Population genomic analyses reveal high diversity, recombination and nosocomial transmission among Candida glabrata (Nakaseomyces glabrata) isolates causing invasive infections
Bibliographic record
Abstract
Candida glabrata is a commensal yeast of the gastrointestinal tract and skin of humans. However, it causes opportunistic infections in immunocompromised patients, and is the second most common Candida pathogen causing bloodstream infections. Although there are many studies on the epidemiology of C. glabrata infections, the fine- and large-scale geographical nature of C. glabrata remain incompletely understood. Here we investigated both the fine- and large-scale population structure of C. glabrata through genome sequencing of 80 clinical isolates obtained from six tertiary hospitals in Qatar and by comparing with global collections. Our fine-scale analyses revealed high genetic diversity within the Qatari population of C. glabrata and identified signatures of recombination, inbreeding and clonal expansion within and between hospitals, including evidence for nosocomial transmission among coronavirus disease 2019 (COVID-19) patients. In addition to signatures of recombination at the population level, both MATa and MATα alleles were detected in most hospitals, indicating the potential for sexual reproduction in clinical environments. Comparisons with global samples showed that the Qatari C. glabrata population was very similar to those from other parts of the world, consistent with the significant role of recent anthropogenic activities in shaping its population structure. Genome-wide association studies identified both known and novel genomic variants associated with reduced susceptibilities to fluconazole, 5-flucytosine and echinocandins. Together, our genomic analyses revealed the diversity, transmission patterns and antifungal drug resistance mechanisms of C. glabrata in Qatar as well as the relationships between Qatari isolates and those from other parts of the world.
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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 | 0.001 | 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".