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CiBER-seq通过表达表现型的定量CRISPRi剖析遗传网络

Ryan Muller¹, Zuriah A Meacham¹, Lucas Ferguson¹

¹Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

Science (New York, N.Y.)

|December 11, 2020

概括

我们开发了CiBER-seq来测量全基因组图书馆中的基因功能. 这种CRISPR干扰 (CRISPRi) 方法将遗传干扰与特定的分子结果联系起来,推进遗传网络解剖.

科学领域:

分子生物学
遗传学
系统生物学

背景情况:

CRISPR-Cas9技术能够对全基因组进行基因选,但对分子表型的量化仍然是一个挑战.
现有的方法很难将特定的遗传乱与它们在大型图书馆中的精确分子后果联系起来.
了解复杂的生物途径需要能够同时分析多个分子水平的工具.

研究的目的:

开发和验证一种新的方法,即CiBER-seq,用于高吞吐量,全基因组的分子表型.
将CRISPR干扰 (CRISPRi) 的遗传乱与转录,转化和后转化报告输出联系起来.
应用CiBER-seq来剖析酵母中的综合应激反应 (ISR) 途径.

主要方法:

采用了CRISPR干扰 (CRISPRi) 与条码表达记者测序 (CiBER-seq) 的方法.
一个合并的图书馆方法允许同时选多种遗传乱.
在转录,转化和转化后的基因表达通过条码测序进行了量化.

主要成果:

在酵母中,CiBER-seq成功地重复了已知的综合应激反应 (ISR) 途径.
导致未充电转移RNA (tRNA) 积累的遗传乱被证明可以激活ISR记者转录.
该研究确定了ISR激活的新触发因素,包括独立于正规传感器的tRNA不足.

结论:

CiBER-seq是解剖复杂遗传网络的强大且广泛应用的工具.
该方法可以从全基因组遗传乱中精确,全面地描述分子表型.
这种方法提升了我们对基因功能和途径调节的理解,通过对ISR途径的新见解来说明这一点.

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