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Daloso DDM, Morais EG, Oliveira E Silva KF, Williams TCR. Cell-type-specific metabolism in plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 114:1093-1114. [PMID: 36987968 DOI: 10.1111/tpj.16214] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/20/2023] [Accepted: 03/25/2023] [Indexed: 05/31/2023]
Abstract
Every plant organ contains tens of different cell types, each with a specialized function. These functions are intrinsically associated with specific metabolic flux distributions that permit the synthesis of the ATP, reducing equivalents and biosynthetic precursors demanded by the cell. Investigating such cell-type-specific metabolism is complicated by the mosaic of different cells within each tissue combined with the relative scarcity of certain types. However, techniques for the isolation of specific cells, their analysis in situ by microscopy, or modeling of their function in silico have permitted insight into cell-type-specific metabolism. In this review we present some of the methods used in the analysis of cell-type-specific metabolism before describing what we know about metabolism in several cell types that have been studied in depth; (i) leaf source and sink cells; (ii) glandular trichomes that are capable of rapid synthesis of specialized metabolites; (iii) guard cells that must accumulate large quantities of the osmolytes needed for stomatal opening; (iv) cells of seeds involved in storage of reserves; and (v) the mesophyll and bundle sheath cells of C4 plants that participate in a CO2 concentrating cycle. Metabolism is discussed in terms of its principal features, connection to cell function and what factors affect the flux distribution. Demand for precursors and energy, availability of substrates and suppression of deleterious processes are identified as key factors in shaping cell-type-specific metabolism.
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Affiliation(s)
- Danilo de Menezes Daloso
- Lab Plant, Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Ceará, Fortaleza-CA, 60451-970, Brazil
| | - Eva Gomes Morais
- Lab Plant, Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Ceará, Fortaleza-CA, 60451-970, Brazil
| | - Karen Fernanda Oliveira E Silva
- Departamento de Botânica, Instituto de Ciências Biológicas, Universidade de Brasília, Asa Norte, Brasília-DF, 70910-900, Brazil
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2
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Punyasu N, Kalapanulak S, Saithong T. CO 2 recycling by phospho enolpyruvate carboxylase enables cassava leaf metabolism to tolerate low water availability. FRONTIERS IN PLANT SCIENCE 2023; 14:1159247. [PMID: 37229106 PMCID: PMC10204807 DOI: 10.3389/fpls.2023.1159247] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/12/2023] [Indexed: 05/27/2023]
Abstract
Cassava is a staple crop that acclimatizes well to dry weather and limited water availability. The drought response mechanism of quick stomatal closure observed in cassava has no explicit link to the metabolism connecting its physiological response and yield. Here, a genome-scale metabolic model of cassava photosynthetic leaves (leaf-MeCBM) was constructed to study on the metabolic response to drought and stomatal closure. As demonstrated by leaf-MeCBM, leaf metabolism reinforced the physiological response by increasing the internal CO2 and then maintaining the normal operation of photosynthetic carbon fixation. We found that phosphoenolpyruvate carboxylase (PEPC) played a crucial role in the accumulation of the internal CO2 pool when the CO2 uptake rate was limited during stomatal closure. Based on the model simulation, PEPC mechanistically enhanced drought tolerance in cassava by providing sufficient CO2 for carbon fixation by RuBisCO, resulting in high production of sucrose in cassava leaves. The metabolic reprogramming decreased leaf biomass production, which may lead to maintaining intracellular water balance by reducing the overall leaf area. This study indicates the association of metabolic and physiological responses to enhance tolerance, growth, and production of cassava in drought conditions.
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Affiliation(s)
- Nattharat Punyasu
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, and School of Information Technology, King Mongkut’s University of Technology Thonburi (Bang Khun Thian), Bangkok, Thailand
| | - Saowalak Kalapanulak
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, and School of Information Technology, King Mongkut’s University of Technology Thonburi (Bang Khun Thian), Bangkok, Thailand
- School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi (Bang Khun Thian), Bangkok, Thailand
- Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut’s University of Technology Thonburi (Bang Khun Thian), Bangkok, Thailand
| | - Treenut Saithong
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, and School of Information Technology, King Mongkut’s University of Technology Thonburi (Bang Khun Thian), Bangkok, Thailand
- School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi (Bang Khun Thian), Bangkok, Thailand
- Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut’s University of Technology Thonburi (Bang Khun Thian), Bangkok, Thailand
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3
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Babele PK, Srivastava A, Selim KA, Kumar A. Millet-inspired systems metabolic engineering of NUE in crops. Trends Biotechnol 2022; 41:701-713. [PMID: 36566140 DOI: 10.1016/j.tibtech.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 12/24/2022]
Abstract
The use of nitrogen (N) fertilizers in agriculture has a great ability to increase crop productivity. However, their excessive use has detrimental effects on the environment. Therefore, it is necessary to develop crop varieties with improved nitrogen use efficiency (NUE) that require less N but have substantial yields. Orphan crops such as millets are cultivated in limited regions and are well adapted to lower input conditions. Therefore, they serve as a rich source of beneficial traits that can be transferred into major crops to improve their NUE. This review highlights the tremendous potential of systems biology to unravel the enzymes and pathways involved in the N metabolism of millets, which can open new possibilities to generate transgenic crops with improved NUE.
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Affiliation(s)
- Piyoosh K Babele
- Rani Lakshmi Bai Central Agricultural University, Jhansi 284003, Uttar Pradesh, India.
| | - Amit Srivastava
- University of Jyväskylä, Nanoscience Centre, Department of Biological and Environmental Science, 40014 Jyväskylä, Finland
| | - Khaled A Selim
- Organismic Interactions Department, Interfaculty Institute for Microbiology and Infection Medicine, Cluster of Excellence 'Controlling Microbes to Fight Infections', Tübingen University, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Anil Kumar
- Rani Lakshmi Bai Central Agricultural University, Jhansi 284003, Uttar Pradesh, India
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4
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Chowdhury NB, Schroeder WL, Sarkar D, Amiour N, Quilleré I, Hirel B, Maranas CD, Saha R. Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:275-291. [PMID: 34554248 DOI: 10.1093/jxb/erab435] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.
