Prediction of the sequence-specific cleavage activity of Cas9 variants

Effect of the Phosphoryl Guanidine Modification in Chimeric DNA-RNA crRNAs on the Activity of the CRISPR-Cas9 System In Vitro

Currently, the CRISPR-Cas9 system serves as a prevalent tool for genome editing and gene expression regulation. Its therapeutic application is limited by off-target effects that can affect genomic integrity through nonspecific, undesirable changes in the genome. Various strategies have been explored to mitigate the off-target effects. Many approaches focus on modifying components of the system, namely, Cas9 and guide RNAs, to enhance specificity. However, a common challenge is that methods aiming to increase specificity often result in a significant reduction in the editing efficiency. Here, we introduce a novel approach to modifying crRNA to balance CRISPR-Cas9 specificity and efficiency. Our approach involves incorporating nucleoside modifications, such as replacing ribo- to deoxyribonucleosides and backbone modifications, using phosphoryl guanidine groups, specifically 1,3-dimethylimidazolidin-2-ylidene phosphoramidate. In this case, within the first 10 nucleotides from the 5' crRNA end, phosphodiester bonds are substituted with phosphoryl guanidine groups. We demonstrate that crRNAs containing a combination of deoxyribonucleosides and single or multiple phosphoryl guanidine groups facilitate the modulation of CRISPR-Cas9 system activity while improving its specificity in vitro.

Secondary Conformational Checkpoint in CRISPR-Cas9

A specific checkpoint between target DNA binding and cleavage primarily governs the precision of Cas9 gene editing. Although various CRISPR-Cas9 variants have been developed to improve DNA cleavage accuracy, we still lack a comprehensive understanding of how they work at the molecular level. Herein, we have focused on studying the late-stage conformational transitions of Cas9 and an evolved Cas9 mutant (evoCas9) that start from the precleavage state. Our submilliseconds of dynamic simulations reveal that the presence of base mismatches leads the HNH nuclease domain of Cas9 to alter its principal functional modes of motion, thereby impairing its conformational activation. This observation suggests the existence of a secondary conformational checkpoint that fine-tunes the final DNA cleavage activation. Remarkably, evoCas9 is prone to deviating from the normal activation pathway with base mismatches. This is characterized by a noticeable shift in the positioning of the HNH domain and a significantly perturbed allosteric communication network within the enzyme. Therefore, the mutations evolved in evoCas9 also reinforce the secondary checkpoint in addition to the previously identified primary checkpoint, collectively ensuring this variant's high gene-editing accuracy. This mechanism should also apply to other Cas9-guide RNA variants with enhanced fidelity.

Mutational rescue of the activity of high-fidelity Cas9 enzymes

Programmable DNA endonucleases derived from bacterial genetic defense systems, exemplified by CRISPR-Cas9, have made it significantly easier to perform genomic modifications in living cells. However, unprogrammed, off-target modifications can have serious consequences, as they often disrupt the function or regulation of non-targeted genes and compromise the safety of therapeutic gene editing applications. High-fidelity mutants of Cas9 have been established to enable more accurate gene editing, but these are typically less efficient. Here, we merge the strengths of high-fidelity Cas9 and hyperactive Cas9 variants to provide an enzyme, which we dub HyperDriveCas9, that yields the desirable properties of both parents. HyperDriveCas9 functions efficiently in mammalian cells and introduces insertion and deletion mutations into targeted genomic regions while maintaining a favorable off-target profile. HyperDriveCas9 is a precise and efficient tool for gene editing applications in science and medicine.

CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction

CRISPR/Cas9 is a powerful genome-editing tool in biology, but its wide applications are challenged by a lack of knowledge governing single-guide RNA (sgRNA) activity. Several deep-learning-based methods have been developed for the prediction of on-target activity. However, there is still room for improvement. Here, we proposed a hybrid neural network named CrnnCrispr, which integrates a convolutional neural network and a recurrent neural network for on-target activity prediction. We performed unbiased experiments with four mainstream methods on nine public datasets with varying sample sizes. Additionally, we incorporated a transfer learning strategy to boost the prediction power on small-scale datasets. Our results showed that CrnnCrispr outperformed existing methods in terms of accuracy and generalizability. Finally, we applied a visualization approach to investigate the generalizable nucleotide-position-dependent patterns of sgRNAs for on-target activity, which shows potential in terms of model interpretability and further helps in understanding the principles of sgRNA design.

Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning

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 and 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 targeted inhibition of gene expression via gRNA design (TIGER) to predict efficacy from guide sequence and context. TIGER outperforms the existing models at predicting on-target 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 the use of RNA-targeting CRISPRs to precisely control gene dosage.

