Predictive modeling of Pseudomonas syringae virulence on bean using gradient boosted decision trees

TolRad, a model for predicting radiation tolerance using Pfam annotations, identifies novel radiosensitive bacterial species from reference genomes and MAGs

The trait of ionizing radiation (IR) tolerance is variable between bacterium, with species succumbing to acute doses as low as 60 Gy and extremophiles able to survive doses exceeding 10,000 Gy. While survival screens have identified multiple highly radioresistant bacteria, such systemic searches have not been conducted for IR-sensitive bacteria. The taxonomy-level diversity of IR sensitivity is poorly understood, as are genetic elements that influence IR sensitivity. Using the protein domain (Pfam) frequencies from 61 bacterial species with experimentally determined D10 values (the dose at which only 10% of the population survives), we trained TolRad, a random forest binary classifier, to distinguish between radiosensitive (D10 < 200 Gy) and radiation-tolerant (D10 > 200 Gy) bacteria. On untrained species, TolRad had an accuracy of 0.900. We applied TolRad to 152 UniProt-hosted bacterial proteomes associated with the human microbiome, including 37 strains from the ATCC Human Microbiome Collection, and classified 34 species as radiosensitive. Whereas IR-sensitive species (D10 < 200 Gy) in the training data set had been confined to the phylum Proteobacterium, this initial TolRad screen identified radiosensitive bacteria in two additional phyla. We experimentally validated the predicted radiosensitivity of a Bacteroidota species from the human microbiome. To demonstrate that TolRad can be applied to metagenome-assembled genomes (MAGs), we tested the accuracy of TolRad on Egg-NOG assembled proteomes (0.965) and partial proteomes. Finally, three collections of MAGs were screened using TolRad, identifying further phyla with radiosensitive species and suggesting that environmental conditions influence the abundance of radiosensitive bacteria.

IMPORTANCE

Bacterial species have vast genetic diversity, allowing for life in extreme environments and the conduction of complex chemistry. The ability to harness the full potential of bacterial diversity is hampered by the lack of high-throughput experimental or bioinformatic methods for characterizing bacterial traits. Here, we present a computational model that uses de novo-generated genome annotations to classify a bacterium as tolerant of ionizing radiation (IR) or as radiosensitive. This model allows for rapid screening of bacterial communities for low-tolerance species that are of interest for both mechanistic studies into bacterial sensitivity to IR and biomarkers of IR exposure.

The effector-triggered immunity landscape of tomato against Pseudomonas syringae

Tomato (Solanum lycopersicum) is one of the world's most important food crops, and as such, its production needs to be protected from infectious diseases that can significantly reduce yield and quality. Here, we survey the effector-triggered immunity (ETI) landscape of tomato against the bacterial pathogen Pseudomonas syringae. We perform comprehensive ETI screens in five cultivated tomato varieties and two wild relatives, as well as an immunodiversity screen on a collection of 149 tomato varieties that includes both wild and cultivated varieties. The screens reveal a tomato ETI landscape that is more limited than what was previously found in the model plant Arabidopsis thaliana. We also demonstrate that ETI eliciting effectors can protect tomato against P. syringae infection when the effector is delivered by a non-virulent strain either prior to or simultaneously with a virulent strain. Overall, our findings provide a snapshot of the ETI landscape of tomatoes and demonstrate that ETI can be used as a biocontrol treatment to protect crop plants.

Genomic characterization of Pseudomonas syringae pv. syringae from Callery pear and the efficiency of associated phages in disease protection

