tool to study plant behavior, which will be useful for several other applications, such as plant metabolic engineering or plant nutrition.
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Affiliation(s)
- Léo Gerlin
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | - Ludovic Cottret
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | - Antoine Escourrou
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | - Stéphane Genin
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | - Caroline Baroukh
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
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6
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Zhao HL, Chang TG, Xiao Y, Zhu XG. Potential metabolic mechanisms for inhibited chloroplast nitrogen assimilation under high CO2. PLANT PHYSIOLOGY 2021; 187:1812-1833. [PMID: 34618071 PMCID: PMC8566258 DOI: 10.1093/plphys/kiab345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/28/2021] [Indexed: 05/31/2023]
Abstract
Improving photosynthesis is considered a major and feasible option to dramatically increase crop yield potential. Increased atmospheric CO2 concentration often stimulates both photosynthesis and crop yield, but decreases protein content in the main C3 cereal crops. This decreased protein content in crops constrains the benefits of elevated CO2 on crop yield and affects their nutritional value for humans. To support studies of photosynthetic nitrogen assimilation and its complex interaction with photosynthetic carbon metabolism for crop improvement, we developed a dynamic systems model of plant primary metabolism, which includes the Calvin-Benson cycle, the photorespiration pathway, starch synthesis, glycolysis-gluconeogenesis, the tricarboxylic acid cycle, and chloroplastic nitrogen assimilation. This model successfully captures responses of net photosynthetic CO2 uptake rate (A), respiration rate, and nitrogen assimilation rate to different irradiance and CO2 levels. We then used this model to predict inhibition of nitrogen assimilation under elevated CO2. The potential mechanisms underlying inhibited nitrogen assimilation under elevated CO2 were further explored with this model. Simulations suggest that enhancing the supply of α-ketoglutarate is a potential strategy to maintain high rates of nitrogen assimilation under elevated CO2. This model can be used as a heuristic tool to support research on interactions between photosynthesis, respiration, and nitrogen assimilation. It also provides a basic framework to support the design and engineering of C3 plant primary metabolism for enhanced photosynthetic efficiency and nitrogen assimilation in the coming high-CO2 world.
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Affiliation(s)
- Hong-Long Zhao
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Key Laboratory for Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Tian-Gen Chang
- National Key Laboratory for Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yi Xiao
- National Key Laboratory for Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200031, China
- Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, Illinois 61801, USA
- Department of Plant Biology, University of Illinois at Urbana Champaign, Urbana, Illinois 61801, USA
| | - Xin-Guang Zhu
- National Key Laboratory for Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200031, China
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7
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Sahu A, Blätke MA, Szymański JJ, Töpfer N. Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput Struct Biotechnol J 2021; 19:4626-4640. [PMID: 34471504 PMCID: PMC8382995 DOI: 10.1016/j.csbj.2021.08.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
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Affiliation(s)
- Ankur Sahu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Mary-Ann Blätke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Jędrzej Jakub Szymański
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Nadine Töpfer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
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Gloaguen P, Vandenbrouck Y, Joyard J, Curien G. ChloroKB, a cell metabolism reconstruction of the model plant Arabidopsis thaliana. C R Biol 2021; 344:157-163. [PMID: 34213853 DOI: 10.5802/crbiol.49] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 04/26/2021] [Indexed: 11/24/2022]
Abstract
Can we understand how plant cell metabolism really works? An integrated large-scale modelling of plant metabolism predictive model would make possible to analyse the impact of disturbances in environmental conditions on cellular functioning and diversity of plant-made molecules of interest. ChloroKB, a Web application initially developed for exploration of Arabidopsis chloroplast metabolic network now covers Arabidopsis mesophyll cell metabolism. Interconnected metabolic maps show subcellular compartments, metabolites, proteins, complexes, reactions, and transport. Data in ChloroKB have been structured to allow for mathematical modelling and will be used as a reference for modelling work dedicated to a particular issue.
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Affiliation(s)
- Pauline Gloaguen
- Université Grenoble Alpes, CNRS, CEA, INRAe, IRIG-LPCV, UMR5168, 38000 Grenoble, France
| | - Yves Vandenbrouck
- Université Grenoble Alpes, Inserm, CEA, IRIG-BGE, U1038, 38000, Grenoble, France
| | - Jacques Joyard
- Université Grenoble Alpes, CNRS, CEA, INRAe, IRIG-LPCV, UMR5168, 38000 Grenoble, France
| | - Gilles Curien
- Université Grenoble Alpes, CNRS, CEA, INRAe, IRIG-LPCV, UMR5168, 38000 Grenoble, France
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Jansson C, Faiola C, Wingler A, Zhu XG, Kravchenko A, de Graaff MA, Ogden AJ, Handakumbura PP, Werner C, Beckles DM. Crops for Carbon Farming. FRONTIERS IN PLANT SCIENCE 2021; 12:636709. [PMID: 34149744 PMCID: PMC8211891 DOI: 10.3389/fpls.2021.636709] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 04/26/2021] [Indexed: 05/03/2023]
Abstract
Agricultural cropping systems and pasture comprise one third of the world's arable land and have the potential to draw down a considerable amount of atmospheric CO2 for storage as soil organic carbon (SOC) and improving the soil carbon budget. An improved soil carbon budget serves the dual purpose of promoting soil health, which supports crop productivity, and constituting a pool from which carbon can be converted to recalcitrant forms for long-term storage as a mitigation measure for global warming. In this perspective, we propose the design of crop ideotypes with the dual functionality of being highly productive for the purposes of food, feed, and fuel, while at the same time being able to facilitate higher contribution to soil carbon and improve the below ground ecology. We advocate a holistic approach of the integrated plant-microbe-soil system and suggest that significant improvements in soil carbon storage can be achieved by a three-pronged approach: (1) design plants with an increased root strength to further allocation of carbon belowground; (2) balance the increase in belowground carbon allocation with increased source strength for enhanced photosynthesis and biomass accumulation; and (3) design soil microbial consortia for increased rhizosphere sink strength and plant growth-promoting (PGP) properties.
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Affiliation(s)
- Christer Jansson
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Celia Faiola
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, United States
| | - Astrid Wingler
- School of Biological, Earth & Environmental Sciences and Environmental Research Institute, University College Cork, Cork, Ireland
| | - Xin-Guang Zhu
- National Key Laboratory for Plant Molecular Genetics, Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Alexandra Kravchenko
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, United States
| | - Marie-Anne de Graaff
- Department of Biological Sciences, Boise State University, Boise, ID, United States
| | - Aaron J. Ogden
- Pacific Northwest National Laboratory, Richland, WA, United States
| | | | | | - Diane M. Beckles
- Department of Plant Sciences, University of California, Davis, Davis, CA, United States
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Abstract
Plants depend on the enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) for CO2 fixation. However, especially in C3 plants, photosynthetic yield is reduced by formation of 2-phosphoglycolate, a toxic oxygenation product of Rubisco, which needs to be recycled in a high-flux-demanding metabolic process called photorespiration. Canonical photorespiration dissipates energy and causes carbon and nitrogen losses. Reducing photorespiration through carbon-concentrating mechanisms, such as C4 photosynthesis, or bypassing photorespiration through metabolic engineering is expected to improve plant growth and yield. The β-hydroxyaspartate cycle (BHAC) is a recently described microbial pathway that converts glyoxylate, a metabolite of plant photorespiration, into oxaloacetate in a highly efficient carbon-, nitrogen-, and energy-conserving manner. Here, we engineered a functional BHAC in plant peroxisomes to create a photorespiratory bypass that is independent of 3-phosphoglycerate regeneration or decarboxylation of photorespiratory precursors. While efficient oxaloacetate conversion in Arabidopsis thaliana still masks the full potential of the BHAC, nitrogen conservation and accumulation of signature C4 metabolites demonstrate the proof of principle, opening the door to engineering a photorespiration-dependent synthetic carbon-concentrating mechanism in C3 plants.
