Proteasome inhibition as a therapeutic target for the fungal pathogen <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
ABSTRACT The current therapeutic challenges for treating fungal diseases demand new approaches and new drugs. A promising strategy involves combination therapy with agents of distinct mechanisms of action to increase fungicidal activity and limit the impact of mutations leading to resistance. In this study, we evaluated the antifungal potential of bortezomib by examining the inhibition of proteasome activity, cell proliferation, and capsule production by Cryptococcus neoformans , the causative agent of fungal meningoencephalitis. Chemical genetic screens with collections of deletion mutants identified potential druggable targets for combination therapy with bortezomib. In vitro assays of combinations of bortezomib with flucytosine, chlorpromazine, bafilomycin A1, copper sulfate, or hydroxyurea revealed antifungal effects against C. neoformans . Furthermore, combination treatment with bortezomib and flucytosine in a murine inhalation model of cryptococcosis resulted in the improvement of neurological functions and reduced fungal replication and dissemination, leading to a delay in disease progression. This study therefore highlights the utility of chemical genetic screens to identify new therapeutic approaches as well as the antifungal potential of proteasome inhibition. IMPORTANCE Fungal diseases of humans are difficult to treat, and there is a clear need for additional antifungal drugs, better diagnostics, effective vaccines, and new approaches to deal with emerging drug resistance. Fungi are challenging to control because they share many common biochemical functions with their mammalian hosts and it is therefore difficult to identify fungal-specific targets for drug development. One approach is to employ existing antifungal drugs in combination with agents that target common cellular processes at levels that are (ideally) not toxic for the host. We pursued this approach in this study by examining the potential of the clinically approved proteasome inhibitor bortezomib to influence the proliferation and virulence of Cryptococcus neoformans . We found that the combination of bortezomib with the anti-cryptococcal drug flucytosine improved the survival of infected mice, thus demonstrating the potential of this strategy for antifungal therapy.
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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".