The National Plant Diagnostic Network

Everything Is Faster: How Do Land-Grant University-Based Plant Diagnostic Laboratories Keep Up with a Rapidly Changing World?

Plant diagnostic laboratories (PDLs) are at the heart of land-grant universities (LGUs) and their extension mission to connect citizens with research-based information. Although research and technological advances have led to many modern methods and technologies in plant pathology diagnostics, the pace of adopting those methods into services at PDLs has many complexities we aim to explore in this review. We seek to identify current challenges in plant disease diagnostics, as well as diagnosticians' and administrators'perceptions of PDLs' many roles. Surveys of diagnosticians and administrators were conducted to understand the current climate on these topics. We hope this article reaches researchers developing diagnostic methods with modern and new technologies to foster a better understanding of PDL diagnosticians' perspective on method implementation. Ultimately, increasing researchers' awareness of the factors influencing method adoption by PDLs encourages support, collaboration, and partnerships to advance plant diagnostics.

Dissecting the economic impact of soybean diseases in the United States over two decades

Soybean (Glycine max L. Merrill) is an economically important commodity for United States agriculture. Nonetheless, the profitability of soybean production has been negatively impacted by soybean diseases. The economic impacts of 23 common soybean diseases were estimated in 28 soybean-producing states in the U.S., from 1996 to 2016 (the entire data set consisted of 13,524 data points). Estimated losses were investigated using a variety of statistical approaches. The main effects of state, year, pre- and post-discovery of soybean rust, region, and zones based on yield, harvest area, and production, were significant on "total economic loss" as a function of diseases. Across states and years, the soybean cyst nematode, charcoal rot, and seedling diseases were the most economically damaging diseases while soybean rust, bacterial blight, and southern blight were the least economically damaging. A significantly greater mean loss (51%) was observed in states/years after the discovery of soybean rust (2004 to 2016) compared to the pre-discovery (1996 to 2003). From 1996 to 2016, the total estimated economic loss due to soybean diseases in the U.S. was $95.48 billion, with $80.89 billion and $14.59 billion accounting for the northern and southern U.S. losses, respectively. Over the entire time period, the average annual economic loss due to soybean diseases in the U.S. reached nearly $4.55 billion, with approximately 85% of the losses occurring in the northern U.S. Low yield/harvest/production zones had significantly lower mean economic losses due to diseases in comparison to high yield/harvest/production zones. This observation was further bolstered by the observed positive linear correlation of mean soybean yield loss (in each state, due to all diseases considered in this study, across 21 years) with the mean state wide soybean production (MT), mean soybean yield (kg ha-1), and mean soybean harvest area (ha). Results of this investigation provide useful insights into how research, policy, and educational efforts should be prioritized in soybean disease management.

Capacity of United States federal government and its partners to rapidly and accurately report the identity (taxonomy) of non-native organisms intercepted in early detection programs

The early detection of and rapid response to invasive species (EDRR) depends on accurate and rapid identification of non-native species. The 2016–2018 National Invasive Species Council Management Plan called for an assessment of US government (federal) capacity to report on the identity of non-native organisms intercepted through early detection programs. This paper serves as the response to that action item. Here we summarize survey-based findings and make recommendations for improving the federal government’s capacity to identify non-native species authoritatively in a timely manner. We conclude with recommendations to improve accurate identification within the context of EDRR by increasing coordination, maintaining taxonomic expertise, creating an identification tools clearinghouse, developing and using taxonomic standards for naming and identification protocols, expanding the content of DNA and DNA Barcode libraries, ensuring long-term sustainability of biological collections, and engaging and empowering citizens and citizen science groups.

Network Analysis: A Systems Framework to Address Grand Challenges in Plant Pathology

Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.

The Role of Viruses in the Phytobiome

Viruses are an important but sequence-diverse and often understudied component of the phytobiome. We succinctly review current information on how plant viruses directly affect plant health and physiology and consequently have the capacity to modulate plant interactions with their biotic and abiotic environments. Virus interactions with other biota in the phytobiome, including arthropods, fungi, and nematodes, may also impact plant health. For example, viruses interact with and modulate the interface between plants and insects. This has been extensively studied for insect-vectored plant viruses, some of which also infect their vectors. Other viruses have been shown to alter the impacts of plant-interacting phytopathogenic and nonpathogenic fungi and bacteria. Viruses that infect nematodes have also recently been discovered, but the impact of these and phage infecting soil bacteria on plant health remain largely unexplored.

