Crop loss from plant disease poses a
serious threat to global food/fiber/feed security. The re-appearance and subsequent epidemic of wheat and
barley scab (causal agent Fusarium graminearum) in North America resulted in yield and
quality losses estimated at $1 billion US, in 1993 alone (McMullen et al., 1997). Some diseases (e.g., karnal bunt of wheat)
have incurred a huge indirect cost due to international trade restrictions. In
addition, certain groups of fungi produce mycotoxins
in infected crops, directly posing a health hazard to humans and animals. After
the September 11 attacks, and the subsequent anthrax release, it became
apparent that the threat to US agriculture from the deliberate release of
pathogens should not be underestimated (NRC,
2003). The impact of
agriculture on our country ranges from supplying food for our tables, to
serving as one of the largest sectors in our economy, employing 17% of our
civilian workforce in agriculture-related enterprises (farm production, food
processing, manufacturing, exporting, and related services), and accounting for
8% of our export value, as well as generating nearly 16% of the US gross
domestic product. Disruption of the food/fiber/feed production system by the
intentional release of pathogens, therefore, would be catastrophic to the
The ability to accurately and rapidly identify the causal agent is essential for implementing disease management and regulatory measures to prevent major crop disease epidemics and mitigate long-term impacts that could result from such events. Because approximately 10,000 fungal/oomycete species are considered plant pathogenic (Farr et al., 1989; Agrios, 1997), accurate identification of new isolates is a challenge, even to experts. Considering that the number of fungal species identified to date (72,000 to 100,000) only represent a small fraction of the fungal kingdom, estimated to include ~1.5 million species (Hawksworth, 1991; Hawksworth and Rossman, 1997; Hawksworth, 2001), the actual number of plant pathogenic fungi/oomycetes is likely to be much higher than 10,000. Given the expected magnitude of pathogen diversity in nature, the importance of systematically cataloging and sharing taxonomic and phylogenetic information for agricultural security cannot be overemphasized. Furthermore, the observation that fungal/oomycete species easily recognized using phylogenetics are not necessarily morphologically distinguishable (e.g., O’Donnell et al., 2004) indicates that molecular identification is critical for recognizing new and emerging threats. In addition, because crop loss is caused by populations of strains that vary within species in traits such as virulence, host range, mating type, fungicide resistance, and/or toxin production, determining species identity is often insufficient. We need to generate a comprehensive picture of the genetic and phenotypic diversity within key pathogens. The potential for the emergence of new pathogens through hybridization (Brasier et al., 1998; Brasier et al., 1999; Brasier, 2000), global migration, and accidental release due to expanding agricultural activities and trade, as well as increased concerns about agroterrorism, further underscore the importance of cataloging the genetic and phenotypic diversity of fungal/oomycete pathogens (Kang et al., 2002).
(A) Objectives: Toward the goal of enhancing our ability to detect, diagnose, monitor, and manage Phytophthora diseases, we propose to (i) systematically catalog genotypic and phenotypic data of Phytophthora spp. in a forensic database format that can be easily accessed and utilized by the global community of plant health professionals, and (ii) develop and optimize molecular diagnostic tools for Phytophthora at both the species and population levels (Fig. 1). Oomycete plant pathogens, such as Phytophthora, belong to a unique group of protists, the Stramenopila (Gunderson et al., 1987; Patterson, 1989; Förster et al., 1990b; Leipe et al., 1994). While they are fungus-like in that they produce hyphae, Phytophthora species show a distant evolutionary relationship with “true” fungi. Instead, they reside in a more basally derived eukaryotic lineage that includes chromophyte algae (Förster et al., 1990a). Their high virulence and ability to spread rapidly throughout the world establishes Phytophthora as one of the most important groups of plant pathogens. The destructive potential of Phytophthora diseases is well illustrated by the re-emergence of late blight of potato and tomato due to the introduction of new, fungicide-resistant lineages of P. infestans (Fry and Goodwin, 1997), and the recent outbreak of sudden oak death (SOD) in the US (Rizzo et al., 2002) and ramorum canker and blight of ornamental plants in Europe (Werres et al., 2001) caused by P. ramorum. Recent discoveries of inter-specific hybridization among Phytophthora spp. in nature (Man in 't Veld et al., 1998; Brasier et al., 1999; Bonants et al., 2000; Brasier, 2000; Olson and Stenlid, 2002), which could yield novel pathogens and play an important role in the exploitation of new host plant species, further underscore the threat posed by Phytophthora species.
