使用“PopOff”对由常见多态性改变的基因编辑脱靶位点进行生物信息学分析。
A bioinformatic analysis of gene editing off-target loci altered by common polymorphisms, using 'PopOff'.
作者信息
Samson Christopher, du Rand Alex, Hunt John, Whitford Whitney, Jacobsen Jessie, Sheppard Hilary
机构信息
School of Biological Sciences, University of Auckland, Auckland, New Zealand.
出版信息
J R Soc N Z. 2024 May 9;55(6):2440-2463. doi: 10.1080/03036758.2024.2347968. eCollection 2025.
Gene editing therapies are designed to minimise off-target editing. However, it is not widespread practice for common polymorphisms to be considered when identifying potential off-target sites . Nevertheless, genetic variants should be included as they have the potential to alter existing, or to generate new, off-target sites. To facilitate the consideration of common polymorphisms when designing targeted gene therapies we developed PopOff, a web-based tool that integrates minor allele frequencies from the gnomAD variant database into an off-target analysis. We used PopOff to analyse predicted off-target loci from guide RNAs used in four clinical trials and thirty-four research publications. From an analysis of sixty guides, we identified that approximately 20% of off-target loci overlap with a common polymorphism. Of these sites, 6.93% contained variants that reduce the level of mismatch between the off-target locus and guide, and therefore may increase off-target cleavage. In addition, we identified that 0.34% of common polymorphisms generated novel PAM sites, resulting in off-target loci that standard workflows would miss. Our findings demonstrate that common polymorphisms should be considered when designing guides to maximise the safety of CRISPR-based gene therapies. However, this may be problematic in populations where the breadth of genetic diversity remains uncharacterised.
基因编辑疗法旨在尽量减少脱靶编辑。然而,在识别潜在脱靶位点时,通常不会普遍考虑常见的多态性。尽管如此,基因变异仍应被纳入考虑,因为它们有可能改变现有的脱靶位点或产生新的脱靶位点。为了在设计靶向基因疗法时便于考虑常见的多态性,我们开发了PopOff,这是一种基于网络的工具,它将gnomAD变异数据库中的次要等位基因频率整合到脱靶分析中。我们使用PopOff分析了四项临床试验和34篇研究出版物中使用的引导RNA预测的脱靶位点。通过对60个引导序列的分析,我们发现约20%的脱靶位点与常见的多态性重叠。在这些位点中,6.93%包含可降低脱靶位点与引导序列之间错配水平的变异,因此可能会增加脱靶切割。此外,我们发现0.34%的常见多态性产生了新的PAM位点,导致标准工作流程会遗漏的脱靶位点。我们的研究结果表明,在设计引导序列时应考虑常见的多态性,以最大限度地提高基于CRISPR的基因疗法的安全性。然而,在遗传多样性广度仍未明确的人群中,这可能会有问题。