High-throughput genome engineering

高通量基因组工程

• 批准号: 10700703
• 负责人: Meru Sadhu
• 金额: $ 85.98万
• 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/10700703
• 关键词: Biology; CRISPR/Cas technology; Cells; 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; Reporting; Systems Biology; Variant; Work; Yeast Model System; Yeasts; genetic information; genetic variant; interest; novel; repaired; 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.

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。我们正在通过使用高通量基因组工程方法来产生和研究数千种遗传变异来实现这一目标。这将使我们能够理解我们所研究的特定变体的后果，并了解变体后果的一般原则，以便扩展到更广阔的未经测试的变体空间。 为了实现高通量基因组工程，我们使用大规模并行寡核苷酸合成来生成数千个独特酵母质粒的池，每个质粒都携带一个指导RNA（gRNA）基因和一个编码特定突变的配对修复模板。在使用gRNA通过Cas9进行定向DNA切割后，修复模板在酵母基因组中引入突变。然后对编辑的酵母细胞池进行目标表型的选择。因此，所选池中独特质粒的丰度报告了每种遗传变异对所研究表型的影响。 我们还在开发新的高通量基因组工程方法。这些包括CRISPR技术的进步，通过扩大CRISPR可以靶向的遗传结果的空间，以及高通量编辑的新应用，例如CRISPR定向缺失的精心部署。