Recent advances in CRISPR-based functional genomics for the study of disease-associated genetic variants

Advances in sequencing technology have greatly increased our ability to gather genomic data, yet understanding the impact of genetic mutations, particularly variants of uncertain significance (VUSs), remains a challenge in precision medicine. The CRISPR‒Cas system has emerged as a pivotal tool for genome engineering, enabling the precise incorporation of specific genetic variations, including VUSs, into DNA to facilitate their functional characterization. Additionally, the integration of CRISPR‒Cas technology with sequencing tools allows the high-throughput evaluation of mutations, transforming uncertain genetic data into actionable insights. This allows researchers to comprehensively study the functional consequences of point mutations, paving the way for enhanced understanding and increasing application to precision medicine. This review summarizes the current genome editing tools utilizing CRISPR‒Cas systems and their combination with sequencing tools for functional genomics, with a focus on point mutations.

A deep profile of gene expression across 18 human cancers

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of novel cancer biology from existing gene expression data.

Targeted nonviral delivery of genome editors in vivo

Cell-type-specific in vivo delivery of genome editing molecules is the next breakthrough that will drive biological discovery and transform the field of cell and gene therapy. Here, we discuss recent advances in the delivery of CRISPR-Cas genome editors either as preassembled ribonucleoproteins or encoded in mRNA. Both strategies avoid pitfalls of viral vector-mediated delivery and offer advantages including transient editor lifetime and potentially streamlined manufacturing capability that are already proving valuable for clinical use. We review current applications and future opportunities of these emerging delivery approaches that could make genome editing more efficacious and accessible in the future.

HyCas9-12aGEP: an efficient genome editing platform for Corynebacterium glutamicum

Corynebacterium glutamicum plays a crucial role as a significant industrial producer of metabolites. Despite the successful development of CRISPR-Cas9 and CRISPR-Cas12a-assisted genome editing technologies in C. glutamicum, their editing resolution and efficiency are hampered by the diverse on-target activities of guide RNAs (gRNAs). To address this problem, a hybrid CRISPR-Cas9-Cas12a genome editing platform (HyCas9-12aGEP) was developed in C. glutamicum in this study to co-express sgRNA (corresponding to SpCas9 guide RNA), crRNA (corresponding to FnCas12a guide RNA), or hfgRNA (formed by the fusion of sgRNA and crRNA). HyCas9-12aGEP improves the efficiency of mapping active gRNAs and outperforms both CRISPR-Cas9 and CRISPR-Cas12a in genome editing resolution and efficiency. In the experiment involving the deletion of the cg0697-0740 gene segment, an unexpected phenotype was observed, and HyCas9-12aGEP efficiently identified the responsible genotype from more than 40 genes. Here, HyCas9-12aGEP greatly improve our capability in terms of genome reprogramming in C. glutamicum.

High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9

The high-fidelity (HF1), hyper-accurate (Hypa), and evolved (Evo) variants of the CRISPR-associated protein 9 (Cas9) endonuclease are critical tools to mitigate off-target effects in the application of CRISPR-Cas9 technology. The mechanisms by which mutations in recognition subdomain 3 (Rec3) mediate specificity in these variants are poorly understood. Here, solution nuclear magnetic resonance and molecular dynamics simulations establish the structural and dynamic effects of high-specificity mutations in Rec3, and how they propagate the allosteric signal of Cas9. We reveal conserved structural changes and dynamic differences at regions of Rec3 that interface with the RNA:DNA hybrid, transducing chemical signals from Rec3 to the catalytic His-Asn-His (HNH) domain. The variants remodel the communication sourcing from the Rec3 α helix 37, previously shown to sense target DNA complementarity, either directly or allosterically. This mechanism increases communication between the DNA mismatch recognition helix and the HNH active site, shedding light on the structure and dynamics underlying Cas9 specificity and providing insight for future engineering principles.

Enhancing prime editor activity by directed protein evolution in yeast

Prime editing is a highly versatile genome editing technology that enables the introduction of base substitutions, insertions, and deletions. However, compared to traditional Cas9 nucleases prime editors (PEs) are less active. In this study we use OrthoRep, a yeast-based platform for directed protein evolution, to enhance the editing efficiency of PEs. After several rounds of evolution with increased selection pressure, we identify multiple mutations that have a positive effect on PE activity in yeast cells and in biochemical assays. Combining the two most effective mutations - the A259D amino acid substitution in nCas9 and the K445T substitution in M-MLV RT - results in the variant PE_Y18. Delivery of PE_Y18, encoded on DNA, mRNA or as a ribonucleoprotein complex into mammalian cell lines increases editing rates up to 3.5-fold compared to PEmax. In addition, PE_Y18 supports higher prime editing rates when delivered in vivo into the liver or brain. Our study demonstrates proof-of-concept for the application of OrthoRep to optimize genome editing tools in eukaryotic cells.

