Targeting epigenetic regulators to overcome drug resistance in the emerging human fungal pathogen Candida auris
Bibliographic record
Abstract
Abstract The rise of drug-resistant fungal species, such as Candida auris , poses a serious threat to global health, with mortality rates exceeding 40% and resistance rates surpassing 90%. The limited arsenal of effective antifungal agents underscores the urgent need for novel strategies. Here, we systematically evaluate the role of histone H3 post-translational modifications in C. auris drug resistance, focusing on acetylation mediated by Gcn5 and Rtt109, and methylation mediated by Set1, Set2, and Dot1. Mutants deficient in these enzymes exhibit varying degrees of antifungal drug sensitivity. Notably, we discover that GCN5 depletion and the subsequent loss of histone H3 acetylation downregulates key genes involved in ergosterol biosynthesis and drug efflux, resulting in increased susceptibility to azoles and polyenes. Additionally, Gcn5 regulates cell wall integrity and echinocandin resistance through the calcineurin signaling pathway and transcription factor Cas5. In infection models using Galleria mellonella and immunocompromised mice, GCN5 deletion significantly reduces the virulence of C. auris . Furthermore, the Gcn5 inhibitor CPTH 2 synergizes with caspofungin in vitro and in vivo without notable toxicity. These findings highlight the critical role of Gcn5 in the resistance and pathogenicity of C. auris , positioning it as a promising therapeutic target for combating invasive 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 | 0.000 |
| 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".