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Content archived on 2024-05-29

An integrated, interdisciplinary approach to Nod factor perception

Objective

The project will train Early-stage Researchers and Experienced Researchers on a multidisciplinary project to unravel the mechanisms of the perception of Nod factors in the establishment of the Legume-Rhizobia symbiosis. This symbiosis is of great agronomic and ecological importance. The establishment of the symbiosis is induced by rhizobial lipo-chitooligosaccharidic Nod factor signals, which induce symbiotic responses on roots of the host plant. Recently receptors have been cloned which are involved in Nod factor perception and signal transduction. The aim of the project is to integrate multidisciplinary approaches to decipher the molecular mechanisms by which these receptors perceive Nod factors leading to specificity of partner recognition and activation of the plant infection and nodulation pathways. Approaches to be used include plant genetics, functional genomics, proteomics, cellular, molecular and structural biology, computational analysis, biochemistry, organic and analytical chemistry, and biophysics. The Project will be based on the model legume Medicago truncatula, which is taxonomically closely-related to major European legume crops. Training of researchers in this area is important for contributing to European aims of a more sustainable agriculture and safer food. The multidisciplinary research training programme will involve both local and network-wide components and will include secondments to other partner's laboratories, workshops, meetings and courses. In addition the researchers will be trained in complementary skills such as communication, presentation, project management and coordination

Fields of science (EuroSciVoc)

Keywords

Topic(s)

Call for proposal

FP6-2005-MOBILITY-1
See other projects for this call

Funding Scheme

RTN - Marie Curie actions-Research Training Networks

Coordinator

INSTITUT NATIONAL DE LA RECHERCHE AGRONOMIQUE (INRA)
EU contribution
No data
Total cost
No data

Participants (7)