Identification of the pea factors involved in aphid-mediated infection of Pea Enation Mosaic Virus

Due to the recent pesticide ban in Europe, crops are suffering from the diseases caused by insect-transmitted viruses. In particular, aphid-borne Pea Enation Mosaic Virus (PEMV) causes serious yield loss in pea (Pisum sativum), an important source of protein. Although some PEMV-resistant pea genotypes were identified, resistance genes are not cloned, and the infection process and the mechanisms of symptom development are not understood at a molecular level. To identify pea genes that are involved in PEMV transmission by aphid, within-host propagation and symptom development, we are screening a collection of 240 pea genotypes, completed with some known PEMV resistant and susceptible genotypes, for partial or total resistance against PEMV. On this collection of pea genotypes, we are evaluating PEMV spread and symptom development after aphid inoculation. The PhD student will use these phenotypic and genomic data to conduct several GWAS (Genome Wide Association Study) to identify pea genes involved in the resistance to different steps of the infection process. The student will also conduct transcriptomics analyses to examine the interactions between resistant and susceptible pea genotypes with PEMV. The identified genes and pathways involved in virus infection and pea-virus interactions will be examined by functional analyses. This PhD project will identify candidate pea genes that are involved in virus resistance or viral infection process and contribute to understand the mechanisms of their interactions.

The interviews will most likely be held via Zoom on June 20, 2025. The second possible interview date is June 27, 2025. Although the official application deadline is June 18, we recommend submitting your application sooner rather than later.

The student must have a basic knowledge of quantitative genetics.

The student must have basic knowledge of molecular biology and plant-pathogen interaction.

Experience in statistical analysis of large datasets (GWAS, omics) is essential.