Competitive Index in Mixed Infection:A Sensitive and Accurate Method to Quantify Growth of Pseudomonas syringaein Different Plants

摘要

The ability of Pseudomonas syringae to replicate in susceptible hostplants depends on a large number of virulence factors, and is directly related toits pathogenicity. Absence of one or more of these factors frequently causes areduction in bacterial growth in the host and an attenuation of the induced symp-toms. Determining the role that these factors play in the infection process requiressensitive methods to quantify virulence. In animal models, mixed infections haveallowed the development of this type of analysis; from measuring otherwise unde-tectable virulence defects of type III effector mutants to allowing genetic analysisof multiple mutants to determine functional relationships between different genes.Virulence factors of pathogens such as Salmonella or Streptococcus have oftenbeen assigned to regulons or to secretion or transport systems. Furthermore, theestablishment of mixed infections in different animal models has been key to thedevelopment of high-throughput methods of screening for virulence factors (e.g.signature-tagged mutagenesis). In this work, we have established that determin-ing competitive indexes within mixed infections is an accurate method to quantifyvirulence and carry out genetic analysis of Pseudomonas syringae interaction withthe plant. If the right inoculation dose is used, the strains within the mixed infectionneither compete for the resources nor interfere with each other, thus multiplyingwithin the plant in a manner that reflects their respective pathogenic capabilities.We have set up the method using P syringae pv phaseolicola in its interactionwith bean plants. In addition, we have tested the application of the method toother established models using P. syringae pv tomato in both its interactions withtomato plants and Arabidopsis, and P. syringae pv syringae in its interaction withbean plants. We found that this method is highly sensitive and reproducible therebygreatly reducing experimental variation and consequently the number of plantsrequired to obtain significant results. The method allows a variety of applicationssome of which are discussed.