Considering that SOD and late blight are unlikely to be the last major Phytophthora disease outbreaks on plants of economic and/or environmental significance, in order to protect agriculture from established Phytophthora diseases as well as any new or re-emerging ones, we need to map and catalog the diversity, distribution pattern and dynamics of Phytophthora species and their populations at both the temporal and spatial scales. This information is crucial for developing regulatory and disease management strategies, should facilitate monitoring the dynamics of the pathogen community in response to agricultural management practices and environmental changes, and help track the movement of high-risk pathogens via agricultural commodity trades. However, currently available information on the diversity and dynamics of Phytophthora is limited and fragmentary, thus impeding the subsequent integration and utilization of the resulting data. The proposed database and associated data analysis and visualization tools will provide an effective means for linking these activities and will serve as a central hub for facilitating the access, analysis, and sharing of data on genetic and phenotypic diversity and dynamics of Phytophthora spp. by researchers and regulatory agency workers. Specifically, we aim to accomplish the following objectives:
· To establish a comprehensive phylogenetic framework for Phytophthora spp. Successful disease management strategies require accurate and rapid identification of the causal agents. Given the vast diversity of pathogens in nature and the limited resolution of phenotypic traits for species identification, the use of genetic markers to complement pathogen identification is critical for prompt implementation of suitable control and regulatory measures. Owing to increased survey efforts and application of molecular phylogenetic approaches, many novel species of Phytophthora have been reported in recent years, including P. ramorum (Werres et al., 2001), P. nemorosa (Hansen et al., 2003), P. ipomoeae (Flier et al., 2002), P. europaea, P. psychrophila, P. uliginosa (Jung et al., 2002), P. pseudosyringae (Jung et al., 2003), and about eight other unique but unnamed taxa (Brasier et al., 2003). The proliferation of newly described species in this genus is indicative of our limited understanding of the diversity and dynamics of Phytophthora in ecosystems. Relative to major pathogenic fungi, phylogenetic analyses of Phytophthora mainly have been conducted based on the sequence of the internal transcribed spacer (ITS) of ribosomal RNA (rRNA) encoding genes (Cooke et al., 2000). Although ITS has emerged as one of the most commonly used genetic markers for identifying and defining fungal/oomycete species, its phylogenetic utility typically ends at the level of related species, and it often fails to resolve close relatives (Geiser, 2004). In fungi, conserved protein-coding genes that contain highly variable introns, have provided much higher resolution than the ITS at the species level. Sequences from the 28S/18S rRNA genes and amino acid sequences are better suited than ITS in addressing phylogenetic questions at the genus (or higher) level. In a recent publication (Martin and Tooley, 2003a), phylogenetic relationships of 27 Phytophthora species were assessed by sequence analysis of the mitochondrially-encoded cytochrome oxidase II (coxII) gene, and determined that relationships within these species were in general agreement with those observed for ITS region, and these two loci provided similar resolution. In addition, Ivors et al. (2004) sequenced a portion of the mitochondrially-encoded NADH dehydrogenase subunit 5 (nad5) gene for investigating the phylogenetic relationships of 21 Phytophthora species. We will develop at least four robust marker loci for recognizing and identifying species within the genus Phytophthora (Taylor et al., 2000). Once suitable markers are identified, a comprehensive phylogenetic analysis of Phytophthora, at both the species and population levels, will be carried out using the existing culture collections in the World Phytophthora Collection (WPC; http://phytophthora.ucr.edu; ~6,000 accessions) at UC-Riverside and the Pennsylvania Department of Agriculture (PDA; ~1,000 accessions). Together these represent the world’s largest collection of Phytophthora isolates. For species underrepresented in these collections, we will obtain additional isolates from our collaborators (see letters of collaboration). Once this project is completed, the resulting data should serve as an invaluable resource for identifying and tracking Phytophthora species and their populations and will establish a baseline for monitoring the emergence of new/foreign pathogens in agroecosystems.
· To develop and optimize molecular diagnostic tools. The importance of developing sensitive molecular tools for diagnosis of Phytophthora spp. is highlighted by recent epidemics by P. ramorum (Rizzo and Garbelotto, 2003). To effectively combat Phytophthora diseases, an assessment of the diversity, distribution and dynamics of Phytophthora in agroecosystems is urgently needed. Such an assessment will require the development of good sampling strategies and deployment of molecular diagnostic methods. Molecular diagnostic tools have been successfully applied to detect various viral, bacterial, fungal and oomycete pathogens (Duncan and Torrance, 1992; Martin et al., 2000). Based on data from the proposed phylogenetic analysis, we will develop and test various types of molecular diagnostic tools/protocols for detecting and differentiating major Phytophthora pathogens at both the species and population levels that can be used in research, regulatory and diagnostic laboratories. RFLP analysis of PCR amplified templates has been utilized for differentiating Phytophthora at both the species and population levels (Cooke and Duncan, 1997; Kroon et al., 2004). Sequence data from the proposed phylogenetic analysis will allow users of the Phytophthora database to generate and evaluate virtual RFLP patterns at a chosen locus within and between species (Fig. 1). We also plan on further improving currently available protocols for detection of various Phytophthora species in different substrates, including soil, roots, water and plant tissues (Bonants et al., 1997; Tooley et al., 1997; Liew et al., 1998; Winton and Hansen, 2001). Four of the PDs (Coffey, Kang, Ivors, and Martin) are actively engaged in developing and applying molecular diagnostic tools for detecting and differentiating Phytophthora (Ivors et al., 2004; Kong et al., 2004; Martin and Tooley, 2004). The sensitivity and specificity of resulting diagnostic tools will be evaluated using reference cultures as well as infected plant material collected from field surveys in multiple states. Tested diagnostic protocols will immediately be made available to the Phytophthora community through the proposed database (Fig. 1).
build a comprehensive genotype, phenotype, and specimen database for Phytophthora.
Archiving and sharing existing data is as important as generating new data. A
relational internet database will be constructed to catalog systematically the
resulting phylogenetic data and link them to
available phenotypes (morphology, virulence, mating type, host, fungicide
resistance, geographic origin, etc.) of individual isolates in a format that
can be easily accessed, analyzed, and visualized (Fig. 1). To serve as a comprehensive resource
for various groups of plant health professionals, including regulatory agency
workers, extension agents, diagnosticians and researchers in industry,
government and academia, information relevant to dealing with major pathogenic Phytophthora diseases will be assembled and
presented in the database. The information may include, but will not be limited
to, diagnostic protocols, photographs illustrating typical morphological traits
of the species and disease symptoms, recommended eradication and containment
procedures, and contact information for experts on individual species and
corresponding diseases. To aid
researchers in analyzing and visualizing the temporal, spatial, and
evolutionary structure and dynamics of Phytophthora via the database, a
cohesive array of computational tools (‘cyber-infrastructure’) will be built
around the database (Fig. 1& below). This cyber-infrastructure will support the capture, integration, analysis,
visualization, and dissemination of data within the Phytophthora
research community. Not only will the cyber-infrastructure facilitate
sharing data/results and developing new questions, it will also increase the
pace at which new knowledge is transmitted to the research community and
regulatory agencies. Due to the increase in international plant trade,
generating data on foreign Phytophthora species would be crucial for the detection and
identification of exotic Phytophthora strains, i.e. before they become
established in the
(B) Cyber-Infrastructure Supporting the Database: To aid users of the Phytophthora database in utilizing and analyzing the data in the database, an array of data search, analysis and visualization tools are needed. Although the development of such tools is not part of the current proposal, an overview of these tools is needed to illustrate how data from our project can be accessed and utilized. The database can be accessed via web portal technology. The web interface is divided into three main components: data submission, database query, and GeoVISTA Studio application. The fundamental goal of the GeoVISTA Studio (http://www.geovista.psu.edu/index.jsp), a Java-based, visual programming environment, is to improve geoscientific analysis by providing an environment that operationally integrates a wide range of analysis activities, including those both computational and visual (Takatsuka and Gahegan, 2002). With a 2-year (11/02-10/04) support from USDA-ARS, we are currently working with Dr. Mark Gehagan, Director of the GeoVISTA Studio project, to build a number of new data analysis and visualization tools for analyzing and visualizing the temporal and spatial dynamics of plant pathogens in the context of agroecosystems.