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Affiliation(s)
- Niaz Bahar Chowdhury
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Wheaton L Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Nardjis Amiour
- Institut National de Recherche pour l'Agriculure, l'Alimentation et l'Envionnement (INRAE), Centre de Versailles-Grignon, Versailles cedex, France
| | - Isabelle Quilleré
- Institut National de Recherche pour l'Agriculure, l'Alimentation et l'Envionnement (INRAE), Centre de Versailles-Grignon, Versailles cedex, France
| | - Bertrand Hirel
- Institut National de Recherche pour l'Agriculure, l'Alimentation et l'Envionnement (INRAE), Centre de Versailles-Grignon, Versailles cedex, France
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Root and Rhizobiome Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
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5
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Ramirez-Malule H, López-Agudelo VA, Gómez-Ríos D, Ochoa S, Ríos-Estepa R, Junne S, Neubauer P. TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus. Bioengineering (Basel) 2021; 8:103. [PMID: 34436106 PMCID: PMC8389198 DOI: 10.3390/bioengineering8080103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 07/04/2021] [Accepted: 07/16/2021] [Indexed: 11/26/2022] Open
Abstract
Streptomyces clavuligerus (S. clavuligerus) has been widely studied for its ability to produce clavulanic acid (CA), a potent inhibitor of β-lactamase enzymes. In this study, S. clavuligerus cultivated in 2D rocking bioreactor in fed-batch operation produced CA at comparable rates to those observed in stirred tank bioreactors. A reduced model of S. clavuligerus metabolism was constructed by using a bottom-up approach and validated using experimental data. The reduced model was implemented for in silico studies of the metabolic scenarios arisen during the cultivations. Constraint-based analysis confirmed the interrelations between succinate, oxaloacetate, malate, pyruvate, and acetate accumulations at high CA synthesis rates in submerged cultures of S. clavuligerus. Further analysis using shadow prices provided a first view of the metabolites positive and negatively associated with the scenarios of low and high CA production.
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Affiliation(s)
| | | | - David Gómez-Ríos
- Grupo de Investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP), Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín 050010, Colombia; (D.G.-R.); (S.O.)
| | - Silvia Ochoa
- Grupo de Investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP), Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín 050010, Colombia; (D.G.-R.); (S.O.)
| | - Rigoberto Ríos-Estepa
- Escuela de Biociencias, Universidad Nacional de Colombia sede Medellín, Medellín 050010, Colombia;
| | - Stefan Junne
- Chair of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, D-13355 Berlin, Germany; (S.J.); (P.N.)
| | - Peter Neubauer
- Chair of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, D-13355 Berlin, Germany; (S.J.); (P.N.)
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6
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Characterization of effects of genetic variants via genome-scale metabolic modelling. Cell Mol Life Sci 2021; 78:5123-5138. [PMID: 33950314 PMCID: PMC8254712 DOI: 10.1007/s00018-021-03844-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 04/23/2021] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.
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7
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Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava. Sci Rep 2021; 11:8758. [PMID: 33888810 PMCID: PMC8062692 DOI: 10.1038/s41598-021-88129-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/08/2021] [Indexed: 02/02/2023] Open
Abstract
The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.
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8
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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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9
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Correa SM, Fernie AR, Nikoloski Z, Brotman Y. Towards model-driven characterization and manipulation of plant lipid metabolism. Prog Lipid Res 2020; 80:101051. [PMID: 32640289 DOI: 10.1016/j.plipres.2020.101051] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 06/20/2020] [Accepted: 06/21/2020] [Indexed: 01/09/2023]
Abstract
Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.
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Affiliation(s)
- Sandra M Correa
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel; Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín 050010, Colombia.
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Zoran Nikoloski
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modelling Group, Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm 14476, Germany.
| | - Yariv Brotman
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
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10
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Sarkar D, Maranas CD. SNPeffect: identifying functional roles of SNPs using metabolic networks. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:512-531. [PMID: 32167625 PMCID: PMC9328443 DOI: 10.1111/tpj.14746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/20/2020] [Indexed: 05/04/2023]
Abstract
Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.
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Affiliation(s)
- Debolina Sarkar
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
| | - Costas D. Maranas
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
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11
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Schroeder WL, Saha R. Introducing an Optimization- and explicit Runge-Kutta- based Approach to Perform Dynamic Flux Balance Analysis. Sci Rep 2020; 10:9241. [PMID: 32514037 PMCID: PMC7280247 DOI: 10.1038/s41598-020-65457-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 05/04/2020] [Indexed: 11/17/2022] Open
Abstract
In this work we introduce the generalized Optimization- and explicit Runge-Kutta-based Approach (ORKA) to perform dynamic Flux Balance Analysis (dFBA), which is numerically more accurate and computationally tractable than existing approaches. ORKA is applied to a four-tissue (leaf, root, seed, and stem) model of Arabidopsis thaliana, p-ath773, uniquely capturing the core-metabolism of several stages of growth from seedling to senescence at hourly intervals. Model p-ath773 has been designed to show broad agreement with published plant-scale properties such as mass, maintenance, and senescence, yet leaving reaction-level behavior unconstrainted. Hence, it serves as a framework to study the reaction-level behavior necessary for observed plant-scale behavior. Two such case studies of reaction-level behavior include the lifecycle progression of sulfur metabolism and the diurnal flow of water throughout the plant. Specifically, p-ath773 shows how transpiration drives water flow through the plant and how water produced by leaf tissue metabolism may contribute significantly to transpired water. Investigation of sulfur metabolism elucidates frequent cross-compartment exchange of a standing pool of amino acids which is used to regulate the proton flow. Overall, p-ath773 and ORKA serve as scaffolds for dFBA-based lifecycle modeling of plants and other systems to further broaden the scope of in silico metabolic investigation.
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Affiliation(s)
- Wheaton L Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, USA.
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12
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Domergue JB, Abadie C, Limami A, Way D, Tcherkez G. Seed quality and carbon primary metabolism. PLANT, CELL & ENVIRONMENT 2019; 42:2776-2788. [PMID: 31323691 DOI: 10.1111/pce.13618] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 07/05/2019] [Accepted: 07/13/2019] [Indexed: 05/28/2023]
Abstract
Improving seed quality is amongst the most important challenges of contemporary agriculture. In fact, using plant varieties with better germination rates that are more tolerant to stress during seedling establishment may improve crop yield considerably. Therefore, intense efforts are currently being devoted to improve seed quality in many species, mostly using genomics tools. However, despite its considerable importance during seed imbibition and germination processes, primary carbon metabolism in seeds is less studied. Our knowledge of the physiology of seed respiration and energy generation and the impact of these processes on seed performance have made limited progress over the past three decades. In particular, (isotope-assisted) metabolomics of seeds has only been assessed occasionally, and there is limited information on possible quantitative relationships between metabolic fluxes and seed quality. Here, we review the recent literature and provide an overview of potential links between metabolic efficiency, metabolic biomarkers, and seed quality and discuss implications for future research, including a climate change context.
