Population-scale chemical response revealed by a barcoded yeast collection
Dutta, A.; Garin, M.; Loegler, V.; Brach, G.; Friedrich, A.; Yoshimura, M.; Hirano, H.; Osada, H.; Boone, C.; Yashiroda, Y.; Hou, J.; Schacherer, J.
Show 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction 95%
- Variation in Ubiquitin System Genes Creates Substrate-Specific Effects on Proteasomal Protein Degradation 94%
- Pleiotropic win-win mutations can rapidly evolve in a nascent cooperative community despite unfavorable conditions 94%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.