Evolution of cross-tolerance to metals in yeast
Bibliographic record
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 adapted Saccharomyces cerevisiae to high concentrations of six single metals in a short-term evolutionary rescue experiment. We then measured cross-tolerance of each metal-adapted line to the other five metals. We tested three predictors for the degree of cross-tolerance, based on similarity in 1) the physiochemical properties of each metal pair, 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, the strongest predictor was the metal in which adaptation occurred: Cobalt-adapted lines performed well in most metals (generalists) while manganese-adapted lines typically performed poorly (specialists). To determine the genetic basis, we sequenced the genomes of 109 metal-adapted lines. Broader cross-tolerance characterized lines bearing mutations affecting phosphorus metabolism, with three genes related to phosphate metabolism bearing several independent mutations ( PHO84 , SIW14 , VTC4 ). Thus, while a genome-wide analysis failed to predict cross-tolerance, a subset of genes facilitated growth in multiple metals. We also observed two “mutator” lines (both in manganese) and report evidence that cadmium, cobalt, and manganese altered the mutation spectrum. While it is challenging to predict how evolutionary adaptation to one stressor will impact tolerance to other stresses, our work helps reveal the environments and pathways that contribute to cross-tolerance among metals.
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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 | 0.001 |
| 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 | 0.001 | 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".