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

Advances in biomacromolecule-functionalized magnetic particles for phytopathogen detection

Due to the increasing crop losses caused by common and newly emerging phytopathogens, there is a pressing need for the development of rapid and reliable methods for phytopathogen detection and analysis. Leveraging advancements in biochemical engineering technologies and nanomaterial sciences, researchers have put considerable efforts on utilizing biofunctionalized magnetic micro- and nanoparticles (MPs) to develop rapid and reliable systems for phytopathogen detection. MPs facilitate the rapid, high-throughput analysis and in-field applications, while the biomacromolecules, which play key roles in the biorecognitions, interactions and signal amplification, determine the specificity, sensitivity, reliability, and portability of pathogen detection systems. The integration of MPs and biomacromolecules provides dimensionality- and composition-dependent properties, representing a novel approach to develop phytopathogen detection systems. In this review, we summarize and discuss the general properties, synthesis and characterization of MPs, and focus on biomacromolecule-functionalized MPs as well as their representative applications for phytopathogen detection and analysis reported over the past decade. Extensively studied bioreceptors, such as antibodies, phages and phage proteins, nucleic acids, and glycans that are involved in the recognitions and interactions, are covered and discussed. Additionally, the integration of MPs-based detection system with portable microfluidic devices to facilitate their in-field applications is also discussed. Overall, this review focuses on biomacromolecule-functionalized MPs and their applications for phytopathogen detection, aiming to highlight their potential in developing advanced biosensing systems for effective plant protection.

Developing Predictive Models and Early Warning Systems for Invading Pathogens: Wheat Rusts

Innovations in aerobiological and epidemiological modeling are enabling the development of powerful techniques to infer connectivity networks for transboundary pathogens in ways that were not previously possible. The innovations are supported by improved access to historical and near real-time highly resolved weather data, multi-country disease surveillance data, and enhanced computing power. Using wheat rusts as an exemplar, we introduce a flexible modeling framework to identify characteristic pathways for long-distance spore dispersal within countries and beyond national borders. We show how the models are used for near real-time early warning systems to support smallholder farmers in East Africa and South Asia. Wheat rust pathogens are ideal exemplars because they continue to pose threats to food security, especially in regions of the world where resources for control are limited. The risks are exacerbated by the rapid appearance and spread of new pathogenic strains, prodigious spore production, and long-distance dispersal for transboundary and pandemic spread.

A Review of Probe-Based Enrichment Methods to Inform Plant Virus Diagnostics

Modern diagnostic techniques based on DNA sequence similarity are currently the gold standard for the detection of existing and emerging pathogens. Whilst individual assays are inexpensive to use, assay development is costly and carries risks of not being sensitive or specific enough to capture an increasingly diverse range of targets. Sequencing can provide the entire nucleic acid content of a sample and may be used to identify all pathogens present in the sample when the depth of coverage is sufficient. Targeted enrichment techniques have been used to increase sequence coverage and improve the sensitivity of detection within virus samples, specifically, to capture sequences for a range of different viruses or increase the number of reads from low-titre virus infections. Vertebrate viruses have been well characterised using in-solution hybridisation capture to target diverse virus families. The use of probes for genotyping and strain identification has been limited in plants, and uncertainty around sensitivity is an impediment to the development of a large-scale virus panel to use within regulatory settings and diagnostic pipelines. This review aims to compare significant studies that have used targeted enrichment of viruses to identify approaches to probe design and potential for use in plant virus detection and characterisation.

Next-generation sequencing technologies for helminth diagnostics and surveillance in ruminants: shifting diagnostic barriers

Helminth infections in grazing ruminants are a major issue for livestock farming globally, but are unavoidable in outdoor grazing systems and must be effectively managed to avoid deleterious effects to animal health, and productivity. Next-generation sequencing (NGS) technologies are transforming our understanding of the genetic basis of anthelmintic resistance (AR) and epidemiological studies of ruminant gastrointestinal parasites. They also have the potential to not only help develop and validate molecular diagnostic tests but to be directly used in routine diagnostics integrating species-specific identification and AR into a single test. Here, we review how these developments have opened the pathway for the development of multi-AR and multispecies identification in a single test, with widespread implications for sustainable livestock farming for the future.

Forensic DNA phenotyping using Oxford Nanopore Sequencing system

In forensic science, the demand for precision, consistency, and cost-effectiveness has driven the exploration of next-generation sequencing technologies. This study investigates the potential of Oxford Nanopore Sequencing (ONT) Technology for analyzing the HIrisPlex-S panel, a set of 41 single nucleotide polymorphism (SNP) markers used to predict eye, hair, and skin color. Using ONT sequencing, we assessed the accuracy and reliability of ONT-generated data by comparing it with conventional capillary electrophoresis (CE) in 18 samples. The Guppy v6.1 was used as a basecaller, and sample profiles were obtained using Burrows-Wheeler Aligner, Samtools, BCFtools, and Python. Comparing accuracy with CE, we found that 62% of SNPs in ONT-unligated samples were correctly genotyped, with 36% showing allele dropout, and 2% being incorrectly genotyped. In the ONT-ligated samples, 85% of SNPs were correctly genotyped, with 10% showing allele dropout, and 5% being incorrectly genotyped. Our findings indicate that ONT, particularly when combined with ligation, enhances genotyping accuracy and coverage, thereby reducing allele dropouts. However, challenges associated with the technology's error rates and the impact on genotyping accuracy are recognized. Phenotype predictions based on ONT data demonstrate varying degrees of success, with the technology showing high accuracy in several cases. Although ONT technology holds promise in forensic genetics, further optimization and quality control measures are essential to harness its full potential. This study contributes to the ongoing efforts to refine sequence read tuning and improve correction tools in the context of ONT technology's application in forensic genetics.

