Pooled and dual-guided CRISPRi, a genome-wide tool for genetic interaction mapping in high-throughput

项目摘要 没有一个基因是单独起作用的，相反，基因组被组织成一个相互作用的复杂网络。 以确保生物体对其环境做出适当的反应。遗传相互作用网络（GIN） 表示这些关系的全局视图，例如，可以将单元描绘为功能布线图。 因此，GIN是发展对细胞或生物体中所有过程的综合理解的关键。遗传 相互作用被定义为突变的组合，这些突变在效应方面具有意想不到的表型。 个体的扰动。例如，两个突变在结合时几乎没有影响 可能是致命的（负面相互作用），或者两个单独具有负面影响的突变可能没有影响 当结合时（积极的相互作用）。对于包括酵母在内的模型系统， 存在允许在全基因组范围内对双基因敲除取样的分析。这种方法有 能够对超过2300万种相互作用进行采样，并产生了最详细的遗传相互作用网络， 约900，000个基因相互作用。相比之下，对于细菌， 能够高通量地绘制全基因组遗传相互作用的方法是缺乏的。在本提案中，我们解决了 通过在细菌病原体中开发合并和双重指导的CRISPRi（p&dgCRISPRi）来挑战 肺炎链球菌。作为原理验证，我们开发了一个相对较小的p&dgCRISPRi版本。 为了实现这一点，我们设计了一种克隆策略，旨在将两个单一的指导RNA（gRNA）组合成一个单一的指导RNA。 基因组靶向S.肺炎。因此，约5000对 在库中筛选相互作用，产生约500个阴性相互作用和约200个阳性相互作用。在 目标1，我们扩大该方法的规模，并生成饱和库，总计约120万个相互作用。我们首先 对基因组中的每个开放阅读框（ORF）评估10个gRNA，并选择两个有效的gRNA。这些 gRNA然后用于产生超过120万个汇集的S。肺炎CRISPRi菌株，其中每个细菌 表达2种gRNA。每个gRNA对与两个随机条形码连接，并且这些条形码的频率变化是随机的。 使用通过Illumina测序确定的群体中的条形码来计算它们对细胞生长的影响。 健身在目标2中，我们建立 第一个全基因组的遗传相互作用网络的S。肺炎通过筛选 p&dgCRISPRi文库在丰富和基本培养基中，以及在补充有来自以下之一的抗生素的丰富培养基中， 四大类。网络被详细分析，并与额外的（组学）数据相结合和融合 提供背景，并挖掘新的生物学见解，而30-50相互作用的验证，以确认高- 信任互动。最重要的是，这些GIN将被证明是开发综合 了解生物体中的所有过程，例如可以帮助设计新的抗菌剂 战略布局

Project Summary No single gene acts by itself, instead the genome is organized into an intricate network of interacting components to ensure the organism mounts an appropriate response to its environment. A genetic interaction network (GIN) represents a global view of these relationships and, for instance, can depict a cell as a functional wiring diagram. Thereby GINs are key to develop an integrated understanding of all processes in a cell or organism. A genetic interaction is defined as a combination of mutations that have an unexpected phenotype with respect to the effect of the individual perturbations. For instance, two mutations that have little effect by themselves when combined may be lethal (a negative interaction) or two mutations that have a negative effect individually may have no effect when combined (a positive interaction). For model systems including yeast, tools such as synthetic genetic array analyses exist that allows for sampling of double gene knockouts on a genome-wide scale. This approach has enabled sampling of >23 million interactions and has resulted in the most detailed genetic interaction network to date consisting of ~900,000 genetic interactions. In contrast, an easily implementable approach for bacteria that can map genome-wide genetic interactions in high-throughput is lacking. In this proposal we solve this challenge by developing pooled and dual-guided CRISPRi (p&dgCRISPRi) in the bacterial pathogen Streptococcus pneumoniae. As a proof-of-principle we developed a relatively small version of p&dgCRISPRi. To enable this, we designed a cloning strategy aimed at combining two single guide RNAs (gRNAs) into a single genome targeting all pairwise combinations of a set of 105 genes in S. pneumoniae. Thereby ~5000 pairwise interactions were screened in a pool, resulting in ~500 negative interactions and ~200 positive interactions. In Aim 1, we scale-up the approach and generate saturated libraries totaling ~1.2 million interactions. We first evaluate 10 gRNAs for each open reading frame (ORF) in the genome, and select two efficient ones. These gRNAs are than used to generate over 1.2 million pooled S. pneumoniae CRISPRi strains where each bacterium expresses 2 gRNAs. Each gRNA-pair is linked to two random barcodes, and the change in frequency of these barcodes in the population, which is determined by Illumina sequencing, is used to calculate their effect on fitness. In Aim 2, we build the first genome-wide genetic interaction network for S. pneumoniae by screening the p&dgCRISPRi libraries in rich and minimal media, and in rich media supplemented with an antibiotic from one of the four major classes. Networks are analyzed in detail and are combined and fused with additional (omics)data to provide context, and mined for new biological insights, while 30-50 interactions are validated to confirm high- confidence interactions. Most importantly, these GINs will proof central to developing an integrated understanding of all processes in an organism and may for instance aid in the design of new antimicrobial strategies.