<scp>RAS</scp> signalling genes can be used as host‐induced gene silencing targets to control fungal diseases caused by <i>Sclerotinia sclerotiorum</i> and <i>Botrytis cinerea</i>
Bibliographic record
Abstract
Sclerotinia sclerotiorum causes white mold (also called stem rot, Sclerotinia blight, etc.) in many economically important plants. It is a notorious soilborne fungal pathogen due to its wide host range and ability to survive in soil for long periods of time as sclerotia. Although host-induced gene silencing (HIGS) was recently demonstrated to be an effective method for controlling white mold, limited gene targets are available. Here, using a forward genetics approach, we identified a RAS-GTPase activating protein, SsGAP1, which plays essential roles in sclerotia formation, compound appressoria production and virulence. In parallel, as revealed by our knockout analysis, the SsGAP1 ortholog in Botrytis cinerea, BcGAP1, plays similar roles in fungal development and virulence. By knocking down SsRAS1 and SsRAS2, we also revealed that both SsRAS1 and SsRAS2 are required for vegetative growth, sclerotia development, compound appressoria production and virulence in S. sclerotiorum. Due to the major roles these RAS signalling components play in Sclerotiniaceae biology, they can be used as HIGS targets to control diseases caused by both S. sclerotiorum and B. cinerea. Indeed, when we introduced HIGS constructs targeting SsGAP1, SsRAS1 and SsRAS2 in Nicotiana benthamiana and Arabidopsis thaliana, we observed reduced virulence. Taken together, our forward genetics gene discovery pipeline in S. sclerotiorum is highly effective in identifying novel HIGS targets to control S. sclerotiorum and B. cinerea.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".