Systematic decomposition of sequence determinants governing CRISPR/Cas9 specificity

Construction of multi-targeted CRISPR libraries in tomato to overcome functional redundancy at genome-scale level

Genetic variance is vital for breeding programs and mutant screening, yet traditional mutagenesis methods wrestle with genetic redundancy and a lack of specificity in gene targeting. CRISPR-Cas9 offers precise, site-specific gene editing, but its application in crop improvement has been limited by scalability challenges. In this study, we develop genome-wide multi-targeted CRISPR libraries in tomato, enhancing the scalability of CRISPR gene editing in crops and addressing the challenges of redundancy while maintaining its precision. We design 15,804 unique single guide RNAs (sgRNAs), each targeting multiple genes within the same gene families. These sgRNAs are classified into 10 sub-libraries based on gene function. We generate approximately 1300 independent CRISPR lines and successfully identify mutants with distinct phenotypes related to fruit development, fruit flavor, nutrient uptake, and pathogen response. Additionally, we develop CRISPR-GuideMap, a double-barcode tagging system to enable large-scale sgRNA tracking in generated plants. Our results demonstrate that multi-targeted CRISPR libraries are scalable and effective for large-scale gene editing and offer an approach to overcome gene functional redundancy in basic plant research and crop breeding.

Optimizing CRISPR/Cas9 precision: Mitigating off-target effects for safe integration with photodynamic and stem cell therapies in cancer treatment

CRISPR/Cas9 precision genome editing has revolutionized cancer treatment by introducing specific alterations to the cancer genome. But the therapeutic potential of CRISPR/Cas9 is limited by off-target effects, which can cause undesired changes to genomic regions and create major safety concerns. The primary emphasis lies in their implications within the realm of cancer photodynamic therapy (PDT), where precision is paramount. PDT is a promising cancer treatment method; nevertheless, its effectiveness is severely limited and readily leads to recurrence due to the therapeutic resistance of cancer stem cells (CSCs). With a focus on targeted genome editing into cancer cells during PDT and stem cell treatment (SCT), the review aims to further the ongoing search for safer and more accurate CRISPR/Cas9-mediated methods. At the core of this exploration are recent advancements and novel techniques that offer promise in mitigating the risks associated with off-target effects. With a focus on cancer PDT and SCT, this review critically assesses the landscape of off-target effects in CRISPR/Cas9 applications, offering a comprehensive knowledge of their nature and prevalence. A key component of the review is the assessment of cutting-edge delivery methods, such as technologies based on nanoparticles (NPs), to optimize the distribution of CRISPR components. Additionally, the study delves into the intricacies of guide RNA design, focusing on advancements that bolster specificity and minimize off-target effects, crucial elements in ensuring the precision required for effective cancer PDT and SCT. By synthesizing insights from various methodologies, including the exploration of innovative genome editing tools and leveraging robust validation methods and bioinformatics tools, the review aspires to chart a course towards more reliable and precise CRISPR-Cas9 applications in cancer PDT and SCT. For safe PDT and SCT integration in cancer therapy, CRISPR/Cas9 precision optimization is essential. Utilizing sophisticated molecular and computational techniques to address off-target effects is crucial to realizing the therapeutic promise of these technologies, which will ultimately lead to the development of individualized and successful cancer treatment strategies. Our long-term goals are to improve precision genome editing for more potent cancer therapy approaches by refining the way CRISPR/Cas9 is integrated with photodynamic and stem cell therapies.

