Population-scale chemical response revealed by a barcoded yeast collection
Bibliographic record
Abstract
Abstract Natural genetic variation shapes how microbial populations adapt to environmental and chemical challenges, but scalable approaches to map genotype-phenotype relationships across diverse genetic backgrounds remain limited. Here, we developed a systematically barcoded collection of 520 Saccharomyces cerevisiae natural isolates that captures the ecological, geographical and genetic diversity of the species. Using pooled barcode sequencing, we profiled fitness responses to over 600 bioactive and natural compounds, revealing broader and more polarized bioactivity than the standard yeast gene-deletion collection. Fitness-based clustering defined six major compound groups with reproducible, population-structured sensitivity patterns. Genome-wide association analysis identified significant genetic variants across 107 compounds, linking natural polymorphisms to chemical responses and involving genes in genome maintenance, ribosome biogenesis, vesicular trafficking and stress tolerance. Together, our barcoded natural population provides a scalable framework for chemical-genetic screening, enabling systematic dissection of how genetic diversity shapes microbial fitness and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".