Identification of novel Xanthomonas euvesicatoria type III effector proteins by a machine-learning approach

Doron Teper, David Burstein, Dor Salomon, Michael Gershovitz, Tal Pupko, Guido Sessa

Research output: Contribution to journalArticlepeer-review

Abstract

The Gram-negative bacterium Xanthomonas euvesicatoria (Xcv) is the causal agent of bacterial spot disease in pepper and tomato. Xcv pathogenicity depends on a type III secretion (T3S) system that delivers effector proteins into host cells to suppress plant immunity and promote disease. The pool of known Xcv effectors includes approximately 30 proteins, most identified in the 85-10 strain by various experimental and computational techniques. To identify additional Xcv 85-10 effectors, we applied a genome-wide machine-learning approach, in which all open reading frames (ORFs) were scored according to their propensity to encode effectors. Scoring was based on a large set of features, including genomic organization, taxonomic dispersion, hypersensitive response and pathogenicity (hrp)-dependent expression, 5′ regulatory sequences, amino acid composition bias and GC content. Thirty-six predicted effectors were tested for translocation into plant cells using the hypersensitive response (HR)-inducing domain of AvrBs2 as a reporter. Seven proteins (XopAU, XopAV, XopAW, XopAP, XopAX, XopAK and XopAD) harboured a functional translocation signal and their translocation relied on the HrpF translocon, indicating that they are bona fideT3S effectors. Remarkably, four belong to novel effector families. Inactivation of the xopAP gene reduced the severity of disease symptoms in infected plants. A decrease in cell death and chlorophyll content was observed in pepper leaves inoculated with the xopAP mutant when compared with the wild-type strain. However, populations of the xopAP mutant in infected leaves were similar in size to those of wild-type bacteria, suggesting that the reduction in virulence was not caused by impaired bacterial growth.

Original languageEnglish
Pages (from-to)398-411
Number of pages14
JournalMolecular Plant Pathology
Volume17
Issue number3
DOIs
StatePublished - 1 Apr 2016

Keywords

  • Effector
  • Machine learning
  • Pathogenomics
  • Pepper
  • Type III secretion system
  • Xanthomonas euvesicatoria

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