Aeciospore ejection in the rust pathogen Puccinia graminis is driven by moisture ingress.

Fungi have evolved an array of spore discharge and dispersal processes. Here, we developed a theoretical model that explains the ejection mechanics of aeciospore liberation in the stem rust pathogen Puccinia graminis. Aeciospores are released from cluster cups formed on its Berberis host, spreading early-season inoculum into neighboring small-grain crops. Our model illustrates that during dew or rainfall, changes in aeciospore turgidity exerts substantial force on neighboring aeciospores in cluster cups whilst gaps between spores become perfused with water. This perfusion coats aeciospores with a lubrication film that facilitates expulsion, with single aeciospores reaching speeds of 0.053 to 0.754 m·s-1. We also used aeciospore source strength estimates to simulate the aeciospore dispersal gradient and incorporated this into a publicly available web interface. This aids farmers and legislators to assess current local risk of dispersal and facilitates development of sophisticated epidemiological models to potentially curtail stem rust epidemics originating on Berberis.

MARPLE, a point-of-care, strain-level disease diagnostics and surveillance tool for complex fungal pathogens.

BACKGROUND: Effective disease management depends on timely and accurate diagnosis to guide control measures. The capacity to distinguish between individuals in a pathogen population with specific properties such as fungicide resistance, toxin production and virulence profiles is often essential to inform disease management approaches. The genomics revolution has led to technologies that can rapidly produce high-resolution genotypic information to define individual variants of a pathogen species. However, their application to complex fungal pathogens has remained limited due to the frequent inability to culture these pathogens in the absence of their host and their large genome sizes. RESULTS: Here, we describe the development of Mobile And Real-time PLant disEase (MARPLE) diagnostics, a portable, genomics-based, point-of-care approach specifically tailored to identify individual strains of complex fungal plant pathogens. We used targeted sequencing to overcome limitations associated with the size of fungal genomes and their often obligately biotrophic nature. Focusing on the wheat yellow rust pathogen, Puccinia striiformis f.sp. tritici (Pst), we demonstrate that our approach can be used to rapidly define individual strains, assign strains to distinct genetic lineages that have been shown to correlate tightly with their virulence profiles and monitor genes of importance. CONCLUSIONS: MARPLE diagnostics enables rapid identification of individual pathogen strains and has the potential to monitor those with specific properties such as fungicide resistance directly from field-collected infected plant tissue in situ. Generating results within 48 h of field sampling, this new strategy has far-reaching implications for tracking plant health threats.

Potential for re-emergence of wheat stem rust in the United Kingdom.

Wheat stem rust, a devastating disease of wheat and barley caused by the fungal pathogen Puccinia graminis f. sp. tritici, was largely eradicated in Western Europe during the mid-to-late twentieth century. However, isolated outbreaks have occurred in recent years. Here we investigate whether a lack of resistance in modern European varieties, increased presence of its alternate host barberry and changes in climatic conditions could be facilitating its resurgence. We report the first wheat stem rust occurrence in the United Kingdom in nearly 60 years, with only 20% of UK wheat varieties resistant to this strain. Climate changes over the past 25 years also suggest increasingly conducive conditions for infection. Furthermore, we document the first occurrence in decades of P. graminis on barberry in the UK . Our data illustrate that wheat stem rust does occur in the UK and, when climatic conditions are conducive, could severely harm wheat and barley production.

Pathogenomic Analysis of Wheat Yellow Rust Lineages Detects Seasonal Variation and Host Specificity.

Recent disease outbreaks caused by (re-)emerging plant pathogens have been associated with expansions in pathogen geographic distribution and increased virulence. For example, in the past two decades' wheat yellow (stripe) rust, Puccinia striiformis f. sp. tritici, has seen the emergence of new races that are adapted to warmer temperatures, have expanded virulence profiles, and are more aggressive than previous races, leading to wide-scale epidemics. Here, we used field-based genotyping to generate high-resolution data on P. striiformis genetics and carried out global population analysis. We also undertook comparative analysis of the 2014 and 2013 UK populations and assessed the temporal dynamics and host specificity of distinct pathogen genotypes. Our analysis revealed that P. striiformis lineages recently detected in Europe are extremely diverse and in fact similar to globally dispersed populations. In addition, we identified a considerable shift in the UK P. striiformis population structure including the first identification of one infamous race known as Kranich. Next, by establishing the genotype of both the pathogen and host within a single infected field sample, we uncovered evidence for varietal specificity for genetic groups of P. striiformis. Finally, we found potential seasonal specificity for certain genotypes of the pathogen with several lineages identified only in samples collected in late spring and into the summer, whereas one lineage was identified throughout the wheat growing season. Our discovery of which wheat varieties are susceptible to which specific P. striiformis isolates, and when those isolates are prevalent throughout the year, represents a powerful tool for disease management.

Field Pathogenomics: An Advanced Tool for Wheat Rust Surveillance.

Traditionally, diagnostic tools for plant pathogens were limited to the analysis of purified pathogen isolates subjected to phenotypic characterization and/or PCR-based genotypic analysis. However, these approaches detect only already known pathogenic agents, may not always recognize novel races, and can introduce bias in the results. Recent advances in next-generation sequencing technologies have provided new opportunities to integrate high-resolution genotype data into pathogen surveillance programs. Here, we describe some of the key bioinformatics analysis used in the newly developed "Field Pathogenomics" pathogen surveillance technique. This technique is based on RNA-seq data generated directly form pathogen-infected plant leaf samples collected in the field, providing a unique opportunity to characterize the pathogen population and its host directly in their natural environment. We describe two main analyses: (1) a phylogenetic analysis of the pathogen isolates that have been collected to understand how they are related to each other, and (2) a population structure analysis to provide insight into the genetic substructure within the pathogen population. This provides a high-resolution representation of pathogen population dynamics directly in the field, providing new insights into pathogen biology, population structure, and pathogenesis.