A sugarcane smut fungus effector simulates the host endogenous elicitor peptide to suppress plant immunity
Bibliographic record
Abstract
The smut fungus Sporisorium scitamineum causes the most prevalent disease on sugarcane. The mechanism of its pathogenesis, especially the functions and host targets of its effector proteins, are unknown. In order to identify putative effectors involving in S. scitamineum infection, a weighted gene co-expression network analysis was conducted based on the transcriptome profiles of both smut fungus and sugarcane using a customized microarray. A smut effector gene, termed SsPele1, showed strong co-expression with sugarcane PLANT ELICITOR PEPTIDE RECEPTOR1 (ScPEPR1), which encodes a receptor like kinase for perception of plant elicitor peptide1 (ScPep1). The relationship between SsPele1 and ScPEPR1, and the biological function of SsPele1 were characterized in this study. The SsPele1 C-terminus contains a plant elicitor peptide-like motif, by which SsPele1 interacts strongly with ScPEPR1. Strikingly, the perception of ScPep1 on ScPEPR1 is competed by SsPele1 association, leading to the suppression of ScPEPR1-mediated immune responses. Moreover, the Ustilago maydis effector UmPele1, an ortholog of SsPele1, promotes fungal virulence using the same strategy. This study reveals a novel strategy by which a fungal effector can mimic the plant elicitor peptide to complete its perception and attenuate receptor-activated immunity.
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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".