CR26-BAP-1 - The Fruit Biology and Pathology joint research unit offers a multidisciplinary environment (understanding of the underlying mechanisms of plant development or their interactions with pathogens or with the environment through to the application of this knowledge in translational research). The unit has a range of experimental facilities dedicated to growing and phenotyping plants. MeTA is a highly multidisciplinary team using approaches combining experimentation and modelling to study plant metabolism. Moreover, the team hosts the Bordeaux Metabolome platform that provides the tools necessary to explore metabolomic diversity. You will also benefit from its affiliation to the INBS MetaboHUB (metabolomics) and PHENOME (phenomic and phenotyping) networks.The metabolome gathers information about metabolic functioning that is at the heart of all living processes. Consequently, metabolomic analyses of biodiversity combined with artificial intelligence show the highly predictive nature of the metabolome, particularly during adaptation to extreme habitats. Metabolomics has been used to predict yield and understand the basics, and study plant/pest interactions and multi-stress responses. Your role will be to study agroecosystems using approaches combining metabolomics and modelling to update the mechanisms governing the relationships between organisms (plants, insects, microbiota, etc.) and/or determine ecosystem services. The ultimate purpose will be to develop predictive models dedicated to the ecological transition of agriculture.The project extends the understanding of links between metabolism programming and trade-offs with plant performance to agroecology, from plant to ecosystem scale.Your short-term objective will be to formulate a strategy drawing on the bibliography and to establish a network of national and international collaborations to identify high-performance agroecosystems adapted to different contexts or, on the contrary, to imbalanced agroecosystems. This is an essential step to fine-tune the project objectives. It concerns more particularly the identification of ecosystem services that will be targeted by modelling. Your medium-term objectives will be to conduct experiments on agroecosystems comprising various compartments (plants, microorganisms, soil, air, etc.) to obtain climatic and physiological data, assess ecosystem services, and collect samples from each compartment. Reproducible and interoperable data from organism metabolomes and phenomes could be obtained thanks to numerous possibilities provided by the Bordeaux Metabolome Platform, particularly high-throughput combined with high-resolution mass spectrometry. Statistical analyses and predictive models could be developed to establish links between metabolism and ecosystem services.Your long-term objectives will be to understand the metabolic and physiological mechanisms involved in agroecological system dynamics. Functional genomics approaches could be used to validate the strong hypotheses generated by modelling thanks to collaborations with other researchers from the team and the unit. The purpose of the project will be to propose new strategies for agriculture, such as decision-making tools based on predictive metabolomics.

Motivation Metabolic networks represent genome-derived information about the biochemical reactions that cells are capable of performing. Mapping omic data onto these networks is important to refine mo...

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DOSSIER DE PRESSE - Le programme France 2030 « Accélération de la recherche à risque en Agriculture, Alimentation et Environnement » EXPLOR’AE offre un cadre inédit pour soutenir des projets de recher...

Pathway analysis, originally developed for gene expression data, has been adapted for metabolomics. However, owing to the unique characteristics and constraints of metabolites compared with genes, pat...

Grantee accounts remain frozen, while union accuses NSF of ignoring rules governing peer review

AbstractBackground. Descriptive metadata are vital for reporting, discovering, leveraging, and mobilizing research datasets. However, resolving metadata is