Using Aspergillus nidulans To Identify Antifungal Drug Resistance Mutations
Bibliographic record
Abstract
Systemic fungal infections contribute to at least 10% of deaths in hospital settings. Most antifungal drugs target ergosterol (polyenes) or its biosynthetic pathway (azoles and allylamines), or beta-glucan synthesis (echinocandins). Antifungal drugs that target proteins are prone to the emergence of resistant strains. Identification of genes whose mutations lead to targeted resistance can provide new information on those pathways. We used Aspergillus nidulans as a model system to exploit its tractable sexual cycle and calcofluor white as a model antifungal agent to cross-reference our results with other studies. Within 2 weeks from inoculation on sublethal doses of calcofluor white, we isolated 24 A. nidulans adaptive strains from sectoring colonies. Meiotic analysis showed that these strains had single-gene mutations. In each case, the resistance was specific to calcofluor white, since there was no cross-resistance to caspofungin (echinocandin). Mutation sites were identified in two mutants by next-generation sequencing. These were confirmed by reengineering the mutation in a wild-type strain using a gene replacement strategy. One of these mutated genes was related to cell wall synthesis, and the other one was related to drug metabolism. Our strategy has wide application for many fungal species, for antifungal compounds used in agriculture as well as health care, and potentially during protracted drug therapy once drug resistance arises. We suggest that our strategy will be useful for keeping ahead in the drug resistance arms race.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".