A GDP-mannose-1-phosphate guanylyltransferase as a potential HIGS target against Sclerotinia sclerotiorum
Bibliographic record
Abstract
Sclerotinia stem rot is a devastating disease affecting vegetables and oil crops worldwide. It is caused by the necrotrophic ascomycete Sclerotinia (S.) sclerotiorum. Host-induced gene silencing (HIGS) has shown promise in disease control against insects and fungal pathogens, but effective HIGS target genes against S. sclerotiorum remain limited. In this study, we identified a GDP-mannose pyrophosphorylase (GMPP) SsMPG2 through forward genetic analysis. Ssmpg2 mutants exhibit abnormal sclerotia and compound appressoria, along with defective cell wall integrity and attenuated virulence. Meanwhile, knocking out SsMPG2 reduced the GMPP activity and glycosylation of proteins. In addition, SsMPG2 interacts with SsMPG1, which is essential in S. sclerotiorum. Downstream of the SsMPG1-SsMPG2 complex, SsPMT4, which encodes an O-mannosyltransferase, is also critical for compound appressoria formation and virulence. Notably, MPG2 is essential for the virulence of several other fungal pathogens such as Botrytis cinerea, Magnaporthe oryzae, and Fusarium graminearum. Furthermore, expressing hairpin RNAs against SsMPG1 and SsMPG2 in Nicotiana benthamiana and Arabidopsis thaliana significantly reduced disease symptoms caused by S. sclerotiorum. Collectively, our findings demonstrate the critical roles of GMPP in the virulence of phytopathogenic fungi and suggest that MPGs are promising HlGS targets for controlling S. sclerotiorum.
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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".