Control of white mold (Sclerotinia sclerotiorum) through plant-mediated RNA interference
Bibliographic record
Abstract
The causative agent of white mold, Sclerotinia sclerotiorum, is capable of infecting over 600 plant species and is responsible for significant crop losses across the globe. Control is currently dependent on broad-spectrum chemical agents that can negatively impact the agroecological environment, presenting a need to develop alternative control measures. In this study, we developed transgenic Arabidopsis thaliana (AT1703) expressing hairpin (hp)RNA to silence S. sclerotiorum ABHYDROLASE-3 and slow infection through host induced gene silencing (HIGS). Leaf infection assays show reduced S. sclerotiorum lesion size, fungal load, and ABHYDROLASE-3 transcript abundance in AT1703 compared to wild-type Col-0. To better understand how HIGS influences host-pathogen interactions, we performed global RNA sequencing on AT1703 and wild-type Col-0 directly at the site of S. sclerotiorum infection. RNA sequencing data reveals enrichment of the salicylic acid (SA)-mediated systemic acquired resistance (SAR) pathway, as well as transcription factors predicted to regulate plant immunity. Using RT-qPCR, we identified predicted interacting partners of ABHYDROLASE-3 in the polyamine synthesis pathway of S. sclerotiorum that demonstrate co-reduction with ABHYDROLASE-3 transcript levels during infection. Together, these results demonstrate the utility of HIGS technology in slowing S. sclerotiorum infection and provide insight into the role of ABHYDROLASE-3 in the A. thaliana-S. sclerotiorum pathosystem.
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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 | 0.001 |
| 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".