A Genetic Incompatibility Accelerates Adaptation in Yeast
Bibliographic record
Abstract
During mismatch repair (MMR) MSH proteins bind to mismatches that form as the result of DNA replication errors and recruit MLH factors such as Mlh1-Pms1 to initiate excision and repair steps. Previously, we identified a negative epistatic interaction involving naturally occurring polymorphisms in the MLH1 and PMS1 genes of baker's yeast. Here we hypothesize that a mutagenic state resulting from this negative epistatic interaction increases the likelihood of obtaining beneficial mutations that can promote adaptation to stress conditions. We tested this by stressing yeast strains bearing mutagenic (incompatible) and non-mutagenic (compatible) mismatch repair genotypes. Our data show that incompatible populations adapted more rapidly and without an apparent fitness cost to high salt stress. The fitness advantage of incompatible populations was rapid but disappeared over time. The fitness gains in both compatible and incompatible strains were due primarily to mutations in PMR1 that appeared earlier in incompatible evolving populations. These data demonstrate a rapid and reversible role (by mating) for genetic incompatibilities in accelerating adaptation in eukaryotes. They also provide an approach to link experimental studies to observational population genomics.
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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 | 0.000 |
| 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".