Evolution of cross-tolerance to metals in yeast
Bibliographic record
Abstract
Abstract Organisms often face multiple selective pressures simultaneously (e.g., mine tailings with multiple heavy metal contaminants), yet we know little about when adaptation to one stressor provides cross-tolerance or cross-intolerance to other stressors. To explore the potential for cross-tolerance, we first adapted Saccharomyces cerevisiae to high concentrations of six single metals in a short-term evolutionary rescue experiment. We then measured the cross-tolerance of each metal-adapted line in the other five metals. We generated and tested three predictors for the degree of cross-tolerance, based on the similarity between pairs of metal environments in (1) their physiochemical properties, (2) the overlap in genes known to impact tolerance to both metals, and (3) their co-occurrence in the environment. None of these predictors explained significant variation in cross-tolerance. Instead, we observed that adapted lines in one metal were frequently cross-tolerant to certain metals (manganese and nickel) and intolerant to others (cobalt and zinc). Furthermore, cross-tolerance between pairs of metals was not reciprocal, with mutations accumulating in one metal (e.g., copper) providing adaptation to another metal (e.g., manganese), but not vice versa . Evolved lines also differed in their degree of specialization, with lines evolved in manganese or copper more specialized to that metal, but lines evolved in cobalt or zinc more generally tolerant. To determine the genetic basis of these metal adaptations, we sequenced the genomes of 109 metal-adapted yeast lines. The SNP mutation spectrum was significantly different in cadmium, cobalt, and manganese than expected in a mutation accumulation experiment in S. cerevisiae . In addition, two lines were highly mutated, bearing defects in DNA repair genes (both in manganese). Thirteen genes exhibited parallel adaptation to different metals; three of these genes generated broad cross-tolerance. Several mutations were found in vacuolar transporter genes, suggesting an important role for vacuolar proteins in adapting to metal stress. Our results with these metal-adapted lines indicate that cross-tolerance is challenging to predict, depending on the combined stressors experienced and the nature of the mutations involved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".