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Identification of Guide-Intrinsic Determinants of Cas9 Specificity.
The CRISPR Journal ( IF 3.7 ) Pub Date : 2019-06-01 , DOI: 10.1089/crispr.2019.0009
Nicholas C Huston 1 , Josh Tycko 1 , Eric L Tillotson 1 , Christopher J Wilson 1 , Vic E Myer 1 , Hariharan Jayaram 1 , Barrett E Steinberg 1
Affiliation  

Considerable effort has been devoted to developing a comprehensive understanding of CRISPR nuclease specificity. In silico predictions and multiple genome-wide cellular and biochemical approaches have revealed a basic understanding of the Cas9 specificity profile. However, none of these approaches has delivered a model that allows accurate prediction of a CRISPR nuclease's ability to cleave a site based entirely on the sequence of the guide RNA (gRNA) and the target. We describe a library-based biochemical assay that directly reports the cleavage efficiency of a particular Cas9-guide complex by measuring both uncleaved and cleaved target molecules over a wide range of mismatched library members. We applied our assay using libraries of targets to evaluate the specificity of Staphylococcus aureus Cas9 under a variety of experimental conditions. Surprisingly, our data show an unexpectedly high variation in the random gRNA:target DNA mismatch tolerance when cleaving with different gRNAs, indicating guide-intrinsic mismatch permissiveness and challenging the assumption of universal specificity models. We use data generated by our assay to create the first off-target, guide-specific cleavage models. The barcoded libraries of targets approach is rapid, highly modular, and capable of generating protein- and guide-specific models, as well as illuminating the biophysics of Cas9 binding versus cutting. These models may be useful in identifying potential off-targets, and the gRNA-intrinsic nature of mismatch tolerance argues for coupling these specificity models with orthogonal methods for a more complete assessment of gRNA specificity.

中文翻译:

Cas9 特异性的指南内在决定因素的鉴定。

为了全面了解 CRISPR 核酸酶特异性,人们付出了巨大的努力。计算机预测和多种全基因组细胞和生化方法揭示了对 Cas9 特异性谱的基本了解。然而,这些方法都没有提供一个模型,可以完全根据向导 RNA (gRNA) 和靶标的序列准确预测 CRISPR 核酸酶切割位点的能力。我们描述了一种基于文库的生化测定,通过测量各种不匹配的文库成员中未切割和切割的目标分子,直接报告特定 Cas9 引导复合物的切割效率。我们使用靶标库进行检测,以评估各种实验条件下金黄色葡萄球菌 Cas9 的特异性。令人惊讶的是,我们的数据显示,当用不同的 gRNA 进行切割时,随机 gRNA:目标 DNA 错配耐受性出现了意想不到的高变化,这表明引导内在错配的宽容性,并挑战了通用特异性模型的假设。我们使用我们的检测生成的数据来创建第一个脱靶、特定于指南的切割模型。目标条形码库方法快速、高度模块化,能够生成蛋白质和指南特异性模型,并阐明 Cas9 结合与切割的生物物理学。这些模型可能有助于识别潜在的脱靶,并且 gRNA 内在的错配耐受性主张将这些特异性模型与正交方法结合起来,以更完整地评估 gRNA 特异性。
更新日期:2019-11-01
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