Genetic and Functional Diversity Help Explain Pathogenic, Weakly Pathogenic, and Commensal Lifestyles in the Genus Xanthomonas.

The genus Xanthomonas has been primarily studied for pathogenic interactions with plants. However, besides host and tissue-specific pathogenic strains, this genus also comprises nonpathogenic strains isolated from a broad range of hosts, sometimes in association with pathogenic strains, and other environments, including rainwater. Based on their incapacity or limited capacity to cause symptoms on the host of isolation, nonpathogenic xanthomonads can be further characterized as commensal and weakly pathogenic. This study aimed to understand the diversity and evolution of nonpathogenic xanthomonads compared to their pathogenic counterparts based on their cooccurrence and phylogenetic relationship and to identify genomic traits that form the basis of a life history framework that groups xanthomonads by ecological strategies. We sequenced genomes of 83 strains spanning the genus phylogeny and identified eight novel species, indicating unexplored diversity. While some nonpathogenic species have experienced a recent loss of a type III secretion system, specifically the hrp2 cluster, we observed an apparent lack of association of the hrp2 cluster with lifestyles of diverse species. We performed association analysis on a large data set of 337 Xanthomonas strains to explain how xanthomonads may have established association with the plants across the continuum of lifestyles from commensals to weak pathogens to pathogens. Presence of distinct transcriptional regulators, distinct nutrient utilization and assimilation genes, transcriptional regulators, and chemotaxis genes may explain lifestyle-specific adaptations of xanthomonads.

Open Access and Reproducibility in Plant Pathology Research: Guidelines and Best Practices.

The landscape of scientific publishing is experiencing a transformative shift toward open access, a paradigm that mandates the availability of research outputs such as data, code, materials, and publications. Open access provides increased reproducibility and allows for reuse of these resources. This article provides guidance for best publishing practices of scientific research, data, and associated resources, including code, in The American Phytopathological Society journals. Key areas such as diagnostic assays, experimental design, data sharing, and code deposition are explored in detail. This guidance aligns with that observed by other leading journals. We hope the information assembled in this paper will raise awareness of best practices and enable greater appraisal of the true effects of biological phenomena in plant pathology.

A Survey of Xylella fastidiosa in the U.S. State of Virginia Reveals Wide Distribution of Both Subspecies fastidiosa and multiplex in Grapevine.

Global travel and trade in combination with climate change are expanding the geographic distribution of plant pathogens. The bacterium Xylella fastidiosa is a prime example. Native to the Americas, it has spread to Europe, Asia, and the Middle East. To assess the risk that pathogen introductions pose to crops in newly invaded areas, it is key to survey their diversity, host range, and disease incidence in relation to climatic conditions where they are already present. We performed a survey of X. fastidiosa in grapevine in Virginia using a combination of quantitative PCR, multilocus sequencing, and metagenomics. We also analyzed samples from deciduous trees with leaf scorch symptoms. X. fastidiosa subspecies fastidiosa was identified in grapevines in all regions of the state, even in Northern Virginia, where the temperature was below -9°C for 10 days per year on average in the years preceding sampling. Unexpectedly, we also found for the first time grapevine samples infected with X. fastidiosa subspecies multiplex (Xfm). The Xfm lineage found in grapevines had been previously isolated from blueberries in the Southeastern United States and was distinct from that found in deciduous trees in Virginia. The obtained results will be important for risk assessment of X. fastidiosa introductions in other parts of the world.

Genomic delineation and description of species and within-species lineages in the genus Pantoea.