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Chen J, Beauvoit B, Génard M, Colombié S, Moing A, Vercambre G, Gomès E, Gibon Y, Dai Z. Modelling predicts tomatoes can be bigger and sweeter if biophysical factors and transmembrane transports are fine-tuned during fruit development. THE NEW PHYTOLOGIST 2021; 230:1489-1502. [PMID: 33550584 DOI: 10.1111/nph.17260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/30/2021] [Indexed: 06/12/2023]
Abstract
The trade-off between yield and quality, a major problem for the production of fleshy fruits, involves fruit expansive growth and sugar metabolism. Here we developed an integrative model by coupling a biophysical model of fleshy fruit growth processes, including water and carbon fluxes and organ expansion, with an enzyme-based kinetic model of sugar metabolism to better understand the interactions between these two processes. The integrative model was initially tested on tomato fruit, a model system for fleshy fruit. The integrative model closely simulated the biomass and major carbon metabolites of tomato fruit developing under optimal or stress conditions. The model also performed robustly when simulating the fruit size and sugar concentrations of different tomato genotypes including wild species. The validated model was used to explore ways of uncoupling the size-sweetness trade-off in fruit. Model-based virtual experiments suggested that larger sweeter tomatoes could be obtained by simultaneously manipulating certain biophysical factors and transmembrane transports. The integrative fleshy fruit model provides a promising tool to facilitate the targeted bioengineering and breeding of tomatoes and other fruits.
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Affiliation(s)
- Jinliang Chen
- INRAE, Bordeaux Science Agro, EGFV, UMR 1287, Univ. Bordeaux, Villenave d'Ornon, F-33140, France
- Beijing Key Laboratory of Grape Science and Enology and Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, China
| | - Bertrand Beauvoit
- INRAE, Biologie du Fruit et Pathologie, UMR 1332, Univ. Bordeaux, Villenave d'Ornon, F-33140, France
| | - Michel Génard
- UR 1115 Plantes et Systèmes de Culture Horticoles, INRAE, Avignon Cedex 9, F-84914, France
| | - Sophie Colombié
- INRAE, Biologie du Fruit et Pathologie, UMR 1332, Univ. Bordeaux, Villenave d'Ornon, F-33140, France
| | - Annick Moing
- INRAE, Biologie du Fruit et Pathologie, UMR 1332, Univ. Bordeaux, Villenave d'Ornon, F-33140, France
| | - Gilles Vercambre
- UR 1115 Plantes et Systèmes de Culture Horticoles, INRAE, Avignon Cedex 9, F-84914, France
| | - Eric Gomès
- INRAE, Bordeaux Science Agro, EGFV, UMR 1287, Univ. Bordeaux, Villenave d'Ornon, F-33140, France
| | - Yves Gibon
- INRAE, Biologie du Fruit et Pathologie, UMR 1332, Univ. Bordeaux, Villenave d'Ornon, F-33140, France
| | - Zhanwu Dai
- INRAE, Bordeaux Science Agro, EGFV, UMR 1287, Univ. Bordeaux, Villenave d'Ornon, F-33140, France
- Beijing Key Laboratory of Grape Science and Enology and Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, China
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12
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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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13
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Tan XLJ, Cheung CYM. A multiphase flux balance model reveals flexibility of central carbon metabolism in guard cells of C 3 plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 104:1648-1656. [PMID: 33070390 DOI: 10.1111/tpj.15027] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 09/19/2020] [Accepted: 10/02/2020] [Indexed: 06/11/2023]
Abstract
Experimental research into guard cell metabolism has revealed the roles of the accumulation of various metabolites in guard cell function, but a comprehensive understanding of their metabolism over the diel cycle is still incomplete due to the limitations of current experimental methods. In this study we constructed a four-phase flux balance model of guard cell metabolism to investigate the changes in guard cell metabolism over the diel cycle, including the day and night and stomatal opening and closing. Our model predicted metabolic flexibility in guard cells of C3 plants, showing that multiple metabolic processes can contribute to the synthesis and metabolism of malate and sucrose as osmolytes during stomatal opening and closing. Our model showed the possibility of guard cells adapting to varying light availability and sucrose uptake from the apoplast during the day by operating in a mixotrophic mode with a switch between sucrose synthesis via the Calvin-Benson cycle and sucrose degradation via the oxidative pentose phosphate pathway. During stomatal opening, our model predicted an alternative flux mode of the Calvin-Benson cycle with all dephosphorylating steps diverted to diphosphate-fructose-6-phosphate 1-phosphotransferase to produce inorganic pyrophosphate, which is used to pump protons across the tonoplast for the accumulation of osmolytes. An analysis of the energetics of the use of different osmolytes in guard cells showed that malate and Cl- are similarly efficient as the counterion of K+ during stomatal opening.
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Affiliation(s)
- X L Joshua Tan
- Yale-NUS College, 16 College Avenue West, 138527, Singapore
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14
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Correa SM, Alseekh S, Atehortúa L, Brotman Y, Ríos-Estepa R, Fernie AR, Nikoloski Z. Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 104:76-95. [PMID: 33001507 DOI: 10.1111/tpj.14906] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 06/03/2020] [Accepted: 06/12/2020] [Indexed: 06/11/2023]
Abstract
Efficient approaches to increase plant lipid production are necessary to meet current industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires an understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom-up metabolic reconstruction of J. curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint-based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping stone for future rational optimization of other agronomically relevant traits in J. curcas.
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Affiliation(s)
- Sandra M Correa
- Genetics of Metabolic Traits Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Grupo de Biotecnología, Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín, 050010, Colombia
| | - Saleh Alseekh
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Centre for Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Lucía Atehortúa
- Grupo de Biotecnología, Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín, 050010, Colombia
| | - Yariv Brotman
- Genetics of Metabolic Traits Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Rigoberto Ríos-Estepa
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia, Medellín, 050010, Colombia
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Centre for Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Zoran Nikoloski
- Centre for Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, 14476, Germany
- Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, 14476, Germany
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15
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Fitzpatrick TB, Chapman LM. The importance of thiamine (vitamin B 1) in plant health: From crop yield to biofortification. J Biol Chem 2020; 295:12002-12013. [PMID: 32554808 PMCID: PMC7443482 DOI: 10.1074/jbc.rev120.010918] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/17/2020] [Indexed: 12/14/2022] Open
Abstract
Ensuring that people have access to sufficient and nutritious food is necessary for a healthy life and the core tenet of food security. With the global population set to reach 9.8 billion by 2050, and the compounding effects of climate change, the planet is facing challenges that necessitate significant and rapid changes in agricultural practices. In the effort to provide food in terms of calories, the essential contribution of micronutrients (vitamins and minerals) to nutrition is often overlooked. Here, we focus on the importance of thiamine (vitamin B1) in plant health and discuss its impact on human health. Vitamin B1 is an essential dietary component, and deficiencies in this micronutrient underlie several diseases, notably nervous system disorders. The predominant source of dietary vitamin B1 is plant-based foods. Moreover, vitamin B1 is also vital for plants themselves, and its benefits in plant health have received less attention than in the human health sphere. In general, vitamin B1 is well-characterized for its role as a coenzyme in metabolic pathways, particularly those involved in energy production and central metabolism, including carbon assimilation and respiration. Vitamin B1 is also emerging as an important component of plant stress responses, and several noncoenzyme roles of this vitamin are being characterized. We summarize the importance of vitamin B1 in plants from the perspective of food security, including its roles in plant disease resistance, stress tolerance, and crop yield, and review the potential benefits of biofortification of crops with increased vitamin B1 content to improve human health.
