<i>Verticillium dahliae</i> effector VDAL protects MYB6 from degradation by interacting with PUB25 and PUB26 E3 ligases to enhance Verticillium wilt resistance
Bibliographic record
Abstract
Verticillium wilt is a severe plant disease that causes massive losses in multiple crops. Increasing the plant resistance to Verticillium wilt is a critical challenge worldwide. Here, we report that the hemibiotrophic Verticillium dahliae-secreted Asp f2-like protein VDAL causes leaf wilting when applied to cotton leaves in vitro but enhances the resistance to V. dahliae when overexpressed in Arabidopsis or cotton without affecting the plant growth and development. VDAL protein interacts with Arabidopsis E3 ligases plant U-box 25 (PUB25) and PUB26 and is ubiquitinated by PUBs in vitro. However, VDAL is not degraded by PUB25 or PUB26 in planta. Besides, the pub25 pub26 double mutant shows higher resistance to V. dahliae than the wild-type. PUBs interact with the transcription factor MYB6 in a yeast two-hybrid screen. MYB6 promotes plant resistance to Verticillium wilt while PUBs ubiquitinate MYB6 and mediate its degradation. VDAL competes with MYB6 for binding to PUBs, and the role of VDAL in increasing Verticillium wilt resistance depends on MYB6. Taken together, these results suggest that plants evolute a strategy to utilize the invaded effector protein VDAL to resist the V. dahliae infection without causing a hypersensitive response (HR); alternatively, hemibiotrophic pathogens may use some effectors to keep plant cells alive during its infection in order to take nutrients from host cells. This study provides the molecular mechanism for plants increasing disease resistance when overexpressing some effector proteins without inducing HR, and may promote searching for more genes from pathogenic fungi or bacteria to engineer plant disease resistance.
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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 | 0.001 |
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".