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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.<br/>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. <br/>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. <br/>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. <br/>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. <br/>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.<br/>","abstract_html":"Phytophthora 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.&lt;br/&gt;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. &lt;br/&gt;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. &lt;br/&gt;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. &lt;br/&gt;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. &lt;br/&gt;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.&lt;br/&gt;","abstract_has_math":false,"creators":["Motion, Graham B."],"institution":"University of Dundee","degree_name":"Doctor of Philosophy","degree_level":"Doctoral Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Huitema, Edgar"],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-24T02:08:12Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier 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