Deep learning models to predict the editing efficiencies and outcomes of diverse base editors

Applications of base editing are frequently restricted by the requirement for a protospacer adjacent motif (PAM), and selecting the optimal base editor (BE) and single-guide RNA pair (sgRNA) for a given target can be difficult. To select for BEs and sgRNAs without extensive experimental work, we systematically compared the editing windows, outcomes and preferred motifs for seven BEs, including two cytosine BEs, two adenine BEs and three C•G to G•C BEs at thousands of target sequences. We also evaluated nine Cas9 variants that recognize different PAM sequences and developed a deep learning model, DeepCas9variants, for predicting which variants function most efficiently at sites with a given target sequence. We then develop a computational model, DeepBE, that predicts editing efficiencies and outcomes of 63 BEs that were generated by incorporating nine Cas9 variants as nickase domains into the seven BE variants. The predicted median efficiencies of BEs with DeepBE-based design were 2.9- to 20-fold higher than those of rationally designed SpCas9-containing BEs.

Past, present, and future of CRISPR genome editing technologies

Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.

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.

Revolutionizing in vivo therapy with CRISPR/Cas genome editing: breakthroughs, opportunities and challenges

Genome editing using the CRISPR/Cas system has revolutionized the field of genetic engineering, offering unprecedented opportunities for therapeutic applications in vivo. Despite the numerous ongoing clinical trials focusing on ex vivo genome editing, recent studies emphasize the therapeutic promise of in vivo gene editing using CRISPR/Cas technology. However, it is worth noting that the complete attainment of the inherent capabilities of in vivo therapy in humans is yet to be accomplished. Before the full realization of in vivo therapeutic potential, it is crucial to achieve enhanced specificity in selectively targeting defective cells while minimizing harm to healthy cells. This review examines emerging studies, focusing on CRISPR/Cas-based pre-clinical and clinical trials for innovative therapeutic approaches for a wide range of diseases. Furthermore, we emphasize targeting cancer-specific sequences target in genes associated with tumors, shedding light on the diverse strategies employed in cancer treatment. We highlight the various challenges associated with in vivo CRISPR/Cas-based cancer therapy and explore their prospective clinical translatability and the strategies employed to overcome these obstacles.

Discovery of Diverse CRISPR-Cas Systems and Expansion of the Genome Engineering Toolbox

CRISPR systems mediate adaptive immunity in bacteria and archaea through diverse effector mechanisms and have been repurposed for versatile applications in therapeutics and diagnostics thanks to their facile reprogramming with RNA guides. RNA-guided CRISPR-Cas targeting and interference are mediated by effectors that are either components of multisubunit complexes in class 1 systems or multidomain single-effector proteins in class 2. The compact class 2 CRISPR systems have been broadly adopted for multiple applications, especially genome editing, leading to a transformation of the molecular biology and biotechnology toolkit. The diversity of class 2 effector enzymes, initially limited to the Cas9 nuclease, was substantially expanded via computational genome and metagenome mining to include numerous variants of Cas12 and Cas13, providing substrates for the development of versatile, orthogonal molecular tools. Characterization of these diverse CRISPR effectors uncovered many new features, including distinct protospacer adjacent motifs (PAMs) that expand the targeting space, improved editing specificity, RNA rather than DNA targeting, smaller crRNAs, staggered and blunt end cuts, miniature enzymes, promiscuous RNA and DNA cleavage, etc. These unique properties enabled multiple applications, such as harnessing the promiscuous RNase activity of the type VI effector, Cas13, for supersensitive nucleic acid detection. class 1 CRISPR systems have been adopted for genome editing, as well, despite the challenge of expressing and delivering the multiprotein class 1 effectors. The rich diversity of CRISPR enzymes led to rapid maturation of the genome editing toolbox, with capabilities such as gene knockout, base editing, prime editing, gene insertion, DNA imaging, epigenetic modulation, transcriptional modulation, and RNA editing. Combined with rational design and engineering of the effector proteins and associated RNAs, the natural diversity of CRISPR and related bacterial RNA-guided systems provides a vast resource for expanding the repertoire of tools for molecular biology and biotechnology.

Unified Model to Predict gRNA Efficiency across Diverse Cell Lines and CRISPR-Cas9 Systems

Computationally predicting the efficiency of a guide RNA (gRNA) from its sequence is crucial to designing the CRISPR-Cas9 system. Currently, machine learning (ML)-based models are widely used for such predictions. However, these ML models often show performance imbalance when applied to multiple data sets from diverse sources, hindering the practical utilization of these tools. To address this issue, we propose a Michaelis-Menten theoretical framework that integrates information from multiple data sets. We demonstrate that the binding free energy can serve as a useful invariant that bridges the data from different experimental setups. Building upon this framework, we develop a new ML model called Uni-deepSG. This model exhibits broad applicability on 27 data sets with different cell types, Cas9 variants, and gRNA designs. Our work confirms the existence of a generalized model for predicting gRNA efficiency and lays the theoretical groundwork necessary to finalize such a model.

