Piotrowski Jeff S, Li Sheena C, Deshpande Raamesh, Simpkins Scott W, Nelson Justin, Yashiroda Yoko, Barber Jacqueline M, Safizadeh Hamid, Wilson Erin, Okada Hiroki, Gebre Abraham A, Kubo Karen, Torres Nikko P, LeBlanc Marissa A, Andrusiak Kerry, Okamoto Reika, Yoshimura Mami, DeRango-Adem Eva, van Leeuwen Jolanda, Shirahige Katsuhiko, Baryshnikova Anastasia, Brown Grant W, Hirano Hiroyuki, Costanzo Michael, Andrews Brenda, Ohya Yoshikazu, Osada Hiroyuki, Yoshida Minoru, Myers Chad L, Boone Charles
RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan.
Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota, USA.
Nat Chem Biol. 2017 Sep;13(9):982-993. doi: 10.1038/nchembio.2436. Epub 2017 Jul 24.
Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.
化学遗传学方法为化学文库的无偏向性功能注释提供了可能。突变可改变细胞在化合物存在时的反应，揭示能阐明化合物作用模式的化学遗传相互作用。我们开发了一个高度并行、无偏向性的酵母化学遗传筛选系统，该系统包含三个关键组件。首先，在药物敏感的遗传背景下，我们构建了一个优化的诊断突变体集合，该集合可预测所有主要的酵母生物学过程。其次，我们实施了一种多重（768重）条形码测序方案，能够组装数千个化学遗传图谱。最后，基于化学遗传图谱与全基因组遗传相互作用图谱汇编的比较，我们预测了化合物的功能。应用这种高通量方法，我们筛选了七个不同的化合物文库，并注释了它们的功能多样性。我们进一步验证了生物学过程预测，对一系列不同的化合物进行了优先级排序，并鉴定出似乎具有双重作用模式的化合物。
