Phylogenomic analysis and genetic mechanisms of antifungal resistance in clinical isolates of <i>Candida glabrata</i> ( <i>Nakaseomyces glabratus</i> ) from across Canada, 2013–2020
Bibliographic record
Abstract
ABSTRACT Candida glabrata ( Nakaseomyces glabratus ) is an important cause of invasive fungal infections and may exhibit reduced susceptibility toward antifungal drugs. Here, we used whole-genome sequencing to investigate the genomic phylogeny and identify genetic determinants of antifungal resistance in a collection of 142 clinical C. glabrata isolates obtained from the 10 provinces of Canada between 2013 and 2020. Our study prioritized resistant isolates ( n = 62, 43.7%) from invasive infections to better understand antifungal resistance and represents the largest national genomic survey of clinical C. glabrata isolates performed to date in Canada. Phylogenomic analysis based on single-nucleotide variants in the core genome revealed 15 genetically related clusters of C. glabrata . The largest cluster (cluster I, n = 29) was significantly associated with antifungal resistance ( P value = 0.0112). Antifungal-resistant isolates were present in almost all clusters, suggesting that resistance most likely arises from selective pressure during antifungal therapy, rather than dissemination of resistant clones. Thirty-six unique PDR1 variants were found in 38/52 (73.1%) fluconazole-resistant C. glabrata isolates, with more than half identified in this study as potential novel azole resistance variants. Well-characterized hot-spot variants in FKS genes ( n = 5) were found in 12/13 (92.3%) micafungin-resistant C. glabrata isolates. Our genomic analysis highlights the diversity in strain types and sheds light on potential genetic mechanisms of resistance in Canadian isolates of C. glabrata . IMPORTANCE Candida glabrata , also known as Nakaseomyces glabratus , is a type of yeast that can cause infections in individuals with weakened immune systems. Invasive infections can be difficult to treat since some C. glabrata isolates may not respond well to common antifungals. We studied a large collection of C. glabrata isolates collected from across Canada to better understand how C. glabrata spreads and why this fungal pathogen sometimes resists treatment. Using whole-genome sequence analysis, we found that drug resistance appears in different strains independently, likely as a result of treatment, rather than spreading from a single clone. We also identified specific mutations that may be linked to resistance to commonly used antifungal drugs, such as fluconazole and micafungin. Our research shows how valuable whole-genome sequencing is for understanding the spread and drug resistance of C. glabrata , which can help improve treatment and infection control.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".