Loss of Heterozygosity Drives Adaptation in Hybrid Yeast
Bibliographic record
Abstract
Hybridization is often considered maladaptive, but sometimes hybrids can invade new ecological niches and adapt to novel or stressful environments better than their parents. The genomic changes that occur following hybridization that facilitate genome resolution and/or adaptation are not well understood. Here, we examine hybrid genome evolution using experimental evolution of de novo interspecific hybrid yeast Saccharomyces cerevisiae × Saccharomyces uvarum and their parentals. We evolved these strains in nutrient-limited conditions for hundreds of generations and sequenced the resulting cultures identifying numerous point mutations, copy number changes, and loss of heterozygosity (LOH) events, including species-biased amplification of nutrient transporters. We focused on a particularly interesting example, in which we saw repeated LOH at the high-affinity phosphate transporter gene PHO84 in both intra- and interspecific hybrids. Using allele replacement methods, we tested the fitness of different alleles in hybrid and S. cerevisiae strain backgrounds and found that the LOH is indeed the result of selection on one allele over the other in both S. cerevisiae and the hybrids. This is an example where hybrid genome resolution is driven by positive selection on existing heterozygosity and demonstrates that even infrequent outcrossing may have lasting impacts on adaptation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".