The plant immune system: From discovery to deployment

Plant diseases cause famines, drive human migration, and present challenges to agricultural sustainability as pathogen ranges shift under climate change. Plant breeders discovered Mendelian genetic loci conferring disease resistance to specific pathogen isolates over 100 years ago. Subsequent breeding for disease resistance underpins modern agriculture and, along with the emergence and focus on model plants for genetics and genomics research, has provided rich resources for molecular biological exploration over the last 50 years. These studies led to the identification of extracellular and intracellular receptors that convert recognition of extracellular microbe-encoded molecular patterns or intracellular pathogen-delivered virulence effectors into defense activation. These receptor systems, and downstream responses, define plant immune systems that have evolved since the migration of plants to land ∼500 million years ago. Our current understanding of plant immune systems provides the platform for development of rational resistance enhancement to control the many diseases that continue to plague crop production.

Preventing multi-resistance: New insights for managing fungal adaptation

Sustainable crop protection is vital for food security, yet it is under threat due to the adaptation of a diverse and evolving pathogen population. Resistance can be managed by maximising the diversity of selection pressure through dose variation and the spatial and temporal combination of active ingredients. This study explores the interplay between operational drivers for maximising the sustainability of management strategies in relation to the resistance status of fungal populations. We applied an experimental evolution approach to three artificial populations of Zymoseptoria tritici, an economically significant wheat pathogen, each differing in initial resistance status. Our findings reveal that diversified selection pressure curtails the selection of resistance in naïve populations and those with low frequencies of single resistance. Increasing the number of modes of action most effectively delays resistance development, surpassing the increase in the number of fungicides, fungicide choice based on resistance risk, and temporal variation in fungicide exposure. However, this approach favours generalism in the evolved populations. The prior presence of multiple resistant isolates and their subsequent selection in populations override the effects of diversity in management strategies, thereby invalidating any universal ranking. Therefore, the initial resistance composition must be specifically considered in sustainable resistance management to address real-world field situations.

Exploiting long read sequencing to detect azole fungicide resistance mutations in Pyrenophora teres using unique molecular identifiers

Resistance to fungicides is a global challenge as target proteins under selection can evolve rapidly, reducing fungicide efficacy. To manage resistance, detection technologies must be fast and flexible enough to cope with a rapidly increasing number of mutations. The most important agricultural fungicides are azoles that target the ergosterol biosynthetic enzyme sterol 14α-demethylase (CYP51). Mutations associated with azole resistance in the Cyp51 promoter and coding sequence can co-occur in the same allele at different positions and codons, increasing the complexity of resistance detection. Resistance mutations arise rapidly and cannot be detected using traditional amplification-based methods if they are not known. To capture the complexity of azole resistance in two net blotch pathogens of barley we used the Oxford Nanopore MinION to sequence the promoter and coding sequence of Cyp51A. This approach detected all currently known mutations from biologically complex samples increasing the simplicity of resistance detection as multiple alleles can be profiled in a single assay. With the mobility and decreasing cost of long read sequencing, we demonstrate this approach is broadly applicable for characterizing resistance within known agrochemical target sites.

A targeted long-read sequencing approach questions the association of OXTR methylation with high-functioning autism

BACKGROUND

DNA sequence variation and altered epigenetic regulation of the oxytocin receptor gene (OXTR) have been implicated in autism and autistic-like behaviors. While previous studies have examined subsegments of OXTR, nanopore Cas9-targeted sequencing (nCATS) allows deep characterization of entire genes with simultaneous assessment of epigenetic 5-methylcytosine (5mC) modification and without the need for prior DNA amplification or bisulfite conversion. This pilot study uses an nCATS approach to sequence the entire OXTR gene and its regulatory construct and screen for 5mC modification to compare results between individuals with high-functioning autism (HFA) and neurotypical controls (NC).

METHODS

Using DNA extracted from peripheral blood, OXTR (Hg38, chr3: 8750381-8770434, 20,054 base pairs) was analyzed by nCATS. 5mC modification probabilities were calculated and visualized across the gene and differential methylation analysis was performed.

RESULTS

Twenty adults with HFA (10 males, 10 females) and 20 age- and sex-matched NC (± 5 years) were included. There were no apparent group differences in the entire OXTR gene sequence, except for the intron variant rs918316, which was clustered in the HFA group. However, differential methylation analysis did not reveal a single significant group-dependent differentially methylated site among the 412 CpG sites captured.

LIMITATIONS

Limitations of this study include the small number of samples due to the pilot nature of the study, which particularly limits the relevance of the sequence variants found. It should also be noted that the use of peripheral blood material limits the ability to draw conclusions about central processes.

CONCLUSIONS

Previous findings of autism-associated OXTR epigenetic alterations were not reproducible with our method. In our opinion, this may lead to a reconsideration of the relevance of altered methylation at individual OXTR CpG positions in autism research. However, given the pilot nature of the study, these results need to be replicated in independent cohorts and with larger sample sizes.

Uncovering the history of recombination and population structure in western Canadian stripe rust populations through mating type alleles

BACKGROUND

The population structure of crop pathogens such as Puccinia striiformis f. sp. tritici (Pst), the cause of wheat stripe rust, is of interest to researchers looking to understand these pathogens on a molecular level as well as those with an applied focus such as disease epidemiology. Cereal rusts can reproduce sexually or asexually, and the emergence of novel lineages has the potential to cause serious epidemics such as the one caused by the 'Warrior' lineage in Europe. In a global context, Pst lineages in Canada were not well-characterized and the origin of foreign incursions was not known. Additionally, while some Pst mating type genes have been identified in published genomes, there has been no rigorous assessment of mating type diversity and distribution across the species.

RESULTS

We used a whole-genome/transcriptome sequencing approach for the Canadian Pst population to identify lineages in their global context and evidence tracing foreign incursions. More importantly: for the first time ever, we identified nine alleles of the homeodomain mating type locus in the worldwide Pst population and show that previously identified lineages exhibit a single pair of these alleles. Consistently with the literature, we find only two pheromone receptor mating type alleles. We show that the recent population shift from the 'PstS1' lineage to the 'PstS1-related' lineage is also associated with the introduction of a novel mating type allele (Pst-b3-HD) to the Canadian population. We also show evidence for high levels of mating type diversity in samples associated with the Himalayan center of diversity for Pst, including a single Canadian race previously identified as 'PstPr' (probable recombinant) which we identify as a foreign incursion, most closely related to isolates sampled from China circa 2015.