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Affiliation(s)
- Jean-Baptiste Domergue
- IRHS Institut de Recherche en Horticultures et Séances, UMR 1345, INRA, Agrocampus-Ouest, Université d'Angers SFR 4207 QuaSaV, Beaucouzé, 49070, France
| | - Cyril Abadie
- IRHS Institut de Recherche en Horticultures et Séances, UMR 1345, INRA, Agrocampus-Ouest, Université d'Angers SFR 4207 QuaSaV, Beaucouzé, 49070, France
| | - Anis Limami
- IRHS Institut de Recherche en Horticultures et Séances, UMR 1345, INRA, Agrocampus-Ouest, Université d'Angers SFR 4207 QuaSaV, Beaucouzé, 49070, France
| | - Danielle Way
- Department of Biology, University of Western Ontario, London, ON, N6A 5B7, Canada
- Research School of Biology, ANU Joint College of Sciences, Australian National University, Canberra, ACT, 2601, Australia
- Nicholas School of the Environment, Duke University, Durham, NC, 27708, USA
| | - Guillaume Tcherkez
- IRHS Institut de Recherche en Horticultures et Séances, UMR 1345, INRA, Agrocampus-Ouest, Université d'Angers SFR 4207 QuaSaV, Beaucouzé, 49070, France
- Research School of Biology, ANU Joint College of Sciences, Australian National University, Canberra, ACT, 2601, Australia
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13
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Extended Utilization of Constraint-Based Metabolic Model in a Long-Growing Crop. Processes (Basel) 2019. [DOI: 10.3390/pr7050259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The constraint-based rMeCBM-KU50 model of cassava storage root growth was analyzed to evaluate its sensitivity, with respect to reaction flux distribution and storage root growth rate, to changes in model inputted data and constraints, including sucrose uptake rate-related data—photosynthetic rate, total leaf area, total photosynthetic rate, storage root dry weight, and biomass function-related data. These mainly varied within ±90% of the model default values, although exceptions were made for the carbohydrate (−90% to 8%) and starch (−90% to 9%) contents. The results indicated that the predicted storage root growth rate was highly affected by specific sucrose uptake rates through the total photosynthetic rate and storage root dry weight variations; whereas the carbon flux distribution, direction and partitioning inclusive, was more sensitive to the variation in biomass content, particularly the carbohydrate content. This study showed that the specific sucrose uptake rate based on the total photosynthetic rate, storage root dry weight, and carbohydrate content were critical to the constraint-based metabolic modeling and deepened our understanding of the input–output relationship—specifically regarding the rMeCBM-KU50 model—providing a valuable platform for the modeling of plant metabolic systems, especially long-growing crops.
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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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15
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Botero K, Restrepo S, Pinzón A. A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism. BMC Genomics 2018; 19:863. [PMID: 30537923 PMCID: PMC6288859 DOI: 10.1186/s12864-018-5192-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Phytophthora infestans is a plant pathogen that causes an important plant disease known as late blight in potato plants (Solanum tuberosum) and several other solanaceous hosts. This disease is the main factor affecting potato crop production worldwide. In spite of the importance of the disease, the molecular mechanisms underlying the compatibility between the pathogen and its hosts are still unknown. RESULTS To explain the metabolic response of late blight, specifically photosynthesis inhibition in infected plants, we reconstructed a genome-scale metabolic network of the S. tuberosum leaf, PstM1. This metabolic network simulates the effect of this disease in the leaf metabolism. PstM1 accounts for 2751 genes, 1113 metabolic functions, 1773 gene-protein-reaction associations and 1938 metabolites involved in 2072 reactions. The optimization of the model for biomass synthesis maximization in three infection time points suggested a suppression of the photosynthetic capacity related to the decrease of metabolic flux in light reactions and carbon fixation reactions. In addition, a variation pattern in the flux of carboxylation to oxygenation reactions catalyzed by RuBisCO was also identified, likely to be associated to a defense response in the compatible interaction between P. infestans and S. tuberosum. CONCLUSIONS In this work, we introduced simultaneously the first metabolic network of S. tuberosum and the first genome-scale metabolic model of the compatible interaction of a plant with P. infestans.
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Affiliation(s)
- Kelly Botero
- Grupo de Bioinformática y Biología de Sistemas, Universidad Nacional del Colombia - Instituto de Genética, Calle 53- Carrera 32, Edificio 426, Bogotá, Colombia.,Centro de Bioinformática y Biología Computacional, Manizales, Colombia
| | - Silvia Restrepo
- Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia
| | - Andres Pinzón
- Grupo de Bioinformática y Biología de Sistemas, Universidad Nacional del Colombia - Instituto de Genética, Calle 53- Carrera 32, Edificio 426, Bogotá, Colombia.
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16
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Pfau T, Christian N, Masakapalli SK, Sweetlove LJ, Poolman MG, Ebenhöh O. The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling. Sci Rep 2018; 8:12504. [PMID: 30131500 PMCID: PMC6104047 DOI: 10.1038/s41598-018-30884-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 08/07/2018] [Indexed: 11/09/2022] Open
Abstract
Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.
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Affiliation(s)
- Thomas Pfau
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Nils Christian
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
| | - Shyam K Masakapalli
- School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford, UK
| | - Mark G Poolman
- Department Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
| | - Oliver Ebenhöh
- Institute of Quantitative and Theoretical Biology, Cluster of Excellence on Plant Sciences CEPLAS, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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17
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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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18
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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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19
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Goh HH. Integrative Multi-Omics Through Bioinformatics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1102:69-80. [DOI: 10.1007/978-3-319-98758-3_5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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20
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Computational Approaches on Stoichiometric and Kinetic Modeling for Efficient Strain Design. Methods Mol Biol 2018; 1671:63-82. [PMID: 29170953 DOI: 10.1007/978-1-4939-7295-1_5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Engineering biological systems that are capable of overproducing products of interest is the ultimate goal of any biotechnology application. To this end, stoichiometric (or steady state) and kinetic models are increasingly becoming available for a variety of organisms including prokaryotes, eukaryotes, and microbial communities. This ever-accelerating pace of such model reconstructions has also spurred the development of optimization-based strain design techniques. This chapter highlights a number of such frameworks developed in recent years in order to generate testable hypotheses (in terms of genetic interventions), thus addressing the challenges in metabolic engineering. In particular, three major methods are covered in detail including two methods for designing strains (i.e., one stoichiometric model-based and the other by integrating kinetic information into a stoichiometric model) and one method for analyzing microbial communities.
