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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.

项目摘要没有单一的基因本身起作用，相反，基因组被组织成一个相互作用的复杂网络。以确保有机体对其环境做出适当的反应。遗传互作网络(GIN) 表示这些关系的全局视图，例如，可以将单元描述为功能接线图。因此，GINS是发展对细胞或有机体中所有过程的综合理解的关键。一种基因相互作用被定义为具有与效应相关的意外表型的突变的组合单个扰动的影响。例如，当两个突变组合在一起时，它们本身影响很小可能是致命的(负相互作用)，或者两个单独产生负面影响的突变可能没有影响当结合在一起时(一种积极的互动)。对于包括酵母在内的模型系统，合成基因阵列等工具现有的分析允许在全基因组范围内对双基因敲除进行采样。这种方法具有实现了对2300万个交互作用的采样，并产生了最详细的遗传交互作用网络由大约900,000个遗传交互作用组成的日期。相比之下，对于细菌来说，一种容易实现的方法目前还缺乏能够在全基因组范围内进行高通量遗传互作图谱的研究。在这个提案中，我们解决了这个问题在细菌病原体中发展池和双引导CRISPRi(p&dgCRISPRi)的挑战肺炎链球菌。作为原则证明，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，旨在将两个单引导RNA(GRNA)组合成一个以肺炎链球菌105个基因的所有成对组合为目标的基因组。因此~5000对成对在池中筛选交互，产生约500个负面交互和约200个正交互。在……里面目标1，我们将该方法放大并生成总计约120万个交互的饱和库。我们首先评估基因组中每个开放阅读框架(ORF)的10个gRNAs，并选择两个有效的。这些然后，gRNA被用来产生超过120万个肺炎链球菌的混合菌株，其中每个细菌表达2个gRNA。每个gRNA对链接到两个随机条形码，以及这些条形码的频率变化种群中的条形码由Illumina测序确定，用于计算它们对健身。在目标2中，我们构建首个肺炎链球菌全基因组遗传互作网络的筛选 P&dgCRISPRi文库，在富媒体和最少的媒体中，以及在富媒体中补充了来自以下一种抗生素的四大类。对网络进行详细分析，并与其他(组学)数据进行组合和融合提供背景，并挖掘新的生物学见解，同时验证30-50个交互以确认高- 信心互动。最重要的是，这些GINS将证明是开发集成的了解生物体中的所有过程，例如有助于设计新的抗菌剂战略。