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Recombineering using RecTE from Pseudomonas syringae.

ABSTRACT: In this report, we describe the identification of functions that promote genomic recombination of linear DNA introduced into Pseudomonas cells by electroporation. The genes encoding these functions were identified in Pseudomonas syringae pv. syringae B728a based on similarity to the lambda Red Exo/Beta and RecET proteins encoded by the lambda and Rac bacteriophages of Escherichia coli. The ability of the pseudomonad-encoded proteins to promote recombination was tested in P. syringae pv. tomato DC3000 using a quantitative assay based on recombination frequency. The results show that the Pseudomonas RecT homolog is sufficient to promote recombination of single-stranded DNA oligonucleotides and that efficient recombination of double-stranded DNA requires the expression of both the RecT and RecE homologs. Additionally, we illustrate the utility of this recombineering system to make targeted gene disruptions in the P. syringae chromosome.

SUBMITTER: Swingle B

PROVIDER: S-EPMC2916495 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Recombineering using RecTE from Pseudomonas syringae.

Swingle Bryan B Bao Zhongmeng Z Markel Eric E Chambers Alan A Cartinhour Samuel S

Applied and environmental microbiology 20100611 15

In this report, we describe the identification of functions that promote genomic recombination of linear DNA introduced into Pseudomonas cells by electroporation. The genes encoding these functions were identified in Pseudomonas syringae pv. syringae B728a based on similarity to the lambda Red Exo/Beta and RecET proteins encoded by the lambda and Rac bacteriophages of Escherichia coli. The ability of the pseudomonad-encoded proteins to promote recombination was tested in P. syringae pv. tomato D ...[more]

PMID: 20543050

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Project description:BACKGROUND: Every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets. RESULTS: In this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between Arabidopsis and PstDC3000 in genome scale. The interolog method relies on protein sequence similarity to conduct the PPI prediction. A Pseudomonas protein and an Arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If a Pseudomonas and an Arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. The interolog-based method predicts ~0.79M PPIs involving around 7700 Arabidopsis and 1068 Pseudomonas proteins in the full genome. The domain-based method predicts 85650 PPIs comprising 11432 Arabidopsis and 887 Pseudomonas proteins. Further, around 11000 PPIs have been identified as interacting from both the methods as a consensus. CONCLUSION: The present work predicts the protein-protein interaction network between Arabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 in a genome wide scale with a high confidence. Although the predicted PPIs may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. This can be a useful resource to the plant community to characterize the host-pathogen interaction in Arabidopsis and Pseudomonas system. Further, these prediction models can be applied to the agriculturally relevant crops.

Project description: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.

Project description:The Pseudomonas syringae complex comprises different genetic groups that include strains from both agricultural and environmental habitats. This complex group has been used for decades as a "hodgepodge," including many taxonomically related species. More than 60 pathovars of P. syringae have been described based on distinct host ranges and disease symptoms they cause. These pathovars cause disease relying on an array of virulence mechanisms. However, P. syringae pv. syringae (Pss) is the most polyphagous bacterium in the P. syringae complex, based on its wide host range, that primarily affects woody and herbaceous host plants. In early 1990s, bacterial apical necrosis (BAN) of mango trees, a critical disease elicited by Pss in Southern Spain was described for the first time. Pss exhibits important epiphytic traits and virulence factors, which may promote its survival and pathogenicity in mango trees and in other plant hosts. Over more than two decades, Pss strains isolated from mango trees have been comprehensively investigated to elucidate the mechanisms that governs their epiphytic and pathogenic lifestyles. In particular, the vast majority of Pss strains isolated from mango trees produce an antimetabolite toxin, called mangotoxin, whose leading role in virulence has been clearly demonstrated. Moreover, phenotypic, genetic and phylogenetic approaches support that Pss strains producers of BAN symptoms on mango trees all belong to a single phylotype within phylogroup 2, are adapted to the mango host, and produce mangotoxin. Remarkably, a genome sequencing project of the Pss model strain UMAF0158 revealed the presence of other factors that may play major roles in its different lifestyles, such as the presence of two different type III secretion systems, two type VI secretion systems and an operon for cellulose biosynthesis. The role of cellulose in increasing mango leaf colonization and biofilm formation, and impairing virulence of Pss, suggests that cellulose may play a pivotal role with regards to the balance of its different lifestyles. In addition, 62-kb plasmids belonging to the pPT23A-family of plasmids (PFPs) have been strongly associated with Pss strains that inhabit mango trees. Further, complete sequence and comparative genomic analyses revealed major roles of PFPs in detoxification of copper compounds and ultraviolet radiation resistance, both improving the epiphytic lifestyle of Pss on mango surfaces. Hence, in this review we summarize the research that has been conducted on Pss by our research group to elucidate the molecular mechanisms that underpin the epiphytic and pathogenic lifestyle on mango trees. Finally, future directions in this particular plant-pathogen story are discussed.

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Recombineering using RecTE from Pseudomonas syringae.

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Recombineering using RecTE from Pseudomonas syringae.

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