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Massively parallel Cas13 screens reveal principles for guide RNA design.

ABSTRACT: Type VI CRISPR enzymes are RNA-targeting proteins with nuclease activity that enable specific and robust target gene knockdown without altering the genome. To define rules for the design of Cas13d guide RNAs (gRNAs), we conducted massively parallel screens targeting messenger RNAs (mRNAs) of a green fluorescent protein transgene, and CD46, CD55 and CD71 cell-surface proteins in human cells. In total, we measured the activity of 24,460 gRNAs with and without mismatches relative to the target sequences. Knockdown efficacy is driven by gRNA-specific features and target site context. Single mismatches generally reduce knockdown to a modest degree, but spacer nucleotides 15-21 are largely intolerant of target site mismatches. We developed a computational model to identify optimal gRNAs and confirm their generalizability, testing 3,979 guides targeting mRNAs of 48 endogenous genes. We show that Cas13 can be used in forward transcriptomic pooled screens and, using our model, predict optimized Cas13 gRNAs for all protein-coding transcripts in the human genome.

SUBMITTER: Wessels HH

PROVIDER: S-EPMC7294996 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Massively parallel Cas13 screens reveal principles for guide RNA design.

Wessels Hans-Hermann HH Méndez-Mancilla Alejandro A Guo Xinyi X Legut Mateusz M Daniloski Zharko Z Sanjana Neville E NE

Nature biotechnology 20200316 6

Type VI CRISPR enzymes are RNA-targeting proteins with nuclease activity that enable specific and robust target gene knockdown without altering the genome. To define rules for the design of Cas13d guide RNAs (gRNAs), we conducted massively parallel screens targeting messenger RNAs (mRNAs) of a green fluorescent protein transgene, and CD46, CD55 and CD71 cell-surface proteins in human cells. In total, we measured the activity of 24,460 gRNAs with and without mismatches relative to the target sequ ...[more]

PMID: 32518401

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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.

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Massively parallel Cas13 screens reveal principles for guide RNA design.

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Massively parallel Cas13 screens reveal principles for guide RNA design.

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