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).
· To
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.
The
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.
III. APPROACH
In summary, this project will be a team effort of the WPC,
(A) Generating Sequence Data for Phylogenetic Analysis:
· DNA
extraction: Isolates
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
· PCR
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
· Management
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
Phytophthora
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.
(E)
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
arrays (Lévesque
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
in the
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