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Prediction of on-target and off-target activity of CRISPR-Cas13dguide RNAs using deep learning

ABSTRACT: Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here, we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes in human cells with systematically-designed mismatches, insertions and deletions (indels). We find that mismatches and indels have a position- and context-dependent impact on Cas13d activity, and mismatches that result in G:U wobble pairings are better tolerated than other single-base mismatches. Using this large-scale dataset, we train a convolutional neural network that we term TIGER (Targeted Inhibition of Gene Expression via gRNA design) to predict efficacy from guide sequence and context. TIGER outperforms existing models at predicting on- and off-target activity on our dataset and published datasets. We show that TIGER scoring combined with specific mismatches yields the first general framework to modulate transcript expression, enabling use of RNA-targeting CRISPRs to precisely control gene dosage.

ORGANISM(S): synthetic construct Homo sapiens

PROVIDER: GSE232228 | GEO | 2023/05/12

REPOSITORIES: GEO

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Project description:Clustered regularly interspaced short palindromic repeat (CRISPR) RNA-guided nucleases have gathered considerable excitement as a tool for genome engineering. However, questions remain about the specificity of their target site recognition. Most previous studies have examined predicted off-target binding sites that differ from the perfect target site by one to four mismatches, which represent only a subset of genomic regions. Here, we used ChIP-seq to examine genome-wide CRISPR binding specificity at gRNA-specific and gRNA-independent sites. For two guide RNAs targeting the murine Snurf gene promoter, we observed very high binding specificity at the intended target site while off-target binding was observed at 2- to 6-fold lower intensities. We also identified significant gRNA-independent off-target binding. Interestingly, we found that these regions are highly enriched in the PAM site, a sequence required for target site recognition by CRISPR. To determine the relationship between Cas9 binding and endonuclease activity, we used targeted sequence capture as a high-throughput approach to survey a large number of the potential off-target sites identified by ChIP-seq or computational prediction. A high frequency of indels was observed at both target sites and one off-target site, while no cleavage activity could be detected at other ChIP-bound regions. Our results demonstrate that even a simple configuration of a Cas9:gRNA nuclease can support very specific DNA cleavage activity and that most interactions between the CRISPR nuclease complex and genomic PAM sites do not lead to DNA cleavage. ChIP-seq using dCas9 to determine genome-wide binding of CRISPR/Cas9 noED: Cas9 doublemutant protein without an effector domain KRAB: Cas9 doublemutant protein fused to the KRAB repressor domain S1 gRNA: guide RNA targeting GCTCCCTACGCATGCGTCCC(AGG) in the mouse genome S2 gRNA: guide RNA targeting AATGGCTCAGGTTTGTCGCG(CGG) in the mouse genome VEGFA TS3 gRNA: guide RNA targeting GGTGAGTGAGTGTGTGCGTG(TGG) in the human genome

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Prediction of on-target and off-target activity of CRISPR-Cas13dguide RNAs using deep learning

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