What Are the Best Parents for Hybrid Progeny? An Investigation into the Human Pathogenic Fungus Cryptococcus
Bibliographic record
Abstract
Hybridization between more divergent organisms is likely to generate progeny with more novel genetic interactions and genetic variations. However, the relationship between parental genetic divergence and progeny phenotypic variation remains largely unknown. Here, using strains of the human pathogenic Cryptococcus, we investigated the patterns of such a relationship. Twenty-two strains with up to 15% sequence divergence were mated. Progeny were genotyped at 16 loci. Parental strains and their progeny were phenotyped for growth ability at two temperatures, melanin production at seven conditions, and susceptibility to the antifungal drug fluconazole. We observed three patterns of relationships between parents and progeny for each phenotypic trait, including (i) similar to one of the parents, (ii) intermediate between the parents, and (iii) outside the parental phenotypic range. We found that as genetic distance increases between parental strains, progeny showed increased fluconazole resistance and growth at 37 °C but decreased melanin production under various oxidative and nitrosative stresses. Our findings demonstrate that, depending on the traits, both evolutionarily more similar strains and more divergent strains may be better parents to generate progeny with hybrid vigor. Together, the results indicate the enormous potential of Cryptococcus hybrids in their evolution and adaptation to diverse conditions.
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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 | 0.001 | 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 | 0.001 |
| 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".