Through the home page (http://fppd.cbio.psu.edu) users can access general information about the database (description of database organization, information about how to use the database, etc.) and data submission, search, and analysis. Given that our main goal is to establish a community resource, we do not want to create an excessively invasive verification process to ensure legitimate use of the database. Instead, we will implement a simple user registration system that will allow only registered users to access the database through use of password. Because creating a comprehensive database requires data input from the Phytophthora community, user-friendly methods for submitting new information to the database have been developed. From individual species web sites, one will be able to access varied information useful in dealing with selected species: (i) General biology; (ii) Images of disease symptoms and the various morphological features used to identify the species; (iii) Protocols for amplifying marker genes by PCR, including primer sequences; and (iv) A literature reference guide. Each database query will lead to the GeoVISTA Studio application. The available tools will include a program for building phylogenetic trees using selected strains. The geographic distribution of strains matching the search criteria, as well as data regarding host species, mating type, and virulence, for example, can be analyzed at this point by using a different GeoVISTA Studio tool. Further analysis of correlations between phylogenetic position, geographic origin, host of origin, and any available epidemiological factors can be performed within these components.
Genotypic data will serve as the main anchor for linking any available phenotypic information for individual species/strains, which will include bibliographic information, host and geographic origin, fungicide resistance, known diagnostic tools, and strain histories. A two-tiered DNA-sequence based search tool will allow users of the database to match a new pathogen isolate to a known species, or closest relative, and in species for which a large number of isolates have been characterized, to a population (i.e., genetic lineage) within a species. For identification of an unknown isolate to the best species or species group, users can query the database using phylogenetically informative marker loci such as an ITS sequence, and ones we develop in this proposed research, via the Gapped BLAST program (Altschul et al., 1990; Altschul et al., 1997). If the user knows the species identity of a newly isolated strain, one can go directly to the chosen species for a population search. When a user chooses population level, the BLAST (Altschul et al., 1990) submission page with a list of available markers (introns of protein-coding genes and/or intergenic regions that are expected to provide higher phylogenetic resolution than ITS) will be created. This process will be interactive, and the page will be created on the fly so that we can always present an up-to-date marker list.
The list of species/strains returned from the sequence-based search will be accessible in various visualization tools such as analyzing phylogenetic relationships between the new isolate and related isolates in the database (i.e., phylogenetic tree building) and visualizing via supporting maps the geographic origins of selected strains along with environmental contexts of these geographic areas. For instance, a list of strains carrying identical/similar sequences returned by the BLAST search are first processed through a multiple sequence alignment algorithm, and then numeric calculations are made for the creation of a distance-based phylogenetic tree (Neighbor-Joining). All sequence data, including sequence alignments, stored in the database will be available by downloading so users can perform analyses on their own.
Presenting phylogenetic relationships among isolates in the form of phylogenetic trees and visualizing their geographic origins, will provide insights into the temporal and spatial distribution patterns and dynamics of selected strains/species. Because increasing interstate and international commerce and travel can significantly increase the movement of pathogens, tools for visualizing the spatial and temporal distribution pattern of pathogens in a geographical context are essential. The geographic location of each strain on the phylogenetic tree will be displayed in map form. Such distribution patterns may provide important insights into the mechanisms related to the emergence of new pathogens. Knowing the geographic location of a strain or a collection of strains also opens access to vast digital archives of geographic and environmental data that can be spatially associated with the strains. Considering the importance of environmental factors (such as temperature and humidity, precipitation, prevailing direction of wind, and soil chemistry and physics) on the disease dynamics, we will develop tools that will permit the analysis of possible links between such environmental factors and distribution patterns of specific pathogens (Fig. 1). These tools will help us (i) identify the environmental characteristics of the regions where the new pathogen and related strains are found, (ii) uncover a rapid, or surprising change in pathogen dynamics, (iii) predict the potential geographic trajectory of the newly emerging pathogen, and (iv) assess potential risk after pathogen migration to other regions with similar environmental conditions. In the long run, this cyber-infrastructure could serve as a bridge between population genetics/phylogenetics and epidemiology.
(C) Expected Benefits to Plant Biosecurity: The following needs should be addressed in order to secure agriculture from devastating Phytophthora diseases. Through this collaborative project, we aim to address these needs:
· Need for establishing a comprehensive identification system for pathogen surveillance. Genotyping is a highly effective tool for tracking the movement and determining the origin of newly emerging pathogens. Since many pathogens can migrate from one region to another through various means, including shipment on agricultural products, creating mechanisms that would allow for the rapid analysis of species and population identity of newly discovered pathogens is imperative to effectively diminish their impact. For instance, in the ornamentals industry, where pests and pathogens may accompany plant material shipped throughout the world, the ability to trace pathogens from a shipment to its geographic origin would allow the producer to remedy the problem at its source. Although it has been several years since P. ramorum was first identified (Werres et al., 2001; Rizzo et al., 2002), its origin remains unknown. Comprehensive data on the genotypes and phenotypes of diverse Phytophthora spp. on a global scale will greatly assist in this detective work. Since SOD is unlikely to be the last major disease outbreak to plants of economic and/or environmental significance, we need to map and catalogue the diversity, distribution and dynamics of Phytophthora in the context of global agroecosystems in order to establish a baseline for monitoring the emergence of new/foreign pathogens and implement appropriate control and regulatory measures. We also must archive reference culture collections along with corresponding genotypic/phenotypic data in a format that can be easily accessible and searchable by the global scientific community. Given the limitations of phenotypic characters for strain and/or species identification, development and validation of robust genetic markers for strain identification is therefore critical for prompt implementation of suitable control and regulatory measures. We anticipate this project will serve regulatory agencies, such as state plant inspection agencies and USDA-APHIS, industry, and land-grant universities in a way similar to what the forensic DNA database does for the federal and state law enforcement agencies. Particularly, the use of genotyping to identify strains and species of interest will greatly assist the study of newly isolated pathogens by those researchers who have limited experience in morpho-taxonomy, or by regulatory agency scientists who must quickly assess the threat of a new isolate for rapid deployment of containment and/or eradication measures.
