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

汇集和双引导 CRISPRi，一种用于高通量遗传相互作用图谱的全基因组工具

基本信息

批准号：
10305684
负责人：
Juan Cesar Federico Ortiz-Marquez
金额：
$ 19.56万
依托单位：
BOSTON COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-11-19 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10305684
关键词：
Antibiotics Bacteria Bar Codes Behavior Biological Biological Models Biological Process CRISPR interference Cell model Cells Chromosome Mapping Cloning Data Development Drug Targeting Engineering Ensure Environment Evaluation Frequencies Genes Genetic Genome Guide RNA Individual Isopropyl Thiogalactoside Libraries Link Maps Metabolic Pathway Mutation Open Reading Frames Organism Pathway Analysis Pathway interactions Pharmaceutical Preparations Phenotype Population Process Resources Saccharomyces cerevisiae Sampling Screening Result Standardization Streptococcus pneumoniae Streptococcus pyogenes Work Yeasts antimicrobial design experimental study fitness gene discovery gene function genome-wide insight knockout gene pathogenic bacteria promoter protein complex reconstruction response scale up screening tool transposon sequencing

项目摘要

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.

项目摘要没有单个基因的作用本身，而是将基因组组织成复杂的相互作用网络确保生物体对其环境产生适当的反应。遗传相互作用网络（GIN）代表这些关系的全局视图，例如，可以将单元描绘成功能性接线图。因此，杜松是对细胞或生物体中所有过程的综合理解的关键。遗传相互作用定义为具有意外表型相对于效应的突变的组合个人扰动。例如，两个突变在合并时几乎没有效果可能是致命的（负相互作用）或两个具有负面影响的突变可能没有效果当组合时（正相互作用）。对于包括酵母在内的模型系统，诸如合成遗传阵列之类的工具存在分析，可以在全基因组范围内对双基因敲除取样。这种方法有启用了> 2300万相互作用的采样，并导致了最详细的遗传相互作用网络日期由约900,000个遗传相互作用组成。相反，一种易于实施的细菌方法缺乏在高通量中绘制全基因组的遗传相互作用。在这个建议中，我们解决了这个问题通过在细菌病原体中开发汇集和双引导的CRISPRI（P＆DGCRISPRI）来挑战肺炎链球菌。作为原理证明，我们开发了一个相对较小的P＆Dgcrispri版本。为了实现这一目标，我们设计了一种克隆策略靶向肺炎链球菌中一组105个基因的所有成对组合的基因组。因此〜5000成对在池中筛选了相互作用，导致约500个负相互作用和约200个正相互作用。在 AIM 1，我们扩大方法并产生饱和的库，总计约120万个相互作用。我们首先评估基因组中每个开放式阅读框（ORF）的10个GRNA，并选择两个有效的grnas。这些 GRNA所用来产生超过120万张肺炎链球菌Crispri菌株，每种细菌表达2个grnas。每个GRNA对与两个随机条形码有关，并且这些频率的变化通过Illumina测序确定的人口中的条形码用于计算其对健康。在AIM 2中，我们构建肺炎链球菌的第一个全基因组遗传相互作用网络通过筛查 P＆Dgcrispri图书馆中的富裕媒体和富裕媒体中，并补充了一种抗生素四个主要班级。网络进行详细分析，并与其他（OMIC）数据结合并融合提供背景并为新的生物学见解而开采，而30-50个相互作用得到验证以确认高置信度相互作用。最重要的是，这些杜松子会证明开发综合的核心了解有机体中的所有过程，例如，可能有助于设计新抗菌剂的设计策略。