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

Pseudomonas syringae is a genetically diverse bacterial species complex responsible for numerous agronomically important crop diseases. Individual P. syringae isolates are assigned pathovar designations based on their host of isolation and the associated disease symptoms, and these pathovar designations are often assumed to reflect host specificity although this assumption has rarely been rigorously tested. Here we developed a rapid seed infection assay to measure the virulence of 121 diverse P. syringae isolates on common bean (Phaseolus vulgaris). This collection includes P. syringae phylogroup 2 (PG2) bean isolates (pathovar syringae) that cause bacterial spot disease and P. syringae phylogroup 3 (PG3) bean isolates (pathovar phaseolicola) that cause the more serious halo blight disease. We found that bean isolates in general were significantly more virulent on bean than non-bean isolates and observed no significant virulence difference between the PG2 and PG3 bean isolates. However, when we compared virulence within PGs we found that PG3 bean isolates were significantly more virulent than PG3 non-bean isolates, while there was no significant difference in virulence between PG2 bean and non-bean isolates. These results indicate that PG3 strains have a higher level of host specificity than PG2 strains. We then used gradient boosting machine learning to predict each strain's virulence on bean based on whole genome k-mers, type III secreted effector k-mers, and the presence/absence of type III effectors and phytotoxins. Our model performed best using whole genome data and was able to predict virulence with high accuracy (mean absolute error = 0.05). Finally, we functionally validated the model by predicting virulence for 16 strains and found that 15 (94%) had virulence levels within the bounds of estimated predictions. This study strengthens the hypothesis that P. syringae PG2 strains have evolved a different lifestyle than other P. syringae strains as reflected in their lower level of host specificity. It also acts as a proof-of-principle to demonstrate the power of machine learning for predicting host specific adaptation.

Comparative genomic insights into the epidemiology and virulence of plant pathogenic pseudomonads from Turkey.

Pseudomonas is a highly diverse genus that includes species that cause disease in both plants and animals. Recently, pathogenic pseudomonads from the Pseudomonas syringae and Pseudomonas fluorescens species complexes have caused significant outbreaks in several agronomically important crops in Turkey, including tomato, citrus, artichoke and melon. We characterized 169 pathogenic Pseudomonas strains associated with recent outbreaks in Turkey via multilocus sequence analysis and whole-genome sequencing, then used comparative and evolutionary genomics to characterize putative virulence mechanisms. Most of the isolates are closely related to other plant pathogens distributed among the primary phylogroups of P. syringae, although there are significant numbers of P. fluorescens isolates, which is a species better known as a rhizosphere-inhabiting plant-growth promoter. We found that all 39 citrus blast pathogens cluster in P. syringae phylogroup 2, although strains isolated from the same host do not cluster monophyletically, with lemon, mandarin orange and sweet orange isolates all being intermixed throughout the phylogroup. In contrast, 20 tomato pith pathogens are found in two independent lineages: one in the P. syringae secondary phylogroups, and the other from the P. fluorescens species complex. These divergent pith necrosis strains lack characteristic virulence factors like the canonical tripartite type III secretion system, large effector repertoires and the ability to synthesize multiple bacterial phytotoxins, suggesting they have alternative molecular mechanisms to cause disease. These findings highlight the complex nature of host specificity among plant pathogenic pseudomonads.

Identifying Pseudomonas syringae Type III Secreted Effector Function via a Yeast Genomic Screen.

Gram-negative bacterial pathogens inject type III secreted effectors (T3SEs) directly into host cells to promote pathogen fitness by manipulating host cellular processes. Despite their crucial role in promoting virulence, relatively few T3SEs have well-characterized enzymatic activities or host targets. This is in part due to functional redundancy within pathogen T3SE repertoires as well as the promiscuity of individual T3SEs that can have multiple host targets. To overcome these challenges, we generated and characterized a collection of yeast strains stably expressing 75 T3SE constructs from the plant pathogen Pseudomonas syringae This collection is devised to facilitate heterologous genetic screens in yeast, a non-host organism, to identify T3SEs that target conserved eukaryotic processes. Among 75 T3SEs tested, we identified 16 that inhibited yeast growth on rich media and eight that inhibited growth on stress-inducing media. We utilized Pathogenic Genetic Array (PGA) screens to identify potential host targets of P. syringae T3SEs. We focused on the acetyltransferase, HopZ1a, which interacts with plant tubulin and alters microtubule networks. To uncover putative HopZ1a host targets, we identified yeast genes with genetic interaction profiles most similar (i.e., congruent) to the PGA profile of HopZ1a and performed a functional enrichment analysis of these HopZ1a-congruent genes. We compared the congruence analyses above to previously described HopZ physical interaction datasets and identified kinesins as potential HopZ1a targets. Finally, we demonstrated that HopZ1a can target kinesins by acetylating the plant kinesins HINKEL and MKRP1, illustrating the utility of our T3SE-expressing yeast library to characterize T3SE functions.

