Foliar application of plant-derived peptides decreases the severity of leaf rust (Puccinia triticina) infection in bread wheat (Triticum aestivum L.)
Bibliographic record
Abstract
BACKGROUND: Screening and developing novel antifungal agents with minimal environmental impact are needed to maintain and increase crop production, which is constantly threatened by various pathogens. Small peptides with antimicrobial and antifungal activities have been known to play an important role in plant defense both at the pathogen level by suppressing its growth and proliferation as well as at the host level through activation or priming of the plant's immune system for a faster, more robust response against fungi. Rust fungi (Pucciniales) are plant pathogens that can infect key crops and overcome resistance genes introduced in elite wheat cultivars. RESULTS: We performed an in vitro screening of 18 peptides predominantly of plant origin with antifungal or antimicrobial activity for their ability to inhibit leaf rust (Puccinia triticina, CCDS-96-14-1 isolate) urediniospore germination. Nine peptides demonstrated significant fungicidal properties compared to the control. Foliar application of the top three candidates, β-purothionin, Purothionin-α2 and Defensin-2, decreased the severity of leaf rust infection in wheat (Triticum aestivum L.) seedlings. Additionally, increased pathogen resistance was paralleled by elevated expression of defense-related genes. CONCLUSIONS: Identified antifungal peptides could potentially be engineered in the wheat genome to provide an alternative source of genetic resistance to leaf rust.
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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 | 0.001 |
| 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".