A Short Review of Anti-Rust Fungi Peptides: Diversity and Bioassays
Bibliographic record
Abstract
Pucciniales are fungal pathogens of plants that cause devastating rust diseases in agriculture. Chemically-synthesized pesticides help farmers to control rust epidemics, but governing bodies aim at limiting their use over the next decade. Defense peptides with antimicrobial activities may help to innovate a next generation of phytosanitary products for sustainable crop protection. This review comprehensively inventories the proteins or peptides exhibiting a biochemically-demonstrated antifungal activity toward Pucciniales (i.e., anti-rust proteins or peptides; hereafter ‘ARPs’), and also analyses the bioassays used to characterize them. In total, the review scrutinizes sixteen publications, which collectively report 35 ARPs. These studies used either in vitro or in planta bioassays, or a combination of both, to characterize ARPs; mostly by evaluating their ability to inhibit the spore germination process in vitro or to inhibit fungal growth and rust disease development in planta. Also, the manuscript shows that almost no mode of action against rust fungi was elucidated, although some might be inferred from studies performed on other fungi. This short review may serve as a knowledge and methodological basis to inform future studies addressing ARPs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".