A Framework for Optimizing Phytosanitary Thresholds in Seed Systems

A New Multiplex TaqMan qPCR for Precise Detection and Quantification of Clavibacter michiganensis in Seeds and Plant Tissue

Bacterial canker of tomato caused by Clavibacter michiganensis (Cm) is one of the most devastating bacterial diseases affecting the tomato industry worldwide. As the result of Cm colonization of the xylem, the susceptible host shows typical symptoms of wilt, marginal leaf necrosis, stem cankers, and ultimately plant death. However, what makes Cm an even more dangerous pathogen is its ability to infect seeds and plants without causing symptoms. Unfortunately, there are no resistant cultivars or effective chemical or biological control methods available to growers against Cm. Its control relies heavily on prevention. The implementation of a rapid and accurate detection tool is imperative to monitor the presence of Cm and prevent its spread. In this study, we developed a specific and sensitive multiplex TaqMan qPCR assay to detect Cm and distinguish it from related bacterial species that affect tomato plants. Two Cm chromosomal virulence-related genes, rhuM and tomA, were used as specific targets. The plant internal control tubulin alpha-3 was included in each of the multiplexes to improve the reliability of the assay. Specificity was evaluated with 37 bacterial strains including other Clavibacter spp. and related and unrelated bacterial pathogens from different geographic locations affecting a wide variety of hosts. Results showed that the assay is able to discriminate Cm strains from other related bacteria. The assay was validated on tissue and seed samples following artificial infection, and all tested samples accurately detected the presence of Cm. The tool described here is highly specific, sensitive, and reliable for the detection of Cm and allows the quantification of Cm in seeds, roots, stems, and leaves. The diagnostic assay can also be adapted for multiple purposes such as seed certification programs, surveillance, biosafety, the effectiveness of control methods, border protection, and epidemiological studies.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Disaster Plant Pathology: Smart Solutions for Threats to Global Plant Health from Natural and Human-Driven Disasters

Disaster plant pathology addresses how natural and human-driven disasters impact plant diseases and the requirements for smart management solutions. Local to global drivers of plant disease change in response to disasters, often creating environments more conducive to plant disease. Most disasters have indirect effects on plant health through factors such as disrupted supply chains and damaged infrastructure. There is also the potential for direct effects from disasters, such as pathogen or vector dispersal due to floods, hurricanes, and human migration driven by war. Pulse stressors such as hurricanes and war require rapid responses, whereas press stressors such as climate change leave more time for management adaptation but may ultimately cause broader challenges. Smart solutions for the effects of disasters can be deployed through digital agriculture and decision support systems supporting disaster preparedness and optimized humanitarian aid across scales. Here, we use the disaster plant pathology framework to synthesize the effects of disasters in plant pathology and outline solutions to maintain food security and plant health in catastrophic scenarios. We recommend actions for improving food security before and following disasters, including (i) strengthening regional and global cooperation, (ii) capacity building for rapid implementation of new technologies, (iii) effective clean seed systems that can act quickly to replace seed lost in disasters, (iv) resilient biosecurity infrastructure and risk assessment ready for rapid implementation, and (v) decision support systems that can adapt rapidly to unexpected scenarios. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Transmission of Spinach Downy Mildew via Seed and Infested Leaf Debris

Spinach downy mildew, caused by the obligate oomycete pathogen Peronospora effusa, is a worldwide constraint on spinach production. The role of airborne sporangia in the disease cycle of P. effusa is well established, but the role of the sexual oospores in the epidemiology of P. effusa is less clear and has been a major challenge to examine experimentally. To evaluate seed transmission of spinach downy mildew via oospores in this study, isolated glass chambers were employed in two independent experiments to grow out oospore-infested spinach seed and noninfested seeds mixed with oospore-infested crop debris. Downy mildew diseased spinach plants were observed 37 and 34 days after planting in the two isolator experiments, respectively, in the chambers that contained one of two oospore-infested seed lots or seeds coated with oospore-infested leaves. Spinach plants in isolated glass chambers initiated from seeds without oospores did not show downy mildew symptoms. Similar findings were obtained using the same seed lot samples in a third experiment conducted in a growth chamber. In direct grow out tests to examine oospore infection on seedlings performed in a containment greenhouse with oospore-infested seed of two different cultivars, characteristic Peronospora sporangiophores were observed growing from a seedling of each cultivar. The frequency of seedlings developing symptoms from 82 of these oospore-infested seed indicated that approximately 2.4% of seedlings from infested seed developed symptoms, and 0.55% of seedlings from total seeds assayed developed symptoms. The results provide evidence that oospores can serve as a source of inoculum for downy mildew and provide further evidence of direct seed transmission of the downy mildew pathogen to seedlings in spinach via seedborne oospores.