Single-base tiled screen unveils design principles of PspCas13b for potent and off-target-free RNA silencing

The development of precise RNA-editing tools is essential for the advancement of RNA therapeutics. CRISPR (clustered regularly interspaced short palindromic repeats) PspCas13b is a programmable RNA nuclease predicted to offer superior specificity because of its 30-nucleotide spacer sequence. However, its design principles and its on-target, off-target and collateral activities remain poorly characterized. Here, we present single-base tiled screening and computational analyses that identify key design principles for potent and highly selective RNA recognition and cleavage in human cells. We show that the de novo design of spacers containing guanosine bases at precise positions can greatly enhance the catalytic activity of inefficient CRISPR RNAs (crRNAs). These validated design principles (integrated into an online tool, https://cas13target.azurewebsites.net/ ) can predict highly effective crRNAs with ~90% accuracy. Furthermore, the comprehensive spacer-target mutagenesis revealed that PspCas13b can tolerate only up to four mismatches and requires ~26-nucleotide base pairing with the target to activate its nuclease domains, highlighting its superior specificity compared to other RNA or DNA interference tools. On the basis of this targeting resolution, we predict an extremely low probability of PspCas13b having off-target effects on other cellular transcripts. Proteomic analysis validated this prediction and showed that, unlike other Cas13 orthologs, PspCas13b exhibits potent on-target activity and lacks collateral effects.

Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs

CRISPR-based genome editing technologies have revolutionised the field of molecular biology, offering unprecedented opportunities for precise genetic manipulation. However, off-target effects remain a significant challenge, potentially leading to unintended consequences and limiting the applicability of CRISPR-based genome editing technologies in clinical settings. Current literature predominantly focuses on point predictions for off-target activity, which may not fully capture the range of possible outcomes and associated risks. Here, we present crispAI, a neural network architecture-based approach for predicting uncertainty estimates for off-target cleavage activity, providing a more comprehensive risk assessment and facilitating improved decision-making in single guide RNA (sgRNA) design. Our approach makes use of the count noise model Zero Inflated Negative Binomial (ZINB) to model the uncertainty in the off-target cleavage activity data. In addition, we present the first-of-its-kind genome-wide sgRNA efficiency score, crispAI-aggregate, enabling prioritization among sgRNAs with similar point aggregate predictions by providing richer information compared to existing aggregate scores. We show that uncertainty estimates of our approach are calibrated and its predictive performance is superior to the state-of-the-art in silico off-target cleavage activity prediction methods. The tool and the trained models are available at https://github.com/furkanozdenn/crispr-offtarget-uncertainty.

Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

The CRISPR/Cas9 system is a highly accurate gene-editing technique, but it can also lead to unintended off-target sites (OTS). Consequently, many high-throughput assays have been developed to measure OTS in a genome-wide manner, and their data was used to train machine-learning models to predict OTS. However, these models are inaccurate when considering OTS with bulges due to limited data compared to OTS without bulges. Recently, CHANGE-seq, a new in vitro technique to detect OTS, was used to produce a dataset of unprecedented scale and quality. In addition, the same study produced in cellula GUIDE-seq experiments, but none of these GUIDE-seq experiments included bulges. Here, we generated the most comprehensive GUIDE-seq dataset with bulges, and trained and evaluated state-of-the-art machine-learning models that consider OTS with bulges. We first reprocessed the publicly available experimental raw data of the CHANGE-seq study to generate 20 new GUIDE-seq experiments, and hundreds of OTS with bulges among the original and new GUIDE-seq experiments. We then trained multiple machine-learning models, and demonstrated their state-of-the-art performance both in vitro and in cellula over all OTS and when focusing on OTS with bulges. Last, we visualized the key features learned by our models on OTS with bulges in a unique representation.

Sensing the DNA-mismatch tolerance of catalytically inactive Cas9 via barcoded DNA nanostructures in solid-state nanopores

Single-molecule quantification of the strength and sequence specificity of interactions between proteins and nucleic acids would facilitate the probing of protein-DNA binding. Here we show that binding events between the catalytically inactive Cas9 ribonucleoprotein and any pre-defined short sequence of double-stranded DNA can be identified by sensing changes in ionic current as suitably designed barcoded linear DNA nanostructures with Cas9-binding double-stranded DNA overhangs translocate through solid-state nanopores. We designed barcoded DNA nanostructures to study the relationships between DNA sequence and the DNA-binding specificity, DNA-binding efficiency and DNA-mismatch tolerance of Cas9 at the single-nucleotide level. Nanopore-based sensing of DNA-barcoded nanostructures may help to improve the design of efficient and specific ribonucleoproteins for biomedical applications, and could be developed into sensitive protein-sensing assays.

Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing

The increasing emergence of Cas9 variants has attracted broad interest, as these variants were designed to expand CRISPR applications. New Cas9 variants typically feature higher editing efficiency, improved editing specificity, or alternative PAM sequences. To select Cas9 variants and gRNAs for high-fidelity and efficient genome editing, it is crucial to systematically quantify the editing performances of gRNAs and develop prediction models based on high-quality datasets. Using synthetic gRNA-target paired libraries and next-generation sequencing, we compared the activity and specificity of gRNAs of four SpCas9 variants. The nucleotide composition in the PAM-distal region had more influence on the editing efficiency of HiFi Cas9 and LZ3 Cas9. We further developed machine learning models to predict the gRNA efficiency and specificity for the four Cas9 variants. To aid users from broad research areas, the machine learning models for the predictions of gRNA editing efficiency within human genome sites are available on our website.

Emerging Gene Therapeutics for Epidermolysis Bullosa under Development

The monogenetic disease epidermolysis bullosa (EB) is characterised by the formation of extended blisters and lesions on the patient's skin upon minimal mechanical stress. Causal for this severe condition are genetic mutations in genes, leading to the functional impairment, reduction, or absence of the encoded protein within the skin's basement membrane zone connecting the epidermis to the underlying dermis. The major burden of affected families justifies the development of long-lasting and curative therapies operating at the genomic level. The landscape of causal therapies for EB is steadily expanding due to recent breakthroughs in the gene therapy field, providing promising outcomes for patients suffering from this severe disease. Currently, two gene therapeutic approaches show promise for EB. The clinically more advanced gene replacement strategy was successfully applied in severe EB forms, leading to a ground-breaking in vivo gene therapy product named beremagene geperpavec (B-VEC) recently approved from the US Food and Drug Administration (FDA). In addition, the continuous innovations in both designer nucleases and gene editing technologies enable the efficient and potentially safe repair of mutations in EB in a potentially permanent manner, inspiring researchers in the field to define and reach new milestones in the therapy of EB.

Interpretable neural architecture search and transfer learning for understanding CRISPR-Cas9 off-target enzymatic reactions

Finely tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Developing predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts. Here we introduce Elektrum, a deep learning framework that addresses these challenges with data-driven and biophysically interpretable models for determining the kinetics of biochemical systems. First, it uses in vitro kinetic assays to rapidly hypothesize an ensemble of high-quality kinetically interpretable neural networks (KINNs) that predict reaction rates. It then employs a transfer learning step, where the KINNs are inserted as intermediary layers into deeper convolutional neural networks, fine-tuning the predictions for reaction-dependent in vivo outcomes. We apply Elektrum to predict CRISPR-Cas9 off-target editing probabilities and demonstrate that Elektrum achieves improved performance, regularizes neural network architectures and maintains physical interpretability.

Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints

The powerful CRISPR genome editing system is hindered by its off-target effects, and existing computational tools achieved limited performance in genome-wide off-target prediction due to the lack of deep understanding of the CRISPR molecular mechanism. In this study, we propose to incorporate molecular dynamics (MD) simulations in the computational analysis of CRISPR system, and present CRISOT, an integrated tool suite containing four related modules, i.e., CRISOT-FP, CRISOT-Score, CRISOT-Spec, CRISORT-Opti for RNA-DNA molecular interaction fingerprint generation, genome-wide CRISPR off-target prediction, sgRNA specificity evaluation and sgRNA optimization of Cas9 system respectively. Our comprehensive computational and experimental tests reveal that CRISOT outperforms existing tools with extensive in silico validations and proof-of-concept experimental validations. In addition, CRISOT shows potential in accurately predicting off-target effects of the base editors and prime editors, indicating that the derived RNA-DNA molecular interaction fingerprint captures the underlying mechanisms of RNA-DNA interaction among distinct CRISPR systems. Collectively, CRISOT provides an efficient and generalizable framework for genome-wide CRISPR off-target prediction, evaluation and sgRNA optimization for improved targeting specificity in CRISPR genome editing.

