High-throughput genome engineering

高通量基因组工程

• 批准号: 10267125
• 负责人: Meru Sadhu
• 金额: $ 79.34万
• 依托单位: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10267125
• 关键词: Biology; CRISPR/Cas technology; Cells; Chromosome Mapping; Clustered Regularly Interspaced Short Palindromic Repeats; DNA; Genes; Genetic; Genetic Variation; Genetic study; Genome engineering; Genomics; Genotype; Goals; Guide RNA; Individual; Learning; Link; Medicine; Methods; Mutation; Oligonucleotides; Outcome; Phenotype; Plasmids; Quantitative Trait Loci; Reporting; Systems Biology; Variant; Work; Yeast Model System; Yeasts; genetic architecture; genetic information; genetic variant; interest; novel; repaired; trait; yeast genome

A major goal in genetics is to use genetic information to predict phenotype, which could have enormous impact in medicine and in our general understanding of biology. We are contributing towards this goal by using high-throughput genome engineering methods to generate and study thousands of genetic variants. This will allow us to understand the consequences of the specific variants we study, and also learn about the general principles underlying variant consequences for extension to the even broader space of untested variants. To accomplish high-throughput genome engineering, we use large-scale parallelized oligonucleotide synthesis to generate pools of thousands of unique yeast plasmids, each of which carries a guide RNA (gRNA) gene and a paired repair template encoding a specific mutation of interest. Following directed DNA cleavage by Cas9 using the gRNA, the repair template introduces the mutation in the yeast genome. The edited pool of yeast cells is then subjected to selection for a phenotype of interest. The abundances of the unique plasmids in the selected pool thus reports on the effect each genetic variant had on the phenotype under study. We are also developing new high-throughput genome engineering approaches. These include advancements to CRISPR technology, by expanding the space of genetic outcomes that CRISPR can target, as well as novel applications of high-throughput editing, such as careful deployment of CRISPR-directed deletions. In the past year, we contributed to a study of the genetic architecture of many traits across a panel of diverse yeast isolates. We found that the most significant effects were due to rare genetic variants. The construction of the linkage mapping panel also allowed many QTLs to be resolved to individual genes, which significantly enriches our understanding of the genetic basis of trait variation in yeasts.

遗传学的一个主要目标是利用遗传信息来预测表型，这可能对医学和我们对生物学的一般理解产生巨大影响。我们通过使用高通量基因组工程方法来生成和研究数千种遗传变异，为实现这一目标做出贡献。这将使我们能够了解我们研究的特定变体的后果，并了解变体后果的一般原则，以便扩展到更广泛的未经测试的变体空间。 为了完成高通量基因组工程，我们使用大规模并行寡核苷酸合成来生成数千个独特的酵母质粒库，每个质粒都携带引导RNA（gRNA）基因和编码特定目标突变的配对修复模板。 Cas9 使用 gRNA 进行定向 DNA 切割后，修复模板在酵母基因组中引入突变。然后对编辑后的酵母细胞池进行感兴趣表型的选择。因此，所选库中独特质粒的丰度报告了每种遗传变异对所研究的表型的影响。 我们还在开发新的高通量基因组工程方法。其中包括通过扩大 CRISPR 可以靶向的遗传结果空间来实现 CRISPR 技术的进步，以及高通量编辑的新颖应用，例如仔细部署 CRISPR 定向删除。 在过去的一年中，我们对一组不同酵母分离株的许多性状的遗传结构进行了研究。我们发现最显着的影响是由罕见的基因变异引起的。连锁作图面板的构建还允许将许多QTL解析为单个基因，这显着丰富了我们对酵母性状变异遗传基础的理解。