· Need for developing molecular diagnostic tools. Because Phytophthora species are often difficult to culture from plants, which can lead to false-negative isolations and misdiagnosis of infected plant material, a number of molecular diagnostic tools based on traditional PCR, as well as real-time (RT)-PCR, have been developed to detect Phytophthora species without prior isolation and characterization of the organism by traditional microbiological methods (Martin et al., 2004). Recently a genus-specific primer pair was developed for Phytophthora spp. that amplifies the spacer sequences between the coxI and II genes, a variable region useful for constructing nested species-specific primers (Martin et al., 2004). Species-specific primers for pathogen identification from diseased tissue have been developed for eight species thus far with additional species currently under evaluation (PD Martin, unpublished). This marker system also has been adopted for RT-PCR (Tooley and Martin, unpublished). However, in order to facilitate the detection and differentiation of Phytophthora in plant and soil extracts, we need to further improve the existing techniques and develop new rapid screening tools. Nation-wide surveys of P. ramorum further underscore the importance of continuously developing such tools. The phylogenetic data from this project will significantly facilitate development of these tools. The USDA launched the National Plant Disease Diagnostic Network (NPDN; http://npdn.ppath.cornell.edu/default.htm) to coordinate interstate communication on disease incidences. The Phytophthora database should complement the NPDN by serving as a resource for genotype-based identification and providing molecular diagnostic tools.
· Need for an effective mechanism linking and integrating community research. The volume of taxonomic, phylogenetic, and population genetic analyses of agriculturally important fungal/oomycete pathogens has increased substantially in recent years. However, complementary efforts to archive and share resulting data have been limited, in part due to the lack of mechanisms and protocols to support such activities. Consequently, it is often difficult to access and compare data generated by various laboratories, causing unnecessary fragmentation of research efforts and duplication of activities. Thus, many hard-won datasets go unused or are under-utilized, when they could instead be leveraged in many other contexts by other researchers. To maximize the value of these data for protecting the food/feed/fiber production system, it is essential that better methods of data storage and sharing be devised (Wilson, 2003). In several science domains, it is already apparent that a coordinated approach to describing, archiving and disseminating research outcomes can provide significant gains in productivity and quality of work. However, in plant pathology, many challenges currently impede the progress of coordinating and linking community research activities toward common goals. Thus, to strengthen agricultural security without infusion of a significant amount of new resources, establishing a community infrastructure that enables distributed teams of researchers to collaborate more closely and leverage each other’s work by providing community-wide access to available datasets and tools is urgently needed. Recent developments in information science are providing computational tools and protocols that can aid scientists in sharing methods and results of their research, and even distributing their computing infrastructure. Federated databases and ontology-based database integration provide tools for coordinated data management, portal technology and web services provide customizable access points to methods and data, and grid technology coupled with high-speed data connections provide distributed but high-powered computational resources (Elmagarmid et al., 1999; Chau et al., 2002; Rodriguez and Egenhofer, 2003). We will develop the cyber-infrastructure supporting the Phytophthora database by taking advantage of these developments so that it can be easily customizable to the emerging needs of research and regulatory communities.
· Need for an integrated set of data analysis and visualization tools to examine complex disease interactions. Because plant diseases result from interactions of multiple factors, including host, pathogen, and the environment (“disease triangle”), an integrative understanding of the role of individual components, as well as synergistic and/or antagonistic interactions among them during disease epidemics is pivotal to implement effective disease management strategies (Milgroom and Peever, 2003). Therefore, it is critical to develop a comprehensive data management system for information capture and integration, coupled with analysis and exploratory visualization apparatus to identify principal risk factors and uncover potential interactions among them (e.g., variables such as temperature, precipitation, wind direction, nutrient management for the environment; virulence, genetic diversity and recombination potential of pathogen). The geographic origin of the strains to be analyzed in this project can be linked to vast digital archives of geographic and environmental data via the GeoVISTA Studio tools. The importance of such a system will grow as we accumulate more data and attempt to understand plant disease epidemics in the context of agroecosystems.
· Need for building and maintaining pathogen culture/data collections as a community resource. Although the importance of preserving microbial strains for future reference and study is widely accepted (Agerer et al., 2000; Grimes et al., 2001), resources for supporting culture collections and genetic diversity studies using these collections have been scarce (Normile, 1999; Wilson, 2003). As witnessed over the past two years, the WPC faced a serious threat of losing its collection due to termination of state support. Although last minute support from academia and industry avoided an imminent closure, depending on the emergency good will of the community is not a viable, long-term solution. Maintaining existing plant pathogen culture collections and associated data, which are main links to past disease epidemics and research activities, is urgently needed as they serve as essential foundations for agricultural security by supporting forensic work, disease management, and regulatory measures. Loss of isolates utilized in previous studies often makes it difficult for other scientists to reproduce and expand these studies. Building and maintaining pathogen culture/data collections should also be viewed as an essential link between pathogen genomics and the analysis of the genetic and phenotypic diversity and variation among populations of individual species and between species. Genomics typically concentrates on a single isolate of a given microbial species. However, because populations of strains that vary in many pathogenic traits cause crop loss, knowing the complete genetic makeup of a single isolate is insufficient for fully understanding disease potential and dynamics in agroecosystems. Materializing the full potential of microbial genomics as a foundation for the development of novel disease control strategies will hinge on achieving a comprehensive understanding of the genetic and phenotypic diversity within pathogen populations in nature, using the biology of sequenced isolates as a reference. The strong endorsement by scientific communities of pathogen genomics as a foundation for plant biosecurity (NRC, 2003; Beale et al., 2002) also underscores the importance of culture collections. To increase the use and value of existing culture collections, it is essential to preserve and expand key culture collections, characterize the genotype and phenotype of isolates within these collections, and archive the resulting data in a format that can be easily shared and accessed. Such data also provide a framework for the efficient use of limited resources in culture collections to insure that under-represented species/strains are preserved.