Guide-specific loss of efficiency and off-target reduction with Cas9 variants

High-fidelity clustered regularly interspaced palindromic repeats (CRISPR)-associated protein 9 (Cas9) variants have been developed to reduce the off-target effects of CRISPR systems at a cost of efficiency loss. To systematically evaluate the efficiency and off-target tolerance of Cas9 variants in complex with different single guide RNAs (sgRNAs), we applied high-throughput viability screens and a synthetic paired sgRNA-target system to assess thousands of sgRNAs in combination with two high-fidelity Cas9 variants HiFi and LZ3. Comparing these variants against wild-type SpCas9, we found that ∼20% of sgRNAs are associated with a significant loss of efficiency when complexed with either HiFi or LZ3. The loss of efficiency is dependent on the sequence context in the seed region of sgRNAs, as well as at positions 15-18 in the non-seed region that interacts with the REC3 domain of Cas9, suggesting that the variant-specific mutations in the REC3 domain account for the loss of efficiency. We also observed various degrees of sequence-dependent off-target reduction when different sgRNAs are used in combination with the variants. Given these observations, we developed GuideVar, a transfer learning-based computational framework for the prediction of on-target efficiency and off-target effects with high-fidelity variants. GuideVar facilitates the prioritization of sgRNAs in the applications with HiFi and LZ3, as demonstrated by the improvement of signal-to-noise ratios in high-throughput viability screens using these high-fidelity variants.

Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions

Finely-tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Creating predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts. Here we introduce Elektrum, a deep learning framework which addresses these challenges with data-driven and biophysically interpretable models for determining the kinetics of biochemical systems. First, it uses in vitro kinetic assays to rapidly hypothesize an ensemble of high-quality Kinetically Interpretable Neural Networks (KINNs) that predict reaction rates. It then employs a novel transfer learning step, where the KINNs are inserted as intermediary layers into deeper convolutional neural networks, fine-tuning the predictions for reaction-dependent in vivo outcomes. Elektrum makes effective use of the limited, but clean in vitro data and the complex, yet plentiful in vivo data that captures cellular context. We apply Elektrum to predict CRISPR-Cas9 off-target editing probabilities and demonstrate that Elektrum achieves state-of-the-art performance, regularizes neural network architectures, and maintains physical interpretability.

DANGER analysis: risk-averse on/off-target assessment for CRISPR editing without a reference genome

Motivation

The CRISPR-Cas9 system has successfully achieved site-specific gene editing in organisms ranging from humans to bacteria. The technology efficiently generates mutants, allowing for phenotypic analysis of the on-target gene. However, some conventional studies did not investigate whether deleterious off-target effects partially affect the phenotype.

Results

Herein, we present a novel phenotypic assessment of CRISPR-mediated gene editing: Deleterious and ANticipatable Guides Evaluated by RNA-sequencing (DANGER) analysis. Using RNA-seq data, this bioinformatics pipeline can elucidate genomic on/off-target sites on mRNA-transcribed regions related to expression changes and then quantify phenotypic risk at the gene ontology term level. We demonstrated the risk-averse on/off-target assessment in RNA-seq data from gene-edited samples of human cells and zebrafish brains. Our DANGER analysis successfully detected off-target sites, and it quantitatively evaluated the potential contribution of deleterious off-targets to the transcriptome phenotypes of the edited mutants. Notably, DANGER analysis harnessed de novo transcriptome assembly to perform risk-averse on/off-target assessments without a reference genome. Thus, our resources would help assess genome editing in non-model organisms, individual human genomes, and atypical genomes from diseases and viruses. In conclusion, DANGER analysis facilitates the safer design of genome editing in all organisms with a transcriptome.

Availability and implementation

The Script for the DANGER analysis pipeline is available at https://github.com/KazukiNakamae/DANGER_analysis. In addition, the software provides a tutorial on reproducing the results presented in this article on the Readme page. The Docker image of DANGER_analysis is also available at https://hub.docker.com/repository/docker/kazukinakamae/dangeranalysis/general.

Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review

CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method of gene and genome editing, which benefits from a large variety of practical applications. For example, in biomedicine, it has been used in research related to cancer, virus infections, pathogen detection, and genetic diseases. Current CRISPR/Cas9 research is based on data-driven models for on- and off-target prediction as a cleavage may occur at non-target sequence locations. Nowadays, conventional machine learning and deep learning methods are applied on a regular basis to accurately predict on-target knockout efficacy and off-target profile of given single-guide RNAs (sgRNAs). In this paper, we present an overview and a comparative analysis of traditional machine learning and deep learning models used in CRISPR/Cas9. We highlight the key research challenges and directions associated with target activity prediction. We discuss recent advances in the sgRNA-DNA sequence encoding used in state-of-the-art on- and off-target prediction models. Furthermore, we present the most popular deep learning neural network architectures used in CRISPR/Cas9 prediction models. Finally, we summarize the existing challenges and discuss possible future investigations in the field of on- and off-target prediction. Our paper provides valuable support for academic and industrial researchers interested in the application of machine learning methods in the field of CRISPR/Cas9 genome editing.

Recent advances in therapeutic CRISPR-Cas9 genome editing: mechanisms and applications

Recently, clustered regularly interspaced palindromic repeats (CRISPR)-Cas9 derived editing tools had significantly improved our ability to make desired changes in the genome. Wild-type Cas9 protein recognizes the target genomic loci and induced local double strand breaks (DSBs) in the guidance of small RNA molecule. In mammalian cells, the DSBs are mainly repaired by endogenous non-homologous end joining (NHEJ) pathway, which is error prone and results in the formation of indels. The indels can be harnessed to interrupt gene coding sequences or regulation elements. The DSBs can also be fixed by homology directed repair (HDR) pathway to introduce desired changes, such as base substitution and fragment insertion, when proper donor templates are provided, albeit in a less efficient manner. Besides making DSBs, Cas9 protein can be mutated to serve as a DNA binding platform to recruit functional modulators to the target loci, performing local transcriptional regulation, epigenetic remolding, base editing or prime editing. These Cas9 derived editing tools, especially base editors and prime editors, can introduce precise changes into the target loci at a single-base resolution and in an efficient and irreversible manner. Such features make these editing tools very promising for therapeutic applications. This review focuses on the evolution and mechanisms of CRISPR-Cas9 derived editing tools and their applications in the field of gene therapy.

Current Bioinformatics Tools to Optimize CRISPR/Cas9 Experiments to Reduce Off-Target Effects

The CRISPR-Cas system has evolved into a cutting-edge technology that has transformed the field of biological sciences through precise genetic manipulation. CRISPR/Cas9 nuclease is evolving into a revolutionizing method to edit any gene of any species with desirable outcomes. The swift advancement of CRISPR-Cas technology is reflected in an ever-expanding ecosystem of bioinformatics tools designed to make CRISPR/Cas9 experiments easier. To assist researchers with efficient guide RNA designs with fewer off-target effects, nuclease target site selection, and experimental validation, bioinformaticians have built and developed a comprehensive set of tools. In this article, we will review the various computational tools available for the assessment of off-target effects, as well as the quantification of nuclease activity and specificity, including web-based search tools and experimental methods, and we will describe how these tools can be optimized for gene knock-out (KO) and gene knock-in (KI) for model organisms. We also discuss future directions in precision genome editing and its applications, as well as challenges in target selection, particularly in predicting off-target effects.