Influence of N1-Methylpseudouridine in Guide RNAs on CRISPR/Cas9 Activity

At present, there are many strategies to improve the activity of CRISPR/Cas9. A well-known and effective approach is guide RNA modification. Many chemical guide RNA modifications have been studied, whereas naturally occurring RNA modifications are largely unexplored. N1-methylpseudouridine (m1Ψ) is an RNA base modification widely used in mRNA therapy, and it holds great promise for application in genome editing systems. The present study focuses on investigating the effect of N1-methylpseudouridine on the functioning of CRISPR/Cas9. In vitro cleavage assays helped determine the level of m1Ψ guide RNA modification that is sufficient to cleave the target substrate. By analyzing FAM-labeled dsDNA substrate cleavage, we calculated the kinetic parameters and the specificity scores of modified guide RNAs. Neon transfection and digital PCR enabled us to assess the activity of modified guide RNAs in mammalian cells. Our study shows that the presence of m1Ψ in guide RNAs can help preserve on-target genome editing while significantly reducing the off-target effects of CRISPR/Cas9 in vitro. We also demonstrate that Cas9 complexes with guide RNAs containing m1Ψ allow for genome editing in human cells. Thus, the incorporation of m1Ψ into guide RNAs supports CRISPR/Cas9 activity both in vitro and in cells.

Harnessing the power of artificial intelligence to advance cell therapy

Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.

CRISPR/Cas9 technology: applications in oocytes and early embryos

CRISPR/Cas9, a highly versatile genome-editing tool, has garnered significant attention in recent years. Despite the unique characteristics of oocytes and early embryos compared to other cell types, this technology has been increasing used in mammalian reproduction. In this comprehensive review, we elucidate the fundamental principles of CRISPR/Cas9-related methodologies and explore their wide-ranging applications in deciphering molecular intricacies during oocyte and early embryo development as well as in addressing associated diseases. However, it is imperative to acknowledge the limitations inherent to these technologies, including the potential for off-target effects, as well as the ethical concerns surrounding the manipulation of human embryos. Thus, a judicious and thoughtful approach is warranted. Regardless of these challenges, CRISPR/Cas9 technology undeniably represents a formidable tool for genome and epigenome manipulation within oocytes and early embryos. Continuous refinements in this field are poised to fortify its future prospects and applications.

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.

CRISPR-Cas-Based Engineering of Probiotics

Probiotics are the treasure of the microbiology fields. They have been widely used in the food industry, clinical treatment, and other fields. The equivocal health-promoting effects and the unknown action mechanism were the largest obstacles for further probiotic's developed applications. In recent years, various genome editing techniques have been developed and applied to explore the mechanisms and functional modifications of probiotics. As important genome editing tools, CRISPR-Cas systems that have opened new improvements in genome editing dedicated to probiotics. The high efficiency, flexibility, and specificity are the advantages of using CRISPR-Cas systems. Here, we summarize the classification and distribution of CRISPR-Cas systems in probiotics, as well as the editing tools developed on the basis of them. Then, we discuss the genome editing of probiotics based on CRISPR-Cas systems and the applications of the engineered probiotics through CRISPR-Cas systems. Finally, we proposed a design route for CRISPR systems that related to the genetically engineered probiotics.

A cleavage rule for selection of increased-fidelity SpCas9 variants with high efficiency and no detectable off-targets

Streptococcus pyogenes Cas9 (SpCas9) has been employed as a genome engineering tool with a promising potential within therapeutics. However, its off-target effects present major safety concerns for applications requiring high specificity. Approaches developed to date to mitigate this effect, including any of the increased-fidelity (i.e., high-fidelity) SpCas9 variants, only provide efficient editing on a relatively small fraction of targets without detectable off-targets. Upon addressing this problem, we reveal a rather unexpected cleavability ranking of target sequences, and a cleavage rule that governs the on-target and off-target cleavage of increased-fidelity SpCas9 variants but not that of SpCas9-NG or xCas9. According to this rule, for each target, an optimal variant with matching fidelity must be identified for efficient cleavage without detectable off-target effects. Based on this insight, we develop here an extended set of variants, the CRISPRecise set, with increased fidelity spanning across a wide range, with differences in fidelity small enough to comprise an optimal variant for each target, regardless of its cleavability ranking. We demonstrate efficient editing with maximum specificity even on those targets that have not been possible in previous studies.

Manipulating and studying gene function in human pluripotent stem cell models

Human pluripotent stem cells (hPSCs) are uniquely suited to study human development and disease and promise to revolutionize regenerative medicine. These applications rely on robust methods to manipulate gene function in hPSC models. This comprehensive review aims to both empower scientists approaching the field and update experienced stem cell biologists. We begin by highlighting challenges with manipulating gene expression in hPSCs and their differentiated derivatives, and relevant solutions (transfection, transduction, transposition, and genomic safe harbor editing). We then outline how to perform robust constitutive or inducible loss-, gain-, and change-of-function experiments in hPSCs models, both using historical methods (RNA interference, transgenesis, and homologous recombination) and modern programmable nucleases (particularly CRISPR/Cas9 and its derivatives, i.e., CRISPR interference, activation, base editing, and prime editing). We further describe extension of these approaches for arrayed or pooled functional studies, including emerging single-cell genomic methods, and the related design and analytical bioinformatic tools. Finally, we suggest some directions for future advancements in all of these areas. Mastering the combination of these transformative technologies will empower unprecedented advances in human biology and medicine.

