A <scp>MAP</scp> kinase cascade broadly regulates the lifestyle of <i>Sclerotinia sclerotiorum</i> and can be targeted by <scp>HIGS</scp> for disease control
Bibliographic record
Abstract
Sclerotinia sclerotiorum causes white mold or stem rot in a wide range of economically important plants, bringing significant yield losses worldwide. Control of this pathogen is difficult as its resting structure sclerotia can survive in soil for years, and no Resistance genes have been identified in S. sclerotiorum hosts. Host-induced gene silencing (HIGS) has shown promising effects in controlling many fungal pathogens, including S. sclerotiorum. However, better molecular genetic understanding of signaling pathways involved in its development and pathogenicity is needed to provide effective HIGS gene targets. Here, by employing a forward genetic screen, we characterized an evolutionarily conserved mitogen-activated protein kinase (MAPK) cascade in S. sclerotiorum, consisting of SsSte50-SsSte11-SsSte7-Smk1, which controls mycelial growth, sclerotia development, compound appressoria formation, virulence, and hyphal fusion. Moreover, disruption of the putative downstream transcription factor SsSte12 led to normal sclerotia but deformed appressoria and attenuated host penetration, as well as impaired apothecia formation, suggestive of diverged regulation downstream of the MAPK cascade. Most importantly, targeting SsSte50 using host-expressed double-stranded RNA resulted in largely reduced virulence of S. sclerotiorum on both Nicotiana benthamiana leaves and transgenic Arabidopsis thaliana plants. Therefore, this MAPK signaling cascade is generally needed for its growth, development, and pathogenesis and can serve as ideal HIGS targets for mitigating economic damages caused by 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".