Considering the vast diversity of plant pathogens, it is critical to base our disease control strategies on a comprehensive understanding of the structure and dynamics of pathogen species/populations in the context of agroecosystems. Such an understanding has the potential to (i) guide the selection of appropriate resistance germplasm for genetic engineering and breeding programs, (ii) assist in deploying crops in such a way that minimizes the spread and evolution of new races and/or hybrid species, (iii) facilitate monitoring the dynamics of the pathogen community in response to certain agricultural practices and environmental changes, (iv) allow us to monitor the build-up of virulent races or strains resistant to crop protectants, and (v) permit us to trace the movement of high-risk pathogens in agricultural production systems. The short-lived “pesticide revolution” and recent resurgence of many microbial diseases due to drug resistance clearly underscores the danger of implementing a disease control strategy without taking into account the diversity and evolutionary plasticity/dynamics of pests and pathogens (Georghiu, 1986; Neu, 1992). Given the high potential of emergence of new Phytophthora species and strains through global migration (Fry and Goodwin, 1997; Punja et al., 1998; Elansky et al., 2001; Peters et al., 2001) and hybridization (Man in 't Veld et al., 1998; Brasier et al., 1999; Bonants et al., 2000; Brasier, 2000; Olson and Stenlid, 2002), as well as increasing concerns about agricultural bioterrorism, systematically cataloging the genetic and phenotypic diversity of Phytophthora warrants immediate attention. Such data is needed to establish a baseline for monitoring the emergence of new/foreign pathogens and implement appropriate control and regulatory measures.
(A) Phytophthora: An ITS sequence-based systematic analysis (Cooke et al., 2000) demonstrated that the genus is monophyletic and comprises eight different clades (Fig. 2), including the obligate biotrophic downy mildews of the genera ‘Peronospora’ and ‘Bremia’, which are now interpreted as highly specialized airborne Phytophthora lineages that have lost the ability to produce zoospores. The emerging realization that Phytophthora spp. possess many unique evolutionary, physiological and biochemical features has increased interest in understanding their biology and ecology (Duncan, 1999; Birch and Whisson, 2001). In addition, this genus includes many important plant pathogens. Despite intensive efforts over 150 years to control P. infestans, it remains one of the world’s, most costly plant pathogens, either in direct losses and/or in the need for intensive use of costly fungicides. The recent migration of new aggressive, fungicide-resistant strains of this pathogen (Fry and Goodwin, 1997) has further exacerbated the problem. Many other Phytophthora species can be equally destructive (Erwin and Ribeiro, 1996). P. sojae causes a root rot of soybean in the US and Canada, causing billions of dollars in annual losses (Schmitthenner, 1985). P. palmivora attacks economically important tropical crops such as cacao, coconut, durian and mango (Mchau and Coffey, 1994b). P. capsici is destructive on chili peppers worldwide (Mchau and Coffey, 1995). P. nicotianae (=P. parasitica) causes severe problems on tomatoes, tobacco and citrus. Multiple species, especially P. cinnamomi, P. cactorum, P. citricola, P. citrophthora, P. cryptogea, and P. nicotianae, cause widespread losses of various ornamental and fruit crops.
most recent threat is P. ramorum, which threatens national and international
forest ecosystems and the global nursery industry. In
(B) Importance of Systematically Cataloguing the Genetic and Phenotypic Diversity of Fungal and Oomycete Pathogens for Agricultural Security: Fungal/oomycete taxa are traditionally defined based on microscopic morphological characters which require an in-depth knowledge of morphological systematics. Morphological traits have limited resolving power in plant pathogens because such traits are not always variable. In the last several years, from molecular systematics studies, it has become increasingly apparent that fungal pathogens traditionally classified as a single species are far more diverse than previously thought (Geiser et al., 1998b; O'Donnell et al., 1998a; Aoki and O'Donnell, 1999; O'Donnell, 2000; O'Donnell et al., 2000b; O'Donnell et al., 2000a; Couch and Kohn, 2002; Steenkamp et al., 2002). Phylogenetic studies of fungi and oomycetes using rDNA sequences are now performed routinely (Bruns et al., 1991; O'Donnell et al., 1998a), although their utility often ends at the level of closely related species. More recently, conserved protein-coding genes such as beta-tubulin and translation elongation factor-1 alpha which contain highly variable introns have been shown to out perform ITS at resolving species boundaries (Tsai et al., 1994; Geiser et al., 1998a; Geiser et al., 1998b; O'Donnell et al., 1998b; Aoki and O'Donnell, 1999; O'Donnell, 2000; O'Donnell et al., 2000a; O'Donnell et al., 2000b; Geiser et al., 2001; Couch and Kohn, 2002; Steenkamp et al., 2002). Mitochondrial gene sequence data such as cox II also are effective for clarifying the phylogeny of the genus and exhibits greater interspecific sequence divergence than the rRNA regions (Martin and Tooley, 2003, 2004). Rapidly accumulating data from genome sequencing/EST projects of diverse fungi and oomycetes will provide a wealth of new markers for phylogenetic studies.
An ill-defined and/or changing classification of fungal/oomycete species and genera has led to a confused picture of which species are pathogenic to a crop of interest or, as in the case of the genus Fusarium, the mycotoxigenic potential of many species. Therefore, a reevaluation of agriculturally important traits such as toxin production, pathogenicity and host range, in individual species/populations within the framework of their evolutionary relationships will significantly advance our understanding of the evolution and variation of these traits. As indicated in the Introduction, the identity of a particular fungal/oomycete isolate at the species level is often insufficient to predict its pathogenicity and toxigenic potential since these traits are often variable within populations. Mating type of individual isolates is also an important trait to document, as an obvious consequence of sexual recombination in heterothallic species is the creation of new genotypes that are potentially more aggressive. Thus, determining the genetic relationships of individual strains within a species and linking the genotype and phenotype of interest will further our understanding of the mechanisms by which a pathogen population changes in response to host/environmental selection pressures.