Predicting prime editing efficiency and product purity by deep learning

Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423 pegRNAs on a highly diverse set of 13,349 human pathogenic mutations that include base substitutions, insertions and deletions. Based on this dataset, we identified sequence context features that influence prime editing and trained PRIDICT (prime editing guide prediction), an attention-based bidirectional recurrent neural network. PRIDICT reliably predicts editing rates for all small-sized genetic changes with a Spearman's R of 0.85 and 0.78 for intended and unintended edits, respectively. We validated PRIDICT on endogenous editing sites as well as an external dataset and showed that pegRNAs with high (>70) versus low (<70) PRIDICT scores showed substantially increased prime editing efficiencies in different cell types in vitro (12-fold) and in hepatocytes in vivo (tenfold), highlighting the value of PRIDICT for basic and for translational research applications.

Technological Convergence: Highlighting the Power of CRISPR Single-Cell Perturbation Toolkit for Functional Interrogation of Enhancers

Higher eukaryotic enhancers, as a major class of regulatory elements, play a crucial role in the regulation of gene expression. Over the last decade, the development of sequencing technologies has flooded researchers with transcriptome-phenotype data alongside emerging candidate regulatory elements. Since most methods can only provide hints about enhancer function, there have been attempts to develop experimental and computational approaches that can bridge the gap in the causal relationship between regulatory regions and phenotypes. The coupling of two state-of-the-art technologies, also referred to as crisprQTL, has emerged as a promising high-throughput toolkit for addressing this question. This review provides an overview of the importance of studying enhancers, the core molecular foundation of crisprQTL, and recent studies utilizing crisprQTL to interrogate enhancer-phenotype correlations. Additionally, we discuss computational methods currently employed for crisprQTL data analysis. We conclude by pointing out common challenges, making recommendations, and looking at future prospects, with the aim of providing researchers with an overview of crisprQTL as an important toolkit for studying enhancers.

An optimized SpCas9 high-fidelity variant for direct protein delivery

Electroporation of the Cas9 ribonucleoprotein (RNP) complex offers the advantage of preventing off-target cleavages and potential immune responses produced by long-term expression of the nuclease. Nevertheless, the majority of engineered high-fidelity Streptococcus pyogenes Cas9 (SpCas9) variants are less active than the wild-type enzyme and are not compatible with RNP delivery. Building on our previous studies on evoCas9, we developed a high-fidelity SpCas9 variant suitable for RNP delivery. The editing efficacy and precision of the recombinant high-fidelity Cas9 (rCas9HF), characterized by the K526D substitution, was compared with the R691A mutant (HiFi Cas9), which is currently the only available high-fidelity Cas9 that can be used as an RNP. The comparative analysis was extended to gene substitution experiments where the two high fidelities were used in combination with a DNA donor template, generating different ratios of non-homologous end joining (NHEJ) versus homology-directed repair (HDR) for precise editing. The analyses revealed a heterogeneous efficacy and precision indicating different targeting capabilities between the two variants throughout the genome. The development of rCas9HF, characterized by an editing profile diverse from the currently used HiFi Cas9 in RNP electroporation, increases the genome editing solutions for the highest precision and efficient applications.

Deep learning in CRISPR-Cas systems: a review of recent studies

In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019-2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area.

A landing pad system for multicopy gene integration in Issatchenkia orientalis

The robust nature of the non-conventional yeast Issatchenkia orientalis allows it to grow under highly acidic conditions and therefore, has gained increasing interest in producing organic acids using a variety of carbon sources. Recently, the development of a genetic toolbox for I. orientalis, including an episomal plasmid, characterization of multiple promoters and terminators, and CRISPR-Cas9 tools, has eased the metabolic engineering efforts in I. orientalis. However, multiplex engineering is still hampered by the lack of efficient multicopy integration tools. To facilitate the construction of large, complex metabolic pathways by multiplex CRISPR-Cas9-mediated genome editing, we developed a bioinformatics pipeline to identify and prioritize genome-wide intergenic loci and characterized 47 gRNAs located in 21 intergenic regions. These loci are screened for guide RNA cutting efficiency, integration efficiency of a gene cassette, the resulting cellular fitness, and GFP expression level. We further developed a landing pad system using components from these well-characterized loci, which can aid in the integration of multiple genes using single guide RNA and multiple repair templates of the user's choice. We have demonstrated the use of the landing pad for simultaneous integrations of 2, 3, 4, or 5 genes to the target loci with efficiencies greater than 80%. As a proof of concept, we showed how the production of 5-aminolevulinic acid can be improved by integrating five copies of genes at multiple sites in one step. We have further demonstrated the efficiency of this tool by constructing a metabolic pathway for succinic acid production by integrating five gene expression cassettes using a single guide RNA along with five different repair templates, leading to the production of 9 g/L of succinic acid in batch fermentations. This study demonstrates the effectiveness of a single gRNA-mediated CRISPR platform to build complex metabolic pathways in a non-conventional yeast. This landing pad system will be a valuable tool for the metabolic engineering of I. orientalis.

