Team Metabolism - META

Team Metabolism - META

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Team Leader : Yves Gibon (yves.gibon@inrae.fr)

Presentation and research objectives

Meta is a multidisciplinary team (analytical chemistry, biochemistry, molecular biology, physiology, statistics and bioinformatics) interested in metabolism and the way it participates in the performance of plants, whether in terms of yield or adaptation to abiotic and biotic stress. For this, we use Systems Biology approaches alternating experimentation and modelling. We thus generate large datasets that we use to develop, parameterise and validate predictive models that allow us to better understand the functioning of plants, in particular that of the fruit. With bottom-up mathematical models (from mechanisms to the phenotype) we try to understand how the fluxes and concentrations of metabolites are controlled. With top-down models (from the phenotype to mechanisms) we are looking for metabolic markers associated with performance, the first step towards identifying the underlying mechanisms. While in recent years we have focused on central metabolism, now we are also interested in redox metabolism as well as secondary metabolism, in particular because of their involvement in adaptation to the environment.

Meta Team

Experimental base

Our experimental strategy consists of detailed experiments (temporal dynamics, diversity of species and/or genotypes) carried out under realistic conditions, in our greenhouses or in partnership with phenotyping platforms or even technical institutes. We collect as much information as possible on the environmental conditions and physiology of plants and fruits, and we take large collections of samples according to standardised procedures (collection, transport, storage) that we use to perform multilevel analyses (transcriptome, proteome, activome, metabolome, composition of biomass).

The team is behind Bordeaux Metabolome, a shared analytical platform created in 2003, on which we perform targeted or non-targeted analyses of the metabolome, the latter being able to be supplemented by annotation or even structural analyses to identify metabolites. We have an almost unique platform dedicated to robotic profiling of enzyme activities and we have recently started building a microfluidics platform as well as a platform dedicated to redox metabolism. We are continuously developing new tools intended for the study of metabolism: equations and a robotic optimisation protocol for the analysis of enzymatic activities (Bénard & Gibon 2016), NMR-ProcFlow for the processing of NMR data (Jacob et al. 2017), BioStatFlow for statistical analysis of metabolomic data (Jacob et al. 2020), Maggot and ODAM for the organization and future exploitation of FAIR data, and EasyReg for fitting mathematical functions on the data.

With the ERASysBio + FRIM project (2010-2013) we described the metabolism of tomato fruit in detail (Biais et al. 2014), an approach extended to 9 other fruit species with the ANR FRIMOUSS project (2015-2020; Roch et al. . 2020). With the INRAE BAP CLIMAX and EU H2020 GLOMICAVE projects we are now completing the dataset with transcriptomics and proteomics to study fruit metabolism from new angles.
Involved in the ANR PIA MetaboHUB and PHENOME projects, respectively dedicated to metabolomics and to plant phenotyping, we have developed a pipeline combining robotics and mass spectrometry in order to be able to process hundreds or even thousands of samples in order to search for metabolic markers associated with performance and to build predictive metabolomic models.

Meta team

Pipetting robot acquired in the ANR PIA PHENOME project

Meta Team

Kiwi orchard used in the ANR FRIMOUSS project

Research themes

1. “Bottom-up” modelling (from mechanisms to phenotype)

We seek to better understand how metabolism works, in particular the trade-offs between growth and quality and how environmental factors (biotic and abiotic) are integrated. By conducting virtual experiments, we are looking for ways to manipulate the accumulation of metabolites of interest (food quality, defence against biotic or abiotic stress, etc.) or the rate of growth (control of metabolic fluxes, link between protein turnover and growth, etc.). Our objective is to propose new ideotypes or agricultural practices based on predictions obtained from mechanistic models.

Through the development of mathematical models combined with experimental data, we seek to predict or understand biological events. In tomato, this approach allowed us to show the importance of vacuolar transport of sugars in very young fruit (Beauvoit et al. 2014; Shinozaki et al. 2020), to predict changes in the fluxes of the central metabolism (Colombié et al. al 2015) as well as the role of starch and cell wall degradation before maturation, coinciding with the climacteric crisis (Colombié et al. 2017). More recently, by integrating transcriptomic and proteomic data, we estimated the turnover of more than 1000 tomato fruit proteins (Belouah et al. 2019) and characterized the regulation of redox metabolism during its development (Decros et al 2019a).