Community-wide efforts to catalog and integrate data on taxonomy, phylogenetics, and population genetics of fungi/oomycetes into a format that can be easily shared and updated are urgently needed for several reasons. In particular, we run the risk of losing most of the existing data because they are often stored in individual investigators’ laboratories, usually in analog form, making them inaccessible and easily lost. Even when genotypic data are in the public domain, they are usually published as a summary, which is not conducive to comparative studies. The value of such data as a basis for developing effective disease control measures will increase as all available and newly generated data for individual pathogens are integrated, allowing regional and global distribution patterns and temporal dynamics to be mapped. It should also be noted that genotype data alone is not directly sufficient for controlling plant pathogens. The full potential of such data can only be realized when combined with phenotypic data (Wheeler et al., 2004). For example, information on the genetic diversity of a pathogen population alone is not very useful per se with regard to breeding durable resistance into a host. However, this information, coupled with data on the diversity and variation of virulence, pesticide resistance, and host range within individual species in temporal and spatial contexts, will not only direct identification of the most effective means of disease control but will also assist in characterization of the genetic mechanisms by which new pathogen races evolve. Once phenotypic data are permanently linked with genetic data and strain number, the data cannot be lost even as fungal/oomycete species limits become better characterized.
(C) Importance of Culture
Collections: The World Phytophthora
Collection (WPC; http://phytophthora.ucr.edu), maintained by PD Coffey at
UC-Riverside, is a unique and invaluable culture collection used by various
investigators over the last 50 years. It
contains over 6,000 accessions, of which approximately one-third have been
characterized using isozymes (Oudemans and
Coffey, 1991a, b; Mchau and Coffey, 1994a; Oudemans et al., 1994) and/or molecular genetic markers (Förster et
al., 1990a; Förster and Coffey, 1992, 1993; Förster et al., 2000), leaving many groups uncharacterized.
Especially important are the types that form nonpapillate
sporangia, where despite enhanced definition into separate species groups based
on molecular analyses (e.g., P. sojae and P. medicaginis were originally described as P. megasperma),
much remains unresolved and many isolates await initial studies. The strains
stored at PDA (~1,000), isolated from nurseries, greenhouses, vegetable fields,
and Christmas tree plantations in
Culture collections also provide irreplaceable and invaluable resources for advancing future research, including genomics (Kamoun et al., 2002). To date, the genomes of many plant pathogens, including three Phytophthora spp. (P. sojae, P. infestans, and P. ramorum), have been sequenced or are being sequenced. Rapid progress in genome sequencing allows numerous opportunities for elucidating the evolutionary basis of many different aspects of pathogen biology. A better understanding of the phylogenetic structure among fungi/oomycetes will provide guidance for deducing the evolutionary origin and variation of genes and functions of practical significance at the genome scale. Such an understanding is also crucial for selecting appropriate species for genome sequencing in order to address particular evolutionary questions (Eisen and Fraser, 2003). The proposed Phytophthora database will complement and augment community research efforts coordinated through the NSF-sponsored Phytophthora Molecular Genetics Network (http://pmgm.vbi.vt.edu/). The availability of various high-throughput methods for detecting a large number of single nucleotide polymorphisms (SNPs) will also permit us to utilize SNPs associated with those genes controlling the traits of agricultural importance, such as host specificity, virulence, and fungicide resistance. Owing to functional genomics of Phytophthora, such genes are rapidly being discovered (Huitema et al., 2004). Phenotype-specific genetic markers will increase our ability to predict the pathological traits of newly isolated strains via genotyping.
(D) Long-term, Broader Impacts: The real impact of the proposed database is that it will provide highly interactive tools for data archiving and analysis and visualization to plant pathology/mycology communities. Exploratory analysis, data mining and visualization tools have become essential components for fully utilizing the exponentially increasing biological data. The following are examples of important questions we believe researchers will be better able to answer in the long-run: (i) What is the cause of spatially variant distribution patterns of specific pathogens?; (ii) Where did certain pathogen strains originate?; (iii) What roles do disease management and cultural practices play in altering the structure and dynamics of the pathogen community?; and (iv) What regions are vulnerable to the emergence of new variants of pathogens via recombination? The possible applications of the cyber-infrastructure will reach a wider audience than those interested in fungal plant pathogens: (i) Information on the diversity and dynamics of pathogen groups other than Phytophthora, including ones affecting human/animal health, can easily be cataloged and analyzed. The importance of cataloging such information has been recognized (Relman, 1999); (ii) Considering the rapidly increasing interests and activities for surveying biodiversity using genetic markers (Mace et al., 2003; Pennisi, 2003), the database can be used to integrate, analyze and visualize biodiversity data in ecological contexts. We believe this database will allow scientists/policy makers to make better use of available data in assessing the vulnerability of agricultural systems. With this infrastructure, it will be possible to more effectively convey the meaning of risk assessments to other scientists and policy makers, thereby advancing the objective of plant biosecurity.
In summary, this project will be a team effort of the WPC,
(A) Generating Sequence Data for Phylogenetic Analysis:
will be grown in pea broth on a rotary shaker at room temperature for approx.
