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Del Giúdice LZ, Falquetto-Gomes P, de Almeida Costa PM, Martins AO, Omena-Garcia RP, Araújo WL, Zsögön A, Picoli EADT, Nunes-Nesi A. Dynamic shifts in primary metabolism across fruit development stages in Capsicum chinense (cv. Habanero). J Plant Physiol 2023; 291:154121. [PMID: 37924627 DOI: 10.1016/j.jplph.2023.154121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 11/06/2023]
Abstract
The development of fleshy fruits involves changes in size and mass, followed by cell differentiation, which is associated with anatomical and histological changes. Parallel to these changes, metabolic alterations lead to the production of osmolytes and energy that modify cell turgor pressure, thereby promoting cell expansion and fruit growth. Detailed information is known about these processes in climacteric fruits (e.g. tomato); however, the regulation of metabolism and its association with anatomical changes in non-climacteric fruit development are poorly understood. In this study, we used detailed anatomical and histological analyses to define three developmental phases of chili pepper (Capsicum chinense cv. Habanero): cell division, cell expansion, and ripening. We showed that each was marked by distinct metabolic profiles, underpinning the switches in energy metabolism to support cellular processes. Interestingly, mitochondrial activity was high in the early stages of development and declined over time, with a modest increase in O2 consumption by pericarp tissues at the beginning of the ripening stage. This respiratory-like burst was associated with the degradation of starch and malate, which are the sources of energy and carbon required for other processes associated with fruit maturation.
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Affiliation(s)
- Luciana Zacour Del Giúdice
- National Institute of Science and Technology on Plant Physiology Under Stress Conditions, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil
| | - Priscilla Falquetto-Gomes
- National Institute of Science and Technology on Plant Physiology Under Stress Conditions, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil
| | | | - Auxiliadora O Martins
- National Institute of Science and Technology on Plant Physiology Under Stress Conditions, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil
| | - Rebeca Patrícia Omena-Garcia
- National Institute of Science and Technology on Plant Physiology Under Stress Conditions, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil
| | - Wagner L Araújo
- National Institute of Science and Technology on Plant Physiology Under Stress Conditions, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil
| | - Agustín Zsögön
- National Institute of Science and Technology on Plant Physiology Under Stress Conditions, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil
| | | | - Adriano Nunes-Nesi
- National Institute of Science and Technology on Plant Physiology Under Stress Conditions, Departamento de Biologia Vegetal, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil.
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Colombié S, Prigent S, Cassan C, Hilbert-Masson G, Renaud C, Dell'Aversana E, Carillo P, Moing A, Beaumont C, Beauvoit B, McCubbin T, Nielsen LK, Gibon Y. Comparative constraint-based modelling of fruit development across species highlights nitrogen metabolism in the growth-defence trade-off. Plant J 2023; 116:786-803. [PMID: 37531405 DOI: 10.1111/tpj.16409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/19/2023] [Accepted: 07/21/2023] [Indexed: 08/04/2023]
Abstract
Although primary metabolism is well conserved across species, it is useful to explore the specificity of its network to assess the extent to which some pathways may contribute to particular outcomes. Constraint-based metabolic modelling is an established framework for predicting metabolic fluxes and phenotypes and helps to explore how the plant metabolic network delivers specific outcomes from temporal series. After describing the main physiological traits during fruit development, we confirmed the correlations between fruit relative growth rate (RGR), protein content and time to maturity. Then a constraint-based method is applied to a panel of eight fruit species with a knowledge-based metabolic model of heterotrophic cells describing a generic metabolic network of primary metabolism. The metabolic fluxes are estimated by constraining the model using a large set of metabolites and compounds quantified throughout fruit development. Multivariate analyses showed a clear common pattern of flux distribution during fruit development with differences between fast- and slow-growing fruits. Only the latter fruits mobilise the tricarboxylic acid cycle in addition to glycolysis, leading to a higher rate of respiration. More surprisingly, to balance nitrogen, the model suggests, on the one hand, nitrogen uptake by nitrate reductase to support a high RGR at early stages of cucumber and, on the other hand, the accumulation of alkaloids during ripening of pepper and eggplant. Finally, building virtual fruits by combining 12 biomass compounds shows that the growth-defence trade-off is supported mainly by cell wall synthesis for fast-growing fruits and by total polyphenols accumulation for slow-growing fruits.