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21
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Ma Z, Bykova NV, Igamberdiev AU. Cell signaling mechanisms and metabolic regulation of germination and dormancy in barley seeds. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.cj.2017.08.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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22
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Chang TG, Zhu XG, Raines C. Source-sink interaction: a century old concept under the light of modern molecular systems biology. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:4417-4431. [PMID: 28338782 DOI: 10.1093/jxb/erx002] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Many approaches to engineer source strength have been proposed to enhance crop yield potential. However, a well-co-ordinated source-sink relationship is required finally to realize the promised increase in crop yield potential in the farmer's field. Source-sink interaction has been intensively studied for decades, and a vast amount of knowledge about the interaction in different crops and under different environments has been accumulated. In this review, we first introduce the basic concepts of source, sink and their interactions, then summarize current understanding of how source and sink can be manipulated through both environmental control and genetic manipulations. We show that the source-sink interaction underlies the diverse responses of crops to the same perturbations and argue that development of a molecular systems model of source-sink interaction is required towards a rational manipulation of the source-sink relationship for increased yield. We finally discuss both bottom-up and top-down routes to develop such a model and emphasize that a community effort is needed for development of this model.
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Affiliation(s)
- Tian-Gen Chang
- CAS Key Laboratory of Computational Biology and State Key Laboratory for Hybrid Rice, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin-Guang Zhu
- CAS Key Laboratory of Computational Biology and State Key Laboratory for Hybrid Rice, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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23
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Koch I, Nöthen J, Schleiff E. Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets. Front Genet 2017; 8:85. [PMID: 28713420 PMCID: PMC5491931 DOI: 10.3389/fgene.2017.00085] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 06/06/2017] [Indexed: 12/16/2022] Open
Abstract
Motivation:Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models.
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Affiliation(s)
- Ina Koch
- Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence “Macromolecular Complexes”, Goethe-University FrankfurtFrankfurt am Main, Germany
| | - Joachim Nöthen
- Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence “Macromolecular Complexes”, Goethe-University FrankfurtFrankfurt am Main, Germany
| | - Enrico Schleiff
- Department of Biosciences, Institute of Molecular Biosciences, Molecular Cell Biology of Plants, Cluster of Excellence “Macromolecular Complexes”, Goethe-University FrankfurtFrankfurt am Main, Germany
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Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii. PLoS One 2017; 12:e0177292. [PMID: 28542252 PMCID: PMC5443493 DOI: 10.1371/journal.pone.0177292] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 04/25/2017] [Indexed: 12/18/2022] Open
Abstract
Despite the wealth of knowledge available for C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, 13C-metabolic flux analysis (13C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments (cytosol, mitochondria and plastid) included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with 13C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.
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25
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Peyraud R, Dubiella U, Barbacci A, Genin S, Raffaele S, Roby D. Advances on plant-pathogen interactions from molecular toward systems biology perspectives. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2017; 90:720-737. [PMID: 27870294 PMCID: PMC5516170 DOI: 10.1111/tpj.13429] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 11/14/2016] [Accepted: 11/14/2016] [Indexed: 05/21/2023]
Abstract
In the past 2 decades, progress in molecular analyses of the plant immune system has revealed key elements of a complex response network. Current paradigms depict the interaction of pathogen-secreted molecules with host target molecules leading to the activation of multiple plant response pathways. Further research will be required to fully understand how these responses are integrated in space and time, and exploit this knowledge in agriculture. In this review, we highlight systems biology as a promising approach to reveal properties of molecular plant-pathogen interactions and predict the outcome of such interactions. We first illustrate a few key concepts in plant immunity with a network and systems biology perspective. Next, we present some basic principles of systems biology and show how they allow integrating multiomics data and predict cell phenotypes. We identify challenges for systems biology of plant-pathogen interactions, including the reconstruction of multiscale mechanistic models and the connection of host and pathogen models. Finally, we outline studies on resistance durability through the robustness of immune system networks, the identification of trade-offs between immunity and growth and in silico plant-pathogen co-evolution as exciting perspectives in the field. We conclude that the development of sophisticated models of plant diseases incorporating plant, pathogen and climate properties represent a major challenge for agriculture in the future.
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Affiliation(s)
- Rémi Peyraud
- LIPMUniversité de ToulouseINRACNRSCastanet‐TolosanFrance
| | | | | | - Stéphane Genin
- LIPMUniversité de ToulouseINRACNRSCastanet‐TolosanFrance
| | | | - Dominique Roby
- LIPMUniversité de ToulouseINRACNRSCastanet‐TolosanFrance
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26
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Salon C, Avice JC, Colombié S, Dieuaide-Noubhani M, Gallardo K, Jeudy C, Ourry A, Prudent M, Voisin AS, Rolin D. Fluxomics links cellular functional analyses to whole-plant phenotyping. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:2083-2098. [PMID: 28444347 DOI: 10.1093/jxb/erx126] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.
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Affiliation(s)
- Christophe Salon
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Jean-Christophe Avice
- UNICAEN, UMR INRA 950 Ecophysiologie Végétale, Agronomie et nutritions N, C, S, Esplanade de la Paix, Université Caen Normandie, 14032 Caen Cedex 5, France
| | - Sophie Colombié
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
| | - Martine Dieuaide-Noubhani
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
| | - Karine Gallardo
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Christian Jeudy
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Alain Ourry
- UNICAEN, UMR INRA 950 Ecophysiologie Végétale, Agronomie et nutritions N, C, S, Esplanade de la Paix, Université Caen Normandie, 14032 Caen Cedex 5, France
| | - Marion Prudent
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Anne-Sophie Voisin
- Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France
| | - Dominique Rolin
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France
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Park JY, Kim YH, Min J. CO2 reduction and organic compounds production by photosynthetic bacteria with surface displayed carbonic anhydrase and inducible expression of phosphoenolpyruvate carboxylase. Enzyme Microb Technol 2017; 96:103-110. [DOI: 10.1016/j.enzmictec.2016.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 10/05/2016] [Accepted: 10/11/2016] [Indexed: 11/30/2022]
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Beckers V, Dersch LM, Lotz K, Melzer G, Bläsing OE, Fuchs R, Ehrhardt T, Wittmann C. In silico metabolic network analysis of Arabidopsis leaves. BMC SYSTEMS BIOLOGY 2016; 10:102. [PMID: 27793154 PMCID: PMC5086045 DOI: 10.1186/s12918-016-0347-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 10/21/2016] [Indexed: 12/23/2022]
Abstract
Background During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants. Results The thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response. Conclusions The successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Veronique Beckers
- Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123, Saarbrücken, Germany
| | - Lisa Maria Dersch
- Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123, Saarbrücken, Germany
| | | | - Guido Melzer
- Institute of Biochemical Engineering, Technical University Braunschweig, Braunschweig, Germany
| | | | | | | | - Christoph Wittmann
- Institute for Systems Biotechnology, Saarland University, Campus A1.5, 66123, Saarbrücken, Germany.
