Molecular Plant-Microbe InteractionsGenomicsPeer-reviewed journal article
Transcriptome analysis of apple leaves infected by the rust fungus Gymnosporangium yamadae at two sporulation stages (spermogonia and aecia) reveals specific host responses, rust pathogenesis-related genes and a shift in the phyllosphere fungal community composition
Siqi TaoiD, Lucas AueriD, Emmanuelle MoriniD, Ying‐Mei LiangiD, Sébastien DuplessisiD
Abstract
Apple rust disease caused by Gymnosporangium yamadae is one of the major threats to apple orchards. In this study, dual RNA-seq analysis was conducted to simultaneously monitor gene expression profiles of G. yamadae and infected apple leaves during the formation of rust spermogonia and aecia. The molecular mechanisms underlying this compatible interaction at 10 and 30 days post inoculation (dpi) indicate a significant reaction from the host plant and comprise detoxication pathways at the earliest stage and the induction of secondary metabolism related pathways at 30dpi. Such host reactions have been previously reported in other rust pathosystems and may represent a general reaction to rust infection. G. yamadae transcript profiling indicates a conserved genetic program in spermogonia and aecia that is shared with other rust fungi, whereas secretome prediction reveals the presence of specific secreted candidate effector proteins expressed during apple infection. Unexpectedly, the survey of fungal unigenes in the transcriptome assemblies of inoculated and mock-inoculated apple leaves reveals that G. yamadae infection modifies the fungal community composition in the apple phyllosphere at 30 dpi. Collectively, our results provide novel insights into the compatible apple-apple rust interaction and advance the knowledge of this heteroecious demicyclic rust fungus.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- Citations
- 26
- FWCI
- 0.83
- Field percentile
- top 30%
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