Effector identification in the lettuce downy mildew Bremia lactucae by massively parallel transcriptome sequencing

J.H.M. Stassen, M.F. Seidl, P.W.J. Vergeer, I.J. Nijman, B. Snel, E. Cuppen, A.F.J.M. van den Ackerveken*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Lettuce downy mildew (Bremia lactucae) is a rapidly adapting oomycete pathogen affecting commercial lettuce cultivation. Oomycetes are known to use a diverse arsenal of secreted proteins (effectors) to manipulate their hosts. Two classes of effector are known to be translocated by the host: the RXLRs and Crinklers. To gain insight into the repertoire of effectors used by B. lactucae to manipulate its host, we performed massively parallel sequencing of cDNA derived from B. lactucae spores and infected lettuce (Lactuca sativa) seedlings. From over 2.3 million 454 GS FLX reads, 59 618 contigs were assembled representing both plant and pathogen transcripts. Of these, 19 663 contigs were determined to be of B. lactucae origin as they matched pathogen genome sequences (SOLiD) that were obtained from >270 million reads of spore-derived genomic DNA. After correction of cDNA sequencing errors with SOLiD data, translation into protein models and filtering, 16 372 protein models remained, 1023 of which were predicted to be secreted. This secretome included elicitins, necrosis and ethylene-inducing peptide 1-like proteins, glucanase inhibitors and lectins, and was enriched in cysteine-rich proteins. Candidate host-translocated effectors included 78 protein models with RXLR effector features. In addition, we found indications for an unknown number of Crinkler-like sequences. Similarity clustering of secreted proteins revealed additional effector candidates. We provide a first look at the transcriptome of B. lactucae and its encoded effector arsenal.
Original languageEnglish
Pages (from-to)719-731
Number of pages13
JournalMolecular Plant Pathology
Volume13
Issue number7
DOIs
Publication statusPublished - Sept 2012

Keywords

  • Econometric and Statistical Methods: General
  • Geneeskunde(GENK)
  • Medical sciences
  • Bescherming en bevordering van de menselijke gezondheid

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