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Interactions between plants and microbial pathogens involve complex signal exchanges at the plant surface and intercellular space interface (Baker et al. 1997; Parniske 2000; Hahn and Mendgen 2001). For example, plant pathogens have the remarkable ability to manipulate biochemical, physiological, and morphological processes in their host plants through a diverse array of extracellular effector molecules that can either promote infection or trigger defense responses (Knogge 1996; Lauge and De Wit 1998; Collmer et al. 2000; Kjemtrup et al. 2000; Staskawicz et al. 2001). Typically, such molecules are secreted into the intercellular interface between the pathogen and the plant or delivered inside the host cell to reach their cellular target. Thus, discovery programs that target genes encoding extracellular proteins can be expected to increase the probability of identifying genes involved in virulence. This approach has been taken successfully in the study of bacterial pathogens and symbionts. For example, an early study showed that Sinorhizobium meliloti mutants deficient in extracellular proteins were five times more likely to be affected in symbiosis than random mutants (Long et al. 1988). More recently, the characterization of effector proteins secreted through the type III secretion system of animal- and plant-associated bacteria has emerged as a key strategy for understanding mechanisms of virulence (Collmer et al. 2000; Kjemtrup et al. 2000; Staskawicz et al. 2001). In eukaryotic plant pathogens, genomic studies that focus systematically on extracellular proteins remain limited to nematodes, in which secretions from the esophageal gland cells are thought to play critical roles in infection (Wang et al. 2001). However, several classes of oomycete and fungal effector molecules, such as elicitor proteins that induce plant defense responses and a programmed cell death response termed the “hypersensitive response” (HR), are known to require secretion (Lauge and De Wit 1998; Jia et al. 2000). Therefore, secretion is an essential mechanism for delivery of virulence factors by eukaryotic plant pathogens to their appropriate site in infected plant tissue. In eukaryotic cells, most secreted and membrane proteins are exported through the general secretory pathway (also known as type II secretion system) via short, N-terminal amino-acid sequences known as signal peptides (von Heijne 1985; Rapoport 1992). Typically, signal peptides contain one or two charged amino acids followed by a hydrophobic core, and the signal peptidase cleavage site is defined by a pair of small uncharged amino acids (von Heijne 1985). Although most of these features can be identified in known extracellular proteins, the particular amino acid sequences are highly degenerate, and cannot be identified using DNA hybridization or PCR-based techniques (Klein et al. 1996). However, with the advent of genomics, large sets of sequence data became available, creating the opportunity to develop and test predictive software to identify extracellular proteins. For example, SignalPv 2.0, a program that was developed using machine learning methods, assigns signal peptide prediction scores and putative cleavage sites to unknown amino acid sequences with a high level of accuracy (Nielsen et al. 1997; Nielsen and Krogh 1998; Menne et al. 2000). The Irish famine pathogen, Phytophthora infestans, is a eukaryotic oomycete microorganism that causes late blight, a worldwide devastating disease of potato and tomato (Fry and Goodwin 1997a,b). Although it is a pathogen of great economic importance, little is known about the molecular mechanisms involved in the pathogenicity and host specificity of P. infestans, and only a handful of genes have been implicated in interaction with host plants (Kamoun 2000, 2001). Structural genomics of Phytophthora is underway. Pilot cDNA sequencing projects were performed for P. infestans and another species, Phytophthora sojae (Kamoun et al. 1999b; Qutob et al. 2000), resulting in a database of expressed sequence tags (ESTs; Waugh et al. 2000). With the accumulation of sequence data for Phytophthora, the challenge is shifting to data mining and functional analyses. One important goal is to be able to associate a biological function with sequences with no significant similarity to known genes. With this objective in mind, we set up to identify systematically P. infestans cDNAs encoding extracellular proteins from EST databases. Here, we describe PexFinder, an algorithm for the automated identification of putative extracellular proteins from ESTs. We applied PexFinder to a P. infestans EST data set and selected 63 candidate Pex (Phytophthora extracellular proteins) cDNAs for functional expression in plants using a viral vector. This functional genomics strategy resulted in the discovery of a novel family of necrosis-inducing genes that are predicted to encode extracellular proteins with no similarity to sequences in public databases.

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.

The approximately 60 species of Phytophthora are all destructive pathogens, causing rots of roots, stems, leaves and fruits of a wide range of agriculturally and ornamentally important plants (1). Some species, such as P. cinnamomi, P. parasitica and P. cactorum, each attack hundreds of different plant host species, whereas others are more restricted. Some of the crops where Phytophthora infections cause the greatest financial losses include potato, soybean, tomato, alfalfa, tobacco, peppers, cucurbits, pineapple, strawberry, raspberry and a wide range of perennial tree crops, especially citrus, avocado, almonds, walnuts, apples and cocoa, and they also heavily affect the ornamental, nursery and forestry industries. The economic damage overall to crops in the United States by Phytophthora species is estimated in the tens of billions of dollars, including the costs of control measures, and worldwide it is many times this amount (1). In the northern midwest of the U.S., P. sojae causes $200 million in annual losses to soybean alone, and worldwide causes around $1-2 billion in losses per year. P. infestans infections resulted in the Irish potato famine last century and continues to be a difficult and worsening problem for potato and tomato growers worldwide, with worldwide costs estimated at $5more » billion per year.« less

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.

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.