AbstractPhytophthora secretes a large repertoire of molecules (effectors) during infection to modulate host processes and enable infection. Microarray analysis on tomato-P. capsici timecourse experiments has revealed dramatic transcriptional changes as a consequence of infection, suggesting a role for pathogen effectors in transcriptional reprogramming throughout its disease cycle.
We hypothesise that Phytophthora genomes encode effectors that translocate into host nuclei during infection, where they bind DNA and modify gene expression. Computational methods for predicting DNA-binding proteins can provide a high-throughput means of candidate selection. However, current prediction algorithms are limited in plants and pathogens.
Here we have created a plant specific prediction model, which we have employed to predict DNA-binding proteins in the tomato (Solanum lycopersicum) genome. By validating these predictions we have demonstrated that this model is suitable for high-throughput prediction of DNA-binding proteins and will be a useful tool in genome annotation efforts.
Applying our prediction model to effectors from P. capsici and P. infestans, we have identified a set of candidates which have been prioritised for experimental characterisation. From these candidates we have identified chromatin-associated effectors which localise to the nucleus and enhance P. capsici virulence. Taken together these results suggest DNA-binding may be an important feature for pathogen effectors.
Finally we have assessed the use of three techniques to validate direct DNA-binding and identify target DNA sequences, for which we show preliminary results and outline planned use. Candidate DNA-binding effectors will be prioritised for use with these techniques.
We conclude that this study has provided the means to identify candidate DNA-binding effectors which can be adopted by others wishing to study pathogen DNA-binding effectors. This will help further our understanding of not only pathogen effectors but also of plant DNA-associated processes during infection.
|Date of Award||2015|
|Sponsors||The James Hutton Institute|
|Supervisor||Edgar Huitema (Supervisor) & Sue Jones (Supervisor)|
Computational Identification and Functional Characterisation of Candidate DNA Binding Effectors in Phytophthora
Motion, G. B. (Author). 2015
Student thesis: Doctoral Thesis › Doctor of Philosophy