Translocation and Functional Analysis of Pseudomonas savastanoi pv. savastanoi NCPPB 3335 Type III Secretion System Effectors Reveals Two Novel Effector Families of the Pseudomonas syringae Complex

Pseudomonas savastanoi pv. savastanoi NCPPB 3335 causes olive knot disease and is a model pathogen for exploring bacterial infection of woody hosts. The type III secretion system (T3SS) effector repertoire of this strain includes 31 effector candidates plus two novel candidates identified in this study which have not been reported to translocate into plant cells. In this work, we demonstrate the delivery of seven NCPPB 3335 effectors into Nicotiana tabacum leaves, including three proteins from two novel families of the P. syringae complex effector super-repertoire (HopBK and HopBL), one of which comprises two proteins (HopBL1 and HopBL2) that harbor a SUMO protease domain. When delivered by P. fluorescens heterologously expressing a P. syringae T3SS, all seven effectors were found to suppress the production of defense-associated reactive oxygen species. Moreover, six of these effectors, including the truncated versions of HopAA1 and HopAZ1 encoded by NCPPB 3335, suppressed callose deposition. The expression of HopAZ1 and HopBL1 by functionally effectorless P. syringae pv. tomato DC3000D28E inhibited the hypersensitive response in tobacco and, additionally, expression of HopBL2 by this strain significantly increased its competitiveness in N. benthamiana. DNA sequences encoding HopBL1 and HopBL2 were uniquely detected in a collection of 31 P. savastanoi pv. savastanoi strains and other P. syringae strains isolated from woody hosts, suggesting a relevant role of these two effectors in bacterial interactions with olive and other woody plants.

Modeling, simulation and application of bacterial transduction in genetic algorithms

At present, all methods in Evolutionary Computation are bioinspired by the fundamental principles of neo-Darwinism, as well as by a vertical gene transfer. Virus transduction is one of the key mechanisms of horizontal gene propagation in microorganisms (e.g. bacteria). In the present paper, we model and simulate a transduction operator, exploring the possible role and usefulness of transduction in a genetic algorithm. The genetic algorithm including transduction has been named PETRI (abbreviation of Promoting Evolution Through Reiterated Infection). Our results showed how PETRI approaches higher fitness values as transduction probability comes close to 100%. The conclusion is that transduction improves the performance of a genetic algorithm, assuming a population divided among several sub-populations or ?bacterial colonies?.