Tissue-specific mRNA profiling of the <i>Brassica napus-Sclerotinia sclerotiorum</i> interaction uncovers novel regulators of plant immunity
Bibliographic record
Abstract
Abstract White mold in Brassica napus (canola) is caused by the fungal pathogen Sclerotinia sclerotiorum and is responsible for significant losses in crop yield across the globe. With advances in high-throughput transcriptomics, our understanding of the B. napus defense response to S. sclerotiorum is becoming clearer; however, 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 in response to S. sclerotiorum . This strategy increases the number of genes detected compared to whole-leaf assessments and provides unprecedented information on tissue-specific gene expression networks in response to pathogen attack. Our findings provide novel insight into the conserved and specific roles of ontogenetically distinct leaf tissue layers in response to infection. Using bioinformatics tools, we identified several defense genes that might coordinate plant immunity responses shared across different tissue layers within the leaf. These genes were functionally characterized by challenging T-DNA insertion lines of Arabidopsis with necrotrophic, hemi-biotrophic, and biotrophic pathogens, ultimately converging on the PR5-like RECEPTOR KINASE (PRK5). Together, these data provide insight on the complexity of the B. napus defense response directly at the site of infection. Highlight Laser microdissection coupled RNA sequencing of the B. napus – S. sclerotiorum interaction identifies candidate genes predicted to guide plant immunity against pathogen attack.
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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".