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Affiliation(s)
- Teresa B Fitzpatrick
- Department of Botany and Plant Biology, University of Geneva, Geneva, Switzerland.
| | - Lottie M Chapman
- Department of Botany and Plant Biology, University of Geneva, Geneva, Switzerland
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16
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Bandehagh A, Taylor NL. Can Alternative Metabolic Pathways and Shunts Overcome Salinity Induced Inhibition of Central Carbon Metabolism in Crops? FRONTIERS IN PLANT SCIENCE 2020; 11:1072. [PMID: 32849676 PMCID: PMC7417600 DOI: 10.3389/fpls.2020.01072] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 06/30/2020] [Indexed: 05/25/2023]
Abstract
The annual cost of lost crop production from exposure to salinity has major impacts on food security in all parts of the world. Salinity stress disturbs energy metabolism and knowledge of the impacts on critical processes controlling plant energy production is key to successfully breeding salt tolerant crops. To date, little progress has been achieved using classic breeding approaches to develop salt tolerance. The hope of some salinity researchers is that through a better understanding of the metabolic responses and adaptation to salinity exposure, new breeding targets can be suggested to help develop salt tolerant crops. Plants sense and react to salinity through a complex system of sensors, receptor systems, transporters, signal transducers, and gene expression regulators in order to control the uptake of salts and to induce tolerant metabolism that jointly leads to changes in growth rate and biomass production. During this response, there must be a balance between supply of energy from mitochondria and chloroplasts and energy demands for water and ion transport, growth, and osmotic adjustment. The photosynthetic response to salinity has been thoroughly researched and generally we see a sharp drop in photosynthesis after exposure to salinity. However, less attention has been given to the effect of salt stress on plant mitochondrial respiration and the metabolic processes that influence respiratory rate. A further complication is the wide range of respiratory responses that have been observed in different plant species, which have included major and minor increases, decreases, and no change in respiratory rate after salt exposure. In this review, we begin by considering physiological and biochemical impacts of salinity on major crop plants. We then summarize and consider recent advances that have characterized changes in abundance of metabolites that are involved in respiratory pathways and their alternative routes and shunts in terms of energy metabolism in crop plants. We will consider the diverse molecular responses of cellular plant metabolism during salinity exposure and suggest how these metabolic responses might aid in salinity tolerance. Finally, we will consider how this commonality and diversity should influence how future research of the salinity responses of crops plants should proceed.
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Affiliation(s)
- Ali Bandehagh
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences and Institute of Agriculture, The University of Western Australia, Crawley, WA, Australia
- Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | - Nicolas L. Taylor
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences and Institute of Agriculture, The University of Western Australia, Crawley, WA, Australia
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17
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Shameer S, Vallarino JG, Fernie AR, Ratcliffe RG, Sweetlove LJ. Flux balance analysis of metabolism during growth by osmotic cell expansion and its application to tomato fruits. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:68-82. [PMID: 31985867 DOI: 10.1111/tpj.14707] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/24/2019] [Accepted: 12/20/2019] [Indexed: 05/27/2023]
Abstract
Cell expansion is a significant contributor to organ growth and is driven by the accumulation of osmolytes to increase cell turgor pressure. Metabolic modelling has the potential to provide insights into the processes that underpin osmolyte synthesis and transport, but the main computational approach for predicting metabolic network fluxes, flux balance analysis, often uses biomass composition as the main output constraint and ignores potential changes in cell volume. Here we present growth-by-osmotic-expansion flux balance analysis (GrOE-FBA), a framework that accounts for both the metabolic and ionic contributions to the osmotica that drive cell expansion, as well as the synthesis of protein, cell wall and cell membrane components required for cell enlargement. Using GrOE-FBA, the metabolic fluxes in dividing and expanding cells were analysed, and the energetic costs for metabolite biosynthesis and accumulation in the two scenarios were found to be surprisingly similar. The expansion phase of tomato fruit growth was also modelled using a multiphase single-optimization GrOE-FBA model and this approach gave accurate predictions of the major metabolite levels throughout fruit development, as well as revealing a role for transitory starch accumulation in ensuring optimal fruit development.
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Affiliation(s)
- Sanu Shameer
- Department of Plant Sciences, University of Oxford, Oxford, UK
| | - José G Vallarino
- Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
| | | | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford, UK
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18
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Sink/Source Balance of Leaves Influences Amino Acid Pools and Their Associated Metabolic Fluxes in Winter Oilseed Rape ( Brassica napus L.). Metabolites 2020; 10:metabo10040150. [PMID: 32295054 PMCID: PMC7240945 DOI: 10.3390/metabo10040150] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 03/26/2020] [Accepted: 04/09/2020] [Indexed: 11/18/2022] Open
Abstract
Nitrogen remobilization processes from source to sink tissues in plants are determinant for seed yield and their implementation results in a complete reorganization of the primary metabolism during sink/source transition. Here, we decided to characterize the impact of the sink/source balance on amino acid metabolism in the leaves of winter oilseed rape grown at the vegetative stage. We combined a quantitative metabolomics approach with an instationary 15N-labeling experiment by using [15N]L-glycine as a metabolic probe on leaf ranks with a gradual increase in their source status. We showed that the acquisition of the source status by leaves was specifically accompanied by a decrease in asparagine, glutamine, proline and S-methyl-l-cysteine sulphoxide contents and an increase in valine and threonine contents. Dynamic analysis of 15N enrichment and concentration of amino acids revealed gradual changes in the dynamics of amino acid metabolism with respect to the sink/source status of leaf ranks. Notably, nitrogen assimilation into valine, threonine and proline were all decreased in source leaves compared to sink leaves. Overall, our results suggested a reduction in de novo amino acid biosynthesis during sink/source transition at the vegetative stage.
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19
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Dellero Y, Heuillet M, Marnet N, Bellvert F, Millard P, Bouchereau A. Sink/Source Balance of Leaves Influences Amino Acid Pools and Their Associated Metabolic Fluxes in Winter Oilseed Rape ( Brassica napus L.). Metabolites 2020; 10:metabo10040150. [PMID: 32295054 DOI: 10.15454/1i9pet] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 03/26/2020] [Accepted: 04/09/2020] [Indexed: 05/27/2023] Open
Abstract
Nitrogen remobilization processes from source to sink tissues in plants are determinant for seed yield and their implementation results in a complete reorganization of the primary metabolism during sink/source transition. Here, we decided to characterize the impact of the sink/source balance on amino acid metabolism in the leaves of winter oilseed rape grown at the vegetative stage. We combined a quantitative metabolomics approach with an instationary 15N-labeling experiment by using [15N]L-glycine as a metabolic probe on leaf ranks with a gradual increase in their source status. We showed that the acquisition of the source status by leaves was specifically accompanied by a decrease in asparagine, glutamine, proline and S-methyl-l-cysteine sulphoxide contents and an increase in valine and threonine contents. Dynamic analysis of 15N enrichment and concentration of amino acids revealed gradual changes in the dynamics of amino acid metabolism with respect to the sink/source status of leaf ranks. Notably, nitrogen assimilation into valine, threonine and proline were all decreased in source leaves compared to sink leaves. Overall, our results suggested a reduction in de novo amino acid biosynthesis during sink/source transition at the vegetative stage.
