Nuclear processes associated with plant immunity and pathogen susceptibility

Plants are sessile organisms that have evolved exquisite and sophisticated mechanisms to adapt to their biotic and abiotic environment. Plants deploy receptors and vast signalling networks to detect, transmit and respond to a given biotic threat by inducing properly dosed defence responses. Genetic analyses and, more recently, next-generation -omics approaches have allowed unprecedented insights into the mechanisms that drive immunity. Similarly, functional genomics and the emergence of pathogen genomes have allowed reciprocal studies on the mechanisms governing pathogen virulence and host susceptibility, collectively allowing more comprehensive views on the processes that govern disease and resistance. Among others, the identification of secreted pathogen molecules (effectors) that modify immunity-associated processes has changed the plant–microbe interactions conceptual landscape. Effectors are now considered both important factors facilitating disease and novel probes, suited to study immunity in plants. In this review, we will describe the various mechanisms and processes that take place in the nucleus and help regulate immune responses in plants. Based on the premise that any process required for immunity could be targeted by pathogen effectors, we highlight and describe a number of functional assays that should help determine effector functions and their impact on immune-related processes. The identification of new effector functions that modify nuclear processes will help dissect nuclear signalling further and assist us in our bid to bolster immunity in crop plants

DNA-binding protein prediction using plant specific support vector machines:validation and application of a new genome annotation tool

There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provide focus for the more costly and time consuming functional assays that follow. DNA-binding proteins are an important class of proteins that require annotation, but current computational methods are not applicable for genome wide predictions in plant species. Here, we explore the use of species and lineage specific models for the prediction of DNA-binding proteins in plants. We show that a species specific support vector machine model based on Arabidopsis sequence data is more accurate (accuracy 81%) than a generic model (74%), and based on this we develop a plant specific model for predicting DNA-binding proteins. We apply this model to the tomato proteome and demonstrate its ability to perform accurate high-throughput prediction of DNA-binding proteins. In doing so, we have annotated 36 currently uncharacterised proteins by assigning a putative DNA-binding function. Our model is publically available and we propose it be used in combination with existing tools to help increase annotation levels of DNA-binding proteins encoded in plant genomes