Tissue-specific mRNA profiling of the Brassica napus-Sclerotinia sclerotiorum interaction uncovers novel regulators of plant immunity.
Philip L WalkerIan J GirardMichael G BeckerShayna GiesbrechtSteve WhyardW G Dilantha FernandoTeresa R de KievitMark F BelmontePublished in: Journal of experimental botany (2022)
White mold is caused by the fungal pathogen Sclerotinia sclerotiorum and leads to rapid and significant loss in plant yield. Among its many brassicaceous hosts, including Brassica napus (canola) and Arabidopsis, the response of individual tissue layers directly at the site of infection has yet to be explored. Using laser microdissection coupled with RNA sequencing, we profiled the epidermis, mesophyll, and vascular leaf tissue layers of B. napus in response to S. sclerotiorum. High-throughput tissue-specific mRNA sequencing increased the total number of detected transcripts compared with whole-leaf assessments and provided novel insight into the conserved and specific roles of ontogenetically distinct leaf tissue layers in response to infection. When subjected to pathogen infection, the epidermis, mesophyll, and vasculature activate both specific and shared gene sets. Putative defense genes identified through transcription factor network analysis were then screened for susceptibility against necrotrophic, hemi-biotrophic, and biotrophic pathogens. Arabidopsis deficient in PR5-like RECEPTOR KINASE (PR5K) mRNA levels were universally susceptible to all pathogens tested and were further characterized to identify putative interacting partners involved in the PR5K signaling pathway. Together, these data provide insight into the complexity of the plant defense response directly at the site of infection.
Keyphrases
- transcription factor
- genome wide identification
- single cell
- high throughput
- signaling pathway
- network analysis
- cell wall
- binding protein
- genome wide analysis
- gram negative
- gene expression
- machine learning
- oxidative stress
- dna methylation
- high resolution
- plant growth
- arabidopsis thaliana
- artificial intelligence
- hepatitis c virus
- human immunodeficiency virus
- deep learning
- big data