Guo Xinyi, Rahman Jahan A, Wessels Hans-Hermann, Méndez-Mancilla Alejandro, Haro Daniel, Chen Xinru, Sanjana Neville E
New York Genome Center, New York, NY 10013, USA.
Department of Biology, New York University, New York, NY 10003, USA.
Cell Genom. 2021 Oct 13;1(1). doi: 10.1016/j.xgen.2021.100001. Epub 2021 Sep 3.
The recent characterization of RNA-targeting CRISPR nucleases has enabled diverse transcriptome engineering and screening applications that depend crucially on prediction and selection of optimized CRISPR guide RNAs (gRNAs). Previously, we developed a computational model to predict Cas13d gRNA activity for all human protein-coding genes. Here, we extend this framework to six model organisms (human, mouse, zebrafish, fly, nematode, and flowering plants) for protein-coding genes and noncoding RNAs (ncRNAs) and also to four RNA virus families (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2], HIV-1, H1N1 influenza, and Middle East respiratory syndrome [MERS]). We include experimental validation of predictions by testing knockdown of multiple ncRNAs (, , and ) in human and mouse cells. We developed a freely available web-based platform () with pre-scored gRNAs for transcriptome-wide targeting in several organisms and an interactive design tool to predict optimal gRNAs for custom RNA targets entered by the user. This resource will facilitate CRISPR-Cas13 RNA targeting in model organisms, emerging viral threats to human health.
最近对靶向RNA的CRISPR核酸酶的特性描述，使得多种转录组工程和筛选应用成为可能，而这些应用关键依赖于对优化的CRISPR引导RNA（gRNA）的预测和选择。此前，我们开发了一种计算模型，用于预测所有人类蛋白质编码基因的Cas13d gRNA活性。在此，我们将该框架扩展到六种模式生物（人类、小鼠、斑马鱼、果蝇、线虫和开花植物）的蛋白质编码基因和非编码RNA（ncRNA），以及四个RNA病毒家族（严重急性呼吸综合征冠状病毒2 [SARS-CoV-2]、HIV-1、H1N1流感病毒和中东呼吸综合征 [MERS]）。我们通过测试人类和小鼠细胞中多种ncRNA（、、和）的敲低情况，对预测结果进行了实验验证。我们开发了一个免费的基于网络的平台（），其中包含针对多种生物全转录组靶向的预评分gRNA，以及一个交互式设计工具，用于预测用户输入的定制RNA靶标的最佳gRNA。该资源将促进在模式生物中进行CRISPR-Cas13 RNA靶向，应对对人类健康构成新出现威胁的病毒。