Translating virome analyses to support biosecurity, on-farm management, and crop breeding

Virome analysis via high-throughput sequencing (HTS) allows rapid and massive virus identification and diagnoses, expanding our focus from individual samples to the ecological distribution of viruses in agroecological landscapes. Decreases in sequencing costs combined with technological advances, such as automation and robotics, allow for efficient processing and analysis of numerous samples in plant disease clinics, tissue culture laboratories, and breeding programs. There are many opportunities for translating virome analysis to support plant health. For example, virome analysis can be employed in the development of biosecurity strategies and policies, including the implementation of virome risk assessments to support regulation and reduce the movement of infected plant material. A challenge is to identify which new viruses discovered through HTS require regulation and which can be allowed to move in germplasm and trade. On-farm management strategies can incorporate information from high-throughput surveillance, monitoring for new and known viruses across scales, to rapidly identify important agricultural viruses and understand their abundance and spread. Virome indexing programs can be used to generate clean germplasm and seed, crucial for the maintenance of seed system production and health, particularly in vegetatively propagated crops such as roots, tubers, and bananas. Virome analysis in breeding programs can provide insight into virus expression levels by generating relative abundance data, aiding in breeding cultivars resistant, or at least tolerant, to viruses. The integration of network analysis and machine learning techniques can facilitate designing and implementing management strategies, using novel forms of information to provide a scalable, replicable, and practical approach to developing management strategies for viromes. In the long run, these management strategies will be designed by generating sequence databases and building on the foundation of pre-existing knowledge about virus taxonomy, distribution, and host range. In conclusion, virome analysis will support the early adoption and implementation of integrated control strategies, impacting global markets, reducing the risk of introducing novel viruses, and limiting virus spread. The effective translation of virome analysis depends on capacity building to make benefits available globally.

Development, Validation, and Utility of Species-Specific Diagnostic Markers for Detection of Peronospora belbahrii

Peronospora belbahrii is an oomycete and the cause of basil downy mildew, one of the most destructive diseases affecting basil production worldwide. Disease management is challenging due to wind-dispersed sporangia and contaminated seed; therefore, identifying P. belbahrii in seed lots before sale or planting or in the field before symptoms develop could allow for timely deployment of disease management strategies. In this study, a draft genome assembly and next-generation sequencing reads for P. belbahrii, as well as publicly available DNA-seq and RNA-seq reads of several other downy mildew pathogens, were incorporated into a bioinformatics pipeline to predict P. belbahrii-specific diagnostic markers. The specificity of each candidate marker was validated against a diverse DNA collection of P. belbahrii, host tissue, and related oomycetes using PCR. Two species-specific markers were identified and used as templates to develop a highly sensitive probe-based real-time quantitative PCR (qPCR) assay that could detect P. belbahrii in leaf tissue and seed samples. Both markers were capable of reliably detecting as low as 500 fg/µl of P. belbahrii genomic DNA and as few as 10 sporangia. The qPCR assay was then validated with seed samples collected from a basil cultivar experiment. In total, 48 seed samples were collected and tested; P. belbahrii was detected in samples of all cultivars at estimated concentrations of 600 fg/µl up to 250 pg/µl and at as few as 10 sporangia up to >1,000 sporangia. The markers and assays are valuable for diagnostics and identifying P. belbahrii-contaminated seed lots to mitigate the effects of future basil downy mildew epidemics.