CONCLUSIONS

These data describe a recent shift in the population of Canadian Pst field isolates and characterize homeodomain-locus mating type alleles in the global Pst population which can now be utilized in testing several research questions and hypotheses around sexuality and hybridization in rust fungi.

Genome-Enabled Insights into Downy Mildew Biology and Evolution

Oomycetes that cause downy mildew diseases are highly specialized, obligately biotrophic phytopathogens that can have major impacts on agriculture and natural ecosystems. Deciphering the genome sequence of these organisms provides foundational tools to study and deploy control strategies against downy mildew pathogens (DMPs). The recent telomere-to-telomere genome assembly of the DMP Peronospora effusa revealed high levels of synteny with distantly related DMPs, higher than expected repeat content, and previously undescribed architectures. This provides a road map for generating similar high-quality genome assemblies for other oomycetes. This review discusses biological insights made using this and other assemblies, including ancestral chromosome architecture, modes of sexual and asexual variation, the occurrence of heterokaryosis, candidate gene identification, functional validation, and population dynamics. We also discuss future avenues of research likely to be fruitful in studies of DMPs and highlight resources necessary for advancing our understanding and ability to forecast and control disease outbreaks.

Nanopore Sequencing with GraphMap for Comprehensive Pathogen Detection in Potato Field Soil

Early detection of causal pathogens is important to prevent crop loss from diseases. However, some diseases, such as soilborne diseases, are difficult to diagnose due to the absence of visible or characteristic symptoms. In the present study, the use of the Oxford Nanopore MinION sequencer as a molecular diagnostic tool was assessed due to its long-read sequencing capabilities and portability. Nucleotide samples (DNA or RNA) from potato field soils were sequenced and analyzed using a locally curated pathogen database, followed by identification via sequence mapping. We performed computational speed tests of three commonly used mapping/annotation tools (BLAST, BWA-BLAST, and BWA-GraphMap) and found BWA-GraphMap to be the fastest tool for local searching against our curated pathogen database. The data collected demonstrate the high potential of Nanopore sequencing as a minimally biased diagnostic tool for comprehensive pathogen detection in soil from potato fields. Our GraphMap-based MinION sequencing method could be useful as a predictive approach for disease management by identifying pathogens present in field soil prior to planting. Although this method still needs further experimentation with a larger sample size for practical use, the data analysis pipeline presented can be applied to other cropping systems and diagnostics for detecting multiple pathogens.

Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis

Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.

Safeguarding wheat yields from cereal fungal invaders in the postgenomic era

Wheat production is under constant threat from pests and pathogens, with fungal foliar diseases causing considerable annual yield losses. However, recent improvements in genomic tools and resources provide an unprecedented opportunity to enhance wheat's resilience in the face of these biotic constraints. Here, we discuss the impact of these advances on three key areas of managing fungal diseases of wheat: (i) enhancing the abundance of resistance traits available for plant breeding, (ii) accelerating the identification of novel fungicide targets and (iii) developing better tools for disease diagnostics and surveillance. Embracing these new genomics-led technological innovations in crop protection could revolutionise our wheat production system to improve resilience and prevent yield losses.

A highly multiplexed assay to monitor pathogenicity, fungicide resistance and gene flow in the fungal wheat pathogen Zymoseptoria tritici

Crop pathogens pose severe risks to global food production due to the rapid rise of resistance to pesticides and host resistance breakdowns. Predicting future risks requires monitoring tools to identify changes in the genetic composition of pathogen populations. Here we report the design of a microfluidics-based amplicon sequencing assay to multiplex 798 loci targeting virulence and fungicide resistance genes, and randomly selected genome-wide markers for the fungal pathogen Zymoseptoria tritici. The fungus causes one of the most devastating diseases on wheat showing rapid adaptation to fungicides and host resistance. We optimized the primer design by integrating polymorphism data from 632 genomes of the same species. To test the performance of the assay, we genotyped 192 samples in two replicates. Analysis of the short-read sequence data generated by the assay showed a fairly stable success rate across samples to amplify a large number of loci. The performance was consistent between samples originating from pure genomic DNA as well as material extracted directly from infected wheat leaves. In samples with mixed genotypes, we found that the assay recovers variations in allele frequencies. We also explored the potential of the amplicon assay to recover transposable element insertion polymorphism relevant for fungicide resistance. As a proof-of-concept, we show that the assay recovers the pathogen population structure across French wheat fields. Genomic monitoring of crop pathogens contributes to more sustainable crop protection and yields.

Diversity, detection and exploitation: linking soil fungi and plant disease

Plant-associated fungi are incredibly diverse, comprising over a million species of mycorrhiza, endophytes, saprophytes and pathogens worldwide. This diverse fungal community is highly important for plant health. Many fungi are effective biocontrol agents that can kill or suppress fungal pathogens, with pathogen biocontrol found for both individual microorganisms and plant-associated fungal consortia. Meanwhile, increased plant community diversity aboveground corresponds to an increase in below-ground fungal community diversity, which contributes in turn to improved rhizosphere soil health and pathogen suppression. In this review, we discuss the role of fungal diversity in soil health and plant disease suppression and the various mechanisms by which mycorrhizal and endophytic fungi combat plant pathogenic fungi. We also discuss the array of diagnostic tools, both well-established and newly developed, which are revolutionising fungal pathogen detection and rhizosphere community analysis.

