Large‐scale essential gene identification in <i>Candida albicans</i> and applications to antifungal drug discovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Candida albicans is the primary fungal pathogen of humans. Despite the need for novel drugs to combat fungal infections [Sobel, J.D. (2000) Clin Infectious Dis 30: 652], antifungal drug discovery is currently limited by both the availability of suitable drug targets and assays to screen corresponding targets. A functional genomics approach based on the diploid C. albicans genome sequence, termed GRACETM (gene replacement and conditional expression), was used to assess gene essentiality through a combination of gene replacement and conditional gene expression. In a systematic application of this approach, we identify 567 essential genes in C. albicans. Interestingly, evaluating the conditional phenotype of all identifiable C. albicans homologues of the Saccharomyces cerevisiae essential gene set [Giaever, G., Chu, A.M., Ni, L., Connelly, C., Riles, L., Veronneau, S., et al. (2002) Nature 418: 387-391] by GRACE revealed only 61% to be essential in C. albicans, emphasizing the importance of performing such studies directly within the pathogen. Construction of this conditional mutant strain collection facilitates large-scale examination of terminal phenotypes of essential genes. This information enables preferred drug targets to be selected from the C. albicans essential gene set by phenotypic information derived both in vitro, such as cidal versus static terminal phenotypes, as well as in vivo through virulence studies using conditional strains in an animal model of infection. In addition, the combination of phenotypic and bioinformatic analyses further improves drug target selection from the C. albicans essential gene set, and their respective conditional mutant strains may be directly used as sensitive whole-cell assays for drug screening.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it