Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany.
Institute of Pharmaceutical Sciences, University of Graz and BioTechMed-Graz, Graz, Austria.
Nat Methods. 2017 Dec;14(12):1213-1221. doi: 10.1038/nmeth.4464. Epub 2017 Oct 16.
The identification of genomic variants in healthy and diseased individuals continues to rapidly outpace our ability to functionally annotate these variants. Techniques that both systematically assay the functional consequences of nucleotide-resolution variation and can scale to hundreds of genes are urgently required. We designed a sensitive yeast two-hybrid-based 'off switch' for positive selection of interaction-disruptive variants from complex genetic libraries. Combined with massively parallel programmed mutagenesis and a sequencing readout, this method enables systematic profiling of protein-interaction determinants at amino-acid resolution. We defined >1,000 interaction-disrupting amino acid mutations across eight subunits of the BBSome, the major human cilia protein complex associated with the pleiotropic genetic disorder Bardet-Biedl syndrome. These high-resolution interaction-perturbation profiles provide a framework for interpreting patient-derived mutations across the entire protein complex and thus highlight how the impact of disease variation on interactome networks can be systematically assessed.
在健康个体和患病个体中鉴定基因组变异的速度继续超过我们对这些变异进行功能注释的能力。迫切需要既能够系统检测核苷酸分辨率变异的功能后果，又能够扩展到数百个基因的技术。我们设计了一种灵敏的酵母双杂交“关闭开关”，用于从复杂的遗传文库中筛选出具有干扰作用的变异。结合大规模平行编程诱变和测序读数，该方法能够以氨基酸分辨率系统地分析蛋白质相互作用决定因素。我们在与多系统遗传疾病 Bardet-Biedl 综合征相关的主要人类纤毛蛋白复合物 BBSome 的八个亚基中鉴定了 >1000 种干扰相互作用的氨基酸突变。这些高分辨率的相互作用扰动图谱为解释整个蛋白质复合物中患者来源的突变提供了一个框架，从而突出了如何系统地评估疾病变异对互作网络的影响。
