Ctt1 catalase activity potentiates antifungal azoles in the emerging opportunistic pathogen Saccharomyces cerevisiae
Bibliographic record
Abstract
Abstract Fungi respond to antifungal drugs by increasing their antioxidant stress response. How this impacts antifungal efficacy remains controversial and not well understood. Here we examine the role of catalase activity in the resistance of Saccharomyces cerevisiae to the common antifungals, fluconazole and miconazole, for which we report minimum inhibitory concentrations (MICs) of 104 and 19 μM, respectively. At sub-MIC concentrations, fluconazole and miconazole stimulate catalase activity 2-3-fold but, unexpectedly, deletion of cytosolic catalase (ctt1) makes cells more resistant to these azoles and to clotrimazole, itraconazole and posaconazole. On the other hand, upregulating Ctt1 activity by preconditioning with 0.2 mM H2O2 potentiates miconazole 32-fold and fluconazole 4-fold. Since H2O2 preconditioning does not alter the resistance of ctt1Δ cells, which possess negligible catalase activity, we link azole potentiation with Ctt1 upregulation. In contrast, sod2Δ cells deleted for mitochondrial superoxide dismutase are 4–8-fold more azole sensitive than wild-type cells, revealing that Sod2 activity protects cells against azole toxicity. In fact, the ctt1Δ mutant has double the Sod2 activity of wild-type cells so ctt1 deletion increases azole resistance in part by Sod2 upregulation. Notably, deletion of peroxisomal/mitochondrial cta1 or cytosolic sod1 does not alter fluconazole or miconazole potency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".