Engineered dual selection for directed evolution of SpCas9 PAM specificity

Goldberg, Gregory W.; Spencer, Jeffrey M.; Giganti, David; Camellato, Brendan; Agmon, Neta; Ichikawa, David M.; Boeke, Jef D.; Noyes, Marcus B.

doi:10.1038/s41467-020-20650-x

Cited by 15 publications

(26 citation statements)

References 71 publications

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“…The data suggest that different PAM sites have different levels of correlation with gRNA efficiency. Specifically, in accordance with previous reports of PAM efficiency associated with cleavage, CGG and GGG PAMs are associated with higher efficiency of knockdown than TGG. , We also notice that AGG is more strongly anticorrelated with knockdown. In terms of the DNA strand, gRNA sequences on the coding strand have a lower knockdown efficiency, again as expected , (for the purposes of correlation analysis, strand 0 is the coding strand and strand 1 is the template strand).…”

Section: Resultssupporting

confidence: 92%

High-Density Guide RNA Tiling and Machine Learning for Designing CRISPR Interference in Synechococcus sp. PCC 7002

et al. 2023

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While CRISPRi was previously established in Synechococcus sp. PCC 7002 (hereafter 7002), the design principles for guide RNA (gRNA) effectiveness remain largely unknown. Here, 76 strains of 7002 were constructed with gRNAs targeting three reporter systems to evaluate features that impact gRNA efficiency. Correlation analysis of the data revealed that important features of gRNA design include the position relative to the start codon, GC content, protospacer adjacent motif (PAM) site, minimum free energy, and targeted DNA strand. Unexpectedly, some gRNAs targeting upstream of the promoter region showed small but significant increases in reporter expression, and gRNAs targeting the terminator region showed greater repression than gRNAs targeting the 3′ end of the coding sequence. Machine learning algorithms enabled prediction of gRNA effectiveness, with Random Forest having the best performance across all training sets. This study demonstrates that high-density gRNA data and machine learning can improve gRNA design for tuning gene expression in 7002.

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Section: Resultssupporting

confidence: 92%

High-Density Guide RNA Tiling and Machine Learning for Designing CRISPR Interference in Synechococcus sp. PCC 7002

et al. 2023

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“…Random mutagenesis, structureguided rational design, chimera generation, and phage-assisted continuous evolution (PACE) were widely employed approaches established for expanding the base-editing target scopes. 22 The xCas-3.7 variant displayed a PAM preference for NGW (W = A or T) and lower off-target activity than SpCas9. 23 SpG and SpRY respectively recognized NG and NR (R = A or G) PAMs, which were widely used in the construction of the microbial BE toolbox.…”

Section: Expanding Pam Compatibility By Harnessing Casmentioning

confidence: 96%

“…Compared to orthologs, the engineered SpCas9 variants generated from protein engineering contribute more greatly to the construction of BEs. Random mutagenesis, structure-guided rational design, chimera generation, and phage-assisted continuous evolution (PACE) were widely employed approaches established for expanding the base-editing target scopes . The xCas-3.7 variant displayed a PAM preference for NGW (W = A or T) and lower off-target activity than SpCas9 .…”

Section: Optimization Strategies Based On Core Component Engineering ...mentioning

confidence: 99%

Powerful Microbial Base-Editing Toolbox: From Optimization Strategies to Versatile Applications

Qin

Zhang

et al. 2023

ACS Synth. Biol.

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Base editors (BE) based on CRISPR systems are practical gene-editing tools which continue to drive frontier advances of life sciences. BEs are able to efficiently induce point mutations at target sites without double-stranded DNA cleavage. Hence, they are widely employed in the fields of microbial genome engineering. As applications of BEs continue to expand, the demands for base-editing efficiency, fidelity, and versatility are also on the rise. In recent years, a series of optimization strategies for BEs have been developed. By engineering the core components of BEs or adopting different assembly methods, the performance of BEs has been well optimized. Moreover, series of newly established BEs have significantly expanded the base-editing toolsets. In this Review, we will summarize the current efforts for BE optimization, introduce several novel BEs with versatility, and look forward to the broadened applications for industrial microorganisms.

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“…The target sites should include a PAM sequence, whereby the most commonly used PAM is NGG. Despite the lower efficiency compared to NGG, various PAMs including NAG, NGA, and NCC have been experimented 25 , 26 . Therefore, we used sgRNA containing CAG PAM sequence to target CAG repeat region in treating HD.…”

Section: Introductionmentioning

confidence: 99%

DNA double-strand break-free CRISPR interference delays Huntington’s disease progression in mice

Seo¹,

Shin²,

Lee³

et al. 2023

Commun Biol

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Huntington’s disease (HD) is caused by a CAG repeat expansion in the huntingtin (HTT) gene. CRISPR-Cas9 nuclease causes double-strand breaks (DSBs) in the targeted DNA that induces toxicity, whereas CRISPR interference (CRISPRi) using dead Cas9 (dCas9) suppresses the target gene expression without DSBs. Delivery of dCas9-sgRNA targeting CAG repeat region does not damage the targeted DNA in HEK293T cells containing CAG repeats. When this study investigates whether CRISPRi can suppress mutant HTT (mHTT), CRISPRi results in reduced expression of mHTT with relative preservation of the wild-type HTT in human HD fibroblasts. Although both dCas9 and Cas9 treatments reduce mHTT by sgRNA targeting the CAG repeat region, CRISPRi delays behavioral deterioration and protects striatal neurons against cell death in HD mice. Collectively, CRISPRi can delay disease progression by suppressing mHtt, suggesting DNA DSB-free CRISPRi is a potential therapy for HD that can compensate for the shortcoming of CRISPR-Cas9 nuclease.

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Engineered dual selection for directed evolution of SpCas9 PAM specificity

Cited by 15 publications

References 71 publications

High-Density Guide RNA Tiling and Machine Learning for Designing CRISPR Interference in Synechococcus sp. PCC 7002

High-Density Guide RNA Tiling and Machine Learning for Designing CRISPR Interference in Synechococcus sp. PCC 7002

Powerful Microbial Base-Editing Toolbox: From Optimization Strategies to Versatile Applications

DNA double-strand break-free CRISPR interference delays Huntington’s disease progression in mice

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