Puccinia striiformis f. sp. tritici effectors in wheat immune responses

The obligate biotrophic fungus Puccinia striiformis f. sp. tritici, which causes yellow (stripe) rust disease, is among the leading biological agents resulting in tremendous yield losses on global wheat productions per annum. The combatting strategies include, but are not limited to, fungicide applications and the development of resistant cultivars. However, evolutionary pressure drives rapid changes, especially in its "effectorome" repertoire, thus allowing pathogens to evade and breach resistance. The extracellular and intracellular effectors, predominantly secreted proteins, are tactical arsenals aiming for many defense processes of plants. Hence, the identity of the effectors and the molecular mechanisms of the interactions between the effectors and the plant immune system have long been targeted in research. The obligate biotrophic nature of P. striiformis f. sp. tritici and the challenging nature of its host, the wheat, impede research on this topic. Next-generation sequencing and novel prediction algorithms in bioinformatics, which are accompanied by in vitro and in vivo validation approaches, offer a speedy pace for the discovery of new effectors and investigations of their biological functions. Here, we briefly review recent findings exploring the roles of P. striiformis f. sp. tritici effectors together with their cellular/subcellular localizations, host responses, and interactors. The current status and the challenges will be discussed. We hope that the overall work will provide a broader view of where we stand and a reference point to compare and evaluate new findings.

Show me your ID: NLR immune receptors with integrated domains in plants

Nucleotide-binding and leucine-rich repeat receptors (NLRs) are intracellular plant immune receptors that recognize pathogen effectors secreted into the plant cell. Canonical NLRs typically contain three conserved domains including a central nucleotide binding (NB-ARC) domain, C-terminal leucine-rich repeats (LRRs) and an N-terminal domain. A subfamily of plant NLRs contain additional noncanonical domain(s) that have potentially evolved from the integration of the effector targets in the canonical NLR structure. These NLRs with extra domains are thus referred to as NLRs with integrated domains (NLR-IDs). Here, we first summarize our current understanding of NLR-ID activation upon effector binding, focusing on the NLR pairs Pik-1/Pik-2, RGA4/RGA5, and RRS1/RPS4. We speculate on their potential oligomerization into resistosomes as it was recently shown for certain canonical plant NLRs. Furthermore, we discuss how our growing understanding of the mode of action of NLR-ID continuously informs engineering approaches to design new resistance specificities in the context of rapidly evolving pathogens.

Exploring Soybean Resistance to Soybean Cyst Nematode

Resistance to the soybean cyst nematode (SCN) is a topic incorporating multiple mechanisms and multiple types of science. It is also a topic of substantial agricultural importance, as SCN is estimated to cause more yield damage than any other pathogen of soybean, one of the world's main food crops. Both soybean and SCN have experienced jumps in experimental tractability in the past decade, and significant advances have been made. The rhg1-b locus, deployed on millions of farm acres, has been durable and will remain important, but local SCN populations are gradually evolving to overcome rhg1-b. Multiple other SCN resistance quantitative trait loci (QTL) of proven value are now in play with soybean breeders. QTL causal gene discovery and mechanistic insights into SCN resistance are contributing to both basic and applied disciplines. Additional understanding of SCN and other cyst nematodes will also grow in importance and lead to novel disease control strategies.

Proof of concept for multiplex amplicon sequencing for mutation identification using the MinION nanopore sequencer

Rapid, cost-effective identification of genetic variants in small candidate genomic regions remains a challenge, particularly for less well equipped or lower throughput laboratories. The application of Oxford Nanopore Technologies’ MinION sequencer has the potential to fulfil this requirement. We demonstrate a proof of concept for a multiplexing assay that pools PCR amplicons for MinION sequencing to enable sequencing of multiple templates from multiple individuals, which could be applied to gene-targeted diagnostics. A combined strategy of barcoding and sample pooling was developed for simultaneous multiplex MinION sequencing of 100 PCR amplicons. The amplicons are family-specific, spanning a total of 30 loci in DNA isolated from 82 human neurodevelopmental cases and family members. The target regions were chosen for further interrogation because a potentially disease-causative variant had been identified in affected individuals following Illumina exome sequencing. The pooled MinION sequences were deconvoluted by aligning to custom references using the minimap2 aligner software. Our multiplexing approach produced an interpretable and expected sequence from 29 of the 30 targeted genetic loci. The sequence variant which was not correctly resolved in the MinION sequence was adjacent to a five nucleotide homopolymer. It is already known that homopolymers present a resolution problem with the MinION approach. Interestingly despite equimolar quantities of PCR amplicon pooled for sequencing, significant variation in the depth of coverage (127×–19,626×; mean = 8321×, std err = 452.99) was observed. We observed independent relationships between depth of coverage and target length, and depth of coverage and GC content. These relationships demonstrate biases of the MinION sequencer for longer templates and those with lower GC content. We demonstrate an efficient approach for variant discovery or confirmation from short DNA templates using the MinION sequencing device. With less than 130 × depth of coverage required for accurate genotyping, the methodology described here allows for rapid highly multiplexed targeted sequencing of large numbers of samples in a minimally equipped laboratory with a potential cost as much 200 × less than that from Sanger sequencing.

Integrated Strategies for Durable Rice Blast Resistance in Sub-Saharan Africa

Rice is a key food security crop in Africa. The importance of rice has led to increasing country-specific, regional, and multinational efforts to develop germplasm and policy initiatives to boost production for a more food-secure continent. Currently, this critically important cereal crop is predominantly cultivated by small-scale farmers under suboptimal conditions in most parts of sub-Saharan Africa (SSA). Rice blast disease, caused by the fungus Magnaporthe oryzae, represents one of the major biotic constraints to rice production under small-scale farming systems of Africa, and developing durable disease resistance is therefore of critical importance. In this review, we provide an overview of the major advances by a multinational collaborative research effort to enhance sustainable rice production across SSA and how it is affected by advances in regional policy. As part of the multinational effort, we highlight the importance of joint international partnerships in tackling multiple crop production constraints through integrated research and outreach programs. More specifically, we highlight recent progress in establishing international networks for rice blast disease surveillance, farmer engagement, monitoring pathogen virulence spectra, and the establishment of regionally based blast resistance breeding programs. To develop blast-resistant, high yielding rice varieties for Africa, we have established a breeding pipeline that utilizes real-time data of pathogen diversity and virulence spectra, to identify major and minor blast resistance genes for introgression into locally adapted rice cultivars. In addition, the project has developed a package to support sustainable rice production through regular stakeholder engagement, training of agricultural extension officers, and establishment of plant clinics.

