RNAi as a Tool to Study Virulence in the Pathogenic Yeast Candida glabrata
Bibliographic record
Abstract
The yeast Candida glabrata is major opportunistic pathogen causing mucosal, and systemic infections in humans. Systemic infections caused by this yeast have high mortality rates, and are difficult to treat due to this yeast’s intrinsic, and frequently adapting antifungal resistance. To understand and treat C. glabrata infections, it is essential to investigate the molecular basis of C. glabrata virulence, and resistance. We established a RNA interference (RNAi) system in C. glabrata by expressing the Dicer and Argonaute genes from Saccharomyces castellii (a budding yeast with natural RNAi). Our experiments with reporter genes, and putative virulence genes showed that the introduction of RNAi resulted in 30-70% gene-knockdown for the construct-types antisense and hairpin, respectively. The resulting C. glabrata RNAi strain was used for the screening of a gene library for new virulence-related genes. Phenotypic profiling with high-resolution quantification of growth identified genes involved in the maintenance of cell integrity, antifungal drugs, and ROS resistance. The genes identified by this approach are promising targets for the treatment of C. glabrata infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".