As the name of the genus Pantoea ("of all sorts and sources") suggests, this genus includes bacteria with a wide range of provenances, including plants, animals, soils, components of the water cycle, and humans. Some members of the genus are pathogenic to plants, and some are suspected to be opportunistic human pathogens; while others are used as microbial pesticides or show promise in biotechnological applications. During its taxonomic history, the genus and its species have seen many revisions. However, evolutionary and comparative genomics studies have started to provide a solid foundation for a more stable taxonomy. To move further toward this goal, we have built a 2,509-gene core genome tree of 437 public genome sequences representing the currently known diversity of the genus Pantoea. Clades were evaluated for being evolutionarily and ecologically significant by determining bootstrap support, gene content differences, and recent recombination events. These results were then integrated with genome metadata, published literature, descriptions of named species with standing in nomenclature, and circumscriptions of yet-unnamed species clusters, 15 of which we assigned names under the nascent SeqCode. Finally, genome-based circumscriptions and descriptions of each species and each significant genetic lineage within species were uploaded to the LINbase Web server so that newly sequenced genomes of isolates belonging to any of these groups could be precisely and accurately identified.

Reference-Free Plant Disease Detection Using Machine Learning and Long-Read Metagenomic Sequencing.

Surveillance for early disease detection is crucial to reduce the threat of plant diseases to food security. Metagenomic sequencing and taxonomic classification have recently been used to detect and identify plant pathogens. However, for an emerging pathogen, its genome may not be similar enough to any public genome to permit reference-based tools to identify infected samples. Also, in the case of point-of care diagnosis in the field, database access may be limited. Therefore, here we explore reference-free detection of plant pathogens using metagenomic sequencing and machine learning (ML). We used long-read metagenomes from healthy and infected plants as our model system and constructed k-mer frequency tables to test eight different ML models. The accuracy in classifying individual reads as coming from a healthy or infected metagenome were compared. Of all models, random forest (RF) had the best combination of short run-time and high accuracy (over 0.90) using tomato metagenomes. We further evaluated the RF model with a different tomato sample infected with the same pathogen or a different pathogen and a grapevine sample infected with a grapevine pathogen and achieved similar performances. ML models can thus learn features to successfully perform reference-free detection of plant diseases whereby a model trained with one pathogen-host system can also be used to detect different pathogens on different hosts. Potential and challenges of applying ML to metagenomics in plant disease detection are discussed. IMPORTANCE Climate change may lead to the emergence of novel plant diseases caused by yet unknown pathogens. Surveillance for emerging plant diseases is crucial to reduce their threat to food security. However, conventional genomic based methods require knowledge of existing plant pathogens and cannot be applied to detecting newly emerged pathogens. In this work, we explored reference-free, meta-genomic sequencing-based disease detection using machine learning. By sequencing the genomes of all microbial species extracted from an infected plant sample, we were able to train machine learning models to accurately classify individual sequencing reads as coming from a healthy or an infected plant sample. This method has the potential to be integrated into a generic pipeline for a meta-genomic based plant disease surveillance approach but also has limitations that still need to be overcome.

Identification of Candidate Ice Nucleation Activity (INA) Genes in Fusarium avenaceum by Combining Phenotypic Characterization with Comparative Genomics and Transcriptomics.

Ice nucleation activity (INA) is the capacity of certain particles to catalyze ice formation at temperatures higher than the temperature at which pure water freezes. INA impacts the ratio of liquid to frozen cloud droplets and, therefore, the formation of precipitation and Earth's radiative balance. Some Fusarium strains secrete ice-nucleating particles (INPs); they travel through the atmosphere and may thus contribute to these atmospheric processes. Fusarium INPs were previously found to consist of proteinaceous aggregates. Here, we determined that in F. avenaceum, the proteins forming these aggregates are smaller than 5 nm and INA is higher after growth at low temperatures and varies among strains. Leveraging these findings, we used comparative genomics and transcriptomics to identify candidate INA genes. Ten candidate INA genes that were predicted to encode secreted proteins were present only in the strains that produced the highest number of INPs. In total, 203 candidate INA genes coding for secreted proteins were induced at low temperatures. Among them, two genes predicted to encode hydrophobins stood out because hydrophobins are small, secreted proteins that form aggregates with amphipathic properties. We discuss the potential of the candidate genes to encode INA proteins and the next steps necessary to identify the molecular basis of INA in F. avenaceum.

