Colistin enhances caspofungin antifungal efficacy against Aspergillus fumigatus by modulating calcium homeostasis and stress responses
Bibliographic record
Abstract
Fungal infections cause more than 2.5 million deaths a year. Due to emerging antifungal drug resistance, novel strategies are urgently needed to combat life-threatening fungal diseases. Here, by screening a collection of 5297 compounds derived from three chemical libraries, we demonstrate that the antibacterial agent colistin (COL) can potentiate the fungistatic echinocandins caspofungin (CAS) and anidulafungin, as well as the structurally distinct cell wall targeting antifungal ibrexafungerp against Aspergillus fumigatus. Chemical and genetic screenings revealed that protein kinase C and the transcription factor SltA are involved in the mechanism of action of COL. SltA is essential for coping with calcium-limiting conditions, and the addition of calcium rescues COL-susceptibility. COL + CAS decreases A. fumigatus infection in human pulmonary cells, Galleria mellonella, and Caenorhabditis elegans. In summary, we demonstrate that the mechanism of COL as a synergizer of CAS against A. fumigatus is the disruption of the cell membrane permeability and calcium homeostasis. The antibacterial agent colistin displays synergistic activity with azoles and echinocandins against various pathogenic fungi. Here, the authors show that the mechanisms involve disruption of cell membrane permeability and calcium homeostasis.
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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".