Naïve Bayes Classifiers and accompanying dataset for Pseudomonas syringae isolate characterization.

The Pseudomonas syringae species complex (PSSC) is a diverse group of plant pathogens with a collective host range encompassing almost every food crop grown today. As a threat to global food security, rapid detection and characterization of epidemic and emerging pathogenic lineages is essential. However, phylogenetic identification is often complicated by an unclarified and ever-changing taxonomy, making practical use of available databases and the proper training of classifiers difficult. As such, while amplicon sequencing is a common method for routine identification of PSSC isolates, there is no efficient method for accurate classification based on this data. Here we present a suite of five Naïve bayes classifiers for PCR primer sets widely used for PSSC identification, trained on in-silico amplicon data from 2,161 published PSSC genomes using the life identification number (LIN) hierarchical clustering algorithm in place of traditional Linnaean taxonomy. Additionally, we include a dataset for translating classification results back into traditional taxonomic nomenclature (i.e. species, phylogroup, pathovar), and for predicting virulence factor repertoires.

Evaluation of the taxonomic accuracy and pathogenicity prediction power of 16 primer sets amplifying single copy marker genes in the Pseudomonas syringae species complex.

The Pseudomonas syringae species complex is composed of several closely related species of bacterial plant pathogens. Here, we used in silico methods to assess 16 PCR primer sets designed for broad identification of isolates throughout the species complex. We evaluated their in silico amplification rate in 2161 publicly available genomes, the correlation between pairwise amplicon sequence distance and whole genome average nucleotide identity, and trained naive Bayes classification models to quantify classification resolution. Furthermore, we show the potential for using single amplicon sequence data to predict type III effector protein repertoires, which are important determinants of host specificity and range.

Genome Mining Shows Ubiquitous Presence and Extensive Diversity of Toxin-Antitoxin Systems in Pseudomonas syringae.

Bacterial toxin-antitoxin (TA) systems consist of two or more adjacent genes, encoding a toxin and an antitoxin. TA systems are implicated in evolutionary and physiological functions including genome maintenance, antibiotics persistence, phage defense, and virulence. Eight classes of TA systems have been described, based on the mechanism of toxin neutralization by the antitoxin. Although studied well in model species of clinical significance, little is known about the TA system abundance and diversity, and their potential roles in stress tolerance and virulence of plant pathogens. In this study, we screened the genomes of 339 strains representing the genetic and lifestyle diversity of the Pseudomonas syringae species complex for TA systems. Using bioinformatic search and prediction tools, including SLING, BLAST, HMMER, TADB2.0, and T1TAdb, we show that P. syringae strains encode 26 different families of TA systems targeting diverse cellular functions. TA systems in this species are almost exclusively type II. We predicted a median of 15 TA systems per genome, and we identified six type II TA families that are found in more than 80% of strains, while others are more sporadic. The majority of predicted TA genes are chromosomally encoded. Further functional characterization of the predicted TA systems could reveal how these widely prevalent gene modules potentially impact P. syringae ecology, virulence, and disease management practices.