Biocipher(x), Inc., San Diego, CA 92121, USA; Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
Mol Cell. 2018 Jan 18;69(2):321-333.e3. doi: 10.1016/j.molcel.2017.12.016.
We have developed a highly parallel strategy, systematic gene-to-phenotype arrays (SGPAs), to comprehensively map the genetic landscape driving molecular phenotypes of interest. By this approach, a complete yeast genetic mutant array is crossed with fluorescent reporters and imaged on membranes at high density and contrast. Importantly, SGPA enables quantification of phenotypes that are not readily detectable in ordinary genetic analysis of cell fitness. We benchmark SGPA by examining two fundamental biological phenotypes: first, we explore glucose repression, in which SGPA identifies a requirement for the Mediator complex and a role for the CDK8/kinase module in regulating transcription. Second, we examine selective protein quality control, in which SGPA identifies most known quality control factors along with U tRNA modification, which acts independently of proteasomal degradation to limit misfolded protein production. Integration of SGPA with other fluorescent readouts will enable genetic dissection of a wide range of biological pathways and conditions.
我们开发了一种高度并行的策略，即系统基因表型阵列（SGPAs），以全面绘制驱动感兴趣的分子表型的遗传景观。通过这种方法，完整的酵母遗传突变体阵列与荧光报告基因杂交，并在高对比度和高密度的膜上成像。重要的是，SGPAs 能够定量分析在普通细胞适应性遗传分析中不易检测到的表型。我们通过检查两个基本的生物学表型来对 SGPAs 进行基准测试：首先，我们探索葡萄糖抑制，其中 SGPAs 确定了中介复合物的需求以及 CDK8/激酶模块在调节转录中的作用。其次，我们检查选择性蛋白质质量控制，其中 SGPAs 确定了大多数已知的质量控制因素以及 U tRNA 修饰，它独立于蛋白酶体降解作用以限制错误折叠蛋白质的产生。将 SGPAs 与其他荧光读数相结合将能够对广泛的生物途径和条件进行遗传剖析。
