Pathway analysis of <i>Candida albicans</i> survival and virulence determinants in a murine infection model
Bibliographic record
Abstract
One potentially rich source of possible targets for antifungal therapy are those Candida albicans genes deemed essential for growth under the standard culture (i.e., in vitro) conditions; however, these genes are largely unexplored as drug targets because essential genes are not experimentally amenable to conventional gene deletion and virulence studies. Using tetracycline-regulatable promoter-based conditional mutants, we investigated a murine model of candidiasis in which repressing essential genes in the host was achieved. By adding doxycycline to the drinking water starting 3 days prior to (dox - 3D) or 2 days post (dox + 2D) infection, the phenotypic consequences of temporal gene inactivation were assessed by monitoring animal survival and fungal burden in prophylaxis and acute infection settings. Of 177 selected conditional shut-off strains tested, the virulence of 102 was blocked under both repressing conditions, suggesting that the corresponding genes are essential for growth and survival in a murine host across early and established infection periods. Among these genes were those previously identified as antifungal drug targets (i.e., FKS1, ERG1, and ERG11), verifying that this methodology can be used to validate potential new targets. We also identify genes either conditionally essential or dispensable for in vitro growth but required for survival and virulence, including those in late stage ergosterol synthesis, or early steps in fatty acid or riboflavin biosynthesis. This study evaluates the role of essential genes with respect to pathogen virulence in a large-scale, systems biology context, and provides a general method for gene target validation and for uncovering unexpected antimicrobial targets.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".