Fitness distribution and transgressive segregation across 40 environments in a hybrid progeny population of the human-pathogenic yeast<i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
The opportunistic human fungal pathogen Cryptococcus neoformans includes two varieties, C. neoformans var. grubii and C. neoformans var. neoformans, which correspond to serotypes A and D, respectively. Recent population genetic studies revealed that multiple natural hybridizations have occurred recently between these two divergent lineages. However, the biological effects of such hybridizations are little understood. In this study, we used colony size as a proxy for vegetative fitness to examine the phenotypic effects of hybridization between these two lineages in a laboratory cross. Two genetically diverged parental strains that differed in their growth at different temperatures and on different media as well as in their susceptibility to the common antifungal drug fluconazole were chosen. A total of 269 progeny were obtained and their vegetative growth was determined in 40 environments that differed in nutrients, temperature, and fluconazole concentration. Our analyses indicated little evidence for outbreeding depression or heterosis in the average vegetative fitness of the hybrid progeny population. The progeny, each of the three environmental variables, and their two-way, three-way, and four-way interactions all contributed significantly to the overall vegetative fitness variation. Interestingly, a variable number of progeny displayed evidence of transgressive segregation in vegetative fitness among the tested environments. Our study suggests that hybridization could play a significant role in the phenotypic evolution of this important human-pathogenic fungus.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".