PLoS ONE (Jan 2019)

Flax rust infection transcriptomics reveals a transcriptional profile that may be indicative for rust Avr genes.

  • Wenjie Wu,
  • Adnane Nemri,
  • Leila M Blackman,
  • Ann-Maree Catanzariti,
  • Jana Sperschneider,
  • Gregory J Lawrence,
  • Peter N Dodds,
  • David A Jones,
  • Adrienne R Hardham

DOI
https://doi.org/10.1371/journal.pone.0226106
Journal volume & issue
Vol. 14, no. 12
p. e0226106

Abstract

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Secreted effectors of fungal pathogens are essential elements for disease development. However, lack of sequence conservation among identified effectors has long been a problem for predicting effector complements in fungi. Here we have explored the expression characteristics of avirulence (Avr) genes and candidate effectors of the flax rust fungus, Melampsora lini. We performed transcriptome sequencing and real-time quantitative PCR (qPCR) on RNA extracted from ungerminated spores, germinated spores, isolated haustoria and flax seedlings inoculated with M. lini isolate CH5 during plant infection. Genes encoding two categories of M. lini proteins, namely Avr proteins and plant cell wall degrading enzymes (CWDEs), were investigated in detail. Analysis of the expression profiles of 623 genes encoding predicted secreted proteins in the M. lini transcriptome shows that the six known Avr genes (i.e. AvrM (avrM), AvrM14, AvrL2, AvrL567, AvrP123 (AvrP) and AvrP4) fall within a group of 64 similarly expressed genes that are induced in planta and show a peak of expression early in infection with a subsequent decline towards sporulation. Other genes within this group include two paralogues of AvrL2, an AvrL567 virulence allele, and a number of genes encoding putative effector proteins. By contrast, M. lini genes encoding CWDEs fall into different expression clusters with their distribution often unrelated to their catalytic activity or substrate targets. These results suggest that synthesis of M. lini Avr proteins may be regulated in a coordinated fashion and that the expression profiling-based analysis has significant predictive power for the identification of candidate Avr genes.