Guide-specific loss of efficiency and off-target reduction with Cas9 variants

High-fidelity Cas9 variants have been developed to reduce the off-target effects of CRISPR systems at a cost of efficiency loss. To systematically evaluate the efficiency and off-target tolerance of Cas9 variants in complex with different single guide RNAs (sgRNAs), we applied high-throughput viability screens and a synthetic paired sgRNA-target system to assess thousands of sgRNAs in combination with two high-fidelity Cas9 variants HiFi and LZ3. Comparing these variants against WT SpCas9, we found that ~20% of sgRNAs are associated with a significant loss of efficiency when complexed with either HiFi or LZ3. The loss of efficiency is dependent on the sequence context in the seed region of sgRNAs, as well as at positions 15-18 in the non-seed region that interacts with the REC3 domain of Cas9, suggesting that the variant-specific mutations in REC3 domain account for the loss of efficiency. We also observed various degrees of sequencedependent off-target reduction when different sgRNAs are used in combination with the variants. Given these observations, we developed GuideVar, a transfer-learning-based computational framework for the prediction of on-target efficiency and off-target effect with high-fidelity variants. GuideVar facilitates the prioritization of sgRNAs in the applications with HiFi and LZ3, as demonstrated by the improvement of signal-to-noise ratios in high-throughput viability screens using these high-fidelity variants.

Assessing and advancing the safety of CRISPR-Cas tools: from DNA to RNA editing

CRISPR-Cas gene editing has revolutionized experimental molecular biology over the past decade and holds great promise for the treatment of human genetic diseases. Here we review the development of CRISPR-Cas9/Cas12/Cas13 nucleases, DNA base editors, prime editors, and RNA base editors, focusing on the assessment and improvement of their editing precision and safety, pushing the limit of editing specificity and efficiency. We summarize the capabilities and limitations of each CRISPR tool from DNA editing to RNA editing, and highlight the opportunities for future improvements and applications in basic research, as well as the therapeutic and clinical considerations for their use in patients.

Cnpy32xHA mice reveal neuronal expression of Cnpy3 in the brain

BACKGROUND

Identification of biallelic CNPY3 mutations in patients with epileptic encephalopathy and abnormal electroencephalography findings of Cnpy3 knock-out mice have indicated that the loss of CNPY3 function causes neurological disorders such as epilepsy. However, the basic property of CNPY3 in the brain remains unclear.

NEW METHOD

We generated C-terminal 2xHA-tag knock-in Cnpy3 mice by i-GONAD in vivo genome editing system to investigate the expression and function of Cnpy3 in the mouse brain.

RESULTS

2xHA-tagged Cnpy3 was confirmed by immunoblot analysis using anti-HA and CNPY3 antibodies, although HA tagging caused the decreased Cnpy3 protein level. Immunohistochemical analysis of Cnpy3^2xHA knock-in mice showed that Cnpy3-2xHA was predominantly expressed in the neuron. In addition, Cnpy3 and Cnpy3-2xHA were both localized in the endoplasmic reticulum and synaptosome and showed age-dependent expression changes in the brain.

COMPARISON WITH EXISTING METHODS

Conventional Cnpy3 antibodies could not allow us to investigate the distribution of Cnpy3 expression in the brain, while HA-tagging revealed the expression of CNPY3 in neuronal cells.

CONCLUSIONS

Taken together, we demonstrated that Cnpy3^2xHA knock-in mice would be useful to further elucidate the property of Cnpy3 in brain function and neurological disorders.

CRISPR/dCas9 for hepatic fibrosis therapy: implications and challenges

Hepatic fibrosis is a pathological reaction of tissue damage and repair caused by various pathogenic factors acting on liver. At present, there is no effective anti-fibrotic specific therapy. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (dCas9) system is a new generation of gene editing technology. The CRISPR/dCas9 system provides a platform for studying site-specific transcriptional regulation, which has high efficiency in gene transcriptional activation for achieving robust. This system holds promise for hepatic fibrosis therapy via acting on liver fibrosis effector cells. However, there are some challenges associated with this novel technology, such as large structural variants at on-target, off-target sites, and targeted delivery efficiency. In this review, we present the potential implications and describe the challenges of CRISPR/dCas9 system that might be encountered in hepatic fibrosis therapy.