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Imam J, Singh PK, Shukla P. Plant Microbe Interactions in Post Genomic Era: Perspectives and Applications. Front Microbiol 2016; 7:1488. [PMID: 27725809 PMCID: PMC5035750 DOI: 10.3389/fmicb.2016.01488] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 09/07/2016] [Indexed: 01/17/2023] Open
Abstract
Deciphering plant-microbe interactions is a promising aspect to understand the benefits and the pathogenic effect of microbes and crop improvement. The advancement in sequencing technologies and various 'omics' tool has impressively accelerated the research in biological sciences in this area. The recent and ongoing developments provide a unique approach to describing these intricate interactions and test hypotheses. In the present review, we discuss the role of plant-pathogen interaction in crop improvement. The plant innate immunity has always been an important aspect of research and leads to some interesting information like the adaptation of unique immune mechanisms of plants against pathogens. The development of new techniques in the post - genomic era has greatly enhanced our understanding of the regulation of plant defense mechanisms against pathogens. The present review also provides an overview of beneficial plant-microbe interactions with special reference to Agrobacterium tumefaciens-plant interactions where plant derived signal molecules and plant immune responses are important in pathogenicity and transformation efficiency. The construction of various Genome-scale metabolic models of microorganisms and plants presented a better understanding of all metabolic interactions activated during the interactions. This review also lists the emerging repertoire of phytopathogens and its impact on plant disease resistance. Outline of different aspects of plant-pathogen interactions is presented in this review to bridge the gap between plant microbial ecology and their immune responses.
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Affiliation(s)
| | | | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand UniversityRohtak, India
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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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31
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Zhu M, Monroe JG, Suhail Y, Villiers F, Mullen J, Pater D, Hauser F, Jeon BW, Bader JS, Kwak JM, Schroeder JI, McKay JK, Assmann SM. Molecular and systems approaches towards drought-tolerant canola crops. THE NEW PHYTOLOGIST 2016; 210:1169-1189. [PMID: 26879345 DOI: 10.1111/nph.13866] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2015] [Accepted: 12/14/2015] [Indexed: 06/05/2023]
Abstract
1169 I. 1170 II. 1170 III. 1172 IV. 1176 V. 1181 VI. 1182 1183 References 1183 SUMMARY: Modern agriculture is facing multiple challenges including the necessity for a substantial increase in production to meet the needs of a burgeoning human population. Water shortage is a deleterious consequence of both population growth and climate change and is one of the most severe factors limiting global crop productivity. Brassica species, particularly canola varieties, are cultivated worldwide for edible oil, animal feed, and biodiesel, and suffer dramatic yield loss upon drought stress. The recent release of the Brassica napus genome supplies essential genetic information to facilitate identification of drought-related genes and provides new information for agricultural improvement in this species. Here we summarize current knowledge regarding drought responses of canola, including physiological and -omics effects of drought. We further discuss knowledge gained through translational biology based on discoveries in the closely related reference species Arabidopsis thaliana and through genetic strategies such as genome-wide association studies and analysis of natural variation. Knowledge of drought tolerance/resistance responses in canola together with research outcomes arising from new technologies and methodologies will inform novel strategies for improvement of drought tolerance and yield in this and other important crop species.
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Affiliation(s)
- Mengmeng Zhu
- Biology Department, Pennsylvania State University, University Park, PA, 16802, USA
| | - J Grey Monroe
- Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, 80523, USA
| | - Yasir Suhail
- Department of Biomedical Engineering, The Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Florent Villiers
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20740, USA
| | - Jack Mullen
- Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, 80523, USA
| | - Dianne Pater
- Division of Biological Sciences, Cell and Developmental Biology Section, Food and Fuel for the 21st Century Center, University of California San Diego, La Jolla, CA, 92093-016, USA
| | - Felix Hauser
- Division of Biological Sciences, Cell and Developmental Biology Section, Food and Fuel for the 21st Century Center, University of California San Diego, La Jolla, CA, 92093-016, USA
| | - Byeong Wook Jeon
- Biology Department, Pennsylvania State University, University Park, PA, 16802, USA
| | - Joel S Bader
- Department of Biomedical Engineering, The Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- School of Medicine, The Johns Hopkins University, Baltimore, MD, 21205, USA
| | - June M Kwak
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20740, USA
- Center for Plant Aging Research, Institute for Basic Science, Department of New Biology, DGIST, Daegu, 42988, Korea
| | - Julian I Schroeder
- Division of Biological Sciences, Cell and Developmental Biology Section, Food and Fuel for the 21st Century Center, University of California San Diego, La Jolla, CA, 92093-016, USA
| | - John K McKay
- Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, 80523, USA
| | - Sarah M Assmann
- Biology Department, Pennsylvania State University, University Park, PA, 16802, 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, Hilbers PAJ, van Riel NAW. Flux Balance Analysis of Plant Metabolism: The Effect of Biomass Composition and Model Structure on Model Predictions. FRONTIERS IN PLANT SCIENCE 2016; 7:537. [PMID: 27200014 PMCID: PMC4845513 DOI: 10.3389/fpls.2016.00537] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 04/05/2016] [Indexed: 05/22/2023]
Abstract
The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.
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Affiliation(s)
- Huili Yuan
- Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
| | | | - Peter A. J. Hilbers
- Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of TechnologyEindhoven, Netherlands
| | - Natal A. W. van Riel
- Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of TechnologyEindhoven, Netherlands
- Natal A. W. van Riel
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Lakshmanan M, Cheung CYM, Mohanty B, Lee DY. Modeling Rice Metabolism: From Elucidating Environmental Effects on Cellular Phenotype to Guiding Crop Improvement. FRONTIERS IN PLANT SCIENCE 2016; 7:1795. [PMID: 27965696 PMCID: PMC5126141 DOI: 10.3389/fpls.2016.01795] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Accepted: 11/15/2016] [Indexed: 05/20/2023]
Abstract
Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently six such networks are available, where five are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.
