Spontaneous and Fungicide-Induced Genomic Variation in Sclerotinia sclerotiorum.

Stress from exposure to sublethal fungicide doses may cause genomic instability in fungal plant pathogens, which may accelerate the emergence of fungicide resistance or other adaptive traits. In a previous study, five strains of Sclerotinia sclerotiorum were exposed to sublethal doses of four fungicides with different modes of action, and genotyping showed that such exposure induced mutations. The goal of the present study was to characterize genome-wide mutations in response to sublethal fungicide stress in S. sclerotiorum and study the effect of genomic background on the mutational repertoire. The objectives were to determine the effect of sublethal dose exposure and genomic background on mutation frequency/type, distribution of mutations, and fitness costs. Fifty-five S. sclerotiorum genomes were sequenced and aligned to the reference genome. Variants were called and quality filtered to obtain high confidence calls for single nucleotide polymorphisms (SNPs), insertions/deletions (INDELs), copy number variants, and transposable element (TE) insertions. Results suggest that sublethal fungicide exposure significantly increased the frequency of INDELs in two strains from one genomic background (P value ≤ 0.05), while TE insertions were generally repressed for all genomic backgrounds and under all fungicide exposures. The frequency and/or distribution of SNPs, INDELs, and TE insertions varied with genomic background. A propensity for large duplications on chromosome 7 and aneuploidy of this chromosome were observed in the S. sclerotiorum genome. Mutation accumulation did not significantly affect the overall in planta strain aggressiveness (P value > 0.05). Understanding factors that affect pathogen mutation rates can inform disease management strategies that delay resistance evolution.

Genetic Differentiation and Clonal Expansion of Chinese Botrytis cinerea Populations from Tomato and Other Crops in China.

Botrytis cinerea is an important pathogen of vegetable and fruit crops but little is known about its population structure and genetics in China. We hypothesized that the geographic populations of B. cinerea in China would be genetically differentiated by host, geographic location, and/or year. In this study, we collected 393 B. cinerea isolates representing 28 populations from tomato, cherry, and nectarine from 2006 to 2014 in China. The isolates were analyzed using 14 microsatellite markers, including six new markers that provided more genotyping power than the eight previously published loci. We also investigated the B. cinerea population structure and inferred its mode of reproduction and dispersal based on genotype data. High genotypic diversity was detected in all populations, and clonal reproduction was dominant. Southern China populations harbored more genotypes than northern populations. Differentiation by host plant was evident. Between 2011 and 2012, genotypes changed only slightly among years for Liaoning populations, but they changed substantially among years for the Shanghai and Fujian populations. Clonal dispersal was detected and the farthest dispersal distance was estimated to be about 1,717 km. Two high-frequency genotypes were widely distributed in more than 10 populations and across several years. Our results provide useful, novel information for plant breeding programs and control of B. cinerea in China.

Outbreak analytics: a developing data science for informing the response to emerging pathogens.

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Epidemic curves made easy using the R package incidence.

The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling. incidence is available from https://www.repidemicsconsortium.org/incidence.

epiflows: an R package for risk assessment of travel-related spread of disease.

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

Population structure and phenotypic variation of Sclerotinia sclerotiorum from dry bean (Phaseolus vulgaris) in the United States.

The ascomycete pathogen Sclerotinia sclerotiorum is a necrotrophic pathogen on over 400 known host plants, and is the causal agent of white mold on dry bean. Currently, there are no known cultivars of dry bean with complete resistance to white mold. For more than 20 years, bean breeders have been using white mold screening nurseries (wmn) with natural populations of S. sclerotiorum to screen new cultivars for resistance. It is thus important to know if the genetic diversity in populations of S. sclerotiorum within these nurseries (a) reflect the genetic diversity of the populations in the surrounding region and (b) are stable over time. Furthermore, previous studies have investigated the correlation between mycelial compatibility groups (MCG) and multilocus haplotypes (MLH), but none have formally tested these patterns. We genotyped 366 isolates of S. sclerotiorum from producer fields and wmn surveyed over 10 years in 2003-2012 representing 11 states in the United States of America, Australia, France, and Mexico at 11 microsatellite loci resulting in 165 MLHs. Populations were loosely structured over space and time based on analysis of molecular variance and discriminant analysis of principal components, but not by cultivar, aggressiveness, or field source. Of all the regions tested, only Mexico (n = 18) shared no MLHs with any other region. Using a bipartite network-based approach, we found no evidence that the MCGs accurately represent MLHs. Our study suggests that breeders should continue to test dry bean lines in several wmn across the United States to account for both the phenotypic and genotypic variation that exists across regions.

Developing educational resources for population genetics in R: an open and collaborative approach.