Where to Invest Project Efforts for Greater Benefit: A Framework for Management Performance Mapping with Examples for Potato Seed Health

Policymakers and donors often need to identify the locations where technologies are most likely to have important effects, to increase the benefits from agricultural development or extension efforts. Higher-quality information may help to target the high-benefit locations, but often actions are needed with limited information. The value of information (VOI) in this context is formalized by evaluating the results of decision making guided by a set of specific information compared with the results of acting without considering that information. We present a framework for management performance mapping that includes evaluating the VOI for decision making about geographic priorities in regional intervention strategies, in case studies of Andean and Kenyan potato seed systems. We illustrate the use of recursive partitioning, XGBoost, and Bayesian network models to characterize the relationships among seed health and yield responses and environmental and management predictors used in studies of seed degeneration. These analyses address the expected performance of an intervention based on geographic predictor variables. In the Andean example, positive selection of seed from asymptomatic plants was more effective at high altitudes in Ecuador. In the Kenyan example, there was the potential to target locations with higher technology adoption rates and with higher potato cropland connectivity, i.e., a likely more important role in regional epidemics. Targeting training to high management performance areas would often provide more benefits than would random selection of target areas. We illustrate how assessing the VOI can contribute to targeted development programs and support a culture of continuous improvement for interventions.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Detection and Quantification of Stagonosporopsis cucurbitacearum in Seeds of Cucurbita maxima Using Droplet Digital Polymerase Chain Reaction

Stagonosporopsis cucurbitacearum is an important seedborne pathogen of squash (Cucurbita maxima). The aim of our work was to develop a rapid and sensitive diagnostic tool for detection and quantification of S. cucurbitacearum in squash seed samples, to be compared with blotter analysis, that is the current official seed test. In blotter analysis, 29 of 31 seed samples were identified as infected, with contamination from 1.5 to 65.4%. A new set of primers (DB1F/R) was validated in silico and in conventional, quantitative real-time PCR (qPCR) and droplet digital (dd) PCR. The limit of detection of S. cucurbitacearum DNA for conventional PCR was ∼1.82 × 10^–2 ng, with 17 of 19 seed samples positive. The limit of detection for ddPCR was 3.6 × 10^–3 ng, which corresponded to 0.2 copies/μl. Detection carried out with artificial samples revealed no interference in the absolute quantification when the seed samples were diluted to 20 ng. All seed samples that showed S. cucurbitacearum contamination in the blotter analysis were highly correlated with the absolute quantification of S. cucurbitacearum DNA (copies/μl) in ddPCR (R² = 0.986; p ≤ 0.01). Our ddPCR protocol provided rapid detection and absolute quantification of S. cucurbitacearum, offering a useful support to the standard procedure.

Multiplex qPCR Assay for Direct Detection and Quantification of Colletotrichum truncatum, Corynespora cassiicola, and Sclerotinia sclerotiorum in Soybean Seeds

Precise diagnosis of plant diseases is one of the most effective tools to minimize yield losses. Colletotrichum truncatum, Corynespora cassiicola, and Sclerotinia sclerotiorum are common soilborne pathogens that affect soybeans all over the world. We developed a multiplex quantitative real-time polymerase chain reaction (qPCR) assay to simultaneously detect and quantify the three pathogens in soybean seeds and to survey their occurrence in the main soybean production areas in Brazil. Species-specific primers and probes for C. truncatum and C. cassiicola were designed based on GAPDH and TEF1 genes, respectively, to be combined with qPCR detection of S. sclerotiorum previously reported. The multiplex qPCR assay was successful in the simultaneous detection of C. truncatum, C. cassiicola, and S. sclerotiorum, along with a host internal control. The four pathogens were detected and quantified in artificially and naturally infested soybean seeds, even in the lowest incidence level tested of 0.0625% or 1 infected seed out of 1,599 healthy ones. From 81 seed samples tested, C. truncatum was the most frequently detected pathogen and with higher incidence levels (0.25 to 0.125%), followed by S. sclerotiorum and C. cassiicola, both with lower incidence levels (0.125 to 0.0625%). Together, the results evidenced the high sensitivity of the multiplex qPCR assay, indicating its usefulness for a quick and reliable diagnosis of soybean diseases in seeds.

