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Primary metabolism provides energy and building blocks for growth, maintenance, and adaptation to the environment. Whereas the framework of primary metabolism is well known and significant progress has been made toward understanding the regulation of individual metabolic reactions, we know little regarding the control of metabolic fluxes. Our goal is to broaden this knowledge in order to produce better plants, particularly with respect to fruit quality.
We employ two general approaches to study metabolism and its influence on plant performance. One is Systems Biology to search for metabolic features and, subsequently, their underlying genetic control. The other entails a direct screen for metabolic markers associated with plant traits.
Systems Biology: We exploit our diverse range of skills in analytical chemistry, molecular biology, biochemistry, physiology, statistics and bioinformatics to combine both experimentation and modelling. We develop models to predict the metabolic composition of fruits from molecular, physiological and environmental data. The Eranet ERASysBio+ FRIM project (2010-2013) that we coordinated initiated the enormous task of modelling the metabolic behaviour of developing fruit. Although we focus on tomato, we also employ other model systems, such as melon, peach, grape, corn, wheat, and sunflower. We are convinced that comparing multiple systems will yield a better understand on how the organisation and control of primary metabolism determines crop and plant performance. The remit of our recently awarded ANR FRIMOUSS project (2015-2019) is to compare the primary metabolism of the fruit of 10 different species. This project will permit us to investigate a wide range of fruit characteristics, such as taste, form and texture. Furthermore, we can address fundamental questions on the nature of fruit bearing plants, such as the speed of fruit development and the advantages and disadvantages of climacteric development, which varies within botanical families and even between closely related species. We have assembled a strong community of collaborative partners to undertake this comprehensive research program.
Biomarkers for plant performance: We work under the postulate that metabolism contributes to plant performance. Therefore, we are taking the logic approach to look for metabolic features, which alone or in combination, influence plant traits, such as growth rate, yield, quality, or resistance to abiotic or biotic stresses. This approach is built around the Bordeaux Metabolome Platform, a state-of-the-art metabolomics facility that is constantly updated and strengthened through new technical developments. Our current biomarker screening focuses on the crops corn and sunflower, but we plan to address other issues involving metabolism and biodiversity in relation to the FRIMOUSS project. A critical, subsequent step will be to collaborate with geneticists in order to unravel the genetic control of these markers and to design selection strategies that employ them directly.
We are organized into work groups covering the different aspects of our project. Each module is led by one or two members of the team and most team members contribute to several modules. The following information outlines these work groups.
Phenotyping. In order to obtain the data needed for the development, parameterization and validation of our models, we conduct an annual phenotyping campaign with up to several hundreds of plants grown under conditions simulating the production environment or growth under duress. This interest has led to a collaboration with INVENIO, which is a regional research and experimentation centre for professionals growing fruits and vegetables. Climate and ecophysiological data are recorded, and samples of leaves, stems and fruits are collected throughout the culture period to study metabolism.
In collaboration with Macha Nikolsky and Benjamin Dartigues at the Bordeaux Bioinformatics Centre, we are continuing to develop ‘Xeml Lab’, a program originally developed at the Max Planck Institute of Molecular Plant Physiology (Hannemann et al, 2009). This software package will enable the capture and mining of all numerical data and metadata that describes the samples, including climate data, genotype, physiology, and developmental stage of the samples. Further tools for analysis and integration have been adapted or developed de novo (http://biostatflow.org/).
Data for gene expression, protein levels, enzyme activities and metabolite levels have been collected at a number of stages throughout the development of tomato fruit. We have used RNA-seq and label-free detection to quantify transcripts and proteins, respectively. The proteomic studies are being conducted in collaboration with the team of Michel Zivy and PAPPSO at INRA-Moulon. These studies will complement the physiological, enzymatic and metabolomic data collected by the team to enhance our understanding of the integration of molecular processes during fruit development. We continuously adapt or develop new methods for metabolites and enzyme activities, which are then made available for the Bordeaux Metabolome Platform users. For example, we are currently exploring possibilities offered by microfluidics in collaboration with Jean-Christophe Baret and his group at the CRPP Bordeaux.