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Affiliation(s)
- Younès Dellero
- Department Plant Biology and Breeding, Agrocampus Ouest, Institute for Genetics, Environment and Plant Protection, French National Research Institute for Agriculture, Food and Environment, University of Rennes II, 35653 Le Rheu, France
| | - Maud Heuillet
- Department Plant Biology and Breeding, Department Microbiology and Food Chain, INSA, TBI, French National Center for Scientific Research, French National Research Institute for Agriculture, Food and Environment, University of Toulouse, 31400 Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, 33140 Toulouse, France
| | - Nathalie Marnet
- Department Plant Biology and Breeding and Department Transform, Agrocampus Ouest, Plateau de Profilage Métabolique et Métabolique (P2M2), Biopolymers Interactions Assemblies, Institute for Genetics, Environment and Plant Protection, French National Research Institute for Agriculture, Food and Environment, University of Rennes II, 35653 Le Rheu, France
| | - Floriant Bellvert
- Department Plant Biology and Breeding, Department Microbiology and Food Chain, INSA, TBI, French National Center for Scientific Research, French National Research Institute for Agriculture, Food and Environment, University of Toulouse, 31400 Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, 33140 Toulouse, France
| | - Pierre Millard
- Department Plant Biology and Breeding, Department Microbiology and Food Chain, INSA, TBI, French National Center for Scientific Research, French National Research Institute for Agriculture, Food and Environment, University of Toulouse, 31400 Toulouse, France
| | - Alain Bouchereau
- Department Plant Biology and Breeding, Agrocampus Ouest, Institute for Genetics, Environment and Plant Protection, French National Research Institute for Agriculture, Food and Environment, University of Rennes II, 35653 Le Rheu, France
- Department Plant Biology and Breeding and Department Transform, Agrocampus Ouest, Plateau de Profilage Métabolique et Métabolique (P2M2), Biopolymers Interactions Assemblies, Institute for Genetics, Environment and Plant Protection, French National Research Institute for Agriculture, Food and Environment, University of Rennes II, 35653 Le Rheu, France
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20
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Shaw R, Cheung CYM. Multi-tissue to whole plant metabolic modelling. Cell Mol Life Sci 2020; 77:489-495. [PMID: 31748916 PMCID: PMC11104929 DOI: 10.1007/s00018-019-03384-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 11/06/2019] [Accepted: 11/12/2019] [Indexed: 10/25/2022]
Abstract
Genome-scale metabolic models have been successfully applied to study the metabolism of multiple plant species in the past decade. While most existing genome-scale modelling studies have focussed on studying the metabolic behaviour of individual plant metabolic systems, there is an increasing focus on combining models of multiple tissues or organs to produce multi-tissue models that allow the investigation of metabolic interactions between tissues and organs. Multi-tissue metabolic models were constructed for multiple plants including Arabidopsis, barley, soybean and Setaria. These models were applied to study various aspects of plant physiology including the division of labour between organs, source and sink tissue relationship, growth of different tissues and organs and charge and proton balancing. In this review, we outline the process of constructing multi-tissue genome-scale metabolic models, discuss the strengths and challenges in using multi-tissue models, review the current status of plant multi-tissue and whole plant metabolic models and explore the approaches for integrating genome-scale metabolic models into multi-scale plant models.
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Affiliation(s)
- Rahul Shaw
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, Singapore
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21
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Sadeghnezhad E, Sharifi M, Zare-Maivan H, Ahmadian Chashmi N. Time-dependent behavior of phenylpropanoid pathway in response to methyl jasmonate in Scrophularia striata cell cultures. PLANT CELL REPORTS 2020; 39:227-243. [PMID: 31707473 DOI: 10.1007/s00299-019-02486-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 11/02/2019] [Indexed: 05/13/2023]
Abstract
MeJA triggers a time-dependent behavior of the phenylpropanoid compounds. Plant cells produce a large number of metabolites in response to environmental factors. The cellular responses to environmental changes are orchestrated by signaling molecules, such as methyl jasmonate (MeJA). To understand how the MeJA changes the behavior of amino acids, carbohydrates, and phenylpropanoid compounds such as phenolic acids, phenylethanoid-glycosides, and flavonoids in Scrophularia striata cells; we monitored the metabolic responses for different times of exposure. In this study, we performed a time course analysis of metabolites and enzymes in S. striata cells exposed to MeJA (100 µM) and evaluated the metabolic flux towards carbon-rich secondary metabolites production. Moreover, we calculated the biosynthetic energy cost for free amino acids. Our results indicated that MeJA accelerates the sucrose degradation and directs the metabolic fluxes towards a pool of flavonoids and phenylethanoid glycosides through a change in enzyme behavior in the entry point and center of the phenylpropanoid pathway. MeJA also decreased and then raised the amino acid biosynthesis cost in S. striata cells in a time-dependent manner, indicating the cells evolve to utilize amino acids more economically by reducing cell growth. Finally, we classified the marked changes in the metabolites level and enzyme activities into three groups including early-, late-, and oscillatory-response groups to MeJA and summarized our findings as a model depicting pathway interactions during MeJA elicitation. Determination of metabolic levels in response to MeJA suggests that the changes in metabolic responses are time-dependent.
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Affiliation(s)
- Ehsan Sadeghnezhad
- Department of Plant Biology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohsen Sharifi
- Department of Plant Biology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
| | - Hassan Zare-Maivan
- Department of Plant Biology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
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22
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Shameer S, Ratcliffe RG, Sweetlove LJ. Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light. PLANT PHYSIOLOGY 2019; 180:1947-1961. [PMID: 31213510 PMCID: PMC6670072 DOI: 10.1104/pp.19.00624] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 06/04/2019] [Indexed: 05/04/2023]
Abstract
Key aspects of leaf mitochondrial metabolism in the light remain unresolved. For example, there is debate about the relative importance of exporting reducing equivalents from mitochondria for the peroxisomal steps of photorespiration versus oxidation of NADH to generate ATP by oxidative phosphorylation. Here, we address this and explore energetic coupling between organelles in the light using a diel flux balance analysis model. The model included more than 600 reactions of central metabolism with full stoichiometric accounting of energy production and consumption. Different scenarios of energy availability (light intensity) and demand (source leaf versus a growing leaf) were considered, and the model was constrained by the nonlinear relationship between light and CO2 assimilation rate. The analysis demonstrated that the chloroplast can theoretically generate sufficient ATP to satisfy the energy requirements of the rest of the cell in addition to its own. However, this requires unrealistic high light use efficiency and, in practice, the availability of chloroplast-derived ATP is limited by chloroplast energy dissipation systems, such as nonphotochemical quenching, and the capacity of the chloroplast ATP export shuttles. Given these limitations, substantial mitochondrial ATP synthesis is required to fulfill cytosolic ATP requirements, with only minimal, or zero, export of mitochondrial reducing equivalents. The analysis also revealed the importance of exporting reducing equivalents from chloroplasts to sustain photorespiration. Hence, the chloroplast malate valve and triose phosphate-3-phosphoglycerate shuttle are predicted to have important metabolic roles, in addition to their more commonly discussed contribution to the avoidance of photooxidative stress.
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Affiliation(s)
- Sanu Shameer
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
| | - R George Ratcliffe
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
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23
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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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24
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Amdoun R, Benyoussef EH, Benamghar A, Khelifi L. Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.01.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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25
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Küken A, Nikoloski Z. Computational Approaches to Design and Test Plant Synthetic Metabolic Pathways. PLANT PHYSIOLOGY 2019; 179:894-906. [PMID: 30647083 PMCID: PMC6393797 DOI: 10.1104/pp.18.01273] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 01/09/2019] [Indexed: 05/05/2023]
Abstract
Successfully designed and implemented plant-specific synthetic metabolic pathways hold promise to increase crop yield and nutritional value. Advances in synthetic biology have already demonstrated the capacity to design artificial biological pathways whose behavior can be predicted and controlled in microbial systems. However, the transfer of these advances to model plants and crops faces the lack of characterization of plant cellular pathways and increased complexity due to compartmentalization and multicellularity. Modern computational developments provide the means to test the feasibility of plant synthetic metabolic pathways despite gaps in the accumulated knowledge of plant metabolism. Here, we provide a succinct systematic review of optimization-based and retrobiosynthesis approaches that can be used to design and in silico test synthetic metabolic pathways in large-scale plant context-specific metabolic models. In addition, by surveying the existing case studies, we highlight the challenges that these approaches face when applied to plants. Emphasis is placed on understanding the effect that metabolic designs can have on native metabolism, particularly with respect to metabolite concentrations and thermodynamics of biochemical reactions. In addition, we discuss the computational developments that may help to transform the identified challenges into opportunities for plant synthetic biology.
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Affiliation(s)
- Anika Küken
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
- Systems Biology and Mathematical Modelling, 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 Modelling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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26
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Chiewchankaset P, Siriwat W, Suksangpanomrung M, Boonseng O, Meechai A, Tanticharoen M, Kalapanulak S, Saithong T. Understanding carbon utilization routes between high and low starch-producing cultivars of cassava through Flux Balance Analysis. Sci Rep 2019; 9:2964. [PMID: 30814632 PMCID: PMC6393550 DOI: 10.1038/s41598-019-39920-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 02/05/2019] [Indexed: 12/15/2022] Open
Abstract
Analysis of metabolic flux was used for system level assessment of carbon partitioning in Kasetsart 50 (KU50) and Hanatee (HN) cassava cultivars to understand the metabolic routes for their distinct phenotypes. First, the constraint-based metabolic model of cassava storage roots, rMeCBM, was developed based on the carbon assimilation pathway of cassava. Following the subcellular compartmentalization and curation to ensure full network connectivity and reflect the complexity of eukaryotic cells, cultivar specific data on sucrose uptake and biomass synthesis were input, and rMeCBM model was used to simulate storage root growth in KU50 and HN. Results showed that rMeCBM-KU50 and rMeCBM-HN models well imitated the storage root growth. The flux-sum analysis revealed that both cultivars utilized different metabolic precursors to produce energy in plastid. More carbon flux was invested in the syntheses of carbohydrates and amino acids in KU50 than in HN. Also, KU50 utilized less flux for respiration and less energy to synthesize one gram of dry storage root. These results may disclose metabolic potential of KU50 underlying its higher storage root and starch yield over HN. Moreover, sensitivity analysis indicated the robustness of rMeCBM model. The knowledge gained might be useful for identifying engineering targets for cassava yield improvement.
