RNA-Seq of in planta-expressed Magnaporthe oryzae genes identifies MoSVP as a highly expressed gene required for pathogenicity at the initial stage of infection.
Motoki ShimizuYuki NakanoAkiko HirabuchiKae YoshinoMichie KobayashiKosuke YamamotoRyohei TerauchiHiromasa SaitohPublished in: Molecular plant pathology (2019)
The ascomycete fungus Magnaporthe oryzae is a hemibiotrophic pathogen that causes rice blast disease. Magnaporthe oryzae infects rice leaves, stems and panicles, and induces severe reductions in yield. Effector proteins secreted by M. oryzae in planta are thought to be involved its virulence activity. Here, using RNA-sequencing (RNA-Seq), we generated transcriptome data for M. oryzae isolate Ina168 during the initial stages of infection. We prepared samples from conidia (the inoculum) and from peeled epidermal cotyledon tissue of susceptible barley Hordeum vulgare 'Nigrate' at 12, 24, 36 and 48 hours post-inoculation (hpi). We also generated a draft genome sequence of M. oryzae isolate Ina168 and used it as a reference for mapping the RNA-Seq reads. Gene expression profiling across all stages of M. oryzae infection revealed 1728 putative secreted effector protein genes. We selected seven such genes that were strongly up-regulated at 12 hpi and down-regulated at 24 or 36 hpi and performed gene knockout analysis to determine their roles in pathogenicity. Knockout of MoSVP, encoding a small putative secreted protein with a hydrophobic surface binding protein A domain, resulted in a reduction in pathogenicity, suggesting that MoSVP is a novel virulence effector of M. oryzae.
Keyphrases
- rna seq
- single cell
- genome wide
- genome wide identification
- binding protein
- biofilm formation
- transcription factor
- dna methylation
- pseudomonas aeruginosa
- escherichia coli
- staphylococcus aureus
- regulatory t cells
- dendritic cells
- copy number
- machine learning
- gene expression
- mass spectrometry
- amino acid
- protein protein
- candida albicans
- small molecule
- big data
- high density
- antimicrobial resistance
- deep learning