Various types of large data sets relating to physiology, transcription, protein levels, enzyme activity and metabolite levels, etc. are then subjected to multivariate statistics and network analyses to reveal possible interactions. Such data integration facilitates the creation of statistical models and their underlying constraints and assumptions that are necessary for describing complex biological systems (Biais et al. 2014). The whole group is involved.
Data and metadata.The data describing the experiments, when combined with the multiple phenotypic datasets, will enable data mining. Collected at different levels (physiological, transcripts, proteins, enzymes, metabolites) they are networked to facilitate their analysis and integration. Tools for their analysis and integration are adapted or developed (http://biostatflow.org/). The integration of the data allows us to go deeper into the description of the system under study (Biais et al., 2014) and to formulate hypotheses underlying the development of predictive models of this system. Daniel Jacob.
Subcellular compartmentation. To better understand the critical role of compartmentation in the control of metabolic fluxes it is essential to quantify metabolites in the different compartments of the cell. For this, a dry fractionation approach is developed in collaboration with Ana Paula Alonso (Ohio State University, USA). Combined with carbon 13 labelling experiments, this approach will enable the quantification subcellular fluxes. Martine Dieuaide-Noubhani.
Mathematical Modelling. In collaboration with Jean -Pierre Mazat (University of Bordeaux) and Christine Nazaret (Mathematics Institute of Bordeaux), we are employing various mathematical approaches to predict and describe key processes of fruit development. In particular, modelling is a tool for discovering emergent properties of the systems under consideration.
• Kinetic modelling of the metabolism of major sugars and organic acids
• Flux balance analysis of central metabolism
• Modelling of protein synthesis
Sophie Colombié and Bertrand Beauvoit
Biomarkers. Through a combination of metabolomics, metabolic phenotyping (metabolites, enzyme activities, combinations of metabolites and/or enzymatic activities), evaluation of agronomic traits and statistics we look for metabolic markers associated with the performance of the plant. This work is conducted under the ANR projects AMAIZING (Maize), BREDWHEAT (Wheat) and SUNRISE (Sunflower). Annick Moing.
2015-2019. FRIMOUSS (Fruit Integrative Modelling for a Unified Selection System; ANR-15-CE20-0009), 2 partners (UMR EGFV INRA Bordeaux and UR PSH INRA-Avignon).
2012-2019. BREEDWHEAT (Breeding for economically and environmentally sustainable wheat varieties; ANR-10-BTBR-03), which involves 13 INRA units, Arvalis and 11 private companies, coordinated by Jacques Le Gouis (INRA Clermont-Ferrand).
2012-2019. AMAIZING (Breeding for economically and environmentally sustainable maize varieties: an integrated approach from genomics to selection; ANR-10-BTBR-03), 14 INRA units, Arvalis and 9 private companies, coordinated by Alain Charcosset (INRA Le Moulon).
2012-2019. SUNRISE (Improve sunflower oil production in a sustainable manner; ANR-10-BTBR-03), 9 INRA units, CETIOM and 6 private companies, coordinated by Nicolas Langlade (INRA-Toulouse).
2012-2019. PHENOME (the French plant phenomic network; ANR-11-INBS-0012), 9 INRA units and 2 technical centres involved, coordinated by François Tardieu (LEPSE, INRA-Montpellier).
2013-2019. MetaboHUB (The French National infrastructure for metabolomics and fluxomics; ANR-11-INBS-0012), 4 metabolomics platforms (Bordeaux, Toulouse, Clermont-Ferrand, Saclay-Paris), coordinated by team member Dominique Rolin.
Because of its strong contribution to the Bordeaux Metabolome Platform of the Bordeaux Functional Genomics Centre, our team is involved in many other regional, national and international projects and participates in the Francophone Network of Metabolomics and Fluxomics (RFMF), and performs analytical and bioinformatics developments for the platform. Two facilities are hosted by the group:
- Metabolomics and fluxomics (MAHD, led by Pierre Petriacq)
- Metabolic Phenotyping (HiTMe, led by Yves Gibon)
Team member Dominique Rolin coordinates the ANR project MetaboHUB and we participate in the PHENOME project (coordinated by François Tardieu, INRA- Montpellier).
The platform is led by team member Pierre Petriacq (scientific management).