Disruption of the ATP-dependent unfoldase ClpX reverses antifungal resistance in Cryptococcus neoformans
Bibliographic record
Abstract
Fungal diseases impact the lives of a millions of people across the globe, and with our current repertoire of therapeutic options dwindling, effective treatment strategies are urgently needed. Critically, the emergence of azole-resistant isolates in the clinic following prolonged treatment regimes, environmental fungicide exposure, and fungal evolution, threatens the outcome of current therapeutics, further endangering the survival of infected individuals. Here, we investigate the underpinnings of antifungal resistance using quantitative proteomics to discover protein-level signatures of fluconazole (FLC) resistance in the opportunistic human fungal pathogen, Cryptococcus neoformans. We explore ClpX, an ATP-dependent unfoldase, as a target to overcome FLC resistance and explore target efficacy through macrophage and murine models of cryptococcal infection. Here we show that disruption of ClpX, following gene deletion or targeted inhibition, re-introduces FLC susceptibility into resistant strains, rendering FLC treatment effective once again. Further, we identify and experimentally confirm mechanisms by which ClpX influences susceptibility to FLC, through association with both heme biosynthesis and ergosterol production. Overall, our results contribute to the understanding of mechanisms driving FLC resistance in a globally important fungal pathogen, and we provide avenues for targeting proteins as a therapeutic strategy to reverse antifungal resistance. Here, the authors reveal that the ATP-dependent unfoldase ClpX is a key driver of fluconazole resistance in Cryptococcus neoformans, and show that targeting ClpX restores drug susceptibility, offering a potential therapeutic strategy to reverse antifungal resistance and to render current drugs effective.
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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 | 0.001 |
| 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".