Yeast Populations Evolve to Resist CdSe Quantum Dot Toxicity
Bibliographic record
Abstract
Abstract Engineered nanomaterials are used globally in biomedical, electronic, and optical devices, and are often discarded into the environment. Cell culture experiments have shown that many inorganic nanoparticles are toxic to eukaryotic cells. Here, we show that populations of eukaryotic cells can evolve to survive chronic exposure to toxic CdSe semiconductor quantum dots (QDs). We grew yeast Saccharomyces cerevisiae for 24 days in liquid medium containing QDs prepared daily at half the minimum inhibitory concentration (MIC50) of the progenitor yeast cells. After 24 days, the cells grew normally under constant exposure to QDs. We concluded that these cells evolved to resist QD toxicity. Surprisingly, when we removed QDs from the growth medium, some of the evolved cells grew poorly, i.e., they grew better in the presence of QDs. Finally, genetic analysis confirmed that the ubiquitin ligase gene bul1 was mutated in the evolved cells, which suggests that this gene may be implicated in increased CdSe QD tolerance. This study shows that chronic exposure to QDs can exert selective pressure causing irreversible genetic changes leading to 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".