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Affiliation(s)
- Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and ResearchSingapore, Singapore
| | - C. Y. Maurice Cheung
- Department of Chemical and Biomolecular Engineering, National University of SingaporeSingapore, Singapore
| | - Bijayalaxmi Mohanty
- Department of Chemical and Biomolecular Engineering, National University of SingaporeSingapore, Singapore
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and ResearchSingapore, Singapore
- Department of Chemical and Biomolecular Engineering, National University of SingaporeSingapore, Singapore
- Synthetic Biology for Clinical and Technological Innovation, Life Sciences Institute, National University of SingaporeSingapore, Singapore
- *Correspondence: Dong-Yup Lee,
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35
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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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Shi H, Schwender J. Mathematical models of plant metabolism. Curr Opin Biotechnol 2015; 37:143-152. [PMID: 26723012 DOI: 10.1016/j.copbio.2015.10.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 10/16/2015] [Accepted: 10/26/2015] [Indexed: 11/24/2022]
Abstract
Among various modeling approaches in plant metabolic research, applications of Constraint-Based modeling are fast increasing in recent years, apparently driven by current advances in genomics and genome sequencing. Constraint-Based modeling, the functional analysis of metabolic networks at the whole cell or genome scale, is more difficult to apply to plants than to microbes. Here we discuss recent developments in Constraint-Based modeling in plants with focus on issues of model reconstruction and flux prediction. Another topic is the emerging application of integration of Constraint-Based modeling with omics data to increase predictive power. Furthermore, advances in experimental measurements of cellular fluxes by (13)C-Metabolic Flux Analysis are highlighted, including instationary (13)C-MFA used to probe autotrophic metabolism in photosynthetic tissue in the light.
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Affiliation(s)
- Hai Shi
- Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Jörg Schwender
- Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, United States.
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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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Krach C, Junker A, Rohn H, Schreiber F, Junker BH. Flux visualization using VANTED/FluxMap. Methods Mol Biol 2015; 1191:225-33. [PMID: 25178794 DOI: 10.1007/978-1-4939-1170-7_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
The calculation of metabolic fluxes has been shown to be a valuable asset in systems biology. Several procedures are commonly used to achieve this. Flux balance analyses or metabolic flux analyses usually result in a list of reaction rates (fluxes) provided in a spreadsheet format. This makes it difficult to quickly assess general characteristics of the solution. A fast and easy mapping of these results to a graphical map template facilitates an easy visual data inspection. Here, we describe a protocol that helps in setting up user-specific network templates, mapping flux results to it, and creating multiple exportable flux maps at one time.
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Affiliation(s)
- Christian Krach
- Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK), Gatersleben, Germany
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Maarleveld TR, Wortel MT, Olivier BG, Teusink B, Bruggeman FJ. Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models. PLoS Comput Biol 2015; 11:e1004166. [PMID: 25849486 PMCID: PMC4388735 DOI: 10.1371/journal.pcbi.1004166] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Accepted: 02/02/2015] [Indexed: 01/31/2023] Open
Abstract
High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important insights into the metabolic capacities of a cell. How the feasible metabolic routes emerge from the interplay between flux constraints, optimality objectives, and the entire metabolic network of a cell is, however, only partially understood. We show how optimal metabolic routes, resulting from flux balance analysis computations, arise out of elementary flux modes, constraints, and optimization objectives. We illustrate our findings with a genome-scale stoichiometric model of Escherichia coli metabolism. In the case of one flux constraint, all feasible optimal flux routes can be derived from elementary flux modes alone. We found up to 120 million of such optimal elementary flux modes. We introduce a new computational method to compute the corner points of the optimal solution space fast and efficiently. Optimal flux routes no longer depend exclusively on elementary flux modes when we impose additional constraints; new optimal metabolic routes arise out of combinations of elementary flux modes. The solution space of feasible metabolic routes shrinks enormously when additional objectives---e.g. those related to pathway expression costs or pathway length---are introduced. In many cases, only a single metabolic route remains that is both feasible and optimal. This paper contributes to reaching a complete topological understanding of the metabolic capacity of organisms in terms of metabolic flux routes, one that is most natural to biochemists and biotechnologists studying and engineering metabolism.
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Affiliation(s)
- Timo R. Maarleveld
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU University, Amsterdam, The Netherlands
- Life Sciences, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands
| | - Meike T. Wortel
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU University, Amsterdam, The Netherlands
- Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands
| | - Brett G. Olivier
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU University, Amsterdam, The Netherlands
| | - Bas Teusink
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU University, Amsterdam, The Netherlands
- Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands
| | - Frank J. Bruggeman
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU University, Amsterdam, The Netherlands
- Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands
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Abstract
Oxygen is an indispensable substrate for many biochemical reactions in plants, including energy metabolism (respiration). Despite its importance, plants lack an active transport mechanism to distribute oxygen to all cells. Therefore, steep oxygen gradients occur within most plant tissues, which can be exacerbated by environmental perturbations that further reduce oxygen availability. Plants possess various responses to cope with spatial and temporal variations in oxygen availability, many of which involve metabolic adaptations to deal with energy crises induced by low oxygen. Responses are induced gradually when oxygen concentrations decrease and are rapidly reversed upon reoxygenation. A direct effect of the oxygen level can be observed in the stability, and thus activity, of various transcription factors that control the expression of hypoxia-induced genes. Additional signaling pathways are activated by the impact of oxygen deficiency on mitochondrial and chloroplast functioning. Here, we describe the molecular components of the oxygen-sensing pathway.
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Affiliation(s)
- Joost T van Dongen
- Institute of Biology I, Aachen Biology and Biotechnology, RWTH Aachen University, 52074 Aachen, Germany;
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Seaver SMD, Bradbury LMT, Frelin O, Zarecki R, Ruppin E, Hanson AD, Henry CS. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm. FRONTIERS IN PLANT SCIENCE 2015; 6:142. [PMID: 25806041 PMCID: PMC4354304 DOI: 10.3389/fpls.2015.00142] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 02/22/2015] [Indexed: 05/08/2023]
Abstract
There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.
