Plant pathogenic microbes have the remarkable ability to manipulate biochemical, physiological, and morphological processes in their host plants. These manipulations are achieved through a diverse array of effector molecules that can either promote infection or trigger defense responses. We describe a general functional genomics approach aimed at identifying extracellular effector proteins from plant pathogenic microorganisms by combining data mining of expressed sequence tags (ESTs) with virus-based high-throughput functional expression assays in plants. PexFinder, an algorithm for automated identification of extracellular proteins from EST data sets, was developed and applied to 2147 ESTs from the oomycete plant pathogen Phytophthora infestans. The program identified 261 ESTs (12.2%) corresponding to a set of 142 nonredundant Pex (Phytophthora extracellular protein) cDNAs. Of these, 78 (55%) Pex cDNAs were novel with no significant matches in public databases. Validation of PexFinder was performed using proteomic analysis of secreted protein of P. infestans. To identify which of the Pex cDNAs encode effector proteins that manipulate plant processes, high-throughput functional expression assays in plants were performed on 63 of the identified cDNAs using an Agrobacterium tumefaciens binary vector carrying the potato virus X (PVX) genome. This led to the discovery of two novel necrosis-inducing cDNAs, crn1 and crn2, encoding extracellular proteins that belong to a large and complex protein family in Phytophthora. Further characterization of the crn genes indicated that they are both expressed in P. infestans during colonization of the host plant tomato and that crn2 induced defense-response genes in tomato. Our results indicate that combining data mining using PexFinder with PVX-based functional assays can facilitate the discovery of novel pathogen effector proteins. In principle, this strategy can be applied to a variety of eukaryotic plant pathogens, including oomycetes, fungi, and nematodes.

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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.