Global mRNA profiling reveals the effect of boron as a crop protection tool against <i>Sclerotinia sclerotiorum</i>
Bibliographic record
Abstract
Abstract Sclerotinia sclerotiorum, the causal agent of white mould, is a necrotrophic fungal pathogen responsible for extensive crop loss. Current control options rely heavily on the application of chemical fungicides that are becoming less effective and may lead to the development of fungal resistance. In the current study, we used a foliar application of boron to protect Brassica napus (canola) from S. sclerotiorum infection using whole-plant infection assays. Application of boron to aerial surfaces of the canola plant reduced the number of S. sclerotiorum-forming lesions by 87 % compared to an untreated control. Dual RNA sequencing revealed the effect of boron on both the host plant and fungal pathogen during the infection process. Differential gene expression analysis and gene ontology term enrichment further revealed the mode of action of a foliar boron spray at the mRNA level. A single foliar application of boron primed the plant defence response through the induction of genes associated with systemic acquired resistance while an application of boron followed by S. sclerotiorum infection-induced genes associated with defence response-related cellular signalling cascades. Additionally, in S. sclerotiorum inoculated on boron-treated B. napus, we uncovered gene activity in response to salicylic acid breakdown, consistent with salicylic acid-dependent systemic acquired resistance induction within the host plant. Taken together, this study demonstrates that a foliar application of boron results in priming of the B. napus plant defence response, likely through systemic acquired resistance, thereby contributing to increased tolerance to S. sclerotiorum 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".