Fungal membrane determinants affecting sensitivity to antifungal cyclic lipopeptides from Bacillus spp.
Bibliographic record
Abstract
Bacillus spp. produce numerous antimicrobial metabolites. Among these metabolites, cyclic lipopeptides (CLP) including fengycins, iturins, and surfactins are known to have varying antifungal activity against phytopathogenic fungi. The differential activities of CLP have been attributed to diverse mechanisms of action on fungal membranes. However, the precise biochemical determinants driving their antifungal modes of action have not been conclusively identified. In this study, three plant pathogenic fungi of varying lipopeptide sensitivities, Alternaria solani, Cladosporium cucumerinum, and Fusarium sambucinum, were studied to determine how their cell membrane lipid compositions may confer sensitivity and/or tolerance to fengycin, iturin, and surfactin. Results indicated that sensitivity to all three lipopeptides correlated with lower ergosterol content and elevated phospholipid fatty acid unsaturation. Fungal sensitivity to surfactin was also notably different than fengycin and iturin, as surfactin was influenced more by lower phosphatidylethanolamine amounts, higher levels of phosphatidylinositol, and less by phospholipid fatty acyl chain length. Results from this study provide insight into the fungal membrane composition of A. solani, F. sambucinum, and C. cucumerinum and the specific membrane characteristics influencing the antifungal effectiveness of fengycin, iturin, and surfactin. Understanding of these determinants should enable more accurate prediction of sensitivity-tolerance outcomes for other fungal species exposed to these important CLP.
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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".