Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Nat Protoc. 2012 Jun 21;7(7):1382-96. doi: 10.1038/nprot.2012.069.
Deep sequencing can accurately measure the relative abundance of hundreds of mutations in a single bulk competition experiment, which can give a direct readout of the fitness of each mutant. Here we describe a protocol that we previously developed and optimized to measure the fitness effects of all possible individual codon substitutions for 10-aa regions of essential genes in yeast. Starting with a conditional strain (i.e., a temperature-sensitive strain), we describe how to efficiently generate plasmid libraries of point mutants that can then be transformed to generate libraries of yeast. The yeast libraries are competed under conditions that select for mutant function. Deep-sequencing analyses are used to determine the relative fitness of all mutants. This approach is faster and cheaper per mutant compared with analyzing individually isolated mutants. The protocol can be performed in ∼4 weeks and many 10-aa regions can be analyzed in parallel.
深度测序可以在单次批量竞争实验中准确测量数百种突变的相对丰度,从而直接反映每个突变体的适应性。在这里,我们描述了一个之前开发和优化的方案,用于测量酵母必需基因 10-aa 区域中所有可能的单个密码子替换的适应性效应。从条件性菌株(即温度敏感株)开始,我们描述了如何有效地生成点突变质粒文库,然后将其转化为酵母文库。在选择突变体功能的条件下对酵母文库进行竞争。深度测序分析用于确定所有突变体的相对适应性。与逐个分析分离的突变体相比,这种方法每个突变体的速度更快,成本更低。该方案大约可以在 4 周内完成,并且可以并行分析许多 10-aa 区域。