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Affiliation(s)
- Porntip Chiewchankaset
- Division of Biotechnology, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Wanatsanan Siriwat
- Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Malinee Suksangpanomrung
- Plant Molecular Genetics and Biotechnology Laboratory, National Center for Genetic Engineering and Biotechnology, Thailand Science Park, Pathumthani, 12120, Thailand
| | - Opas Boonseng
- Rayong Field Crops Research Center, Department of Agriculture, Rayong, 21150, Thailand
| | - Asawin Meechai
- Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
- Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi (Bang Mod), Bangkok, 10140, Thailand
| | - Morakot Tanticharoen
- School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Saowalak Kalapanulak
- Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
| | - Treenut Saithong
- Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
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Advances in metabolic flux analysis toward genome-scale profiling of higher organisms. Biosci Rep 2018; 38:BSR20170224. [PMID: 30341247 PMCID: PMC6250807 DOI: 10.1042/bsr20170224] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 10/06/2018] [Accepted: 10/14/2018] [Indexed: 11/25/2022] Open
Abstract
Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.
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Skraly FA, Ambavaram MMR, Peoples O, Snell KD. Metabolic engineering to increase crop yield: From concept to execution. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2018; 273:23-32. [PMID: 29907305 DOI: 10.1016/j.plantsci.2018.03.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 03/07/2018] [Accepted: 03/10/2018] [Indexed: 05/18/2023]
Abstract
Although the return on investment over the last 20 years for mass screening of individual plant genes to improve crop performance has been low, the investment in these activities was essential to establish the infrastructure and tools of modern plant genomics. Complex traits such as crop yield are likely multigenic, and the exhaustive screening of random gene combinations to achieve yield gains is not realistic. Clearly, smart approaches must be developed. In silico analyses of plant metabolism and gene networks can move a trait discovery program beyond trial-and-error approaches and towards rational design strategies. Metabolic models employing flux-balance analysis are useful to determine the contribution of individual genes to a trait, or to compare, optimize, or even design metabolic pathways. Regulatory association networks provide a transcriptome-based view of the plant and can lead to the identification of transcription factors that control expression of multiple genes affecting a trait. In this review, the use of these models from the perspective of an Ag innovation company's trait discovery and development program will be discussed. Important decisions that can have significant impacts on the cost and timeline to develop a commercial trait will also be presented.
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Affiliation(s)
- Frank A Skraly
- Yield10 Bioscience, Inc., 19 Presidential Way, Woburn, MA 01801, United States
| | | | - Oliver Peoples
- Yield10 Bioscience, Inc., 19 Presidential Way, Woburn, MA 01801, United States
| | - Kristi D Snell
- Yield10 Bioscience, Inc., 19 Presidential Way, Woburn, MA 01801, United States.
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29
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Beauvoit B, Belouah I, Bertin N, Cakpo CB, Colombié S, Dai Z, Gautier H, Génard M, Moing A, Roch L, Vercambre G, Gibon Y. Putting primary metabolism into perspective to obtain better fruits. ANNALS OF BOTANY 2018; 122:1-21. [PMID: 29718072 PMCID: PMC6025238 DOI: 10.1093/aob/mcy057] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 03/29/2017] [Indexed: 05/18/2023]
Abstract
Background One of the key goals of fruit biology is to understand the factors that influence fruit growth and quality, ultimately with a view to manipulating them for improvement of fruit traits. Scope Primary metabolism, which is not only essential for growth but is also a major component of fruit quality, is an obvious target for improvement. However, metabolism is a moving target that undergoes marked changes throughout fruit growth and ripening. Conclusions Agricultural practice and breeding have successfully improved fruit metabolic traits, but both face the complexity of the interplay between development, metabolism and the environment. Thus, more fundamental knowledge is needed to identify further strategies for the manipulation of fruit metabolism. Nearly two decades of post-genomics approaches involving transcriptomics, proteomics and/or metabolomics have generated a lot of information about the behaviour of fruit metabolic networks. Today, the emergence of modelling tools is providing the opportunity to turn this information into a mechanistic understanding of fruits, and ultimately to design better fruits. Since high-quality data are a key requirement in modelling, a range of must-have parameters and variables is proposed.
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Affiliation(s)
| | - Isma Belouah
- UMR 1332 BFP, INRA, Univ. Bordeaux, Villenave d’Ornon, France
| | | | | | - Sophie Colombié
- UMR 1332 BFP, INRA, Univ. Bordeaux, Villenave d’Ornon, France
| | - Zhanwu Dai
- UMR 1287 EGFV, INRA, Univ. Bordeaux, Bordeaux Sci Agro, F-Villenave d’Ornon, France
| | | | | | - Annick Moing
- UMR 1332 BFP, INRA, Univ. Bordeaux, Villenave d’Ornon, France
| | - Léa Roch
- UMR 1332 BFP, INRA, Univ. Bordeaux, Villenave d’Ornon, France
| | | | - Yves Gibon
- UMR 1332 BFP, INRA, Univ. Bordeaux, Villenave d’Ornon, France
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30
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Shaw R, Cheung CYM. A Dynamic Multi-Tissue Flux Balance Model Captures Carbon and Nitrogen Metabolism and Optimal Resource Partitioning During Arabidopsis Growth. FRONTIERS IN PLANT SCIENCE 2018; 9:884. [PMID: 29997643 PMCID: PMC6028781 DOI: 10.3389/fpls.2018.00884] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 06/06/2018] [Indexed: 05/19/2023]
Abstract
Plant metabolism is highly adapted in response to its surrounding for acquiring limiting resources. In this study, a dynamic flux balance modeling framework with a multi-tissue (leaf and root) diel genome-scale metabolic model of Arabidopsis thaliana was developed and applied to investigate the reprogramming of plant metabolism through multiple growth stages under different nutrient availability. The framework allowed the modeling of optimal partitioning of resources and biomass in leaf and root over diel phases. A qualitative flux map of carbon and nitrogen metabolism was identified which was consistent across growth phases under both nitrogen rich and limiting conditions. Results from the model simulations suggested distinct metabolic roles in nitrogen metabolism played by enzymes with different cofactor specificities. Moreover, the dynamic model was used to predict the effect of physiological or environmental perturbation on the growth of Arabidopsis leaves and roots.
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31
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Evans EM, Freund DM, Sondervan VM, Cohen JD, Hegeman AD. Metabolic Patterns in Spirodela polyrhiza Revealed by 15N Stable Isotope Labeling of Amino Acids in Photoautotrophic, Heterotrophic, and Mixotrophic Growth Conditions. Front Chem 2018; 6:191. [PMID: 29904627 PMCID: PMC5990592 DOI: 10.3389/fchem.2018.00191] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 05/14/2018] [Indexed: 11/13/2022] Open
Abstract
In this study we describe a [15N] stable isotopic labeling study of amino acids in Spirodela polyrhiza (common duckweed) grown under three different light and carbon input conditions which represent unique potential metabolic modes. Plants were grown with a light cycle, either with supplemental sucrose (mixotrophic) or without supplemental sucrose (photoautotrophic) and in the dark with supplemental sucrose (heterotrophic). Labeling patterns, pool sizes (both metabolically active and inactive), and kinetics/turnover rates were estimated for 17 of the proteinogenic amino acids. Estimation of these parameters followed several overall trends. First, most amino acids showed plateaus in labeling patterns of <100% [15N]-labeling, indicating the possibility of a large proportion of amino acids residing in metabolically inactive metabolite pools. Second, total pool sizes appear largest in the dark (heterotrophic) condition, whereas active pool sizes appeared to be largest in the light with sucrose (mixotrophic) growth condition. In contrast turnover measurements based on pool size were highest overall in the light with sucrose experiment, with the exception of leucine/isoleucine, lysine, and arginine, which all showed higher turnover in the dark. K-means clustering analysis also revealed more rapid turnover in the light treatments with many amino acids clustering in lower-turnover groups. Emerging insights from other research were also supported, such as the prevalence of alternate pathways for serine metabolism in non-photosynthetic cells. These data provide extensive novel information on amino acid pool size and kinetics in S. polyrhiza and can serve as groundwork for future metabolic studies.