3-7 days. Oomycetes often produce slimy cultures that
are problematic for efficient DNA extraction and further enzymatic activity. In
our experience, DNeasy kits (Qiagen,
Inc.) provide consistently clean and problem-free DNA extractions that work
across a very wide variety of taxa, and
undergraduates can be quickly trained to reliably use them. Genomic DNA will be
isolated from lyophilized mycelium using a DNeasy DNA
extraction protocol and resuspended in TE. DNAs will be run out and quantified on agarose
gels, and photographed digitally to provide an estimate of DNA concentration
for further work. A small fraction of the genomic DNA will be transferred to microtiter dishes and sent to
and DNA sequencing: Standard PCR protocols will be used to
amplify at least five different gene regions, the ITS region and 5’ end of the
28S nuclear rRNA (D1 and D2 domains), at least three phylogenetically informative protein-coding genes (e.g.,
translation elongation factor 1-alpha and beta-tubulin)
and/or intergenic regions, and the mitochondrially encoded cox II gene. Protocols for
amplifying and sequencing the two ribosomal gene regions and coxII are well
established, but we plan to develop new marker loci. Primers will be designed
utilizing the three Phytophthora genomes being sequenced, including P. infestans (Clade 1), P. sojae (Clade 7) and P. ramorum (Clade 8) (Huitema et
al., 2004), which represent three divergent clades
(Fig. 2). Full genome sequences also present the opportunity to design markers
for intergenic regions. Markers will be tested on a phylogenetically diverse set of Phytophthora species to ensure
their utility. PDs Geiser
and O’Donnell have considerable experience in developing protein-coding gene
primers for species-level phylogenetics and
population genetics (Geiser et al.,
1998b; O'Donnell et al., 1998a). PCR products will be generated in microtiter
format, and the products will be cleaned using a Millipore microtiter
filtration system. PCR products will be used directly as templates for DNA
sequencing, using the ABI Big Dye Terminator (v.3.1) kit. Again, reactions will
be performed in a microtiter format. Reaction
products will be cleaned using an ethanol precipitation protocol, dried, and
sent to O’Donnell’s lab in
of sample and data:
Good bookkeeping is essential in this process, given that it involves multiple
labs and multiple individuals. A simple relational database with web interface
will be developed and utilized to keep track of isolates, DNA samples, PCR products,
and sequencing reactions. After sequences have been generated, chromatogram
files will be uploaded to an FTP site at the
(B) Sample and Data Collection from Other Sources: Additional genotype and phenotype data will be obtained primarily from GenBank, laboratories that have generated large datasets for Phytophthora, and users of the database. Through the system’s home, the user will access a submission sheet with a guideline for the entry and coding of data. For those who choose web interface for data submission, we will provide an intuitive, step-by-step, submission form. Each submission will have its own ID number, which will be stored in the database and will be used for any future communications with the submitter. Once the final submission has been made, the submitter will get an acknowledgment with a submission ID. For those data downloaded from GenBank, the NCBI accession number will be directly linked to GenBank so that database users will be able to browse through the sequence source information. Because datasets downloaded from GenBank do not contain detailed phenotypic information about the strains sequenced, whenever possible, we will try to supplement with information from the literature. For example, information on host and geographic origins of isolates are not mandatory for GenBank submission, but such information might be available in publications reporting the characterization of the isolates. For example, we are currently working with the Systematic Botany and Mycology Laboratory in USDA-ARS (see letters of support) to transfer some of the information on the host and geographic distribution of selected fungal pathogens. All submitted data will be directed to a ‘clearinghouse (temporary database)’ in which the curator can verify and format the data prior to admitting them to the database.
We will also collect information from the
community. We will collaborate with scientists in
(C) Preprocessing of Sequence Data: Because the database will house multigene sequences from diverse strains within individual species, multiple isolates may exhibit identical sequences at chosen marker gene(s). To reduce the redundancy of including these identical genotypes in downstream phylogenetic analysis and data visualization, one representative sequence from a group of strains sharing identical sequences at marker loci (haplotype) will be used for phylogenetic analysis. The sequence grouping process for haplotype classifications will be performed outside of the main analysis pipeline. Grouping information will be stored in the database and representative sequence/isolates will be marked.
To group strains exhibiting identical sequences at marker loci, we tested a program called CLEANUP (Grillo et al., 1996). This program searches a set of input sequences for similarity at a user specified level, groups them together, and returns a representative sequence together with the information for individual groups. However CLEANUP is only available via a web interface and outputs are returned via email as a set of three compressed files. Because this limitation renders a routine grouping of sequence data rather difficult, we developed a new program identifying identical sequences based on string matches, grouping these sequences and uploading the information into the database. An automated checking and updating process will be part of this program. Once a new set of sequences is submitted and stored in the temporary table, each sequence will be checked against representatives of individual haplotypes, and if a perfect match is found, that strain will become part of the matching haplotype group. Those isolates without a 100% match will form new haplotype group(s) with unique IDs. Although only one representative strain for each haplotype will be used for phylogenetic analysis, users can access the list of isolates within each haplotype. In addition, users will be able to display the geographic origins of all the isolates within each group on the phylogenetic tree (Fig. 1).
(D) Phylogenetic Analysis: The proposed genotyping has two main goals: (i) To produce databases of DNA sequences that can be used to identify unknowns, and (ii) To make phylogenetic inferences based on sequences that can be used to identify evolutionary groups and define species limits. The former can often be done with the aid of a sequence from a single gene, while the latter requires information from multiple genes (Taylor et al., 2000). BLAST is an excellent tool for finding exact and close matches to DNA sequences, and thus works well for quick and basic identification purposes. However, there are benefits to putting a given query sequence into an evolutionary context, and visualizing that context via a phylogenetic analysis, producing a tree that shows the inferred evolutionary path leading to a given sequence and its relatives. This is particularly valuable when BLAST provides a number of different best matches, none of which are identical or closest to any given sequence.
In fungi, highly variable sequences like those contained in the introns of protein-coding genes tend to be dissimilar within genera, to the point where they are difficult or impossible to align. Looking at the alignments of published ITS sequences submitted in the TREEBASE database (Cooke et al., 2000), subgroups within the genus Phytophthora are generally too divergent to be aligned unambiguously. Based on our experience with ITS and protein-coding genes, we expect some of the informative intron regions to be even less alignable across the entire genus because they tend to evolve at a faster evolutionary rate than ITS. Therefore, we will analyze the genus as a number of evolutionary groups based on coxII gene phylogenies (Martin and Tooley, 2003, 2004) and the ten ITS lineages previously identified (Cooke et al., 2000), and make unique sequence alignments within each group, using alignment tools such as CLUSTALW and Se-Al.
