Prediction of the Diplocarpon rosae secretome reveals candidate genes for effectors and virulence factors

Fungal Biol. 2019 Mar;123(3):231-239. doi: 10.1016/j.funbio.2018.12.003. Epub 2018 Dec 26.

Abstract

Rose black spot is one of the most severe diseases of field-grown roses. Though R-genes have been characterised, little information is known about the molecular details of the interaction between pathogen and host. Based on the recently published genome sequence of the black spot fungus, we analysed gene models with various bioinformatic tools utilising the expression data of infected host tissues, which led to the prediction of 827 secreted proteins. A significant proportion of the predicted secretome comprises enzymes for the degradation of cell wall components, several of which were highly expressed during the first infection stages. As the secretome comprises major factors determining the ability of the fungus to colonise its host, we focused our further analyses on predicted effector candidates. In total, 52 sequences of 251 effector candidates matched several bioinformatic criteria of effectors, contained a Y/F/WxC motif, and did not match annotated proteins from other fungi. Additional sequences were identified based on their high expression levels during the penetration/haustorium formation phase and/or by matching known effectors from other fungi. Several host genotypes that are resistant to the sequenced isolate but differ in the R-genes responsible for this resistance are available. The combination of these genotypes with functional studies of the identified candidate effectors will allow the mechanisms of the rose black spot interaction to be dissected.

Keywords: Black spot; Effector prediction; Fungi; Plant cell wall-degrading enzymes; Plant-pathogen interaction; Roses.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Ascomycota / genetics*
  • Ascomycota / pathogenicity
  • Computational Biology
  • Fungal Proteins / genetics*
  • Fungal Proteins / metabolism*
  • Gene Expression Profiling
  • Plant Diseases / microbiology*
  • Rosa / microbiology*
  • Virulence Factors / genetics*
  • Virulence Factors / metabolism*

Substances

  • Fungal Proteins
  • Virulence Factors