Dataset Information

Transcriptome-wide Cas13 guide RNA design for model organisms and viral RNA pathogens.

ABSTRACT: 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 RfxCas13d 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 (MALAT1, HOTAIRM1, Gas5, and Pvt1) in human and mouse cells. We developed a freely available web-based platform (cas13design) 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.

SUBMITTER: Guo X

PROVIDER: S-EPMC9164475 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Transcriptome-wide Cas13 guide RNA design for model organisms and viral RNA pathogens.

Guo Xinyi X Rahman Jahan A JA Wessels Hans-Hermann HH Méndez-Mancilla Alejandro A Haro Daniel D Chen Xinru X Sanjana Neville E NE

Cell genomics 20210903 1

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 <i>Rfx</i>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 nonc ...[more]

PMID: 35664829

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Project description:BackgroundRecent discovery of the gene editing system - CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) associated proteins (Cas), has resulted in its widespread use for improved understanding of a variety of biological systems. Cas13, a lesser studied Cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The Cas13 system utilizes base complementarity between a crRNA/sgRNA (crispr RNA or single guide RNA) and a target RNA transcript, to preferentially bind to only the target transcript. Unlike targeting the upstream regulatory regions of protein coding genes on the genome, the transcriptome is significantly more redundant, leading to many transcripts having wide stretches of identical nucleotide sequences. Transcripts also exhibit complex three-dimensional structures and interact with an array of RBPs (RNA Binding Proteins), both of which may impact the effectiveness of transcript depletion of target sequences. However, our understanding of the features and corresponding methods which can predict whether a specific sgRNA will effectively knockdown a transcript is very limited.ResultsHere we present a novel machine learning and computational tool, CASowary, to predict the efficacy of a sgRNA. We used publicly available RNA knockdown data from Cas13 characterization experiments for 555 sgRNAs targeting the transcriptome in HEK293 cells, in conjunction with transcriptome-wide protein occupancy information. Our model utilizes a Decision Tree architecture with a set of 112 sequence and target availability features, to classify sgRNA efficacy into one of four classes, based upon expected level of target transcript knockdown. After accounting for noise in the training data set, the noise-normalized accuracy exceeds 70%. Additionally, highly effective sgRNA predictions have been experimentally validated using an independent RNA targeting Cas system - CIRTS, confirming the robustness and reproducibility of our model's sgRNA predictions. Utilizing transcriptome wide protein occupancy map generated using POP-seq in HeLa cells against publicly available protein-RNA interaction map in Hek293 cells, we show that CASowary can predict high quality guides for numerous transcripts in a cell line specific manner.ConclusionsApplication of CASowary to whole transcriptomes should enable rapid deployment of CRISPR/Cas13 systems, facilitating the development of therapeutic interventions linked with aberrations in RNA regulatory processes.

Project description:Recent discovery of the gene editing system - CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) associated proteins (Cas), has resulted in its widespread use for improved understanding of a variety of biological systems, by enabling large-scale perturbation of the genomes and transcriptomes. Cas13, a lesser studied Cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The Cas13 system utilizes base complementarity between a crRNA/sgRNA (crispr RNA or single guide RNA) and a target RNA transcript, to preferentially bind to only the target transcript. Unlike targeting the upstream regulatory regions of protein coding genes on the genome, the transcriptome is significantly more redundant, leading to many transcripts having wide stretches of identical nucleotide sequences. Transcripts also exhibit complex three-dimensional structures and interact with an array of RBPs (RNA Binding Proteins), both of which further limit the scope of effective target sequences. As a result, there currently exists no method to predict whether a specific sgRNA will effectively knockdown a transcript. Here we present a novel machine learning and computational tool, to predict the efficacy of a sgRNA. We used publicly available RNA knockdown data from cas13 characterization experiments1 for 555 sgRNAs targeting the transcriptome in HEK293 cells, in conjunction with transcriptome-wide protein occupancy information on RNA2. Our model utilizes a Decision Tree architecture with a set of 112 sequence and target availability features, to classify sgRNA efficacy into one of four classes, based upon expected level of target transcript knockdown. After accounting for noise in the training data set, the noise-normalized accuracy exceeds 90%. Additionally, highly effective sgRNA predictions have been experimentally validated using an independent RNA targeting cas system – CIRTS3, confirming the robustness and reproducibility of our model’s sgRNA predictions. In particular, several highly efficient sgRNA’s designed using our model against SMARCA4 gene exhibited strong agreement with experimental data supporting a 10-fold decrease in expression. Utilizing transcriptome wide protein occupancy information, CASowary can predict high quality guides for different transcripts in a cell specific manner. Applications of CASowary to whole transcriptomes should enable rapid deployment of CRISPR/Cas13 systems, facilitating the development of therapeutic interventions linked with aberrations in RNA regulatory processes.

Dataset Information

Transcriptome-wide Cas13 guide RNA design for model organisms and viral RNA pathogens.

Publications

Transcriptome-wide Cas13 guide RNA design for model organisms and viral RNA pathogens.

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