Incursions of divergent genotypes, evolution of virulence and host jumps shape a continental clonal population of the stripe rust pathogen Puccinia striiformis

Long-distance migration and host adaptation by transboundary plant pathogens often brings detrimental effects to important agroecosystems. Efficient surveillance as a basis for responding to the dynamics of such pathogens is often hampered by a lack of information on incursion origin, evolutionary pathways and the genetic basis of rapidly evolving virulence across larger timescales. Here, we studied these genetic features by using historical isolates of the obligate biotrophic pathogen Puccinia striiformis f. sp. tritici (Pst), which causes one of the most widespread and devastating diseases, stripe (yellow) rust, of wheat. Through a combination of genotypic, phenotypic and genomic analyses, we assigned eight Pst isolates representing putative exotic Pst incursions into Australia to four previously defined genetic groups, PstS0, PstS1, PstS10 and PstS13. We showed that isolates of an additional incursion of P. striiformis, known locally as P. striiformis f. sp. pseudo-hordei, had a new and unique multilocus SSR genotype (MLG). We provide results of overall genomic variation of representative Pst isolates from each genetic group by comparative genomic analyses. We showed that isolates within the PstS1 and PstS13 genetic groups are most distinct at the whole-genome variant level from isolates belonging to genetic group PstS0, whereas the isolate from the PstS10 genetic group is intermediate. We further explored variable gene content, including putative effectors, representing both shared but also unique genetic changes that have occurred following introduction, some of which may additionally account for local adaptation of these isolates to triticale. Our genotypic and genomic data revealed new genetic insights into the evolution of diverse phenotypes of rust pathogens following incursion into a geographically isolated continental region.

Molecular Characterization of Wheat Stripe Rust Pathogen (Puccinia striiformis f. sp. tritici) Collections from Nine Countries

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most important diseases of wheat worldwide. To understand the worldwide distribution of its molecular groups, as well as the diversity, differentiation, and migration of the Pst populations, 567 isolates collected from nine countries (China, Pakistan, Italy, Egypt, Ethiopia, Canada, Mexico, Ecuador, and the U.S.) in 2010–2018 were genotyped using 14 codominant simple sequence repeat markers. A total of 433, including 333 new multi-locus genotypes (MLGs), were identified, which were clustered into ten molecular groups (MGs). The MGs and country-wise populations differed in genetic diversity, heterozygosity, and correlation coefficient between the marker and virulence data. Many isolates from different countries, especially the isolates from Mexico, Ecuador, and the U.S., were found to be identical or closely related MLGs, and some of the MGs were present in all countries, indicating Pst migrations among different countries. The analysis of molecular variance revealed 78% variation among isolates, 12% variation among countries, and 10% variation within countries. Only low levels of differentiation were found by the pairwise comparisons of country populations. Of the 10 MGs, 5 were found to be involved in sexual and/or somatic recombination. Identical and closely related MLGs identified from different countries indicated international migrations. The study provides information on the distributions of various Pst genetic groups in different countries and evidence for the global migrations, which should be useful in understanding the pathogen evolution and in stressing the need for continual monitoring of the disease and pathogen populations at the global scale.

Creation and judicious application of a wheat resistance gene atlas

Disease-resistance (R) gene cloning in wheat (Triticum aestivum) has been accelerated by the recent surge of genomic resources, facilitated by advances in sequencing technologies and bioinformatics. However, with the challenges of population growth and climate change, it is vital not only to clone and functionally characterize a few handfuls of R genes, but also to do so at a scale that would facilitate the breeding and deployment of crops that can recognize the wide range of pathogen effectors that threaten agroecosystems. Pathogen populations are continually changing, and breeders must have tools and resources available to rapidly respond to those changes if we are to safeguard our daily bread. To meet this challenge, we propose the creation of a wheat R-gene atlas by an international community of researchers and breeders. The atlas would consist of an online directory from which sources of resistance could be identified and deployed to achieve more durable resistance to the major wheat pathogens, such as wheat rusts, blotch diseases, powdery mildew, and wheat blast. We present a costed proposal detailing how the interacting molecular components governing disease resistance could be captured from both the host and the pathogen through biparental mapping, mutational genomics, and whole-genome association genetics. We explore options for the configuration and genotyping of diversity panels of hexaploid and tetraploid wheat, as well as their wild relatives and major pathogens, and discuss how the atlas could inform a dynamic, durable approach to R-gene deployment. Set against the current magnitude of wheat yield losses worldwide, recently estimated at 21%, this endeavor presents one route for bringing R genes from the lab to the field at a considerable speed and quantity.

High frequency of fungicide resistance-associated mutations in the wheat yellow rust pathogen Puccinia striiformis f. sp. tritici

BACKGROUND

Reliance on fungicides to manage disease creates selection pressure for the evolution of resistance in fungal and oomycete pathogens. Rust fungi (Pucciniales) are major pathogens of cereals and other crops and have been classified as low-risk for developing resistance to fungicides; no case of field failure of fungicides in a cereal rust disease has yet been recorded. Recently, the Asian soybean rust pathogen, Phakopsora pachyrhizi evolved resistance to several fungicide classes, prompting us to screen a large sample of the globally widespread wheat yellow rust pathogen, Puccinia striiformis f. sp. tritici (Pst), for mutations associated with fungicide resistance.

