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Members of the oomycete genus Phytophthora cause some of the most devastating plant diseases in the world and are arguably the most destructive pathogens of dicot plants. Phytophthora research has entered the genomics era. Current genomic resources include expressed sequence tags from a variety of developmental and infection stages, as well as sequences of selected regions of Phytophthora genomes. Genomics promise to impact upon our understanding of the molecular basis of infection by Phytophthora, for example, by facilitating the isolation of genes encoding effector molecules with a role in virulence and avirulence. Based on prevalent models of plant-pathogen coevolution, some of these effectors, notably those with avirulence functions, are predicted to exhibit significant sequence variation within populations of the pathogen. This and other features were used to identify candidate avirulence genes from sequence databases. Here, we describe a strategy that combines data mining with intraspecific comparative genomics and functional analyses for the identification of novel avirulence genes from Phytophthora. This approach provides a rapid and efficient alternative to classical positional cloning strategies for identifying avirulence genes that match known resistance genes. In addition, this approach has the potential to uncover 'orphan' avirulence genes for which corresponding resistance genes have not previously been characterized.

Oomycetes, such as Phytophthora, downy mildew causal agents, and Pythium, form a unique branch of eukaryotic-plant pathogens with an independent evolutionary history. Among the oomycetes, Phytophthora spp. cause some of the most destructive plant diseases in the world, and are arguably the most devastating pathogens of dicotyledonous plants. Large scale DNA sequencing (genomics) approaches promise to impact our understanding of the molecular basis of pathogenicity and host specificity in Phytophthora by facilitating the isolation of novel virulence and avirulence genes, as well as by helping to identify targets for chemical control. Structural genomic studies of Phytophthora are well under way. The challenge in the postgenome era is to link a sequence to a phenotype with as little experimental effort as possible, using computational tools for data mining and robust high throughput functional assays. In this article, we review our strategy of applying a sequence-to-phenotype (or functional genomics) paradigm to the discovery of novel virulence and avirulence genes, as well as the identification of novel fungicide targets in Phytophthora.

Endopolygalacturonases (endoPGs) are plant cell wall-degrading enzymes that have been implicated in the invasion of plant tissue by pathogenic microbes. EndoPGs have been described from bacteria, plants, insects and numerous species of phytopathogenic fungi. In this study, we describe the first endoPG sequence from oomycetes, a unique group of eukaryotic plant pathogens that exhibit fungal-like filamentous growth but share little taxonomic affinity to fungi. The characterized gene, pipg1, was identified from the potato late-blight pathogen, Phytophthora infestans, and was predicted to encode a secreted glycoprotein with all the signature sequences of endoPGs. Pipg1 was expressed during preinfection and infection stages. Phylogenetic analysis of endoPGs indicated that pipg1 forms a unique class that is significantly more similar to fungal endoPGs than to plant or bacterial ones. This unexpected affinity between PIPG1 and fungal endoPGs contrasts with phylogenies obtained using ribosomal sequences or compiled protein sequences from mitochondrial and chromosomal genes, raising interesting questions about the evolution of these enzymes in oomycetes.

A diversity of microorganisms establishes intimate associations with plants. Whether pathogenic or symbiotic, such interactions are the result of complex recognition events between plants and microbes, leading to signaling cascades and regulation of countless genes involved in the interaction. A key step in unraveling the mysteries of plant-microbe interactions lies in defining the transcriptional changes that occur in both the host and the microbe during their association. The sum of the transcripts, from both host and microbe, which are produced during their association, has been defined as the interaction transcriptome. One approach to analyze interaction transcriptomes is to perform large-scale sequencing of cDNAs (expressed sequence tags or ESTs) obtained from infected plant tissue and representing a mixture of host and microbe sequences. In some cases, the two organisms have markedly different GC content, allowing most ESTs to be easily distinguished on this basis. In this chapter, we describe a GC counting method to determine the species of origin of ESTs obtained from interactions between plants and oomycetes or other high GC content microbes.