A functional genomics screen for DNA-binding effectors in eukaryotic plant pathogens

Plant pathogens achieve virulence through secretion of proteins (effectors) that are thought to modulate host cell signaling and suppress immunity. The function of many eukaryote effectors however, is currently unknown. An increasing number of effectors in eukaryotes localize to the host nucleus, and the identification of TAL DNA-binding effectors in bacterium of the genus Xanthomonas, make the possibility of DNA-binding effectors in eukaryotic pathogens highly likely. Identification and functional characterisation of DNA-binding effectors in eukaryotes is key to understanding pathogenicity at the molecular level.

Machine-learning methods, such as support vector machines, provide a method of predicting protein function from sequence information. Models are trained on sequences of known function and then used to predict function for sequences of unknown function. Methods have previously been developed that use machine learning techniques to predict DNA-binding. Groups at JHI and UoD have identified >1000 candidate effector proteins from species of Phytophthora based on the presence of signal peptides, translocation motifs (RXLR and LXLFLAK), absence of trans-membrane domains and transcriptional up-regulation during infection. This project will see the screening of these protein sequences with the function prediction method to provide a list of potential DNA-binding effectors followed by experimental studies.