Improved sgRNA design in bacteria via genome-wide activity profiling

Guo, Jiahui; Wang, Tianmin; Guan, Changge; Liu, Bing; Luo, Cheng; Xie, Zhen; Zhang, Chong; Xing, Xin‐Hui

doi:10.1101/272377

Cited by 23 publications

(57 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cas9s, 12 to successfully compare on-target activity of wild type Cas9 with eCas9 24 and to engineer new PAM specificities. 25 In E. coli , double stranded DNA (dsDNA) breaks can lead to death and CRISPR/Cas systems have been used for the selection of genetically modified strains.…”

Section: Measuring "On Target" Cutting Rates Via An E Coli Survival mentioning

confidence: 99%

See 1 more Smart Citation

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

Moreb¹,

Hutmacher²,

Lynch³

2020

Preprint

View full text Add to dashboard Cite

CRISPR/Cas systems have become ubiquitous for genome editing in eukaryotic as well as bacterial systems. Cas9 associated with a guide RNA (gRNA) searches DNA for a matching sequence (target site) next to a protospacer adjacent motif (PAM) and once found, cuts the DNA.The number of PAM sites in the genome are effectively a non-target pool of inhibitory substrates, competing with the target site for the Cas9/gRNA complex. We demonstrate that increasing the number of non-target sites for a given gRNA reduces on-target activity in a dose dependent manner. Furthermore, we show that the use of Cas9 mutants with increased PAM specificity towards a smaller subset of PAMs (or smaller pool of competitive substrates) improves cutting rates. Decreasing the non-target pool by increasing PAM specificity provides a path towards improving on-target activity for slower high fidelity Cas9 variants. These results demonstrate the importance of competitive non-target sites on Cas9 activity and, in part, may help to explain sequence and context dependent activities of gRNAs. Engineering improved PAM specificity to reduce the competitive non-target pool offers an alternative strategy to engineer Cas9 variants with increased specificity and maintained on-target activity.

show abstract

Section: Measuring "On Target" Cutting Rates Via An E Coli Survival mentioning

confidence: 99%

“…Although more specific, these variants suffer from reduced cutting rates when compared to wild type Cas9. 9,10,24,29 Using the same set of gRNAs and pNT-15, we compared the killing efficiency of wild type and high fidelity Cas9 variants with and without the D1135E mutation.…”

Section: Reducing the Non-target Pool Increases On-target Activity Wimentioning

confidence: 99%

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

Moreb¹,

Hutmacher²,

Lynch³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…To determine the cRBS sequences of the glucarate biosensors in each sub-library, we first obtained the assorted biosensor plasmids of the five sub-libraries. Then, the mixed PCR products of the five modified sub-libraries were linked with five barcodes and sequenced by NGS 30 ( Accession No. SRR9301175 ; see online methods ).…”

Section: Resultsmentioning

confidence: 99%

Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

Ding

Zhou

Yuan

et al. 2020

Preprint

View full text Add to dashboard Cite

23Currently, predictive translation tuning of regulatory elements to the desired output of 24 transcription factor based biosensors remains a challenge. The gene expression of a biosensor 25 system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding 26 site (RBS), to achieve fine-tuning of its dynamic range (i.e., fold change in gene expression between 27 the presence and absence of inducer) by adjusting the translation initiation rate of the transcription 28 factor and reporter. However, existing genetically encoded biosensors generally suffer from 29 unpredictable translation tuning of regulatory elements to dynamic range. Here, we elucidated the 30 connections and partial mechanisms between RBS, translation initiation rate, protein folding and 31 dynamic range, and presented a rational design platform that predictably tuned the dynamic range 32 of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). A library containing 33 24,000 semi-rationally designed cRBSs was constructed using DNA microarray, and was divided 34into five sub-libraries through fluorescence-activated cell sorting. To explore the relationship 35 between cRBSs and dynamic range, we established a classification model with the cRBSs and 36 average dynamic range of five sub-libraries to accurately predict the dynamic range of biosensors 37 based on convolutional neural network in deep learning. Thus, this work provides a powerful 38 platform to enable predictable translation tuning of RBS to the dynamic range of biosensors. 39 40

show abstract

“…To elucidate determinants of CRISPR-Cas12 offtarget binding, we combine a thermodynamic model of dCas12a binding with a rationally designed CRISPRi assays to map the binding energy landscape of a type V CRISPR-Cas system from Francisella novicida (FnCas12a) as it inspects and binds to its DNA targets. Our approach, inspired by a recent theoretical framework that employs a unified energetic analysis to predict S. pyogenes Cas9 (SpCas9) cleavage activity [31] and recently developed massively parallel multiplexed assays [32][33][34][35], aims to directly measure the energetic and thermodynamic determinants of CRISPR-Cas binding. In other words, our assays excludes sources of variation in DNA cleavage activity caused by unknown physiological factors [28][29][30] by only focusing on the steps leading to final DNA cleavage step.…”

Section: Introductionmentioning

confidence: 99%

Massively parallel CRISPRi assays reveal concealed thermodynamic determinants of dCas12a binding

Specht

Lambert

2019

Preprint

View full text Add to dashboard Cite

The versatility of CRISPR-Cas endonucleases as a tool for biomedical research has lead to diverse applications in gene editing, programmable transcriptional control, and nucleic acid detection. Most CRISPR-Cas systems, however, suffer from off-target effects and unpredictable non-specific binding that negatively impact their reliability and broader applicability. To better evaluate the impact of mismatches on DNA target recognition and binding, we develop a massively parallel CRISPR interference (CRISPRi) assay to measure the binding energy between tens of thousands of CRISPR RNA (crRNA) and target DNA sequences. By developing a general thermodynamic model of CRISPR-Cas binding dynamics, our results unravel a comprehensive map of the energetic landscape of Francisella novicida Cas12a (FnCas12a) as it searches for its DNA target. Our results reveal concealed thermodynamic factors affecting FnCas12a DNA binding which should guide the design and optimization of crRNA that limit off-target effects, including the crucial role of an extended PAM sequence and the impact of the specific base composition of crRNA-DNA mismatches. Our generalizable approach should also provide a mechanistic understanding of target recognition and DNA binding when applied to other CRISPR-Cas systems.

show abstract

Improved sgRNA design in bacteria via genome-wide activity profiling

Cited by 23 publications

References 58 publications

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

Massively parallel CRISPRi assays reveal concealed thermodynamic determinants of dCas12a binding

Contact Info

Product

Resources

About