Position-dependent sequence motif preferences of SpCas9 are largely determined by scaffold-complementary spacer motifs

Streptococcus pyogenes Cas9 (SpCas9) nuclease exhibits considerable position-dependent sequence preferences. The reason behind these preferences is not well understood and is difficult to rationalise, since the protein establishes interactions with the target-spacer duplex in a sequence-independent manner. We revealed here that intramolecular interactions within the single guide RNA (sgRNA), between the spacer and the scaffold, cause most of these preferences. By using in cellulo and in vitro SpCas9 activity assays with systematically designed spacer and scaffold sequences and by analysing activity data from a large SpCas9 sequence library, we show that some long (>8 nucleotides) spacer motifs, that are complementary to the RAR unit of the scaffold, interfere with sgRNA loading, and that some motifs of more than 4 nucleotides, that are complementary to the SL1 unit, inhibit DNA binding and cleavage. Furthermore, we show that intramolecular interactions are present in the majority of the inactive sgRNA sequences of the library, suggesting that they are the most important intrinsic determinants of the activity of the SpCas9 ribonucleoprotein complex. We also found that in pegRNAs, sequences at the 3' extension of the sgRNA that are complementary to the SL2 unit are also inhibitory to prime editing, but not to the nuclease activity of SpCas9.

Single-swap editing for the correction of common Duchenne muscular dystrophy mutations

Duchenne muscular dystrophy (DMD) is a fatal X-linked recessive disease of progressive muscle weakness and wasting caused by the absence of dystrophin protein. Current gene therapy approaches using antisense oligonucleotides require lifelong dosing and have limited efficacy in restoring dystrophin production. A gene editing approach could permanently correct the genome and restore dystrophin protein expression. Here, we describe single-swap editing, in which an adenine base editor edits a single base pair at a splice donor site or splice acceptor site to enable exon skipping or reframing. In human induced pluripotent stem cell-derived cardiomyocytes, we demonstrate that single-swap editing can enable beneficial exon skipping or reframing for the three most therapeutically relevant exons-DMD exons 45, 51, and 53-which could be beneficial for 30% of all DMD patients. Furthermore, an adeno-associated virus delivery method for base editing components can efficiently restore dystrophin production locally and systemically in skeletal and cardiac muscles of a DMD mouse model containing a deletion of Dmd exon 44. Our studies demonstrate single-swap editing as a potential gene editing therapy for common DMD mutations.

Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models

Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes. Here by using feature attribution methods, we show that the quality of the explanations can be increased by leveraging ensembles of explainable machine-learning models. We applied the approach to a dataset of 133 combinations of 46 anticancer drugs tested in ex vivo tumour samples from 285 patients with acute myeloid leukaemia and uncovered a haematopoietic-differentiation signature underlying drug combinations with therapeutic synergy. Ensembles of machine-learning models trained to predict drug combination synergies on the basis of gene-expression data may improve the feature attribution quality of complex machine-learning models.

Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy

Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (m⁵U). To fill this knowledge gap, we first performed a comparative evaluation of various commonly used DL models for epigenetic studies with the help of autoBioSeqpy. We identified optimal architectural variations for m⁵U site classification, optimizing the layer depth and neuron width. Second, we used this knowledge to develop Deepm5U, an improved convolutional-recurrent neural network that accurately predicts m⁵U sites from RNA sequences. We successfully applied Deepm5U to transcriptomewide m⁵U profiling data across different sequencing technologies and cell types. Third, we showed that the techniques for interpreting deep neural networks, including LayerUMAP and DeepSHAP, can provide important insights into the internal operation and behavior of models. Overall, we offered practical guidance for the development, benchmark, and analysis of deep learning models when designing new algorithms for RNA modifications.

Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities

Life science studies involving clustered regularly interspaced short palindromic repeat (CRISPR) editing generally apply the best-performing guide RNA (gRNA) for a gene of interest. Computational models are combined with massive experimental quantification on synthetic gRNA-target libraries to accurately predict gRNA activity and mutational patterns. However, the measurements are inconsistent between studies due to differences in the designs of the gRNA-target pair constructs, and there has not yet been an integrated investigation that concurrently focuses on multiple facets of gRNA capacity. In this study, we analyzed the DNA double-strand break (DSB)-induced repair outcomes and measured SpCas9/gRNA activities at both matched and mismatched locations using 926,476 gRNAs covering 19,111 protein-coding genes and 20,268 non-coding genes. We developed machine learning models to forecast the on-target cleavage efficiency (AIdit_ON), off-target cleavage specificity (AIdit_OFF), and mutational profiles (AIdit_DSB) of SpCas9/gRNA from a uniformly collected and processed dataset by deep sampling and massively quantifying gRNA capabilities in K562 cells. Each of these models exhibited superlative performance in predicting SpCas9/gRNA activities on independent datasets when benchmarked with previous models. A previous unknown parameter was also empirically determined regarding the "sweet spot" in the size of datasets used to establish an effective model to predict gRNA capabilities at a manageable experimental scale. In addition, we observed cell type-specific mutational profiles and were able to link nucleotidylexotransferase as the key factor driving these outcomes. These massive datasets and deep learning algorithms have been implemented into the user-friendly web service http://crispr-aidit.com to evaluate and rank gRNAs for life science studies.