RESULTS

We evaluated 363 Pst isolates from Europe, the USA, Ethiopia, Chile, China and New Zealand for mutations in the target genes of demethylase inhibitor (DMI; Cyp51) and succinate dehydrogenase inhibitor (SDHI; SdhB, SdhC and SdhD) fungicides. A high proportion of Pst isolates carrying a Y134F DMI resistance-associated substitution in the Cyp51 gene was found among those from China and New Zealand. A set of geographically diverse Pst isolates was also found to display a substitution in SdhC (I85V) that is homologous to that reported recently in P. pachyrhizi and linked to SDHI resistance.

CONCLUSION

The identification of resistance-associated alleles confirms that cereal rusts are not immune to fungicide resistance and that selection for resistance evolution is operating at high levels in certain locations. It highlights the need to adopt fungicide resistance management practices and to monitor cereal rust species for development of resistance. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

The persistent threat of emerging plant disease pandemics to global food security

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.

Plant pest surveillance: from satellites to molecules

Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment.

Rust expression browser: an open source database for simultaneous analysis of host and pathogen gene expression profiles with expVIP

Background

Transcriptomics is being increasingly applied to generate new insight into the interactions between plants and their pathogens. For the wheat yellow (stripe) rust pathogen (Puccinia striiformis f. sp. tritici, Pst) RNA-based sequencing (RNA-Seq) has proved particularly valuable, overcoming the barriers associated with its obligate biotrophic nature. This includes the application of RNA-Seq approaches to study Pst and wheat gene expression dynamics over time and the Pst population composition through the use of a novel RNA-Seq based surveillance approach called “field pathogenomics”. As a dual RNA-Seq approach, the field pathogenomics technique also provides gene expression data from the host, giving new insight into host responses. However, this has created a wealth of data for interrogation.

Results

Here, we used the field pathogenomics approach to generate 538 new RNA-Seq datasets from Pst-infected field wheat samples, doubling the amount of transcriptomics data available for this important pathosystem. We then analysed these datasets alongside 66 RNA-Seq datasets from four Pst infection time-courses and 420 Pst-infected plant field and laboratory samples that were publicly available. A database of gene expression values for Pst and wheat was generated for each of these 1024 RNA-Seq datasets and incorporated into the development of the rust expression browser (http://www.rust-expression.com). This enables for the first time simultaneous ‘point-and-click’ access to gene expression profiles for Pst and its wheat host and represents the largest database of processed RNA-Seq datasets available for any of the three Puccinia wheat rust pathogens. We also demonstrated the utility of the browser through investigation of expression of putative Pst virulence genes over time and examined the host plants response to Pst infection.

Conclusions

The rust expression browser offers immense value to the wider community, facilitating data sharing and transparency and the underlying database can be continually expanded as more datasets become publicly available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07488-3.

Fantastic Downy Mildew Pathogens and How to Find Them: Advances in Detection and Diagnostics

Downy mildews affect important crops and cause severe losses in production worldwide. Accurate identification and monitoring of these plant pathogens, especially at early stages of the disease, is fundamental in achieving effective disease control. The rapid development of molecular methods for diagnosis has provided more specific, fast, reliable, sensitive, and portable alternatives for plant pathogen detection and quantification than traditional approaches. In this review, we provide information on the use of molecular markers, serological techniques, and nucleic acid amplification technologies for downy mildew diagnosis, highlighting the benefits and disadvantages of the technologies and target selection. We emphasize the importance of incorporating information on pathogen variability in virulence and fungicide resistance for disease management and how the development and application of diagnostic assays based on standard and promising technologies, including high-throughput sequencing and genomics, are revolutionizing the development of species-specific assays suitable for in-field diagnosis. Our review provides an overview of molecular detection technologies and a practical guide for selecting the best approaches for diagnosis.

Wheat rust epidemics damage Ethiopian wheat production: A decade of field disease surveillance reveals national-scale trends in past outbreaks

Wheat rusts are the key biological constraint to wheat production in Ethiopia-one of Africa's largest wheat producing countries. The fungal diseases cause economic losses and threaten livelihoods of smallholder farmers. While it is known that wheat rust epidemics have occurred in Ethiopia, to date no systematic long-term analysis of past outbreaks has been available. We present results from one of the most comprehensive surveillance campaigns of wheat rusts in Africa. More than 13,000 fields have been surveyed during the last 13 years. Using a combination of spatial data-analysis and visualization, statistical tools, and empirical modelling, we identify trends in the distribution of wheat stem rust (Sr), stripe rust (Yr) and leaf rust (Lr). Results show very high infection levels (mean incidence for Yr: 44%; Sr: 34%; Lr: 18%). These recurrent rust outbreaks lead to substantial economic losses, which we estimate to be of the order of 10s of millions of US-D annually. On the widely adopted wheat variety, Digalu, there is a marked increase in disease prevalence following the incursion of new rust races into Ethiopia, which indicates a pronounced boom-and-bust cycle of major gene resistance. Using spatial analyses, we identify hotspots of disease risk for all three rusts, show a linear correlation between altitude and disease prevalence, and find a pronounced north-south trend in stem rust prevalence. Temporal analyses show a sigmoidal increase in disease levels during the wheat season and strong inter-annual variations. While a simple logistic curve performs satisfactorily in predicting stem rust in some years, it cannot account for the complex outbreak patterns in other years and fails to predict the occurrence of stripe and leaf rust. The empirical insights into wheat rust epidemiology in Ethiopia presented here provide a basis for improving future surveillance and to inform the development of mechanistic models to predict disease spread.