DNA sequences can reveal both species-level relationships and relationships among isolates within a species. Since we propose to characterize many isolates within a number of Phytophthora species, this data will form a foundation for further population genetic studies. First of all, population genetic research on fungal pathogens is often handicapped by poor taxonomy that fails to recognize cryptic species boundaries, causing researchers to apply population genetic analyses to mixtures of species rather than to populations within a species (Taylor et al., 2000). Researchers can characterize Phytophthora populations using the same markers in the database, and are thus assured of putting their isolates in the correct context. By taking advantage of fast evolving markers such as protein-coding genes and intergenic regions, the clonal dynamics of Phytophthora populations can be investigated using nested clade analyses and other approaches (Carbone and Kohn, 2001). Furthermore, including data for mitochondrial genes in the analysis will facilitate identification of isolates that are interspecific hybrids, an important but understudied area of research for this genus.
Correct identification of species lineages relies on the analysis of multiple gene phylogenies, where species limits are recognized as shared nodes on trees inferred from multiple genes (Taylor et al., 2000). We will identify species limits in the genus Phytophthora by identifying such shared nodes. Maximum-parsimony, neighbor-joining, Bayesian and maximum-likelihood approaches will be used to infer evolutionary trees from at least three different genes, and the topologies of the different trees will be compared. Identical sequences will be trimmed out of these analyses to aid in computation, which will utilize PAUP*v.4.1 (Swofford, 2002), MEGA version 3 (Kumar et al., 2001), and MrBayes v.3.0 (Huelsenbeck and Ronquist, 2003) where appropriate. In our experience, different methods of phylogenetic inference at the species level yield very similar results. When major differences occur, there is usually an interesting biological phenomenon as the cause, including extra copies of the gene of interest (O'Donnell et al., 1998a), hybridization in the history of some taxa (Brasier, 2000; Olson and Stenlid, 2002), and strong selection on the locus (Ward et al., 2002). We will sift through the data for signs of such interesting phenomena, particularly conflicts among gene genealogies that could indicate a history of recent hybridization or selection on traits associated with pathogenicity and environmental adaptation.
Development and Evaluation of Molecular Diagnostic Tools: In addition to
designing and testing primers for species/population-specific PCR (traditional
and real-time) detection methods, we will test species/population-specific oligonucleotide
et al., 1998; Martin et al., 2000; Fessehaie et al.,
2003) for detecting and
differentiating a mixture of strains. PCR coupled with ELISA (Bailey et al.,
2002) is another potential way to detect multiple species. A technique
involving PCR-SSCP analysis (Kong
et al., 2003; Kong et al., 2004)
is currently being investigated for direct detection of multiple Phytophthora
species in various types of environmental samples (Kong, personal
communication). PCR-RFLP approaches can also be developed based on sequence
variation to characterize pathogen isolates by population type. For instance,
using a single nucleotide polymorphism (SNP) identified in the mitochondrial cytochrome c oxidase subunit 1 (coxI) gene in
isolates of P. ramorum
originally isolated from Europe and those from the U.S., a novel PCR-RFLP
approach associated with this SNP was developed (Kroon et al., 2004). This technique allowed the rapid
identification of both population types in the same location in
The sensitivity and specificity of these methods will be evaluated using reference cultures as well as Phytophthora samples taken from field surveys (Monrovia Growers, PDA, and NCSU). Results and recommendations from these tests will be provided via the database. One of the nurseries operated by Monrovia Growers recently shipped out plants infected with P. ramorum. It produces ~15 million landscape plants each year (of over 2200 varieties) on 4000 acres, thus providing an ample opportunity to survey Phytophthora in a wide range of plants from a cross-section of climatic conditions. PDs Coffey and Martin will mainly be involved in this survey. PD Ivors at NCSU will also collect Phytophthora infested plant material from numerous different agricultural and horticultural host species for in planta diagnostic testing and optimization. PDA surveys Phytophthora in various ornamentals as part of its SOD surveys. As a service to the community, we will provide genomic DNA and a genotyping service (sequencing ITS or a more informative locus) to those who deposit their strains at the WPC.
(F) Workshop: To provide laboratory training to the Phytophthora and plant pathology research community, workshops will be offered. Rather than developing our own, we will coordinate and present data at the second International Phytophthora workshop at NCSU (http://www.cals.ncsu.edu/plantpath/PPIL/Index.html), as the first one will be offered in July 2004 (see letters of collaboration). This training program will include the followings: (i) Basic culturing, storage, and handling techniques, (ii) How to use recommended molecular tools for studying Phytophthora species and populations, (iii) Identification methods (both molecular and traditional), (iv) Lectures on Phytophthora biology, genomics, and systematics, and (v) How to use the database resource for identification and analysis.
IV. TIME TABLE, Evaluation, and Management
(A) Time Table:
Year 1: Prepare genomic DNA and sequence ITS + 5’ end of 28S rDNA and the cox II gene. Identify and validate other genetic markers for phylogenetic analyses. Begin organizing and transferring phenotype data, general biology and pathology, literature information to the database. Get the database on line. Start developing and evaluating various molecular diagnostic methods
Year 2: Prepare genomic DNA and continue phylogenetic analyses. Offer the first workshop. Start providing molecular identification services for the community. Continue to evaluate molecular diagnostic methods using samples from the field surveys.
Year 3: Continued phylogenetic analyses. Continue to evaluate molecular diagnostic methods.
(B) Evaluation and Management: The
database will be deemed a success if workers in academic, governmental and
industrial research and regulatory organizations find the information provided
useful and effective. A “Feedback” link will be placed on the web site so
individuals can interactively tell us what they think, and will be also used to
evaluate the progress of the database and to make future plans. Counting the
number of “uses” will also help monitor the impact of the database. We will
also record the number of strain and data entries from the community. Input
from participants of the proposed workshop and scientific meetings (such as APS
annual meeting) will also be used to evaluate our progress. We formed an
advisory committee, consisting of representatives from academia (Bill Fry at
Cornell), government (Seong Hwan Kim at PDA and Paul Tooley at USDA), industry (John Keller at Monrovia
Growers), and international community (Peter Bonants
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