Identification and Characterization of Plant Cell Death–Inducing Secreted Proteins From <i>Ustilaginoidea virens</i>
Bibliographic record
Abstract
Ustilaginoidea virens (Cooke) Takah (telemorph Villosiclava virens) is an ascomycetous fungus that causes rice false smut, one of the most important rice diseases. Fungal effectors often play essential roles in host-pathogen coevolutionary interactions. However, little is known about the functions of U. virens effectors. Here, we performed functional studies on putative effectors in U. virens and demonstrated that 13 of 119 putative effectors caused necrosis or necrosis-like phenotypes in Nicotiana benthamiana. Among them, 11 proteins were confirmed to be secreted, using a yeast secretion system, and the corresponding genes are all highly induced during infection, except UV_44 and UV_4753. Eight secreted proteins were proven to trigger cell death or defenses in rice protoplasts and the secretion signal of these proteins is essential for their cell death-inducing activity. The ability of UV_44 and UV_1423 to trigger cell death is dependent on the predicted serine peptidase and ribonuclease catalytic active sites, respectively. We demonstrated that UV_1423 and UV_6205 are N-glycosylated proteins, which glycosylation has different impacts on their abilities to induce cell death. Collectively, the study identified multiple secreted proteins in U. virens with specific structural motifs that induce cell death or defense machinery in nonhost and host plants.
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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) | 0.000 | 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".