Phenotypic Analysis of a Family of Transcriptional Regulators, the Zinc Cluster Proteins, in the Human Fungal Pathogen <i>Candida glabrata</i>
Bibliographic record
Abstract
Candida glabrata is the second most important human fungal pathogen. Despite its formal name, C. glabrata is in fact more closely related to the nonpathogenic budding yeast Saccharomyces cerevisiae. However, less is known about the biology of this pathogen. Zinc cluster proteins form a large family of transcriptional regulators involved in the regulation of numerous processes such as the control of the metabolism of sugars, amino acids, fatty acids, as well as drug resistance. The C. glabrata genome encodes 41 known or putative zinc cluster proteins, and the majority of them are uncharacterized. We have generated a panel of strains carrying individual deletions of zinc cluster genes. Using a novel approach relying on tetracycline for conditional expression in C. glabrata at the translational level, we show that only two zinc cluster genes are essential. We have performed phenotypic analysis of nonessential zinc cluster genes. Our results show that two deletion strains are thermosensitive whereas two strains are sensitive to caffeine, an inhibitor of the target of rapamycin pathway. Increased salt tolerance has been observed for eight deletion strains, whereas one strain showed reduced tolerance to salt. We have also identified a number of strains with increased susceptibility to the antifungal drugs fluconazole and ketoconazole. Interestingly, one deletion strain showed decreased susceptibility to the antifungal micafungin. In summary, we have assigned phenotypes to more than half of the zinc cluster genes in C. glabrata. Our study provides a resource that will be useful to better understand the biological role of these transcription factors.
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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".