Advances in point-of-care nucleic acid extraction technologies for rapid diagnosis of human and plant diseases

Global health and food security constantly face the challenge of emerging human and plant diseases caused by bacteria, viruses, fungi, and other pathogens. Disease outbreaks such as SARS, MERS, Swine Flu, Ebola, and COVID-19 (on-going) have caused suffering, death, and economic losses worldwide. To prevent the spread of disease and protect human populations, rapid point-of-care (POC) molecular diagnosis of human and plant diseases play an increasingly crucial role. Nucleic acid-based molecular diagnosis reveals valuable information at the genomic level about the identity of the disease-causing pathogens and their pathogenesis, which help researchers, healthcare professionals, and patients to detect the presence of pathogens, track the spread of disease, and guide treatment more efficiently. A typical nucleic acid-based diagnostic test consists of three major steps: nucleic acid extraction, amplification, and amplicon detection. Among these steps, nucleic acid extraction is the first step of sample preparation, which remains one of the main challenges when converting laboratory molecular assays into POC tests. Sample preparation from human and plant specimens is a time-consuming and multi-step process, which requires well-equipped laboratories and skilled lab personnel. To perform rapid molecular diagnosis in resource-limited settings, simpler and instrument-free nucleic acid extraction techniques are required to improve the speed of field detection with minimal human intervention. This review summarizes the recent advances in POC nucleic acid extraction technologies. In particular, this review focuses on novel devices or methods that have demonstrated applicability and robustness for the isolation of high-quality nucleic acid from complex raw samples, such as human blood, saliva, sputum, nasal swabs, urine, and plant tissues. The integration of these rapid nucleic acid preparation methods with miniaturized assay and sensor technologies would pave the road for the "sample-in-result-out" diagnosis of human and plant diseases, especially in remote or resource-limited settings.

Proteinaceous effector discovery and characterization in filamentous plant pathogens

The complicated interplay of plant-pathogen interactions occurs on multiple levels as pathogens evolve to constantly evade the immune responses of their hosts. Many economically important crops fall victim to filamentous pathogens that produce small proteins called effectors to manipulate the host and aid infection/colonization. Understanding the effector repertoires of pathogens is facilitating an increased understanding of the molecular mechanisms underlying virulence as well as guiding the development of disease control strategies. The purpose of this review is to give a chronological perspective on the evolution of the methodologies used in effector discovery from physical isolation and in silico predictions, to functional characterization of the effectors of filamentous plant pathogens and identification of their host targets.

Blurred lines: integrating emerging technologies to advance plant biosecurity

Plant diseases threaten global food security and biodiversity. Rapid dispersal of pathogens particularly via human means has accelerated in recent years. Timely detection of plant pathogens is essential to limit their spread. At the same time, international regulations must keep abreast of advances in plant disease diagnostics. In this review we describe recent progress in developing modern plant disease diagnostics based on detection of pathogen components, high-throughput image analysis, remote sensing, and machine learning. We discuss how different diagnostic approaches can be integrated in detection frameworks that can work at different scales and account for sampling biases. Lastly, we briefly discuss the requirements to apply these advances under regulatory settings to improve biosecurity measures globally.

Evolution of virulence in rust fungi - multiple solutions to one problem

Rust fungi are major pathogens that negatively affect crops and ecosystems. Recent rust disease epidemics driven by the emergence of strains with novel virulence profiles demand a better understanding of the evolutionary mechanisms of these organisms. Here, we review research advances in genome-scale analysis coupled with functional validation of effector candidate genes that have been instrumental to elucidate processes that contribute to changes in virulence phenotypes. We highlight how haplotype-phased genome references have paved the road to link these processes to the reproductive phases of rust fungi and have provided evidence for somatic exchange between strains as an important mechanism for generating diversity in asexual populations. With increasing data availability, we envision the future development of molecular virulence diagnostic tools.

Differential loss of effector genes in three recently expanded pandemic clonal lineages of the rice blast fungus

BACKGROUND

Understanding the mechanisms and timescales of plant pathogen outbreaks requires a detailed genome-scale analysis of their population history. The fungus Magnaporthe (Syn. Pyricularia) oryzae-the causal agent of blast disease of cereals- is among the most destructive plant pathogens to world agriculture and a major threat to the production of rice, wheat, and other cereals. Although M. oryzae is a multihost pathogen that infects more than 50 species of cereals and grasses, all rice-infecting isolates belong to a single genetically defined lineage. Here, we combined the two largest genomic datasets to reconstruct the genetic history of the rice-infecting lineage of M. oryzae based on 131 isolates from 21 countries.

RESULTS

The global population of the rice blast fungus consists mainly of three well-defined genetic groups and a diverse set of individuals. Multiple population genetic tests revealed that the rice-infecting lineage of the blast fungus probably originated from a recombining diverse group in Southeast Asia followed by three independent clonal expansions that took place over the last ~ 200 years. Patterns of allele sharing identified a subpopulation from the recombining diverse group that introgressed with one of the clonal lineages before its global expansion. Remarkably, the four genetic lineages of the rice blast fungus vary in the number and patterns of presence and absence of candidate effector genes. These genes encode secreted proteins that modulate plant defense and allow pathogen colonization. In particular, clonal lineages carry a reduced repertoire of effector genes compared with the diverse group, and specific combinations of presence and absence of effector genes define each of the pandemic clonal lineages.

CONCLUSIONS

Our analyses reconstruct the genetic history of the rice-infecting lineage of M. oryzae revealing three clonal lineages associated with rice blast pandemics. Each of these lineages displays a specific pattern of presence and absence of effector genes that may have shaped their adaptation to the rice host and their evolutionary history.

Distinct Life Histories Impact Dikaryotic Genome Evolution in the Rust Fungus Puccinia striiformis Causing Stripe Rust in Wheat

Stripe rust of wheat, caused by the obligate biotrophic fungus Puccinia striiformis f.sp. tritici, is a major threat to wheat production worldwide with an estimated yearly loss of US $1 billion. The recent advances in long-read sequencing technologies and tailored-assembly algorithms enabled us to disentangle the two haploid genomes of Pst. This provides us with haplotype-specific information at a whole-genome level. Exploiting this novel information, we perform whole-genome comparative genomics of two P. striiformis f.sp. tritici isolates with contrasting life histories. We compare one isolate of the old European lineage (PstS0), which has been asexual for over 50 years, and a Warrior isolate (PstS7 lineage) from a novel incursion into Europe in 2011 from a sexual population in the Himalayan region. This comparison provides evidence that long-term asexual evolution leads to genome expansion, accumulation of transposable elements, and increased heterozygosity at the single nucleotide, structural, and allele levels. At the whole-genome level, candidate effectors are not compartmentalized and do not exhibit reduced levels of synteny. Yet we were able to identify two subsets of candidate effector populations. About 70% of candidate effectors are invariant between the two isolates, whereas 30% are hypervariable. The latter might be involved in host adaptation on wheat and explain the different phenotypes of the two isolates. Overall, this detailed comparative analysis of two haplotype-aware assemblies of P. striiformis f.sp. tritici is the first step in understanding the evolution of dikaryotic rust fungi at a whole-genome level.

