Harnessing Hsp90 function as a powerful, broadly effective therapeutic strategy for fungal infectious disease
Bibliographic record
Abstract
Invasive fungal infections are a leading cause of mortality among immunocompromised individuals. Treatment is notoriously difficult with the limited armamentarium of antifungal drugs, whose efficacy is compromised by host toxicity, a limited activity spectrum, or the emergence of drug resistance. We previously established that the molecular chaperone Hsp90 enables the emergence and maintenance of fungal drug resistance. For the most prevalent fungal pathogen of humans, Candida albicans, Hsp90 mediates resistance to azoles, which inhibit ergosterol biosynthesis and are the most widely deployed antifungals in the clinic. For the emerging opportunistic pathogen Aspergillus terreus, Hsp90 is required for basal resistance to echinocandins, which inhibit beta(1, 3)-glucan synthesis and are the only new class of antifungals to reach the clinic in decades. Here, we explore the therapeutic potential of Hsp90 inhibitors in fungal disease using a tractable host-model system, larvae of the greater wax moth Galleria mellonella, and a murine model of disseminated disease. Combination therapy with Hsp90 inhibitors that are well tolerated in humans and an azole rescued larvae from lethal C. albicans infections. Combination therapy with an Hsp90 inhibitor and an echinocandin rescued larvae from infections with the most lethal mold, Aspergillus fumigatus. In a murine model of disseminated candidiasis, genetic compromise of C. albicans HSP90 expression enhanced the therapeutic efficacy of an azole. Thus, harnessing Hsp90 provides a much-needed strategy for improving the treatment of fungal disease because it enhances the efficacy of existing antifungals, blocks the emergence of drug resistance, and exerts broad-spectrum activity against diverse fungal pathogens.
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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".