<i>Candida albicans</i> enhances iron uptake to maintain fluconazole resistance
Bibliographic record
Abstract
ABSTRACT Widespread use of fluconazole has led to the emergence of fluconazole-resistant (FR) Candid a spp. causing challenges in clinical treatment. Iron, an essential nutrient, affects the levels of ergosterol (a fluconazole target) in fungal membranes. Our lab-generated FR strain (fluconazole minimum inhibitory concentration [MIC] >125 µg/mL) showed a twofold lower MIC (4.66 µg/mL) for the iron chelator deferasirox (DFX), compared to its patent strain CAI4 (DFX MIC 9.34 µg/mL), suggesting a greater sensitivity to iron chelation. A sublethal dose of DFX (2.33 µg/mL) was able to effectively synergize with 125 µg/mL fluconazole to kill the FR strain. Iron estimation revealed significantly enhanced intracellular iron accumulation in the FR strain compared to CAI4. Expression of iron-uptake genes ( FRP1 , FRE10 , and RBT5 ) was also significantly upregulated in the FR strain, particularly under high iron. FR strain also showed an increase in the levels of cellular ergosterol, along with an increase in the expression of ergosterol biosynthesis genes ( ERG11 and ERG9 ), compared to CAI4, under both low and high iron. The strain further showed increased β-glucan levels and exposure. Additionally, FR strain showed significantly higher survival in high-iron mice compared to low-iron mice, during fluconazole treatment. Finally, we observed a synergistic fungicidal response between 2.33 µg/mL DFX and 125 µg/mL fluconazole, for FR clinical strains. Overall, this suggests that FR C. albicans actively uptakes more iron to maintain cellular conditions needed to support its resistance against fluconazole; and that DFX alone or in conjugation with fluconazole has the potential to overcome fluconazole drug resistance.
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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) | 0.002 | 0.001 |
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".