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Affiliation(s)
- Sophie Colombié
- Univ. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Sylvain Prigent
- Univ. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Cédric Cassan
- Univ. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Ghislaine Hilbert-Masson
- EGFV, Université de Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, 33882, Villenave d'Ornon, France
| | - Christel Renaud
- EGFV, Université de Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, 33882, Villenave d'Ornon, France
| | - Emilia Dell'Aversana
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "Luigi Vanvitelli", Via Vivaldi 43, 81100, Caserta, Italy
| | - Petronia Carillo
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "Luigi Vanvitelli", Via Vivaldi 43, 81100, Caserta, Italy
| | - Annick Moing
- Univ. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Chloé Beaumont
- Univ. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France
| | - Bertrand Beauvoit
- Univ. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France
| | - Tim McCubbin
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD, 4072, Australia
| | - Lars Keld Nielsen
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD, 4072, Australia
| | - Yves Gibon
- Univ. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
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Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. Plant Phenomics 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
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Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
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Zhou H, Kang S, Génard M, Vercambre G, Chen J. Integrated model simulates bigger, sweeter tomatoes under changing climate under reduced nitrogen and water input. Hortic Res 2023; 10:uhad045. [PMID: 37200840 PMCID: PMC10186270 DOI: 10.1093/hr/uhad045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/05/2023] [Indexed: 05/20/2023]
Abstract
When simulating the response of fruit growth and quality to environmental factors and cultivation practices, the interactions between the mother plant and fruit need to be considered as a whole system. Here, we developed the integrative Tomato plant and fruit Growth and Fruit Sugar metabolism (TGFS) model by coupling equations describing the biophysical processes of leaf gas exchange, water transport, carbon allocation, organ growth and fruit sugar metabolism. The model also accounts for effects of soil nitrogen and atmospheric CO2 concentration on gaseous exchange of water and carbon by the leaf. With different nitrogen and water input values, TGFS performed well at simulating the dry mass of the tomato leaf, stem, root, and fruit, and the concentrations of soluble sugar and starch in fruit. TGFS simulations showed that increasing air temperature and CO2 concentration has positive effects on fruit growth, but not on sugar concentrations. Further model-based analyses of cultivation scenarios suggest that, in the context of climate change, decreasing N by 15%-25% and decreasing irrigation by 10%-20% relative to current levels would increase tomato fresh weight by 27.8%-36.4% while increasing soluble sugar concentration by up to 10%. TGFS provides a promising tool to optimise N and water inputs for sustainable high-quality tomatoes.
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Affiliation(s)
- Huiping Zhou
- Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China
- National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733009, China
| | - Shaozhong Kang
- Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China
- National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733009, China
| | - Michel Génard
- INRAE, UR 1115 Plantes et Systèmes de Culture Horticoles, Avignon Cedex 9 F-84914, France
| | - Gilles Vercambre
- INRAE, UR 1115 Plantes et Systèmes de Culture Horticoles, Avignon Cedex 9 F-84914, France
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Roch L, Prigent S, Klose H, Cakpo CB, Beauvoit B, Deborde C, Fouillen L, van Delft P, Jacob D, Usadel B, Dai Z, Génard M, Vercambre G, Colombié S, Moing A, Gibon Y. Biomass composition explains fruit relative growth rate and discriminates climacteric from non-climacteric species. J Exp Bot 2020; 71:5823-5836. [PMID: 32592486 PMCID: PMC7540837 DOI: 10.1093/jxb/eraa302] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/23/2020] [Indexed: 05/13/2023]
Abstract
Fleshy fruits are very varied, whether in terms of their composition, physiology, or rate and duration of growth. To understand the mechanisms that link metabolism to phenotypes, which would help the targeting of breeding strategies, we compared eight fleshy fruit species during development and ripening. Three herbaceous (eggplant, pepper, and cucumber), three tree (apple, peach, and clementine) and two vine (kiwifruit and grape) species were selected for their diversity. Fruit fresh weight and biomass composition, including the major soluble and insoluble components, were determined throughout fruit development and ripening. Best-fitting models of fruit weight were used to estimate relative growth rate (RGR), which was significantly correlated with several biomass components, especially protein content (R=84), stearate (R=0.72), palmitate (R=0.72), and lignocerate (R=0.68). The strong link between biomass composition and RGR was further evidenced by generalized linear models that predicted RGR with R-values exceeding 0.9. Comparison of the fruit also showed that climacteric fruit (apple, peach, kiwifruit) contained more non-cellulosic cell-wall glucose and fucose, and more starch, than non-climacteric fruit. The rate of starch net accumulation was also higher in climacteric fruit. These results suggest that the way biomass is constructed has a major influence on performance, especially growth rate.
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Affiliation(s)
- Léa Roch
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine – Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Sylvain Prigent
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine – Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Holger Klose
- Institute for Biology, BioSC, RWTH Aachen University, Worringer Weg, Aachen, Germany
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Bertrand Beauvoit
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine – Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Catherine Deborde
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine – Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
- Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Laetitia Fouillen
- Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
- UMR 5200, CNRS, Univ. Bordeaux, Laboratoire de Biogenèse Membranaire, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Pierre van Delft
- Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
- UMR 5200, CNRS, Univ. Bordeaux, Laboratoire de Biogenèse Membranaire, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Daniel Jacob
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine – Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
- Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Björn Usadel
- Institute for Biology, BioSC, RWTH Aachen University, Worringer Weg, Aachen, Germany
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Zhanwu Dai
- UMR 1287 EGFV, INRAE, Univ. Bordeaux, Bordeaux Sci Agro, Villenave d’Ornon, France
| | | | | | - Sophie Colombié
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine – Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Annick Moing
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine – Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
- Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine – Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
- Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d’Ornon, France
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