Disruption of fungal cell wall by antifungal<i>Echinacea</i>extracts
Bibliographic record
Abstract
In addition to widespread use in reducing the symptoms of colds and flu, Echinacea is traditionally employed to treat fungal and bacterial infections. However, to date the mechanism of antimicrobial activity of Echinacea extracts remains unclear. We utilized a set of ∼4,600 viable gene deletion mutants of Saccharomyces cerevisiae to identify mutations that increase sensitivity to Echinacea. Thus, a set of chemical-genetic profiles for 16 different Echinacea treatments was generated, from which a consensus set of 23 Echinacea-sensitive mutants was identified. Of the 23 mutants, only 16 have a reported function. Ten of these 16 are involved in cell wall integrity/structure suggesting that a target for Echinacea is the fungal cell wall. Follow-up analyses revealed an increase in sonication-associated cell death in the yeasts S. cerevisiae and Cryptococcus neoformans after Echinacea extract treatments. Furthermore, fluorescence microscopy showed that Echinacea-treated S. cerevisiae was significantly more prone to cell wall damage than non-treated cells. This study further demonstrates the potential of gene deletion arrays to understand natural product antifungal mode of action and provides compelling evidence that the fungal cell wall is a target of Echinacea extracts and may thus explain the utility of this phytomedicine in treating mycoses.
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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".