Epidemiology of Clonal Pseudomonas aeruginosa Infection in a Canadian Cystic Fibrosis Population.

RATIONALE: The extent of the genetic relatedness among Pseudomonas aeruginosa isolates and its impact on clinical outcomes in the cystic fibrosis (CF) population is poorly understood. OBJECTIVES: The objectives of this study were to determine the prevalence of clonal P. aeruginosa infection in Canada and to associate P. aeruginosa genotypes with clinical outcomes. METHODS: This was an observational study of adult and pediatric patients with CF across Canada. Isolates were typed using multilocus sequence typing. A clone was defined as sharing at least six of seven alleles. Genotyping results were associated with clinical outcomes, including forced expiratory volume in 1 second, body mass index, rate of pulmonary exacerbation, and death/transplant. RESULTS: A total of 1,537 P. aeruginosa isolates were genotyped to 403 unique sequence types (STs) in 402 individuals with CF. Although 39% of STs were shared, most were shared only among a small number of subjects, and the majority (79%) of the genetic diversity in P. aeruginosa isolates was observed between patients. There were no significant differences in clinical outcomes according to genotype. However, patients with a dynamic, changing ST infection pattern had both a steeper decline in forced expiratory volume in 1 second (-2.9% predicted change/yr, 95% confidence interval [CI] = -3.8 to -1.9 compared with 0.4, 95% CI = -0.3 to 1.0; P < 0.001) and body mass index (-1.0 percentile change/yr, 95% CI = -1.6 to -0.3 compared with -0.1, 95% CI = -0.7 to 0.5; P = 0.047) than those with a stable infection with the same ST. CONCLUSIONS: There was no widespread sharing of dominant clones in our CF population, and the majority of the genetic diversity in P. aeruginosa was observed between patients. Changing genotypes over time within an individual was associated with worse clinical outcomes.

Image-Based Quantification of Plant Immunity and Disease.

Measuring the extent and severity of disease is a critical component of plant pathology research and crop breeding. Unfortunately, existing visual scoring systems are qualitative, subjective, and the results are difficult to transfer between research groups, while existing quantitative methods can be quite laborious. Here, we present plant immunity and disease image-based quantification (PIDIQ), a quantitative, semi-automated system to rapidly and objectively measure disease symptoms in a biologically relevant context. PIDIQ applies an ImageJ-based macro to plant photos in order to distinguish healthy tissue from tissue that has yellowed due to disease. It can process a directory of images in an automated manner and report the relative ratios of healthy to diseased leaf area, thereby providing a quantitative measure of plant health that can be statistically compared with appropriate controls. We used the Arabidopsis thaliana-Pseudomonas syringae model system to show that PIDIQ is able to identify both enhanced plant health associated with effector-triggered immunity as well as elevated disease symptoms associated with effector-triggered susceptibility. Finally, we show that the quantitative results provided by PIDIQ correspond to those obtained via traditional in planta pathogen growth assays. PIDIQ provides a simple and effective means to nondestructively quantify disease from whole plants and we believe it will be equally effective for monitoring disease on excised leaves and stems.

Peptides and small molecules of the plant-pathogen apoplastic arena.

Plants reside within an environment rich in potential pathogens. Survival in the presence of such threats requires both effective perception of, and appropriate responses to, pathogenic attack. While plants lack an adaptive immune system, they have a highly developed and responsive innate immune system able to detect and inhibit the growth of the vast majority of potential pathogens. Many of the critical interactions that characterize the relationship between plants and pathogens are played out in the intercellular apoplastic space. The initial perception of pathogen invasion is often achieved through specific plant receptor-like kinases that recognize conserved molecular patterns presented by the pathogen or respond to the molecular debris caused by cellular damage. The perception of either microbial or damage signals by these receptors initiates a response that includes the production of peptides and small molecules to enhance cellular integrity and inhibit pathogen growth. In this review, we discuss the roles of apoplastic peptides and small molecules in modulating plant-pathogen interactions.

Phytopathogen type III effectors as probes of biological systems.

Bacterial phytopathogens utilize a myriad of virulence factors to modulate their plant hosts in order to promote successful pathogenesis. One potent virulence strategy is to inject these virulence proteins into plant cells via the type III secretion system. Characterizing the host targets and the molecular mechanisms of type III secreted proteins, known as effectors, has illuminated our understanding of eukaryotic cell biology. As a result, these effectors can serve as molecular probes to aid in our understanding of plant cellular processes, such as immune signalling, vesicle trafficking, cytoskeleton stability and transcriptional regulation. Furthermore, given that effectors directly and specifically interact with their targets within plant cells, these virulence proteins have enormous biotechnological potential for manipulating eukaryotic systems.