Massively parallel evaluation and computational prediction of the activities and specificities of 17 small Cas9s

Recently, various small Cas9 orthologs and variants have been reported for use in in vivo delivery applications. Although small Cas9s are particularly suited for this purpose, selecting the most optimal small Cas9 for use at a specific target sequence continues to be challenging. Here, to this end, we have systematically compared the activities of 17 small Cas9s for thousands of target sequences. For each small Cas9, we have characterized the protospacer adjacent motif and determined optimal single guide RNA expression formats and scaffold sequence. High-throughput comparative analyses revealed distinct high- and low-activity groups of small Cas9s. We also developed DeepSmallCas9, a set of computational models predicting the activities of the small Cas9s at matched and mismatched target sequences. Together, this analysis and these computational models provide a useful guide for researchers to select the most suitable small Cas9 for specific applications.

Prediction of efficiencies for diverse prime editing systems in multiple cell types

Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors (PEs) to generate a desired edit under various experimental conditions. Here, we evaluated prime editing efficiencies for a total of 338,996 pairs of pegRNAs including 3,979 epegRNAs and target sequences in an error-free manner. These datasets enabled a systematic determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.

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.

Overcoming the Limitations of CRISPR-Cas9 Systems in Saccharomyces cerevisiae: Off-Target Effects, Epigenome, and Mitochondrial Editing

Modification of the genome of the yeast Saccharomyces cerevisiae has great potential for application in biological research and biotechnological advancements, and the CRISPR-Cas9 system has been increasingly employed for these purposes. The CRISPR-Cas9 system enables the precise and simultaneous modification of any genomic region of the yeast to a desired sequence by altering only a 20-nucleotide sequence within the guide RNA expression constructs. However, the conventional CRISPR-Cas9 system has several limitations. In this review, we describe the methods that were developed to overcome these limitations using yeast cells. We focus on three types of developments: reducing the frequency of unintended editing to both non-target and target sequences in the genome, inducing desired changes in the epigenetic state of the target region, and challenging the expansion of the CRISPR-Cas9 system to edit genomes within intracellular organelles such as mitochondria. These developments using yeast cells to overcome the limitations of the CRISPR-Cas9 system are a key factor driving the advancement of the field of genome editing.

Recent Advances in Genome-Editing Technology with CRISPR/Cas9 Variants and Stimuli-Responsive Targeting Approaches within Tumor Cells: A Future Perspective of Cancer Management

The innovative advances in transforming clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR/Cas9) into different variants have taken the art of genome-editing specificity to new heights. Allosteric modulation of Cas9-targeting specificity by sgRNA sequence alterations and protospacer adjacent motif (PAM) modifications have been a good lesson to learn about specificity and activity scores in different Cas9 variants. Some of the high-fidelity Cas9 variants have been ranked as Sniper-Cas9, eSpCas9 (1.1), SpCas9-HF1, HypaCas9, xCas9, and evoCas9. However, the selection of an ideal Cas9 variant for a given target sequence remains a challenging task. A safe and efficient delivery system for the CRISPR/Cas9 complex at tumor target sites faces considerable challenges, and nanotechnology-based stimuli-responsive delivery approaches have significantly contributed to cancer management. Recent innovations in nanoformulation design, such as pH, glutathione (GSH), photo, thermal, and magnetic responsive systems, have modernized the art of CRISPR/Cas9 delivery approaches. These nanoformulations possess enhanced cellular internalization, endosomal membrane disruption/bypass, and controlled release. In this review, we aim to elaborate on different CRISPR/Cas9 variants and advances in stimuli-responsive nanoformulations for the specific delivery of this endonuclease system. Furthermore, the critical constraints of this endonuclease system on clinical translations towards the management of cancer and prospects are described.