We are also convinced that the comparison of biological systems will allow us to better understand the way in which the programming of metabolism influences the performance of plants. Also with the ANR FRIMOUSS project (2015-2019) we are now comparing the fruits of 10 species (Beauvoit et al. 2018).

Meta team

Flow map of the central metabolism in early ripening tomato fruit, obtained by stoichiometric modelling (Colombié et al. 2017)

Meta team

Reconstruction of the genome-wide metabolic network in camelina (Prigent et al. unpublished)

2. Metabolic markers of performance

Because the metabolome of plants contains information about their biology, we assume that it can be used to predict traits such as yield or resistance to stress (Fernandez et al. 2020). By relying on the Bordeaux Metabolome Platform, we are looking for metabolic markers (alone or in combination) associated with traits such as growth rate, yield, quality, or resistance to abiotic and biotic stress. With statistical tools we combine metabolic data (targeted or non-targeted measurements of metabolites, thousands of metabolite signatures, major biomass compounds, and/or enzymatic activities) and agronomic data (traits measured in the field or in the greenhouse, or even on phenotyping platforms) for panels of tens or hundreds of genotypes. Thus, classic univariate and multivariate analyses with or without a priori and modelling or even machine learning approaches are used to update metabolic markers associated with the prediction of plant performance. This work is carried out mainly on plants of agronomic interest, in particular maize (ANR PIA AMAIZING, Lamari et al. 2018), sunflower (ANR PIA SUNRISE, Fernandez et al. 2019)), wheat (ANR PIA BREEDWHEAT) and camelina (EU H2020 UNTWIST), but also in other projects involving metabolism, biodiversity and adaptation. The next step is, in collaboration with geneticists and modellers, to identify the genetic bases that underlie the control of metabolic markers, or to design selection strategies using these markers directly, after having validated them on several experiments. .

Meta team

Volcano plot showing MS signatures of response to a culture condition (Lamari et al. 2018)

Meta team

Prediction of an agronomic trait from biochemical phenotyping data (Prigent et al. in preparation)

3. Redox metabolism and performance

Redox metabolism, and more generally antioxidants, will play a major role in the agriculture of the future because their involvement in the adaptation and acclimation of plants to the environment will increase as the use of pesticides or fertilizers will decrease or even disappear. While decades of research have investigated redox homeostasis in leaves, little work has focused on fruit, which is surprising considering that fruits are a major source of antioxidants for the diet.

We aim to better understand how signalling and detoxification reconcile in developmental processes and biotic or abiotic stress responses. One of our goals is to better understand why domestication has led to a decrease in antioxidants in fleshy fruits.

We are developing a redox platform, a unique tool giving the possibility of high-throughput measurement of multiple biochemical markers of redox metabolism including reactive oxygen species and the main redox buffers and enzymes. This platform develops and implements targeted biochemical analyses and mass spectrometry protocols including the quantification and spatial distribution of redox actors.

Meta team

Plant redox metabolism (Decros et al. 2019b)

Meta team

Central role of redox signalling during fruit development (Decros et al. 2019b)

 

 

 

 

 

Références

Beauvoit B, Colombié S, Monier A, Andrieu M-H, Biais B, Bénard C, Cheniclet C, Dieuaide Noubhani M, Nazaret C, Mazat J-P, Gibon Y (2014) Model-Assisted Analysis of Sugar Metabolism throughout Tomato Fruit Development Reveals Enzyme and Carrier Properties in Relation to Vacuole Expansion. The Plant Cell 26: 3224-3242, doi: 10.1105/tpc.114.127761

Beauvoit B, Belouah I, Bertin N, Cakpo CB, Colombié S, Dai Z, Gautier H, Génard M, Moing A, Roch L, Vercambre G, Gibon Y (2018) Putting primary metabolism into perspective to obtain better fruits. Annals of Botany 122: 1-21, doi: 10.1093/aob/mcy057

Bénard C, Gibon Y (2016) Measurement of enzyme activities and optimization of continuous and discontinuous assays. Current Protocols in Plant Biology 1: 247-262, doi: 10.1002/cppb.20003

Belouah I, Nazaret C, Pétriacq P, Prigent S, Bénard C, Mengin V, Blein-Nicolas M, Denton AK, Balliau T, Augé S, Bouchez O, Mazat J-P, Stitt M, Usadel B, Zivy M, Beauvoit B, Gibon Y, Colombié S (2019) Modeling Protein Destiny in Developing Fruit. Plant Physiology, doi: https://doi.org/10.1104/pp.19.00086