The r computing and statistical language community has developed a myriad of resources for conducting population genetic analyses. However, resources for learning how to carry out population genetic analyses in r are scattered and often incomplete, which can make acquiring this skill unnecessarily difficult and time consuming. To address this gap, we developed an online community resource with guidance and working demonstrations for conducting population genetic analyses in r. The resource is freely available at http://popgen.nescent.org and includes material for both novices and advanced users of r for population genetics. To facilitate continued maintenance and growth of this resource, we developed a toolchain, process and conventions designed to (i) minimize financial and labour costs of upkeep; (ii) to provide a low barrier to contribution; and (iii) to ensure strong quality assurance. The toolchain includes automatic integration testing of every change and rebuilding of the website when new vignettes or edits are accepted. The process and conventions largely follow a common, distributed version control-based contribution workflow, which is used to provide and manage open peer review by designated website editors. The online resources include detailed documentation of this process, including video tutorials. We invite the community of population geneticists working in r to contribute to this resource, whether for a new use case of their own, or as one of the vignettes from the 'wish list' we maintain, or by improving existing vignettes.

Microbe-ID: an open source toolbox for microbial genotyping and species identification.

Development of tools to identify species, genotypes, or novel strains of invasive organisms is critical for monitoring emergence and implementing rapid response measures. Molecular markers, although critical to identifying species or genotypes, require bioinformatic tools for analysis. However, user-friendly analytical tools for fast identification are not readily available. To address this need, we created a web-based set of applications called Microbe-ID that allow for customizing a toolbox for rapid species identification and strain genotyping using any genetic markers of choice. Two components of Microbe-ID, named Sequence-ID and Genotype-ID, implement species and genotype identification, respectively. Sequence-ID allows identification of species by using BLAST to query sequences for any locus of interest against a custom reference sequence database. Genotype-ID allows placement of an unknown multilocus marker in either a minimum spanning network or dendrogram with bootstrap support from a user-created reference database. Microbe-ID can be used for identification of any organism based on nucleotide sequences or any molecular marker type and several examples are provided. We created a public website for demonstration purposes called Microbe-ID (microbe-id.org) and provided a working implementation for the genus Phytophthora (phytophthora-id.org). In Phytophthora-ID, the Sequence-ID application allows identification based on ITS or cox spacer sequences. Genotype-ID groups individuals into clonal lineages based on simple sequence repeat (SSR) markers for the two invasive plant pathogen species P. infestans and P. ramorum. All code is open source and available on github and CRAN. Instructions for installation and use are provided at https://github.com/grunwaldlab/Microbe-ID.

Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality.

To gain a detailed understanding of how plant microbes evolve and adapt to hosts, pesticides, and other factors, knowledge of the population dynamics and evolutionary history of populations is crucial. Plant pathogen populations are often clonal or partially clonal which requires different analytical tools. With the advent of high throughput sequencing technologies, obtaining genome-wide population genetic data has become easier than ever before. We previously contributed the R package poppr specifically addressing issues with analysis of clonal populations. In this paper we provide several significant extensions to poppr with a focus on large, genome-wide SNP data. Specifically, we provide several new functionalities including the new function mlg.filter to define clone boundaries allowing for inspection and definition of what is a clonal lineage, minimum spanning networks with reticulation, a sliding-window analysis of the index of association, modular bootstrapping of any genetic distance, and analyses across any level of hierarchies.

Population Structure of Pythium irregulare, P. ultimum, and P. sylvaticum in Forest Nursery Soils of Oregon and Washington.

Pythium species are important soilborne pathogens occurring in the forest nursery industry of the Pacific Northwest. However, little is known about their genetic diversity or population structure and it is suspected that isolates are moved among forest nurseries on seedling stock and shared field equipment. In order to address these concerns, a total of 115 isolates of three Pythium species (P. irregulare, P. sylvaticum, and P. ultimum) were examined at three forest nurseries using simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) markers. Analyses revealed distinct patterns of intraspecific variation for the three species. P. sylvaticum exhibited the most diversity, followed by P. irregulare, while substantial clonality was found in P. ultimum. For both P. irregulare and P. sylvaticum, but not P. ultimum, there was evidence for significant variation among nurseries. However, all three species also exhibited at least two distinct lineages not associated with the nursery of origin. Finally, evidence was found that certain lineages and clonal genotypes, including fungicide-resistant isolates, are shared among nurseries, indicating that pathogen movement has occurred.

Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction.

Many microbial, fungal, or oomcyete populations violate assumptions for population genetic analysis because these populations are clonal, admixed, partially clonal, and/or sexual. Furthermore, few tools exist that are specifically designed for analyzing data from clonal populations, making analysis difficult and haphazard. We developed the R package poppr providing unique tools for analysis of data from admixed, clonal, mixed, and/or sexual populations. Currently, poppr can be used for dominant/codominant and haploid/diploid genetic data. Data can be imported from several formats including GenAlEx formatted text files and can be analyzed on a user-defined hierarchy that includes unlimited levels of subpopulation structure and clone censoring. New functions include calculation of Bruvo's distance for microsatellites, batch-analysis of the index of association with several indices of genotypic diversity, and graphing including dendrograms with bootstrap support and minimum spanning networks. While functions for genotypic diversity and clone censoring are specific for clonal populations, several functions found in poppr are also valuable to analysis of any populations. A manual with documentation and examples is provided. Poppr is open source and major releases are available on CRAN: http://cran.r-project.org/package=poppr. More supporting documentation and tutorials can be found under 'resources' at: http://grunwaldlab.cgrb.oregonstate.edu/.