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Affiliation(s)
- Samuel M. D. Seaver
- Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA
- Computation Institute, The University of ChicagoChicago, IL, USA
| | - Louis M. T. Bradbury
- Horticultural Sciences Department, University of FloridaGainesville, FL, USA
- Department of Biology, York College, City University of New YorkNew York, NY, USA
| | - Océane Frelin
- Horticultural Sciences Department, University of FloridaGainesville, FL, USA
| | - Raphy Zarecki
- Sackler Faculty of Medicine, Tel Aviv UniversityTel Aviv, Israel
| | - Eytan Ruppin
- Sackler Faculty of Medicine, Tel Aviv UniversityTel Aviv, Israel
| | - Andrew D. Hanson
- Horticultural Sciences Department, University of FloridaGainesville, FL, USA
| | - Christopher S. Henry
- Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA
- Computation Institute, The University of ChicagoChicago, IL, USA
- *Correspondence: Christopher S. Henry, Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, USA
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Colombié S, Nazaret C, Bénard C, Biais B, Mengin V, Solé M, Fouillen L, Dieuaide-Noubhani M, Mazat JP, Beauvoit B, Gibon Y. Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2015; 81:24-39. [PMID: 25279440 PMCID: PMC4309433 DOI: 10.1111/tpj.12685] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 09/19/2014] [Accepted: 09/19/2014] [Indexed: 05/18/2023]
Abstract
Modelling of metabolic networks is a powerful tool to analyse the behaviour of developing plant organs, including fruits. Guided by our current understanding of heterotrophic metabolism of plant cells, a medium-scale stoichiometric model, including the balance of co-factors and energy, was constructed in order to describe metabolic shifts that occur through the nine sequential stages of Solanum lycopersicum (tomato) fruit development. The measured concentrations of the main biomass components and the accumulated metabolites in the pericarp, determined at each stage, were fitted in order to calculate, by derivation, the corresponding external fluxes. They were used as constraints to solve the model by minimizing the internal fluxes. The distribution of the calculated fluxes of central metabolism were then analysed and compared with known metabolic behaviours. For instance, the partition of the main metabolic pathways (glycolysis, pentose phosphate pathway, etc.) was relevant throughout fruit development. We also predicted a valid import of carbon and nitrogen by the fruit, as well as a consistent CO2 release. Interestingly, the energetic balance indicates that excess ATP is dissipated just before the onset of ripening, supporting the concept of the climacteric crisis. Finally, the apparent contradiction between calculated fluxes with low values compared with measured enzyme capacities suggest a complex reprogramming of the metabolic machinery during fruit development. With a powerful set of experimental data and an accurate definition of the metabolic system, this work provides important insight into the metabolic and physiological requirements of the developing tomato fruits.
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Affiliation(s)
- Sophie Colombié
- INRAUMR 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, F-33883, France
- *For correspondence (e-mail )
| | - Christine Nazaret
- Institut de Mathématiques de Bordeaux, ENSTBB-Institut Polytechnique de Bordeaux351 Cours de la Liberation, Talence, F-33400, France
| | - Camille Bénard
- INRAUMR 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, F-33883, France
| | - Benoît Biais
- INRAUMR 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, F-33883, France
| | - Virginie Mengin
- INRAUMR 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, F-33883, France
| | - Marion Solé
- INRAUMR 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, F-33883, France
| | - Laëtitia Fouillen
- CNRS, UMR 5200Laboratoire de Biogenèse Membranaire, Villenave D'Ornon, F-33883, France
- Univ. Bordeaux146 rue Léo-Saignat, Bordeaux Cedex, F-33076, France
| | - Martine Dieuaide-Noubhani
- INRAUMR 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, F-33883, France
- Univ. Bordeaux146 rue Léo-Saignat, Bordeaux Cedex, F-33076, France
| | - Jean-Pierre Mazat
- Univ. Bordeaux146 rue Léo-Saignat, Bordeaux Cedex, F-33076, France
- IBGC-CNRS1 rue Camille Saint-Saëns, Bordeaux Cedex, F-33077, France
| | - Bertrand Beauvoit
- INRAUMR 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, F-33883, France
- Univ. Bordeaux146 rue Léo-Saignat, Bordeaux Cedex, F-33076, France
| | - Yves Gibon
- INRAUMR 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, F-33883, France
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Rezvan A, Marashi SA, Eslahchi C. FCDECOMP: Decomposition of metabolic networks based on flux coupling relations. J Bioinform Comput Biol 2014; 12:1450028. [DOI: 10.1142/s0219720014500280] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.
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Affiliation(s)
- Abolfazl Rezvan
- Department of Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
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Simons M, Saha R, Amiour N, Kumar A, Guillard L, Clément G, Miquel M, Li Z, Mouille G, Lea PJ, Hirel B, Maranas CD. Assessing the metabolic impact of nitrogen availability using a compartmentalized maize leaf genome-scale model. PLANT PHYSIOLOGY 2014; 166:1659-74. [PMID: 25248718 PMCID: PMC4226342 DOI: 10.1104/pp.114.245787] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Maize (Zea mays) is an important C4 plant due to its widespread use as a cereal and energy crop. A second-generation genome-scale metabolic model for the maize leaf was created to capture C4 carbon fixation and investigate nitrogen (N) assimilation by modeling the interactions between the bundle sheath and mesophyll cells. The model contains gene-protein-reaction relationships, elemental and charge-balanced reactions, and incorporates experimental evidence pertaining to the biomass composition, compartmentalization, and flux constraints. Condition-specific biomass descriptions were introduced that account for amino acids, fatty acids, soluble sugars, proteins, chlorophyll, lignocellulose, and nucleic acids as experimentally measured biomass constituents. Compartmentalization of the model is based on proteomic/transcriptomic data and literature evidence. With the incorporation of information from the MetaCrop and MaizeCyc databases, this updated model spans 5,824 genes, 8,525 reactions, and 9,153 metabolites, an increase of approximately 4 times the size of the earlier iRS1563 model. Transcriptomic and proteomic data have also been used to introduce regulatory constraints in the model to simulate an N-limited condition and mutants deficient in glutamine synthetase, gln1-3 and gln1-4. Model-predicted results achieved 90% accuracy when comparing the wild type grown under an N-complete condition with the wild type grown under an N-deficient condition.