Could a Focus on the "Why" of Taxonomy Help Taxonomy Better Respond to the Needs of Science and Society?

Genomics has put prokaryotic rank-based taxonomy on a solid phylogenetic foundation. However, most taxonomic ranks were set long before the advent of DNA sequencing and genomics. In this concept paper, we thus ask the following question: should prokaryotic classification schemes besides the current phylum-to-species ranks be explored, developed, and incorporated into scientific discourse? Could such alternative schemes provide better solutions to the basic need of science and society for which taxonomy was developed, namely, precise and meaningful identification? A neutral genome-similarity based framework is then described that could allow alternative classification schemes to be explored, compared, and translated into each other without having to choose only one as the gold standard. Classification schemes could thus continue to evolve and be selected according to their benefits and based on how well they fulfill the need for prokaryotic identification.

Investigating plant disease outbreaks with long-read metagenomics: sensitive detection and highly resolved phylogenetic reconstruction applied to Xylella fastidiosa.

Early disease detection is a prerequisite for enacting effective interventions for disease control. Strains of the bacterial plant pathogen Xylella fastidiosa have recurrently spread to new crops in new countries causing devastating outbreaks. So far, investigation of outbreak strains and highly resolved phylogenetic reconstruction have required whole-genome sequencing of pure bacterial cultures, which are challenging to obtain due to the fastidious nature of X. fastidiosa. Here, we show that culture-independent metagenomic sequencing, using the Oxford Nanopore Technologies MinION long-read sequencer, can sensitively and specifically detect the causative agent of Pierce's disease of grapevine, X. fastidiosa subspecies fastidiosa. Using a DNA sample from a grapevine in Virginia, USA, it was possible to obtain a metagenome-assembled genome (MAG) of sufficient quality for phylogenetic reconstruction with SNP resolution. The analysis placed the MAG in a clade with isolates from Georgia, USA, suggesting introduction of X. fastidiosa subspecies fastidiosa to Virginia from the south-eastern USA. This proof of concept study, thus, revealed that metagenomic sequencing can replace culture-dependent genome sequencing for reconstructing transmission routes of bacterial plant pathogens.

Metagenomic sequencing for detection and identification of the boxwood blight pathogen Calonectria pseudonaviculata.

Pathogen detection and identification are key elements in outbreak control of human, animal, and plant diseases. Since many fungal plant pathogens cause similar symptoms, are difficult to distinguish morphologically, and grow slowly in culture, culture-independent, sequence-based diagnostic methods are desirable. Whole genome metagenomic sequencing has emerged as a promising technique because it can potentially detect any pathogen without culturing and without the need for pathogen-specific probes. However, efficient DNA extraction protocols, computational tools, and sequence databases are required. Here we applied metagenomic sequencing with the Oxford Nanopore Technologies MinION to the detection of the fungus Calonectria pseudonaviculata, the causal agent of boxwood (Buxus spp.) blight disease. Two DNA extraction protocols, several DNA purification kits, and various computational tools were tested. All DNA extraction methods and purification kits provided sufficient quantity and quality of DNA. Several bioinformatics tools for taxonomic identification were found suitable to assign sequencing reads to the pathogen with an extremely low false positive rate. Over 9% of total reads were identified as C. pseudonaviculata in a severely diseased sample and identification at strain-level resolution was approached as the number of sequencing reads was increased. We discuss how metagenomic sequencing could be implemented in routine plant disease diagnostics.

Ice nucleation in a Gram-positive bacterium isolated from precipitation depends on a polyketide synthase and non-ribosomal peptide synthetase.