The Pseudomonas syringae species complex is a heterogeneous group of plant pathogenic bacteria associated with a wide distribution of plant species. Advances in genomics are revealing the complex evolutionary history of this species complex and the wide array of genetic adaptations underpinning their diverse lifestyles. Here, we genomically characterize two P. syringae isolates collected from diseased Callery pears (Pyrus calleryana) in Berkeley, California in 2019 and 2022. We also isolated a lytic bacteriophage, which we characterized and evaluated for biocontrol efficiency. Using a multilocus sequence analysis and core genome alignment, we classified the P. syringae isolates as members of phylogroup 2, related to other strains previously isolated from Pyrus and Prunus. An analysis of effector proteins demonstrated an evolutionary conservation of effectoromes across isolates classified in PG2 and yet uncovered unique effector profiles for each, including the two newly identified isolates. Whole-genome sequencing of the associated phage uncovered a novel phage genus related to Pseudomonas syringae pv. actinidiae phage PHB09 and the Flaumdravirus genus. Finally, using in planta infection assays, we demonstrate that the phage was equally useful in symptom mitigation of immature pear fruit regardless of the Pss strain tested. Overall, this study demonstrates the diversity of P. syringae and their viruses associated with ornamental pear trees, posing spill-over risks to commercial pear trees and the possibility of using phages as biocontrol agents to reduce the impact of disease.IMPORTANCEGlobal change exacerbates the spread and impact of pathogens, especially in agricultural settings. There is a clear need to better monitor the spread and diversity of plant pathogens, including in potential spillover hosts, and for the development of novel and sustainable control strategies. In this study, we characterize the first described strains of Pseudomonas syringae pv. syringae isolated from Callery pear in Berkeley, California from diseased tissues in an urban environment. We show that these strains have divergent virulence profiles from previously described strains and that they can cause disease in commercial pears. Additionally, we describe a novel bacteriophage that is associated with these strains and explore its potential to act as a biocontrol agent. Together, the data presented here demonstrate that ornamental pear trees harbor novel P. syringae pv. syringae isolates that potentially pose a risk to local fruit production, or vice versa-but also provide us with novel associated phages, effective in disease mitigation.

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.

Features and algorithms: facilitating investigation of secreted effectors in Gram-negative bacteria

Gram-negative bacteria deliver effector proteins through type III, IV, or VI secretion systems (T3SSs, T4SSs, and T6SSs) into host cells, causing infections and diseases. In general, effector proteins for each of these distinct secretion systems lack homology and are difficult to identify. Sequence analysis has disclosed many common features, helping us to understand the evolution, function, and secretion mechanisms of the effectors. In combination with various algorithms, the known common features have facilitated accurate prediction of new effectors. Ensemblers or integrated pipelines achieve a better prediction of performance, which combines multiple computational models or modules with multidimensional features. Natural language processing (NLP) models also show the merits, which could enable discovery of novel features and, in turn, facilitate more precise effector prediction, extending our knowledge about each secretion mechanism.

Leguminous Seedborne Pathogens: Seed Health and Sustainable Crop Management

Pulses have gained popularity over the past few decades due to their use as a source of protein in food and their favorable impact on soil fertility. Despite being essential to modern agriculture, these species face a number of challenges, such as agronomic crop management and threats from plant seed pathogens. This review's goal is to gather information on the distribution, symptomatology, biology, and host range of seedborne pathogens. Important diagnostic techniques are also discussed as a part of a successful process of seed health certification. Additionally, strategies for sustainable control are provided. Altogether, the data collected are suggested as basic criteria to set up a conscious laboratory approach.

Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae

Plant diseases caused by the pathogen Pseudomonas syringae are serious problems for various plant species worldwide. Accurate detection and diagnosis of P. syringae infections are critical for the effective management of these plant diseases. In this review, we summarize the current methods for the detection and diagnosis of P. syringae, including traditional techniques such as culture isolation and microscopy, and relatively newer techniques such as PCR and ELISA. It should be noted that each method has its advantages and disadvantages, and the choice of each method depends on the specific requirements, resources of each laboratory, and field settings. We also discuss the future trends in this field, such as the need for more sensitive and specific methods to detect the pathogens at low concentrations and the methods that can be used to diagnose P. syringae infections that are co-existing with other pathogens. Modern technologies such as genomics and proteomics could lead to the development of new methods of highly accurate detection and diagnosis based on the analysis of genetic and protein markers of the pathogens. Furthermore, using machine learning algorithms to analyze large data sets could yield new insights into the biology of P. syringae and novel diagnostic strategies. This review could enhance our understanding of P. syringae and help foster the development of more effective management techniques of the diseases caused by related pathogens.