Adaptation of the tetracycline-repressible system for modulating the expression of essential genes in <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Abstract The opportunistic human fungal pathogen Cryptococcus neoformans has an enormous impact on human health as the causative agent of cryptococcal meningitis, an AIDS-defining illness that results in significant human mortality. To combat C. neoformans infections, there is a dire need to expand our current antifungal arsenal. Essential gene products often serve as ideal targets for antimicrobials, and thus identifying and characterizing essential genes in a pathogen of interest is critical for drug development. Unfortunately, characterization of essential genes in C. neoformans is limited due to its haploid nature and lack of genetic tools for generating effective conditional-expression mutants. To date, the copper-repressible promoter pCTR4 is the mostly widely used system to regulate essential gene expression, however, its expression is leaky and copper has pleiotropic effects. In diverse fungal species, including Saccharomyces cerevisiae, Candida albicans , and Candida auris , the tetracycline-repressible promoter system is a powerful tool to regulate gene expression, however, it has yet to be adapted for C. neoformans . In this study, we successfully implemented the tetracycline-repressible system in C. neoformans to regulate the expression of the essential gene HSP90 . Supplementation of cultures with the tetracycline analog doxycycline efficiently depleted HSP90 at both transcript and protein levels and inhibited C. neoformans growth and viability. Thus, this work unveils a novel approach to generate conditional-expression mutants in C. neoformans , providing unprecedented potential to systematically study essential gene function in this important human fungal pathogen.
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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) | 0.000 | 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".