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Affiliation(s)
- Erin M Evans
- Department of Horticultural Science, University of Minnesota, Twin Cities, Saint Paul, MN, United States.,Plant and Microbial Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, MN, United States
| | - Dana M Freund
- Department of Horticultural Science, University of Minnesota, Twin Cities, Saint Paul, MN, United States.,Plant and Microbial Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, MN, United States
| | - Veronica M Sondervan
- Department of Horticultural Science, University of Minnesota, Twin Cities, Saint Paul, MN, United States
| | - Jerry D Cohen
- Department of Horticultural Science, University of Minnesota, Twin Cities, Saint Paul, MN, United States.,Plant and Microbial Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, MN, United States
| | - Adrian D Hegeman
- Department of Horticultural Science, University of Minnesota, Twin Cities, Saint Paul, MN, United States.,Plant and Microbial Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, MN, United States.,Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, Saint Paul, MN, United States
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32
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Shen F, Wu X, Shi L, Zhang H, Chen Y, Qi X, Wang Z, Li X. Transcriptomic and metabolic flux analyses reveal shift of metabolic patterns during rice grain development. BMC SYSTEMS BIOLOGY 2018; 12:47. [PMID: 29745852 PMCID: PMC5998905 DOI: 10.1186/s12918-018-0574-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Rice (Oryza sativa) is one of the most important grain crops, which serves as food source for nearly half of the world population. The study of rice development process as well as related strategies for production has made significant progress. However, the comprehensive study on development of different rice tissues at both transcriptomic and metabolic flux level across different stages was lacked. RESULTS In this study, we performed RNA-Seq and characterized the expression profiles of differentiated tissues from Oryza sativa Zhonghua 11, including leaves, sheath, stamen, pistil, lemma and palea of the booting stage, and embryo, endosperm, lemma and palea of the mature grain stage. By integrating this set of transcriptome data of different rice tissues at different stages with a genome-scale rice metabolic model, we generated tissue-specific models and investigated the shift of metabolic patterns, and the discrepancy between transcriptomic and metabolic level. We found although the flux patterns are not very similar with the gene expression pattern, the tissues at booting stage and mature grain stage can be separately clustered by primary metabolism at either level. While the gene expression and flux distribution of secondary metabolism is more diverse across tissues and stages. The critical rate-limiting reactions and pathways were also identified. In addition, we compared the patterns of the same tissue at different stages and the different tissues at same stage. There are more altered pathways at gene expression level than metabolic level, which indicate the metabolism is more robust to reflect the phenotype, and might largely because of the complex post-transcriptional modification. CONCLUSIONS The tissue-specific models revealed more detail metabolic pattern shift among different tissues and stages, which is of great significance to uncover mechanism of rice grain development and further improve production and quality of rice.
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Affiliation(s)
- Fangzhou Shen
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China
| | - Xueting Wu
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200032, People's Republic of China
| | - Luoxi Shi
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.,Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, 55455, USA
| | - Hang Zhang
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China.,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China
| | - Yangmin Chen
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China
| | - Xiaoquan Qi
- The Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, People's Republic of China
| | - Zhuo Wang
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China. .,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
| | - Xuan Li
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200032, People's Republic of China.
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Abstract
Photosynthesis is fundamental to biomass production, but is a dynamic process sensitive to environmental constraints. In recent years, approaches to increase biomass and grain yield by altering photosynthetically related processes in the plant have received considerable attention. However, improving biomass yield requires a predictive understanding of the molecular mechanisms that allow photosynthesis to be adjusted. The important roles of metabolic reactions external to those directly involved in photosynthesis are highlighted in this review; however, our major focus is on the routes taken to improve photosynthetic carbon assimilation and to increase photosynthetic efficiency and consequently biomass yield.
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34
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Noguchi K, Tsunoda T, Miyagi A, Kawai-Yamada M, Sugiura D, Miyazawa SI, Tokida T, Usui Y, Nakamura H, Sakai H, Hasegawa T. Effects of Elevated Atmospheric CO2 on Respiratory Rates in Mature Leaves of Two Rice Cultivars Grown at a Free-Air CO2 Enrichment Site and Analyses of the Underlying Mechanisms. PLANT & CELL PHYSIOLOGY 2018; 59:637-649. [PMID: 29401364 DOI: 10.1093/pcp/pcy017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 01/23/2018] [Indexed: 06/07/2023]
Abstract
Respiratory CO2 efflux and O2 uptake rates in leaves change in response to the growth CO2 concentration ([CO2]). The degrees of change vary depending on the responses of cellular processes such as nitrogen (N) assimilation and accumulation of organic acids to growth [CO2]. However, the underlying mechanisms remain unclear. Here, we examined the respiratory characteristics of mature leaves of two rice varieties with different yield capacities at different growth stages under ambient and elevated [CO2] conditions at a free-air CO2 enrichment site. We also examined the effect of increased water temperature on leaf respiration. We measured the rates of CO2 efflux and O2 uptake, and determined N contents, primary metabolite contents and maximal activities of respiratory enzymes. The leaf CO2 efflux rates decreased in plants grown at elevated [CO2] in both varieties, and were higher in high-yielding Takanari than in Koshihikari. The leaf O2 uptake rates showed little change with respect to growth [CO2] and variety. The increased water temperature did not significantly affect the CO2 efflux and O2 uptake rates. The N and amino acid contents were significantly higher in Takanari than in Koshihikari. The enhanced N assimilation in Takanari may have consumed more respiratory NADH, leading to higher CO2 efflux rates. In Koshihikari, the ratio of tricarboxylic acid (TCA) cycle intermediates changed and maximal activities of enzymes in the TCA cycle decreased at elevated [CO2]. Therefore, the decreased rates of CO2 efflux in Koshihikari may be due to the decreased activities of TCA cycle enzymes at elevated [CO2].