Earth's radiation budget and frequency and intensity of precipitation are influenced by aerosols with ice nucleation activity (INA), i.e., particles that catalyze the formation of ice. Some bacteria, fungi, and pollen are among the most efficient ice nucleators but the molecular basis of INA is poorly understood in most of them. Lysinibacillus parviboronicapiens (Lp) was previously identified as the first Gram-positive bacterium with INA. INA of Lp is associated with a secreted, nanometer-sized, non-proteinaceous macromolecule or particle. Here a combination of comparative genomics, transcriptomics, and a mutant screen showed that INA in Lp depends on a type I iterative polyketide synthase and a non-ribosomal peptide synthetase (PKS-NRPS). Differential filtration in combination with gradient ultracentrifugation revealed that the product of the PKS-NRPS is associated with secreted particles of a density typical of extracellular vesicles and electron microscopy showed that these particles consist in "pearl chain"-like structures not resembling any other known bacterial structures. These findings expand our knowledge of biological INA, may be a model for INA in other organisms for which the molecular basis of INA is unknown, and present another step towards unraveling the role of microbes in atmospheric processes.

Genome Resource: Draft Genome of Fusarium avenaceum, Strain F156N33, Isolated from the Atmosphere Above Virginia and Annotated Based on RNA Sequencing Data.

Fusarium avenaceum is a filamentous fungus commonly associated with plants and soil. It is a causal agent of Fusarium head blight (FHB) on maize and small-grain cereals and blights on other plant species, and is one of the very few fungal species known to have ice nucleation activity (i.e., it catalyzes ice formation). Here, we report the draft genome of the ice-nucleation-active F. avenaceum strain F156N33 isolated from the atmosphere above Virginia. The genome assembly is 41,175,306 bp long, consists of 214 contigs, and is predicted to encode 11,233 proteins, which were annotated using RNA-sequencing data obtained from the same strain.

Draft Genome Sequence of Mortierella alpina Strain LL118, Isolated from an Aspen (Populus tremuloides) Leaf Litter Sample.

Mortierella alpina is a filamentous fungus commonly associated with soil and is one of very few fungal species known to include strains with ice nucleation activity. Here, we report the draft genome sequence of the ice nucleation-active M. alpina strain LL118, isolated from aspen leaf litter collected in Alberta, Canada.

LINflow: a computational pipeline that combines an alignment-free with an alignment-based method to accelerate generation of similarity matrices for prokaryotic genomes.

BACKGROUND: Computing genomic similarity between strains is a prerequisite for genome-based prokaryotic classification and identification. Genomic similarity was first computed as Average Nucleotide Identity (ANI) values based on the alignment of genomic fragments. Since this is computationally expensive, faster and computationally cheaper alignment-free methods have been developed to estimate ANI. However, these methods do not reach the level of accuracy of alignment-based methods. METHODS: Here we introduce LINflow, a computational pipeline that infers pairwise genomic similarity in a set of genomes. LINflow takes advantage of the speed of the alignment-free sourmash tool to identify the genome in a dataset that is most similar to a query genome and the precision of the alignment-based pyani software to precisely compute ANI between the query genome and the most similar genome identified by sourmash. This is repeated for each new genome that is added to a dataset. The sequentially computed ANI values are stored as Life Identification Numbers (LINs), which are then used to infer all other pairwise ANI values in the set. We tested LINflow on four sets, 484 genomes in total, and compared the needed time and the generated similarity matrices with other tools. RESULTS: LINflow is up to 150 times faster than pyani and pairwise ANI values generated by LINflow are highly correlated with those computed by pyani. However, because LINflow infers most pairwise ANI values instead of computing them directly, ANI values occasionally depart from the ANI values computed by pyani. In conclusion, LINflow is a fast and memory-efficient pipeline to infer similarity among a large set of prokaryotic genomes. Its ability to quickly add new genome sequences to an already computed similarity matrix makes LINflow particularly useful for projects when new genome sequences need to be regularly added to an existing dataset.

Population Genomic- and Phylogenomic-Enabled Advances to Increase Insight Into Pathogen Biology and Epidemiology.

Population genetics has been a key discipline in phytopathology for many years. The recent rise in cost-effective, high-throughput DNA sequencing technologies, allows sequencing of dozens, if not hundreds of specimens, turning population genetics into population genomics and opening up new, exciting opportunities as described in this Focus Issue. Without the limitations of genetic markers and the availability of whole or near whole-genome data, population genomics can give new insights into the biology, evolution and adaptation, and dissemination patterns of plant-associated microbes.