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Affiliation(s)
- Margaret Simons
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Rajib Saha
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Nardjis Amiour
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Akhil Kumar
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Lenaïg Guillard
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Gilles Clément
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Martine Miquel
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Zhenni Li
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Gregory Mouille
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Peter J Lea
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Bertrand Hirel
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
| | - Costas D Maranas
- Departments of Chemical Engineering (M.S., R.S., C.D.M.) and Bioinformatics and Genomics, Huck Institutes of the Life Sciences (A.K.), Pennsylvania State University, University Park, Pennsylvania 16802;Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique, Centre de Versailles-Grignon, Unité Mixte de Recherche 1318 Institut National de la Recherche Agronomique-Agro-ParisTech, Equipe de Recherce Labellisée, Centre National de la Recherche Scientifique 3559, F-78026 Versailles cedex, France (N.A., L.G., G.C., M.M., Z.L., G.M., B.H.); andLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom (P.J.L.)
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Simons M, Saha R, Guillard L, Clément G, Armengaud P, Cañas R, Maranas CD, Lea PJ, Hirel B. Nitrogen-use efficiency in maize (Zea mays L.): from 'omics' studies to metabolic modelling. JOURNAL OF EXPERIMENTAL BOTANY 2014; 65:5657-71. [PMID: 24863438 DOI: 10.1093/jxb/eru227] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
In this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when integrated with 'omics' data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.
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Affiliation(s)
- Margaret Simons
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rajib Saha
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Lenaïg Guillard
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Gilles Clément
- Plateau Technique Spécifique de Chimie du Végétal, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Route de St Cyr, F-78026 Versailles Cedex, France
| | - Patrick Armengaud
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Rafael Cañas
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Peter J Lea
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
| | - Bertrand Hirel
- Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
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Baghalian K, Hajirezaei MR, Schreiber F. Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering. THE PLANT CELL 2014; 26:3847-66. [PMID: 25344492 PMCID: PMC4247579 DOI: 10.1105/tpc.114.130328] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.
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Affiliation(s)
- Kambiz Baghalian
- Leibniz Institute of Plant Genetics and Crop Plant Research, D-06466 Gatersleben, Germany Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle, Germany College of Agriculture and Natural Resources, Islamic Azad University-Karaj Branch, Karaj 31485-313, Iran
| | | | - Falk Schreiber
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle, Germany Faculty of IT, Monash University, Clayton, VIC 3800, Australia
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Sweetlove LJ, Obata T, Fernie AR. Systems analysis of metabolic phenotypes: what have we learnt? TRENDS IN PLANT SCIENCE 2014; 19:222-30. [PMID: 24139444 DOI: 10.1016/j.tplants.2013.09.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Revised: 09/12/2013] [Accepted: 09/18/2013] [Indexed: 05/26/2023]
Abstract
Flux is one of the most informative measures of metabolic behavior. Its estimation requires integration of experimental and modeling approaches and, thus, is at the heart of metabolic systems biology. In this review, we argue that flux analysis and modeling of a range of plant systems points to the importance of the supply of metabolic inputs and demand for metabolic end-products as key drivers of metabolic behavior. This has implications for metabolic engineering, and the use of in silico models will be important to help design more effective engineering strategies. We also consider the importance of cell type-specific metabolism and the challenges of characterizing metabolism at this resolution. A combination of new measurement technologies and modeling approaches is bringing us closer to integrating metabolic behavior with whole-plant physiology and growth.
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Affiliation(s)
- Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK.
| | - Toshihiro Obata
- Max-Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max-Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany.
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Junker BH. Flux analysis in plant metabolic networks: increasing throughput and coverage. Curr Opin Biotechnol 2014; 26:183-8. [PMID: 24561560 DOI: 10.1016/j.copbio.2014.01.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Revised: 01/27/2014] [Accepted: 01/27/2014] [Indexed: 12/17/2022]
Abstract
Quantitative information about metabolic networks has been mainly obtained at the level of metabolite contents, transcript abundance, and enzyme activities. However, the active process of metabolism is represented by the flow of matter through the pathways. These metabolic fluxes can be predicted by Flux Balance Analysis or determined experimentally by (13)C-Metabolic Flux Analysis. These relatively complicated and time-consuming methods have recently seen significant improvements at the level of coverage and throughput. Metabolic models have developed from single cell models into whole-organism dynamic models. Advances in lab automation and data handling have significantly increased the throughput of flux measurements. This review summarizes advances to increase coverage and throughput of metabolic flux analysis in plants.
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Affiliation(s)
- Björn H Junker
- Institute of Pharmacy, Martin-Luther-University, Hoher Weg 8, 06120 Halle, Germany.
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Hay JO, Schwender J. Flux variability analysis: application to developing oilseed rape embryos using toolboxes for constraint-based modeling. Methods Mol Biol 2014; 1090:301-316. [PMID: 24222423 DOI: 10.1007/978-1-62703-688-7_18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Flux variability analysis enables comprehensive exploration of alternate optimal routes in a metabolic network. This method is especially useful with models such as bna572 for the developing oilseed rape embryo which is highly compartmentalized. Here, we describe a protocol for carrying out flux variability analysis on reactions and network projections of bna572 using well-established software (CellNetAnalyzer and COBRA) for constraint-based analysis of stoichiometric network reconstructions.
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Affiliation(s)
- Jordan O Hay
- Biosciences Department, Brookhaven National Laboratory, Upton, NY, USA
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Hay JO, Shi H, Heinzel N, Hebbelmann I, Rolletschek H, Schwender J. Integration of a constraint-based metabolic model of Brassica napus developing seeds with (13)C-metabolic flux analysis. FRONTIERS IN PLANT SCIENCE 2014; 5:724. [PMID: 25566296 PMCID: PMC4271587 DOI: 10.3389/fpls.2014.00724] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 12/01/2014] [Indexed: 05/19/2023]
Abstract
The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for (13)C-Metabolic Flux Analysis ((13)C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from (13)C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.
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Affiliation(s)
- Jordan O. Hay
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Hai Shi
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Nicolas Heinzel
- Department of Molecular Genetics, Leibniz-Institut für Pflanzengenetik und KulturpflanzenforschungGatersleben, Germany
| | - Inga Hebbelmann
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Hardy Rolletschek
- Department of Molecular Genetics, Leibniz-Institut für Pflanzengenetik und KulturpflanzenforschungGatersleben, Germany
| | - Jorg Schwender
- Biological, Environment and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
- *Correspondence: Jorg Schwender, Brookhaven National Laboratory, Biological, Environment and Climate Sciences Department, Building 463, Upton, NY 11973, USA e-mail:
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