Chemogenomic Profiling of the Fungal Pathogen Candida albicans
Bibliographic record
Abstract
ABSTRACT There is currently a small number of classes of antifungal drugs, and these drugs are known to target a very limited set of cellular functions. We derived a set of approximately 900 nonessential, transactivator-defective disruption strains from the tetracycline-regulated GRACE collection of strains of the fungal pathogen Candida albicans . This strain set was screened against classic antifungal drugs to identify gene inactivations that conferred either enhanced sensitivity or increased resistance to the compounds. We examined two azoles, fluconazole and posaconazole; two echinocandins, caspofungin and anidulafungin; and a polyene, amphotericin B. Overall, the chemogenomic profiles within drug classes were highly similar, but there was little overlap between classes, suggesting that the different drug classes interacted with discrete networks of genes in C. albicans . We also tested two pyridine amides, designated GPI-LY7 and GPI-C107; these drugs gave very similar profiles that were distinct from those of the echinocandins, azoles, or polyenes, supporting the idea that they target a distinct cellular function. Intriguingly, in cases where these gene sets can be compared to genetic disruptions conferring drug sensitivity in other fungi, we find very little correspondence in genes. Thus, even though the drug targets are the same in the different species, the specific genetic profiles that can lead to drug sensitivity are distinct. This implies that chemogenomic screens of one organism may be poorly predictive of the profiles found in other organisms and that drug sensitivity and resistance profiles can differ significantly among organisms even when the apparent target of the drug is the same.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".