Identification of QTLs Associated with Virulence Related Traits and Drug Resistance in <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Cryptococcus neoformans is a basidiomycete fungus capable of causing deadly meningoenchephilitis, primarily in immunocompromised individuals. Formerly, C. neoformans was composed of two divergent lineages, but these have recently been elevated to species status, now C. neoformans (formerly C. neoformans var. grubii) and C. deneoformans (formerly C. neoformans var. neoformans). While both species can cause deadly infections in humans, C. neoformans is much more prevalent in clinical settings than C. deneoformans However, the genetic factors contributing to their significant differences in virulence remain largely unknown. Quantitative trait locus (QTL) mapping is a powerful tool that can be used to identify genomic regions associated with phenotypic differences between strains. Here, we analyzed a hybrid cross between these two species and identified a total of 23 QTL, including five for melanin production, six for cell size, one for cell wall thickness, five for the frequency of capsule production, three for minimal inhibitory concentration (MIC) of fluconazole in broth, and three for MIC on solid medium. For the fluconazole resistance-associated QTL, three showed environment and/or concentration-specific effects. Our results provide a large number of candidate gene regions from which to explore the molecular bases for phenotypic differences between C. neoformans and C. deneoformans.
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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) | 0.000 | 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 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".