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Affiliation(s)
- Ko Noguchi
- School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji, Tokyo, 192-0392 Japan
| | - Tomonori Tsunoda
- School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji, Tokyo, 192-0392 Japan
| | - Atsuko Miyagi
- Graduate School of Science and Engineering, Saitama University, 255, Shimo-Okubo, Sakura-ku, Saitama, 338-8570 Japan
| | - Maki Kawai-Yamada
- Graduate School of Science and Engineering, Saitama University, 255, Shimo-Okubo, Sakura-ku, Saitama, 338-8570 Japan
| | - Daisuke Sugiura
- Laboratory of Crop Science, Graduate School of Bioagricultural Sciences, Nagoya University, Chikusa, Nagoya, 464-8601 Japan
| | - Shin-Ichi Miyazawa
- Department of Molecular and Cell Biology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, 305-8687 Japan
| | - Takeshi Tokida
- Division of Biogeochemical Cycles, Institute for Agro-Environmental Sciences, NARO, 3-1-3 Kannondai, Tsukuba, Ibaraki, 305-8604 Japan
| | - Yasuhiro Usui
- Division of Farming System Research, Hokkaido Agricultural Research Center, NARO, 9-4 Shinseiminami, Memuro, Kasai, Hokkaido, 082-0081 Japan
| | - Hirofumi Nakamura
- Taiyo Keiki Co. Ltd., 1-12-3 Nakajujo, Kita-ku, Tokyo, 114-0032 Japan
| | - Hidemitsu Sakai
- Division of Climate Change, Institute for Agro-Environmental Sciences, NARO, 3-1-3 Kannondai, Tsukuba, Ibaraki, 305-8604 Japan
| | - Toshihiro Hasegawa
- Division of Agro-Environmental Research, Tohoku Agricultural Research Center, NARO, 4 Akahira, Shimo-kuriyagawa Morioka, Iwate, 020-0198 Japan
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36
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Sajitz-Hermstein M, Töpfer N, Kleessen S, Fernie AR, Nikoloski Z. iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models. Bioinformatics 2017; 32:i755-i762. [PMID: 27587698 DOI: 10.1093/bioinformatics/btw465] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
MOTIVATION Understanding the rerouting of metabolic reaction fluxes upon perturbations has the potential to link changes in molecular state of a cellular system to alteration of growth. Yet, differential flux profiling on a genome-scale level remains one of the biggest challenges in systems biology. This is particularly relevant in plants, for which fluxes in autotrophic growth necessitate time-consuming instationary labeling experiments and costly computations, feasible for small-scale networks. RESULTS Here we present a computationally and experimentally facile approach, termed iReMet-Flux, which integrates relative metabolomics data in a metabolic model to predict differential fluxes at a genome-scale level. Our approach and its variants complement the flux estimation methods based on radioactive tracer labeling. We employ iReMet-Flux with publically available metabolic profiles to predict reactions and pathways with altered fluxes in photo-autotrophically grown Arabidopsis and four photorespiratory mutants undergoing high-to-low CO2 acclimation. We also provide predictions about reactions and pathways which are most strongly regulated in the investigated experiments. The robustness and variability analyses, tailored to the formulation of iReMet-Flux, demonstrate that the findings provide biologically relevant information that is validated with external measurements of net CO2 exchange and biomass production. Therefore, iReMet-Flux paves the wave for mechanistic dissection of the interplay between pathways of primary and secondary metabolisms at a genome-scale. AVAILABILITY AND IMPLEMENTATION The source code is available from the authors upon request. CONTACT nikoloski@mpimp-golm.mpg.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Nadine Töpfer
- Department of Plant and Environmental Sciences, Faculty of Biochemistry, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | | | - Alisdair R Fernie
- Central Metabolism Group, Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam 14476, Germany
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37
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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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38
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Kashaf SS, Angione C, Lió P. Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization. BMC SYSTEMS BIOLOGY 2017; 11:25. [PMID: 28209199 PMCID: PMC5314682 DOI: 10.1186/s12918-017-0395-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 01/13/2017] [Indexed: 11/10/2022]
Abstract
BACKGROUND Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship. RESULTS We present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium's metabolism, such as changes in the bacterium's growth in response to different environmental conditions. CONCLUSIONS After an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens.
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Affiliation(s)
- Sara Saheb Kashaf
- Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Borough road, Middlesbrough, TS1 3BA UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD UK
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39
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Bellasio C. A generalized stoichiometric model of C3, C2, C2+C4, and C4 photosynthetic metabolism. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:269-282. [PMID: 27535993 PMCID: PMC5853385 DOI: 10.1093/jxb/erw303] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 07/21/2016] [Indexed: 05/22/2023]
Abstract
The goal of suppressing photorespiration in crops to maximize assimilation and yield is stimulating considerable interest among researchers looking to bioengineer carbon-concentrating mechanisms into C3 plants. However, detailed quantification of the biochemical activities in the bundle sheath is lacking. This work presents a general stoichiometric model for C3, C2, C2+C4, and C4 assimilation (SMA) in which energetics, metabolite traffic, and the different decarboxylating enzymes (NAD-dependent malic enzyme, NADP-dependent malic enzyme, or phosphoenolpyruvate carboxykinase) are explicitly included. The SMA can be used to refine experimental data analysis or formulate hypothetical scenarios, and is coded in a freely available Microsoft Excel workbook. The theoretical underpinnings and general model behaviour are analysed with a range of simulations, including (i) an analysis of C3, C2, C2+C4, and C4 in operational conditions; (ii) manipulating photorespiration in a C3 plant; (iii) progressively upregulating a C2 shuttle in C3 photosynthesis; (iv) progressively upregulating a C4 cycle in C2 photosynthesis; and (v) manipulating processes that are hypothesized to respond to transient environmental inputs. Results quantify the functional trade-offs, such as the electron transport needed to meet ATP/NADPH demand, as well as metabolite traffic, inherent to different subtypes. The SMA refines our understanding of the stoichiometry of photosynthesis, which is of paramount importance for basic and applied research.
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Affiliation(s)
- Chandra Bellasio
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK
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Beatty PH, Klein MS, Fischer JJ, Lewis IA, Muench DG, Good AG. Understanding Plant Nitrogen Metabolism through Metabolomics and Computational Approaches. PLANTS 2016; 5:plants5040039. [PMID: 27735856 PMCID: PMC5198099 DOI: 10.3390/plants5040039] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 09/21/2016] [Accepted: 09/30/2016] [Indexed: 01/24/2023]
Abstract
A comprehensive understanding of plant metabolism could provide a direct mechanism for improving nitrogen use efficiency (NUE) in crops. One of the major barriers to achieving this outcome is our poor understanding of the complex metabolic networks, physiological factors, and signaling mechanisms that affect NUE in agricultural settings. However, an exciting collection of computational and experimental approaches has begun to elucidate whole-plant nitrogen usage and provides an avenue for connecting nitrogen-related phenotypes to genes. Herein, we describe how metabolomics, computational models of metabolism, and flux balance analysis have been harnessed to advance our understanding of plant nitrogen metabolism. We introduce a model describing the complex flow of nitrogen through crops in a real-world agricultural setting and describe how experimental metabolomics data, such as isotope labeling rates and analyses of nutrient uptake, can be used to refine these models. In summary, the metabolomics/computational approach offers an exciting mechanism for understanding NUE that may ultimately lead to more effective crop management and engineered plants with higher yields.
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Affiliation(s)
- Perrin H Beatty
- Department of Biological Sciences, University of Alberta, 85 Avenue NW, Edmonton, AB T6G 2E9, Canada.
| | - Matthias S Klein
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Jeffrey J Fischer
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Ian A Lewis
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Douglas G Muench
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Allen G Good
- Department of Biological Sciences, University of Alberta, 85 Avenue NW, Edmonton, AB T6G 2E9, Canada.
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Jorge TF, Rodrigues JA, Caldana C, Schmidt R, van Dongen JT, Thomas-Oates J, António C. Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress. MASS SPECTROMETRY REVIEWS 2016; 35:620-49. [PMID: 25589422 DOI: 10.1002/mas.21449] [Citation(s) in RCA: 145] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 10/02/2014] [Accepted: 10/14/2014] [Indexed: 05/08/2023]
Abstract
Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.
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Affiliation(s)
- Tiago F Jorge
- Plant Metabolomics Laboratory, Instituto de Tecnologia Química e Biológica António Xavier-Universidade Nova de Lisboa (ITQB-UNL), Avenida República, 2780-157, Oeiras, Portugal
| | - João A Rodrigues
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal
| | - Camila Caldana
- Max-Planck-partner group at the Brazilian Bioethanol Science and Technology Laboratory/CNPEM, 13083-970, Campinas-SP, Brazil
| | - Romy Schmidt
- Institute of Biology I, RWTH Aachen University, Worringerweg 1, 52074, Aachen, Germany
| | - Joost T van Dongen
- Institute of Biology I, RWTH Aachen University, Worringerweg 1, 52074, Aachen, Germany
| | - Jane Thomas-Oates
- Jane Thomas-Oates, Centre of Excellence in Mass Spectrometry, and Department of Chemistry, University of York, Heslington, York, YO10 5DD, UK
| | - Carla António
- Plant Metabolomics Laboratory, Instituto de Tecnologia Química e Biológica António Xavier-Universidade Nova de Lisboa (ITQB-UNL), Avenida República, 2780-157, Oeiras, Portugal
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Lee CP, Millar AH. The Plant Mitochondrial Transportome: Balancing Metabolic Demands with Energetic Constraints. TRENDS IN PLANT SCIENCE 2016; 21:662-676. [PMID: 27162080 DOI: 10.1016/j.tplants.2016.04.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Revised: 03/25/2016] [Accepted: 04/04/2016] [Indexed: 06/05/2023]
Abstract
In plants, mitochondrial function is associated with hundreds of metabolic reactions. To facilitate these reactions, charged substrates and cofactors move across the charge-impermeable inner mitochondrial membrane via specialized transporters and must work cooperatively with the electrochemical gradient which is essential for mitochondrial function. The regulatory framework for mitochondrial metabolite transport is expected to be more complex in plants than in mammals owing to the close metabolic association between mitochondrial, plastids, and peroxisome metabolism, as well as to the major diurnal fluctuations in plant metabolic function. We propose here how recent advances can be integrated towards defining the mitochondrial transportome in plants. We also discuss what this reveals about sustaining cooperativity between bioenergetics, metabolism, and transport in typical and challenging environments.