Decrypting the mechanistic basis of CRISPR/Cas9 protein

CRISPR/Cas system, a newly but extensively investigated genome-editing method, harbors practical solutions for various genetic problems. It relies on short guide RNAs (gRNAs) to recruit the Cas9 protein, a DNA cleaving enzyme, to its genomic target DNAs. The Cas9 enzyme exhibits some unique properties, like the ability to differentiate self vs. non-self - DNA strands using the base-pairing potential of crRNA, i.e., only CRISPR DNA is entirely complementary to the CRISPR repeat sequences at the crRNA whereas the presence of mismatches in the upstream region of the spacer permit CRISPR interference which is inhibited in case of CRISPR-DNA, allosteric regulation in its domains, and domain reorientation on sgRNA binding. Several groups have contributed their efforts in understanding the functioning of the CRISPR/Cas system, but even then, there is a lot more to explore in this area. The structural and sequence-based understanding of the whole CRISPR-associated bacterial ortholog family landscape is still ambiguous. A better understanding of the underlying energetics of the CRISPR/Cas9 system should reveal critical parameters to design better CRISPR/Cas9s.

Massively targeted evaluation of therapeutic CRISPR off-targets in cells

Methods for sensitive and high-throughput evaluation of CRISPR RNA-guided nucleases (RGNs) off-targets (OTs) are essential for advancing RGN-based gene therapies. Here we report SURRO-seq for simultaneously evaluating thousands of therapeutic RGN OTs in cells. SURRO-seq captures RGN-induced indels in cells by pooled lentiviral OTs libraries and deep sequencing, an approach comparable and complementary to OTs detection by T7 endonuclease 1, GUIDE-seq, and CIRCLE-seq. Application of SURRO-seq to 8150 OTs from 110 therapeutic RGNs identifies significantly detectable indels in 783 OTs, of which 37 OTs are found in cancer genes and 23 OTs are further validated in five human cell lines by targeted amplicon sequencing. Finally, SURRO-seq reveals that thermodynamically stable wobble base pair (rG•dT) and free binding energy strongly affect RGN specificity. Our study emphasizes the necessity of thoroughly evaluating therapeutic RGN OTs to minimize inevitable off-target effects.

Development of clustered regularly interspaced short palindromic repeats/CRISPR-associated technology for potential clinical applications

The clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated (Cas) proteins constitute the innate adaptive immune system in several bacteria and archaea. This immune system helps them in resisting the invasion of phages and foreign DNA by providing sequence-specific acquired immunity. Owing to the numerous advantages such as ease of use, low cost, high efficiency, good accuracy, and a diverse range of applications, the CRISPR-Cas system has become the most widely used genome editing technology. Hence, the advent of the CRISPR/Cas technology highlights a tremendous potential in clinical diagnosis and could become a powerful asset for modern medicine. This study reviews the recently reported application platforms for screening, diagnosis, and treatment of different diseases based on CRISPR/Cas systems. The limitations, current challenges, and future prospectus are summarized; this article would be a valuable reference for future genome-editing practices.

CRISPR/Cas9 gRNA activity depends on free energy changes and on the target PAM context

A major challenge of CRISPR/Cas9-mediated genome engineering is that not all guide RNAs (gRNAs) cleave the DNA efficiently. Although the heterogeneity of gRNA activity is well recognized, the current understanding of how CRISPR/Cas9 activity is regulated remains incomplete. Here, we identify a sweet spot range of binding free energy change for optimal efficiency which largely explains why gRNAs display changes in efficiency at on- and off-target sites, including why gRNAs can cleave an off-target with higher efficiency than the on-target. Using an energy-based model, we show that local gRNA-DNA interactions resulting from Cas9 "sliding" on overlapping protospacer adjacent motifs (PAMs) profoundly impact gRNA activities. Combining the effects of local sliding for a given PAM context with global off-targets allows us to better identify highly specific, and thus efficient, gRNAs. We validate the effects of local sliding on gRNA efficiency using both public data and in-house data generated by measuring SpCas9 cleavage efficiency at 1024 sites designed to cover all possible combinations of 4-nt PAM and context sequences of 4 gRNAs. Our results provide insights into the mechanisms of Cas9-PAM compatibility and cleavage activation, underlining the importance of accounting for local sliding in gRNA design.