Modeling Epidemics in Seed Systems and Landscapes To Guide Management Strategies: The Case of Sweet Potato in Northern Uganda

Seed systems are critical for deployment of improved varieties but also can serve as major conduits for the spread of seedborne pathogens. As in many other epidemic systems, epidemic risk in seed systems often depends on the structure of networks of trade, social interactions, and landscape connectivity. In a case study, we evaluated the structure of an informal sweet potato seed system in the Gulu region of northern Uganda for its vulnerability to the spread of emerging epidemics and its utility for disseminating improved varieties. Seed transaction data were collected by surveying vine sellers weekly during the 2014 growing season. We combined data from these observed seed transactions with estimated dispersal risk based on village-to-village proximity to create a multilayer network or "supranetwork." Both the inverse power law function and negative exponential function, common models for dispersal kernels, were evaluated in a sensitivity analysis/uncertainty quantification across a range of parameters chosen to represent spread based on proximity in the landscape. In a set of simulation experiments, we modeled the introduction of a novel pathogen and evaluated the influence of spread parameters on the selection of villages for surveillance and management. We found that the starting position in the network was critical for epidemic progress and final epidemic outcomes, largely driven by node out-degree. The efficacy of node centrality measures was evaluated for utility in identifying villages in the network to manage and limit disease spread. Node degree often performed as well as other, more complicated centrality measures for the networks where village-to-village spread was modeled by the inverse power law, whereas betweenness centrality was often more effective for negative exponential dispersal. This analysis framework can be applied to provide recommendations for a wide variety of seed systems.[Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Spinach Downy Mildew: Advances in Our Understanding of the Disease Cycle and Prospects for Disease Management

Downy mildew on spinach is caused by Peronospora effusa, an oomycete pathogen that poses a challenge to spinach production worldwide, especially in organic production. Following infection, P. effusa produces abundant amounts of asexual sporangia. Sporangia become windborne and initiate new infections locally or distantly, leading to widespread epidemics. Oospores produced from the union of opposite mating types have been observed within infected leaves and seeds and may remain viable for many years. Sexual reproduction increases the genetic diversity of P. effusa through sexual recombination, and thus, the movement of oospores on seed has likely fueled the rapid explosion of new pathotypes in different regions of the world over the past 20 years. This review summarizes recent advances in spinach downy mildew research, especially in light of the findings of oospores in contemporary commercial spinach seed lots as well as their germination. Knowledge of the role of the oospores and other aspects of the disease cycle can directly translate into new and effective disease management strategies.

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.

Epidemiology: Past, Present, and Future Impacts on Understanding Disease Dynamics and Improving Plant Disease Management-A Summary of Focus Issue Articles

Epidemiology has made significant contributions to plant pathology by elucidating the general principles underlying the development of disease epidemics. This has resulted in a greatly improved theoretical and empirical understanding of the dynamics of disease epidemics in time and space, predictions of disease outbreaks or the need for disease control in real-time basis, and tactical and strategic solutions to disease problems. Availability of high-resolution experimental data at multiple temporal and spatial scales has now provided a platform to test and validate theories on the spread of diseases at a wide range of spatial scales ranging from the local to the landscape level. Relatively new approaches in plant disease epidemiology, ranging from network to information theory, coupled with the availability of large-scale datasets and the rapid development of computer technology, are leading to revolutionary thinking about epidemics that can result in considerable improvement of strategic and tactical decision making in the control and management of plant diseases. Methods that were previously restricted to topics such as population biology or evolution are now being employed in epidemiology to enable a better understanding of the forces that drive the development of plant disease epidemics in space and time. This Focus Issue of Phytopathology features research articles that address broad themes in epidemiology including social and political consequences of disease epidemics, decision theory and support, pathogen dispersal and disease spread, disease assessment and pathogen biology and disease resistance. It is important to emphasize that these articles are just a sample of the types of research projects that are relevant to epidemiology. Below, we provide a succinct summary of the articles that are published in this Focus Issue .