Genome Resource: Ralstonia solanacearum Phylotype II Sequevar 1 (Race 3 Biovar 2) Strain UW848 From the 2020 U.S. Geranium Introduction.

Ralstonia solanacearum phylotype II sequevar 1 (RsII-1, formerly race 3 biovar 2) causes tomato bacterial wilt, potato brown rot, and Southern wilt of geranium. Strains in RsII-1 cause wilting in potato and tomato at cooler temperatures than tropical lowland R. solanacearum strains. Although periodically introduced, RsII-1 has not established in the United States. This pathogen is of quarantine concern and listed as a Federal Select Agent. We report a rapidly sequenced (<2 days) draft genome of UW848, a RsII-1 isolate introduced to the United States in geranium cuttings in spring 2020. UW848 belongs to the near-clonal cluster of RsII-1 global pandemic strains.

LINbase: a web server for genome-based identification of prokaryotes as members of crowdsourced taxa.

High throughput DNA sequencing in combination with efficient algorithms could provide the basis for a highly resolved, genome phylogeny-based and digital prokaryotic taxonomy. However, current taxonomic practice continues to rely on cumbersome journal publications for the description of new species, which still constitute the smallest taxonomic units. In response, we introduce LINbase, a web server that allows users to genomically circumscribe any group of prokaryotes with measurable DNA similarity and that uses the individual isolate as smallest unit. Since LINbase leverages the concept of Life Identification Numbers (LINs), which are codes assigned to individual genomes based on reciprocal average nucleotide identity, we refer to groups circumscribed in LINbase as LINgroups. Users can associate with each LINgroup a name, a short description, and a URL to a peer-reviewed publication. As soon as a LINgroup is circumscribed, any user can immediately identify query genomes as members and submit comments about the LINgroup. Most genomes currently in LINbase were imported from GenBank, but users can upload their own genome sequences as well. In conclusion, LINbase combines the resolution of LINs with the power of crowdsourcing in support of a highly resolved, genome phylogeny-based digital taxonomy. LINbase is available at http://www.LINbase.org.

Exploring Rain as Source of Biological Control Agents for Fire Blight on Apple.

Poor survival on plants can limit the efficacy of Biological Control Agents (BCAs) in the field. Yet bacteria survive in the atmosphere, despite their exposure to high solar radiation and extreme temperatures. If conditions in the atmosphere are similar to, or more extreme than, the environmental conditions on the plant surface, then precipitation may serve as a reservoir of robust BCAs. To test this hypothesis, two hundred and fifty-four rain-borne isolates were screened for in vitro inhibition of Erwinia amylovora, the causal agent of fire blight, as well as of other plant pathogenic bacteria, fungi and oomycetes. Two isolates showed strong activity against E. amylovora and other plant pathogenic bacteria, while other isolates showed activity against fungal and oomycete pathogens. Survival assays suggested that the two isolates that inhibited E. amylovora were able to survive on apple blossoms and branches similarly to E. amylovora. Pathogen population size and associated fire blight symptoms were significantly reduced when detached apple blossoms were treated with the two isolates before pathogen inoculation, however, disease reduction on attached blossoms within an orchard was inconsistent. Using whole genome sequencing, the isolates were identified as Pantoea agglomerans and P. ananatis, respectively. A UV-mutagenesis screen pointed to a phenazine antibiotic D-alanylgriseoluteic acid synthesis gene cluster as being at the base of the antimicrobial activity of the P. agglomerans isolate. Our work reveals the potential of precipitation as an under-explored source of BCAs, whole genome sequencing as an effective approach to precisely identify BCAs, and UV-mutagenesis as a technically simple screen to investigate the genetic basis of BCAs. More field trials are needed to determine the efficacy of the identified BCAs in fire blight control.