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基于阵列的合成基因筛选,用于绘制大肠杆菌中的细菌途径和功能网络。

Array-based synthetic genetic screens to map bacterial pathways and functional networks in Escherichia coli.

作者信息

Babu Mohan, Gagarinova Alla, Emili Andrew

机构信息

Banting and Best Department of Medical Research, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.

出版信息

Methods Mol Biol. 2011;781:99-126. doi: 10.1007/978-1-61779-276-2_7.

Abstract

Cellular processes are carried out through a series of molecular interactions. Various experimental approaches can be used to investigate these functional relationships on a large-scale. Recently, the power of investigating biological systems from the perspective of genetic (gene-gene, or epistatic) interactions has been evidenced by the ability to elucidate novel functional relationships. Examples of functionally related genes include genes that buffer each other's function or impinge on the same biological process. Genetic interactions have traditionally been investigated in bacteria by combining pairs of mutations (for example, gene deletions) and assessing deviation of the phenotype of each double mutant from an expected neutral (or no interaction) phenotype. Fitness is a particularly convenient phenotype to measure: when the double mutant grows faster or slower than expected, the two mutated genes are said to show alleviating or aggravating interactions, respectively. The most commonly used neutral model assumes that the fitness of the double mutant is equal to the product of individual single mutant fitness. A striking genetic interaction is exemplified by the loss of two nonessential genes that buffer each other in performing an essential biological function: deleting only one of these genes produces no detectable fitness defect; however, loss of both genes simultaneously results in systems failure, leading to synthetic sickness or lethality. Systematic large-scale genetic interaction screens have been used to generate functional maps for model eukaryotic organisms, such as yeast, to describe the functional organization of gene products into pathways and protein complexes within a cell. They also reveal the modular arrangement and cross-talk of pathways and complexes within broader functional neighborhoods (Dixon et al. Annu Rev Genet 43:601-625, 2009). Here, we present a high-throughput quantitative Escherichia coli synthetic genetic array (eSGA) screening procedure, which we developed to systematically infer genetic interactions by scoring growth defects among large numbers of double mutants in a classic gram-negative bacterium. The eSGA method exploits the rapid colony growth, ease of genetic manipulation, and natural efficient genetic exchange via conjugation of laboratory E. coli strains. Replica pinning is used to grow and mate arrayed sets of single-gene mutant strains as well as to select double mutants en mass. Strain fitness, which is used as the eSGA readout, is quantified by the digital imaging of the plates and subsequent measuring and comparing single and double mutant colony sizes. While eSGA can be used to screen select mutants to probe the functions of individual genes; using eSGA more broadly to collect genetic interaction data for many combinations of genes can help reconstruct a functional interaction network to reveal novel links and components of biological pathways as well as unexpected connections between pathways. A variety of bacterial systems can be investigated, wherein the genes impinge on a essential biological process (e.g., cell wall assembly, ribosome biogenesis, chromosome replication) that are of interest from the perspective of drug development (Babu et al. Mol Biosyst 12:1439-1455, 2009). We also show how genetic interactions generated by high-throughput eSGA screens can be validated by manual small-scale genetic crosses and by genetic complementation and gene rescue experiments.

摘要

细胞过程是通过一系列分子相互作用来进行的。可以使用各种实验方法大规模研究这些功能关系。最近,从遗传(基因-基因或上位性)相互作用的角度研究生物系统的能力已通过阐明新功能关系的能力得到证明。功能相关基因的例子包括相互缓冲功能或影响同一生物过程的基因。传统上,通过组合成对的突变(例如基因缺失)并评估每个双突变体的表型与预期中性(或无相互作用)表型的偏差来研究细菌中的遗传相互作用。适合度是一种特别方便测量的表型:当双突变体生长得比预期快或慢时,这两个突变基因分别被称为显示缓解或加重相互作用。最常用的中性模型假设双突变体的适合度等于单个单突变体适合度的乘积。一个显著的遗传相互作用的例子是两个非必需基因在执行基本生物学功能时相互缓冲:仅删除其中一个基因不会产生可检测到的适合度缺陷;然而,两个基因同时缺失会导致系统故障,导致合成疾病或致死性。系统性大规模遗传相互作用筛选已被用于为模型真核生物(如酵母)生成功能图谱,以描述基因产物在细胞内进入途径和蛋白质复合物的功能组织。它们还揭示了更广泛功能邻域内途径和复合物的模块化排列和相互作用(Dixon等人,《遗传学年度评论》43:601 - 625,2009年)。在这里,我们展示了一种高通量定量大肠杆菌合成遗传阵列(eSGA)筛选程序,我们开发该程序是为了通过对经典革兰氏阴性细菌中大量双突变体的生长缺陷进行评分来系统地推断遗传相互作用。eSGA方法利用了实验室大肠杆菌菌株的快速菌落生长、易于遗传操作以及通过接合进行的天然高效遗传交换。复制接种用于培养和交配排列好的单基因突变体菌株集,以及批量选择双突变体。用作eSGA读数的菌株适合度通过平板的数字成像以及随后测量和比较单突变体和双突变体菌落大小来量化。虽然eSGA可用于筛选选择突变体以探究单个基因的功能;更广泛地使用eSGA来收集许多基因组合的遗传相互作用数据有助于重建功能相互作用网络以揭示生物途径的新联系和组成部分以及途径之间意想不到的连接。可以研究多种细菌系统,其中基因影响从药物开发角度来看感兴趣的基本生物学过程(例如细胞壁组装、核糖体生物发生、染色体复制)(Babu等人,《分子生物系统》12:1439 - 1455,2009年)。我们还展示了如何通过手动小规模遗传杂交以及遗传互补和基因拯救实验来验证高通量eSGA筛选产生的遗传相互作用。

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