Tissue-specific mRNA profiling of the <i>Brassica napus</i> – <i>Sclerotinia sclerotiorum</i> interaction uncovers novel regulators of plant immunity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".