Beauvericin Potentiates Azole Activity via Inhibition of Multidrug Efflux, Blocks Candida albicans Morphogenesis, and Is Effluxed via Yor1 and Circuitry Controlled by Zcf29
Bibliographic record
Abstract
Invasive fungal infections are a leading cause of human mortality. Effective treatment is hindered by the rapid emergence of resistance to the limited number of antifungal drugs, demanding new strategies to treat life-threatening fungal infections. Here, we explore a powerful strategy to enhance antifungal efficacy against leading human fungal pathogens by using the natural product beauvericin. We found that beauvericin potentiates the activity of azole antifungals against azole-resistant Candida isolates via inhibition of multidrug efflux and that beauvericin itself is effluxed via Yor1. As observed in Saccharomyces cerevisiae, we determined that beauvericin inhibits TOR signaling in Candida albicans To further characterize beauvericin activity in C. albicans, we leveraged genome sequencing of beauvericin-resistant mutants. Resistance was conferred by mutations in transcription factor genes TAC1, a key regulator of multidrug efflux, and ZCF29, which was uncharacterized. Transcriptional profiling and chromatin immunoprecipitation coupled to microarray analyses revealed that Zcf29 binds to and regulates the expression of multidrug transporter genes. Beyond drug resistance, we also discovered that beauvericin blocks the C. albicans morphogenetic transition from yeast to filamentous growth in response to diverse cues. We found that beauvericin represses the expression of many filament-specific genes, including the transcription factor BRG1 Thus, we illuminate novel circuitry regulating multidrug efflux and establish that simultaneously targeting drug resistance and morphogenesis provides a promising strategy to combat life-threatening fungal infections.
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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".