Plant roots are the location of an ongoing struggle between plant parasitic nematodes (PPNs) and their host. PPNs are an important problem in food security and cause billions of euro’s worth of yield losses in agriculture.
The main control measure for to control infections are the use of so-called resistance genes (R-genes). For example, infections of Globodera pallida and Globodera rostochiensis in potato fields are controlled by a few R-genes that were bred into potato varieties. Nematodes in the field are under considerable selection pressure to circumvent R-genes. When an R-gene breaking population establishes itself, it is called ‘virulent’.
In this project we use genomics and population genetic tools to understand how plant parasitic nematodes develop virulence and try to identify the genes that are linked to this. Here, we conduct genome-wide analyses to understand the selection-history of the population. There are multiple thesis opportunities related to this research:
- Virulence in Globodera pallida: Here we try to understand how virulent populations of the potato cyst nematode G. pallida has been selected in the field. We use high-coverage sequencing to measure shifts in allele frequencies between different populations. This thesis would include working with a state-of-the-art G. pallida reference genome and conducting population-genetics analyses on sequencing data (measuring selection, mutation rates, effects of polymorphisms), plus interpreting these results across populations.
- Natural variation in plant parasitic nematodes: To better understand the role of genetic differences within nematode populations, we use the root-knot nematode Meloidogyne hapla on tomato. We test different strains of both Tomato and M. hapla for the severity of the infection, phenotypic effects on the plant, and the transcriptional response to infection. This thesis would include working with a host-parasite model system, experimental design, RNA isolation, transcriptomics (either RNA-sequencing, microarray, and/or qPCR), phenotyping, data analysis (either R or excel).