Comparison of genus-specific primers in RT-PCR for the broad-spectrum detection of viruses in the genus Potyvirus by plant diagnostic laboratories

The Potyvirus genus is one of the largest genera of plant viruses and encompasses many economically important pathogens. While a number of degenerate primers for use in broad spectrum RT-PCR assays have been published, it is not clear which of these primers would be the most useful for use by plant diagnostic laboratories. Twelve sets of primers were tested for their ability to detect nine potyviruses in a two-step RT-PCR. Viruses were extracted from different host backgrounds and were selected to represent eight clades plus one species between clades (sensu Gibbs and Ohshima, 2010). Results of this study indicated that the primers CIFor/CIRev produced easily detectable amplicons from all nine potyviruses without non-specific amplification, false positives, or false negatives. CIFor/CIRev produced single amplicons from potyvirus-infected tissues which could be sequenced directly without gel purification to identify the virus to species.

Coordination of Diagnostic Efforts in the Great Plains: Wheat Virus Survey and Modeling of Disease Onset

Following the discovery of two new wheat virus diseases in the United States, the Great Plains region (Colorado, Kansas, Montana, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wyoming) of the National Plant Diagnostic Network (NPDN) initiated a project to measure the prevalence of five wheat diseases using indirect ELISA. Wheat streak mosaic virus (WSMV), Wheat mosaic virus (WMoV), and Triticum mosaic virus (TriMV) were found in all nine states. WSMV was the most prevalent, averaging 23 to 47% of samples each year. TriMV and WMoV were detected with WSMV (in up to 76% of the samples). All three mite-transmitted viruses were present in 26% or fewer of the samples. Aphid-transmitted viruses in the barley yellow dwarf complex Barley yellow dwarf virus, and Cereal yellow dwarf virus-RPV were less frequent (fewer than 65% of the samples). This paper presents the first case-control methodology paper using plant diagnostic laboratory data and the first signed diagnostic data-sharing agreement between the NPDN and its regulatory stakeholders. Samples collected when <700 cumulative degree-days base 0°C, were twice as likely to be virus negative. This proof-of-concept effort highlights the potential of the NPDN and its National Data Repository to develop knowledge about emerging diseases.

Meta-analysis and other approaches for synthesizing structured and unstructured data in plant pathology

The term data deluge is used widely to describe the rapidly accelerating growth of information in the technical literature, in scientific databases, and in informal sources such as the Internet and social media. The massive volume and increased complexity of information challenge traditional methods of data analysis but at the same time provide unprecedented opportunities to test hypotheses or uncover new relationships via mining of existing databases and literature. In this review, we discuss analytical approaches that are beginning to be applied to help synthesize the vast amount of information generated by the data deluge and thus accelerate the pace of discovery in plant pathology. We begin with a review of meta-analysis as an established approach for summarizing standardized (structured) data across the literature. We then turn to examples of synthesizing more complex, unstructured data sets through a range of data-mining approaches, including the incorporation of 'omics data in epidemiological analyses. We conclude with a discussion of methodologies for leveraging information contained in novel, open-source data sets through web crawling, text mining, and social media analytics, primarily in the context of digital disease surveillance. Rapidly evolving computational resources provide platforms for integrating large and complex data sets, motivating research that will draw on new types and scales of information to address big questions.

The Microbial Rosetta Stone Central Agricultural Database: An Information Resource on High-Consequence Plant Pathogens

Microbial pathogens of humans, animals, and plants can serve as potential agents of biowarfare, bioterrorism, and biocrime. Previously, the Microbial Rosetta Stone (MRS) Central database, an easily accessible informational resource tool, was developed to assist law enforcement personnel in the event of a disease investigation by providing key information on pathogens of concern. Although the database already contained information on a few high-profile plant pathogens, the coverage was insufficient considering the large number of plant pathogens that pose a threat, not only to agricultural production but also to natural plant resources such as forests and rangelands. In this project, 100 plant pathogens of high consequence were selected for study, existing literature on these agents was reviewed, and both the sources and key pathogen information provided therein were curated in the new Agricultural Database (AgDB), an accessory to the existing MRS Central Database. Chosen for inclusion in the MRS Central AgDB were plant pathogens having significant potential for damage to U.S. agricultural and natural ecosystems. The selection process included review of several previously developed plant-pathogen threat lists and recommendations from experts within the U.S. plant biosecurity community. Pathogen information was collected by searching a number of relevant literature databases, sites on the World Wide Web, and other resources. For inclusion in the MRS, the information was curated into categories: pathogen taxonomy, nomenclature synonyms, disease symptoms and geographic distribution, plant hosts, insect vectors, detection and diagnostic methods, laboratory and field protocols, sample collection, and epidemiology. The resulting AgDB enhances the MRS Central Database by summarizing and linking key information on high-threat plant diseases and their causal agents to relevant scientific literature and internet resources. The AgDB contains critical, key information on high-consequence plant pathogens, curated in a format that is readily accessible and easily searched. The resource enhances the existing MRS Central Database and provides law enforcement, forensic, and investigative personnel with an additional tool with which to respond to microbial emergencies, particularly those affecting the agricultural and environmental sectors.

