CASowary: CRISPR-cas13 guide RNA predictor for transcript depletion
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA699563 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13632727.fastq.gz | Fastqsanger.gz | |||
| SRR13632728.fastq.gz | Fastqsanger.gz | |||
| SRR13632729.fastq.gz | Fastqsanger.gz | |||
| SRR13632730.fastq.gz | Fastqsanger.gz | |||
| SRR13632731.fastq.gz | Fastqsanger.gz |
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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.
2021-06-30 | GSE166189 | GEO
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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Project description:The associated experiments document the production of small RNA (sRNA) during the expression of Cas13 and crRNA, crRNA alone, or controls from agrobacterium spot infiltration in Nicotiana benthamiana. We document the specific production of sRNA corresponding to the guide sequence of the targeted mRNA. In cases where a multi-guide crRNA or a hairpin were expressed, abundent sRNA are produced correspinding to the target mRNA, but outside of the corresponding guide sequence site.
2021-12-07 | GSE171980 | GEO
Project description:CRISPR-Cas13 systems have been adapted as versatile toolkits for RNA-related applications. Here we systematically evaluate the performance of several prominent Cas13 family effectors (Cas13a, Cas13b and Cas13d) under lentiviral vectors and reveal surprisingly differential defects and characteristics of these systems. Using RNA immunoprecipitation sequencing, transcriptome profiling, biochemistry analysis and high-throughput CRISPR-Cas13 screening approaches, we determine that each Cas13 system has its intrinsic RNA targets in mammalian cells. Viral process-related host genes can be targeted by Cas13 and affect the production of fertile lentiviral particles, thereby restricting the utility of lentiviral Cas13 systems. Multiple RNase activities of Cas13 are involved in endogenous RNA targeting. Unlike target-induced collateral effect, intrinsic RNA targeting can be specific, target-independent and dynamically tuned by varied states of Cas13 nucleases. Our work not only provides guidance to appropriately utilize lentiviral Cas13 systems, but also raises cautions about intrinsic RNA targeting during Cas13-based basic and therapeutic applications.
2023-10-09 | GSE202841 | GEO
Project description:CRISPR-Cas13 systems have been adapted as versatile toolkits for RNA-related applications. Here we systematically evaluate the performance of several prominent Cas13 family effectors (Cas13a, Cas13b and Cas13d) under lentiviral vectors and reveal surprisingly differential defects and characteristics of these systems. Using RNA immunoprecipitation sequencing, transcriptome profiling, biochemistry analysis and high-throughput CRISPR-Cas13 screening approaches, we determine that each Cas13 system has its intrinsic RNA targets in mammalian cells. Viral process-related host genes can be targeted by Cas13 and affect the production of fertile lentiviral particles, thereby restricting the utility of lentiviral Cas13 systems. Multiple RNase activities of Cas13 are involved in endogenous RNA targeting. Unlike target-induced collateral effect, intrinsic RNA targeting can be specific, target-independent and dynamically tuned by varied states of Cas13 nucleases. Our work not only provides guidance to appropriately utilize lentiviral Cas13 systems, but also raises cautions about intrinsic RNA targeting during Cas13-based basic and therapeutic applications.
2023-10-09 | GSE202838 | GEO
Project description:CRISPR-Cas13 systems have been adapted as versatile toolkits for RNA-related applications. Here we systematically evaluate the performance of several prominent Cas13 family effectors (Cas13a, Cas13b and Cas13d) under lentiviral vectors and reveal surprisingly differential defects and characteristics of these systems. Using RNA immunoprecipitation sequencing, transcriptome profiling, biochemistry analysis and high-throughput CRISPR-Cas13 screening approaches, we determine that each Cas13 system has its intrinsic RNA targets in mammalian cells. Viral process-related host genes can be targeted by Cas13 and affect the production of fertile lentiviral particles, thereby restricting the utility of lentiviral Cas13 systems. Multiple RNase activities of Cas13 are involved in endogenous RNA targeting. Unlike target-induced collateral effect, intrinsic RNA targeting can be specific, target-independent and dynamically tuned by varied states of Cas13 nucleases. Our work not only provides guidance to appropriately utilize lentiviral Cas13 systems, but also raises cautions about intrinsic RNA targeting during Cas13-based basic and therapeutic applications.
2023-10-09 | GSE202898 | GEO
Project description:We used RNA-seq to detect genome-wide gene expression change when a reporter RNA was targeted by Cas13
2023-02-24 | GSE155134 | GEO
Project description:Clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas) technologies have evolved rapidly over the past decade with the continuous discovery of new Cas systems. In particular, RNA-targeting CRISPR-Cas13 proteins are promising single-effector systems to regulate target mRNAs without altering genomic DNA, yet the current Cas13 systems are still restrained by suboptimal efficiencies. Here, we show that U1-driven CRISPR RNAs (crRNAs) can dramatically increase the efficiency of various applications, including RNA knockdown and editing, without modifying the Cas13 protein effectors. We confirm that U1-driven crRNAs are exported into the cytoplasm, while conventional U6 promoter-driven crRNAs are mostly confined in the nucleus. Furthermore, we reveal that the end positions of crRNAs expressed by the U1 promoter are consistent regardless of different guide sequences and lengths. We also demonstrate that U1-driven crRNAs, but not U6-driven crRNAs, can efficiently repress the translation of target genes in combination with catalytically inactive Cas13 proteins. Finally, we show that U1-driven crRNAs can counteract the inhibitory effect of miRNAs. Our simple and effective engineering enables unprecedented cytosolic RNA-targeting applications.
2024-09-15 | GSE275245 | GEO
Project description: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, we conducted massively parallel screens targeting messenger RNAs 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.
2020-01-30 | GSE142675 | GEO
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