Effect of Salt Stress on Mutation and Genetic Architecture for Fitness Components in<i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
Abstract Mutations shape genetic architecture and thus influence the evolvability, adaptation and diversification of populations. Mutations may have different and even opposite effects on separate fitness components, and their rate of origin, distribution of effects and variance-covariance structure may depend on environmental quality. We performed an approximately 1,500-generation mutation-accumulation (MA) study in diploids of the yeast Saccharomyces cerevisiae in stressful (high-salt) and normal environments (50 lines each) to investigate the rate of input of mutational variation (Vm) as well as the mutation rate and distribution of effects on diploid and haploid fitness components, assayed in the normal environment. All four fitness components in both MA treatments exhibited statistically significant mutational variance and mutational heritability. Compared to normal-MA, salt stress increased the mutational variance in growth rate by more than sevenfold in haploids derived from the MA lines. This increase was not detected in diploid growth rate, suggesting masking of mutations in the heterozygous state. The genetic architecture arising from mutation (M-matrix) differed between normal and salt conditions. Salt stress also increased environmental variance in three fitness components, consistent with a reduction in canalization. Maximum-likelihood analysis indicated that stress increased the genomic mutation rate by approximately twofold for maximal growth rate and sporulation rate in diploids and for viability in haploids, and by tenfold for maximal growth rate in haploids, but large confidence intervals precluded distinguishing these values between MA environments. We discuss correlations between fitness components in diploids and haploids and compare the correlations between the two MA environmental treatments.
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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 | 0.000 |
| 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".