Distinct Life Histories Impact Dikaryotic Genome Evolution in the Rust Fungus Puccinia striiformis Causing Stripe Rust in Wheat

Stripe rust of wheat, caused by the obligate biotrophic fungus Puccinia striiformis f.sp. tritici, is a major threat to wheat production worldwide with an estimated yearly loss of US $1 billion. The recent advances in long-read sequencing technologies and tailored-assembly algorithms enabled us to disentangle the two haploid genomes of Pst. This provides us with haplotype-specific information at a whole-genome level. Exploiting this novel information, we perform whole-genome comparative genomics of two P. striiformis f.sp. tritici isolates with contrasting life histories. We compare one isolate of the old European lineage (PstS0), which has been asexual for over 50 years, and a Warrior isolate (PstS7 lineage) from a novel incursion into Europe in 2011 from a sexual population in the Himalayan region. This comparison provides evidence that long-term asexual evolution leads to genome expansion, accumulation of transposable elements, and increased heterozygosity at the single nucleotide, structural, and allele levels. At the whole-genome level, candidate effectors are not compartmentalized and do not exhibit reduced levels of synteny. Yet we were able to identify two subsets of candidate effector populations. About 70% of candidate effectors are invariant between the two isolates, whereas 30% are hypervariable. The latter might be involved in host adaptation on wheat and explain the different phenotypes of the two isolates. Overall, this detailed comparative analysis of two haplotype-aware assemblies of P. striiformis f.sp. tritici is the first step in understanding the evolution of dikaryotic rust fungi at a whole-genome level.

Strain-Level Identification of Bacterial Tomato Pathogens Directly from Metagenomic Sequences

Routine strain-level identification of plant pathogens directly from symptomatic tissue could significantly improve plant disease control and prevention. Here we tested the Oxford Nanopore Technologies (ONT) MinION sequencer for metagenomic sequencing of tomato plants either artificially inoculated with a known strain of the bacterial speck pathogen Pseudomonas syringae pv. tomato or collected in the field and showing bacterial spot symptoms caused by one of four Xanthomonas species. After species-level identification via ONT's WIMP software and the third-party tools Sourmash and MetaMaps, we used Sourmash and MetaMaps with a custom database of representative genomes of bacterial tomato pathogens to attempt strain-level identification. In parallel, each metagenome was assembled and the longest contigs were used as query with the genome-based microbial identification Web service LINbase. Both the read-based and assembly-based approaches correctly identified P. syringae pv. tomato strain T1 in the artificially inoculated samples. The pathogen strain in most field samples was identified as a member of Xanthomonas perforans group 2. This result was confirmed by whole genome sequencing of colonies isolated from one of the samples. Although in our case metagenome-based pathogen identification at the strain level was achieved, caution still must be exercised in interpreting strain-level results because of the challenges inherent to assigning reads to specific strains and the error rate of nanopore sequencing.

Progress on Molecular Genetics and Manipulation of Rust Fungi

Among the thousands of rust species described, many are known for their devastating effects on their hosts, which include major agriculture crops and trees. Hence, for over a century, these basidiomycete pathogenic fungi have been researched and experimented with. However, due to their biotrophic nature, they are challenging organisms to work with and, needing their hosts for propagation, represent pathosystems that are not easily experimentally accessible. Indeed, efforts to perform genetics have been few and far apart for the rust fungi, though one study performed in the 1940s was famously instrumental in formulating the gene-for-gene hypothesis describing pathogen-host interactions. By taking full advantage of the molecular genetic tools developed in the 1980s, research on many plant pathogenic microbes thrived, yet similar work on the rusts remained very challenging though not without some successes. However, the genomics era brought real breakthrough research for the biotrophic fungi and with innovative experimentation and the use of heterologous systems, molecular genetic analyses over the last 2 decades have significantly advanced our insight into the function of many rust fungus genes and their role in the interaction with their hosts. This has allowed optimizing efforts for resistance breeding and the design and testing of various novel strategies to reduce the devastating diseases they cause.

Molecular Approaches for Low-Cost Point-of-Care Pathogen Detection in Agriculture and Forestry

Early detection of plant diseases is a crucial factor to prevent or limit the spread of a rising infection that could cause significant economic loss. Detection test on plant diseases in the laboratory can be laborious, time consuming, expensive, and normally requires specific technical expertise. Moreover, in the developing countries, it is often difficult to find laboratories equipped for this kind of analysis. Therefore, in the past years, a high effort has been made for the development of fast, specific, sensitive, and cost-effective tests that can be successfully used in plant pathology directly in the field by low-specialized personnel using minimal equipment. Nucleic acid-based methods have proven to be a good choice for the development of detection tools in several fields, such as human/animal health, food safety, and water analysis, and their application in plant pathogen detection is becoming more and more common. In the present review, the more recent nucleic acid-based protocols for point-of-care (POC) plant pathogen detection and identification are described and analyzed. All these methods have a high potential for early detection of destructive diseases in agriculture and forestry, they should help make molecular detection for plant pathogens accessible to anyone, anywhere, and at any time. We do not suggest that on-site methods should replace lab testing completely, which remains crucial for more complex researches, such as identification and classification of new pathogens or the study of plant defense mechanisms. Instead, POC analysis can provide a useful, fast, and efficient preliminary on-site screening that is crucial in the struggle against plant pathogens.