Biais B, Bénard C, Beauvoit B, Colombié S, Prodhomme D, Ménard G, Bernillon S, Gehl B, Gautier H, Ballias P, Mazat J-P, Sweetlove LJ, Génard M, Gibon Y (2014) Remarkable reproducibility of enzyme activity profiles in tomato fruits grown under contrasting environments provides a roadmap for studies of fruit metabolism. Plant Physiology 164: 1204-1221, doi: 10.1104/pp.113.231241

Colombié S, Beauvoit B, Nazaret C, Benard C, Vercambre G, Le Gall S, Biais B, Cabasson C, Maucourt M, Bernillon S, Moing A, Dieuaide-Noubhani M, Mazat J-P, Gibon Y (2017). Respiration climacteric in tomato fruits elucidated by constraint-based modelling. New Phytologist 213:1726-1739, doi: 10.1111/nph.14301

Colombié S, Nazaret C, Bénard C, Biais B, Mengin V, Solé M, Fouillen L, Dieuaide Noubhani M, Mazat J-P, Beauvoit B, Gibon Y (2015). Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit. Plant Journal 81: 24-39, doi: 10.1111/tpj.12685

Decros G, Beauvoit B, Colombié S, Cabasson C, Bernillon S, Arrivault S, Guenther M, Prigent S, Gibon Y, Pétriacq P (2019a) Regulation of pyridine nucleotides metabolism along tomato fruit development through transcript and protein profiling. Frontiers in Plant Science 10: 1201, doi: 10.3389/fpls.2019.01201

Decros G, Baldet P, Beauvoit B, Stevens R, Flandin A, Colombié S, Gibon Y, Pétriacq P (2019b). Get the Balance Right: ROS Homeostasis and Redox Signalling in Fruit. Frontiers in Plant Science 10: 1091, doi: 10.3389/fpls.2019.01091

Fernandez O, Urrutia M, Berton T, Bernillon S, Deborde C, Jacob D, Maucourt M, Maury P, Duruflé H, Gibon Y, Langlade NB, Moing A (2019). Metabolomic characterization of sunflower leaf allows discriminating genotype groups or stress levels with a minimal set of metabolic markers. Metabolomics 15: 56, doi: 10.1007/s11306-019-1515-4

Fernandez O, Millet E, Rincent R, Prigent S, Pétriacq P, Gibon Y (2020) Plant metabolomics and breeding. In Plant Metabolomics, Pétriacq P & Bouchereau A (eds). Advances in Botanical Research series. doi.org/10.1016/bs.abr.2020.09.020.

Jacob D, Deborde C, Lefebvre M, Maucourt M, Moing A (2017). NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics. Metabolomics, 13: 36, doi: 10.1007/s11306-017-1178-y

Jacob D, Deborde C, Moing A (2020) BioStatFlow -Statistical Analysis Workflow for "Omics" Data. ArXiv preprint, 2007.04599.

Lamari N, Zhendre V, Urrutia M, Bernillon S, Maucourt M, Deborde C, Prodhomme D, Jacob D, Ballias P, Rolin D, Sellier H, Rabier D, Gibon Y, Giauffret C, Moing A (2018) Metabotyping of 30 maize hybrids under early-sowing conditions reveals potential marker-metabolites for breeding. Metabolomics 14: 132, doi: 10.1007/s11306-018-1427-8

Roch L, Prigent P, Klose H, Cakpo C-B, 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 (2020) Biomass composition explains fruit relative growth rate and discriminates climacteric from non-climacteric species. Journal of Experimental Botany 19: 5823-5836, doi: 10.1093/jxb/eraa302

Shinozaki Y, Beauvoit BP, Takahara M, Hao S, Ezura K, Andrieu M-H, Nishida K, Mori K, Suzuki Y, Kuhara S, Enomoto H, Kusano M, Fukushima A, Mori T, Kojima M, Kobayashi M, Sakakibara H, Saito K, Ohtani Y, Bénard C, Prodhomme D, Gibon Y, Ezura H, Ariizumi T (2020) Fruit setting rewires central metabolism via gibberellin cascades. PNAS 117 : 23970-23981, doi: 10.1073/pnas.2011859117

See also

Modification date: 12 April 2024 | Publication date: 29 April 2011 | By: Muriel Gauthier