Comparison of Tissue-Disruption Methods for PCR-Based Detection of Plant Pathogens

Polymerase chain reaction-based detection of plant-associated microbes depends on physical disruption of tissues of the host and microorganism in order to liberate nucleic acids during extraction. Using six types of plant tissues as well as an oospore preparation of Phytophthora capsici, we evaluated the use of pressure-cycling technology (PCT) compared with several common techniques for physical tissue disruption. With all tissues tested, bead-beating provided excellent yields of amplifiable nucleic acid, with a few inconsistent exceptions. The use of PCT did not consistently improve nucleic acid yields or "amplifiability". The use of a mortar and pestle to physically disrupt plant tissue also provided good results at low cost, though it was not consistently as effective as the bead-beater. Furthermore, handling of ground tissues in an open mortar may present more challenges in minimizing cross-contamination than working with tissues pulverized in a bead-beater tube.

Method: a single nucleotide polymorphism genotyping method for Wheat streak mosaic virus

BACKGROUND

The September 11, 2001 attacks on the World Trade Center and the Pentagon increased the concern about the potential for terrorist attacks on many vulnerable sectors of the US, including agriculture. The concentrated nature of crops, easily obtainable biological agents, and highly detrimental impacts make agroterrorism a potential threat. Although procedures for an effective criminal investigation and attribution following such an attack are available, important enhancements are still needed, one of which is the capability for fine discrimination among pathogen strains. The purpose of this study was to develop a molecular typing assay for use in a forensic investigation, using Wheat streak mosaic virus (WSMV) as a model plant virus.

METHOD

This genotyping technique utilizes single base primer extension to generate a genetic fingerprint. Fifteen single nucleotide polymorphisms (SNPs) within the coat protein and helper component-protease genes were selected as the genetic markers for this assay. Assay optimization and sensitivity testing was conducted using synthetic targets. WSMV strains and field isolates were collected from regions around the world and used to evaluate the assay for discrimination. The assay specificity was tested against a panel of near-neighbors consisting of genetic and environmental near-neighbors.

RESULT

Each WSMV strain or field isolate tested produced a unique SNP fingerprint, with the exception of three isolates collected within the same geographic location that produced indistinguishable fingerprints. The results were consistent among replicates, demonstrating the reproducibility of the assay. No SNP fingerprints were generated from organisms included in the near-neighbor panel, suggesting the assay is specific for WSMV. Using synthetic targets, a complete profile could be generated from as low as 7.15 fmoles of cDNA.

CONCLUSION

The molecular typing method presented is one tool that could be incorporated into the forensic science tool box after a thorough validation study. This method incorporates molecular biology techniques that are already well established in research and diagnostic laboratories, allowing for an easy introduction of this method into existing laboratories.

KEYWORDS

single nucleotide polymorphisms, genotyping, plant pathology, viruses, microbial forensics, Single base primer extension, SNaPshot Multiplex Kit.

Plant disease diagnostic capabilities and networks

Emerging, re-emerging and endemic plant pathogens continue to challege our ability to safeguard plant health worldwide. Further, globalization, climate change, increased human mobility, and pathogen and vector evolution have combined to increase the spread of invasive plant pathogens. Early and accurate diagnoses and pathogen surveillance on local, regional, and global scales are necessary to predict outbreaks and allow time for development and application of mitigation strategies. Plant disease diagnostic networks have developed worldwide to address the problems of efficient and effective disease diagnosis and pathogen detection, engendering cooperation of institutions and experts within countries and across national borders. Networking maximizes impact in the face of shrinking government investments in agriculture and diminishing human resource capacity in diagnostics and applied pathology. New technologies promise to improve the speed and accuracy of disease diagnostics and pathogen detection. Widespread adoption of standard operating procedures and diagnostic laboratory accreditation serve to build trust and confidence among institutions. Case studies of national, regional, and international diagnostic networks are presented.

Plant pathogen forensics: capabilities, needs, and recommendations

A biological attack on U.S. crops, rangelands, or forests could reduce yield and quality, erode consumer confidence, affect economic health and the environment, and possibly impact human nutrition and international relations. Preparedness for a crop bioterror event requires a strong national security plan that includes steps for microbial forensics and criminal attribution. However, U.S. crop producers, consultants, and agricultural scientists have traditionally focused primarily on strategies for prevention and management of diseases introduced naturally or unintentionally rather than on responding appropriately to an intentional pathogen introduction. We assess currently available information, technologies, and resources that were developed originally to ensure plant health but also could be utilized for postintroduction plant pathogen forensics. Recommendations for prioritization of efforts and resource expenditures needed to enhance our plant pathogen forensics capabilities are presented.