Population genomics of drug resistance in the pathogenic yeast Candida Albicans
Bibliographic record
Abstract
The widespread deployment of antimicrobial agents in medicine and agriculture is nearly always followed by the evolution of resistance to these agents in pathogens. The goal of the studies initiated in this thesis is to understand the evolutionary process of the emergence of drug resistance in the pathogenic yeast Candida albicans. I have studied both natural and experimental population samples at genomic scales ranging from small-scale interactions of primary resistance mutations with one or a few genes, to large-scale interactions with many genes. First, I genotyped a natural population sample of C. albicans from HIV-infected patients based on both an assay of nucleotide polymorphisms at 16 loci and DNA fingerprinting. If variation in drug resistance is due to inherent differences in strains of C. albicans, a predominantly clonal organism, then correlation between multilocus genotypes and drug resistance would be expected. Multilocus genotypes were not predictive of resistance to the antifungal drug fluconazole, suggesting that resistance is gained or lost too quickly to be predicted by linkage with neutral markers. Second, I established twelve replicate experimental populations of C. albicans to study the dynamics, fitness consequences, and molecular mechanisms of adaptation to fluconazole. The experimental populations were founded from a single drug-sensitive cell; six populations were evolved with inhibitory concentrations of fluconazole and six were evolved without drug, over 330 generations. While all populations evolved with fluconazole adapted to the presence of drug, they followed strikingly different trajectories, with distinct overexpression patterns of four genes implicated in azole resistance. The experimental populations diverged in fitness, measured in direct competition between each evolved population and the ancestor. I measured changes in genome-wide gene expression that became entrenched during adaptation and persisted in the absence of drug, using DNA microarrays. I profiled four populations that evolved with drug at generation 330 and for two of these populations, I also profiled earlier time points. Cluster analysis of the 301 genes with significantly modulated expression identified three patterns of adaptation to drug. These three patterns of gene expression were also detected in fluconazole-resistant clinical isolates and must represent common transcriptional programs of adaptation to fluconazole.
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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 | 0.001 |
| 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 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".