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Affiliation(s)
- Chun Pong Lee
- Australian Reseach Council (ARC) Centre of Excellence in Plant Energy Biology, The University of Western Australia, 35 Stirling Highway, Crawley 6009, Australia
| | - A Harvey Millar
- Australian Reseach Council (ARC) Centre of Excellence in Plant Energy Biology, The University of Western Australia, 35 Stirling Highway, Crawley 6009, Australia.
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43
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Basler G, Nikoloski Z, Larhlimi A, Barabási AL, Liu YY. Control of fluxes in metabolic networks. Genome Res 2016; 26:956-68. [PMID: 27197218 PMCID: PMC4937563 DOI: 10.1101/gr.202648.115] [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: 12/02/2015] [Accepted: 05/18/2016] [Indexed: 01/09/2023]
Abstract
Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.
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Affiliation(s)
- Georg Basler
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, USA; Department of Environmental Protection, Estación Experimental del Zaidín CSIC, Granada, 18008 Spain
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476 Germany
| | - Abdelhalim Larhlimi
- Laboratoire d'Informatique de Nantes Atlantique, Université de Nantes, Nantes, 44322 France
| | - Albert-László Barabási
- Center for Complex Network Research and Departments of Physics, Computer Science, and Biology, Northeastern University, Boston, Massachusetts 02115, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02215, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02215, USA
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Dersch LM, Beckers V, Wittmann C. Green pathways: Metabolic network analysis of plant systems. Metab Eng 2016; 34:1-24. [DOI: 10.1016/j.ymben.2015.12.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 11/30/2015] [Accepted: 12/01/2015] [Indexed: 12/18/2022]
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Yuan H, Cheung CYM, Poolman MG, Hilbers PAJ, van Riel NAW. A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2016; 85:289-304. [PMID: 26576489 DOI: 10.1111/tpj.13075] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Revised: 11/01/2015] [Accepted: 11/03/2015] [Indexed: 05/09/2023]
Abstract
Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses.
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Affiliation(s)
- Huili Yuan
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Mark G Poolman
- Cell Systems Modelling Group, Department of Biomedical and Medical Science, Oxford Brookes University, Oxford, UK
| | - Peter A J Hilbers
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
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46
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Cheung CYM, Ratcliffe RG, Sweetlove LJ. A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf. PLANT PHYSIOLOGY 2015; 169:1671-82. [PMID: 26265776 PMCID: PMC4634065 DOI: 10.1104/pp.15.00880] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 08/04/2015] [Indexed: 05/02/2023]
Abstract
Flux balance analysis of plant metabolism is an established method for predicting metabolic flux phenotypes and for exploring the way in which the plant metabolic network delivers specific outcomes in different cell types, tissues, and temporal phases. A recurring theme is the need to explore the flexibility of the network in meeting its objectives and, in particular, to establish the extent to which alternative pathways can contribute to achieving specific outcomes. Unfortunately, predictions from conventional flux balance analysis minimize the simultaneous operation of alternative pathways, but by introducing flux-weighting factors to allow for the variable intrinsic cost of supporting each flux, it is possible to activate different pathways in individual simulations and, thus, to explore alternative pathways by averaging thousands of simulations. This new method has been applied to a diel genome-scale model of Arabidopsis (Arabidopsis thaliana) leaf metabolism to explore the flexibility of the network in meeting the metabolic requirements of the leaf in the light. This identified alternative flux modes in the Calvin-Benson cycle revealed the potential for alternative transitory carbon stores in leaves and led to predictions about the light-dependent contribution of alternative electron flow pathways and futile cycles in energy rebalancing. Notable features of the analysis include the light-dependent tradeoff between the use of carbohydrates and four-carbon organic acids as transitory storage forms and the way in which multiple pathways for the consumption of ATP and NADPH can contribute to the balancing of the requirements of photosynthetic metabolism with the energy available from photon capture.
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Affiliation(s)
- C Y Maurice Cheung
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
| | - R George Ratcliffe
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
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47
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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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48
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Rolletschek H, Grafahrend-Belau E, Munz E, Radchuk V, Kartäusch R, Tschiersch H, Melkus G, Schreiber F, Jakob PM, Borisjuk L. Metabolic Architecture of the Cereal Grain and Its Relevance to Maximize Carbon Use Efficiency. PLANT PHYSIOLOGY 2015; 169:1698-713. [PMID: 26395842 PMCID: PMC4634074 DOI: 10.1104/pp.15.00981] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 09/20/2015] [Indexed: 05/20/2023]
Abstract
Here, we have characterized the spatial heterogeneity of the cereal grain's metabolism and demonstrated how, by integrating a distinct set of metabolic strategies, the grain has evolved to become an almost perfect entity for carbon storage. In vivo imaging revealed light-induced cycles in assimilate supply toward the ear/grain of barley (Hordeum vulgare) and wheat (Triticum aestivum). In silico modeling predicted that, in the two grain storage organs (the endosperm and embryo), the light-induced shift in solute influx does cause adjustment in metabolic flux without changes in pathway utilization patterns. The enveloping, leaf-like pericarp, in contrast, shows major shifts in flux distribution (starch metabolism, photosynthesis, remobilization, and tricarboxylic acid cycle activity) allow to refix 79% of the CO2 released by the endosperm and embryo, allowing the grain to achieve an extraordinary high carbon conversion efficiency of 95%. Shading experiments demonstrated that ears are autonomously able to raise the influx of solutes in response to light, but with little effect on the steady-state levels of metabolites or transcripts or on the pattern of sugar distribution within the grain. The finding suggests the presence of a mechanism(s) able to ensure metabolic homeostasis in the face of short-term environmental fluctuation. The proposed multicomponent modeling approach is informative for predicting the metabolic effects of either an altered level of incident light or a momentary change in the supply of sucrose. It is therefore of potential value for assessing the impact of either breeding and/or biotechnological interventions aimed at increasing grain yield.
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Affiliation(s)
- Hardy Rolletschek
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Eva Grafahrend-Belau
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Eberhard Munz
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Volodymyr Radchuk
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Ralf Kartäusch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Henning Tschiersch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Gerd Melkus
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Falk Schreiber
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Peter M Jakob
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
| | - Ljudmilla Borisjuk
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany (H.R., E.M., V.R., H.T., L.B.);Institut für Pharmazie, Martin-Luther-University of Halle, 06120 Halle, Germany (E.G.-B.);Institute of Experimental Physics 5, University of Würzburg, 97074 Würzburg, Germany (E.M., P.M.J.);Research Center Magnetic Resonance Bavaria, 97074 Wurzburg, Germany (R.K., P.M.J.);Department of Medical Imaging, University of Ottawa, Ottawa, Ontario, Canada K1Y 4E9 (G.M.); andClayton School of IT, Monash University, Melbourne, Victoria 3800, Australia (F.S.)
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49
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Horvat P, Koller M, Braunegg G. Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies. World J Microbiol Biotechnol 2015; 31:1315-28. [DOI: 10.1007/s11274-015-1887-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 06/05/2015] [Indexed: 11/25/2022]
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50
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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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