Improving the on-target activity of high-fidelity Cas9 editors by combining rational design and random mutagenesis

Genomic and post-genomic editors based on CRISPR/Cas systems are widely used in basic research and applied sciences, including human gene therapy. Most genome editing tools are based on the CRISPR/Cas9 type IIA system from Streptococcus pyogenes. Unfortunately, a number of drawbacks have hindered its application in therapeutic approaches, the most serious of which is the relatively high level of off-targets. To overcome this obstacle, various high-fidelity Cas9 variants have been created. However, they show reduced on-target activity compared to wild-type Cas9 possibly due to increased sensitivity to eukaryotic chromatin. Here, we combined a rational approach with random mutagenesis to create a set of new Cas9 variants showing high specificity and increased activity in Saccharomyces cerevisiae yeast. Moreover, a novel mutation in the PAM (protospacer adjacent motif)-interacting Cas9 domain was found, which increases the on-target activity of high-fidelity Cas9 variants while retaining their high specificity. The obtained data suggest that this mutation acts by weakening the eukaryotic chromatin barrier for Cas9 and rearranging the RuvC active center. Improved Cas9 variants should further advance genome and post-genome editing technologies. KEY POINTS: • D147Y and P411T mutations increase the activity of high-fidelity Cas9 variants. • The new L1206P mutation further increases the activity of high-fidelity Cas9 variants. • The L1206P mutation weakens the chromatin barrier for Cas9 editors.

Strategies and Methods for Improving the Efficiency of CRISPR/Cas9 Gene Editing in Plant Molecular Breeding

Following recent developments and refinement, CRISPR-Cas9 gene-editing technology has become increasingly mature and is being widely used for crop improvement. The application of CRISPR/Cas9 enables the generation of transgene-free genome-edited plants in a short period and has the advantages of simplicity, high efficiency, high specificity, and low production costs, which greatly facilitate the study of gene functions. In plant molecular breeding, the gene-editing efficiency of the CRISPR-Cas9 system has proven to be a key step in influencing the effectiveness of molecular breeding, with improvements in gene-editing efficiency recently becoming a focus of reported scientific research. This review details strategies and methods for improving the efficiency of CRISPR/Cas9 gene editing in plant molecular breeding, including Cas9 variant enzyme engineering, the effect of multiple promoter driven Cas9, and gRNA efficient optimization and expression strategies. It also briefly introduces the optimization strategies of the CRISPR/Cas12a system and the application of BE and PE precision editing. These strategies are beneficial for the further development and optimization of gene editing systems in the field of plant molecular breeding.

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.

Recent advances in CRISPR-based genome editing technology and its applications in cardiovascular research

The rapid development of genome editing technology has brought major breakthroughs in the fields of life science and medicine. In recent years, the clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing toolbox has been greatly expanded, not only with emerging CRISPR-associated protein (Cas) nucleases, but also novel applications through combination with diverse effectors. Recently, transposon-associated programmable RNA-guided genome editing systems have been uncovered, adding myriads of potential new tools to the genome editing toolbox. CRISPR-based genome editing technology has also revolutionized cardiovascular research. Here we first summarize the advances involving newly identified Cas orthologs, engineered variants and novel genome editing systems, and then discuss the applications of the CRISPR-Cas systems in precise genome editing, such as base editing and prime editing. We also highlight recent progress in cardiovascular research using CRISPR-based genome editing technologies, including the generation of genetically modified in vitro and animal models of cardiovascular diseases (CVD) as well as the applications in treating different types of CVD. Finally, the current limitations and future prospects of genome editing technologies are discussed.

Sniper2L is a high-fidelity Cas9 variant with high activity

Although several high-fidelity SpCas9 variants have been reported, it has been observed that this increased specificity is associated with reduced on-target activity, limiting the applications of the high-fidelity variants when efficient genome editing is required. Here, we developed an improved version of Sniper-Cas9, Sniper2L, which represents an exception to this trade-off trend as it showed higher specificity with retained high activity. We evaluated Sniper2L activities at a large number of target sequences and developed DeepSniper, a deep learning model that can predict the activity of Sniper2L. We also confirmed that Sniper2L can induce highly efficient and specific editing at a large number of target sequences when it is delivered as a ribonucleoprotein complex. Mechanically, the high specificity of Sniper2L originates from its superior ability to avoid unwinding a target DNA containing even a single mismatch. We envision that Sniper2L will be useful when efficient and specific genome editing is required.

TransCrispr: Transformer Based Hybrid Model for Predicting CRISPR/Cas9 Single Guide RNA Cleavage Efficiency

CRISPR/Cas9 is a widely used genome editing tool for site-directed modification of deoxyribonucleic acid (DNA) nucleotide sequences. However, how to accurately predict and evaluate the on- and off-target effects of single guide RNA (sgRNA) is one of the key problems for CRISPR/Cas9 system. Using computational methods to obtain high cell-specific sensitivity and specificity is a prerequisite for the optimal design of sgRNAs. Inspired by the work of predecessors, we found that sgRNA on-target knockout efficacy was not only related to the original sequence but also affected by important biological features. Hence, we introduce a novel approach called TransCrispr, which integrates Transformer and convolutional neural network (CNN) architecture to predict sgRNA knockout efficacy. Firstly, we encode the sequence data and send the transformed sgRNA sequence, positional information, and biological features into the network as input. Then, the convolutional neural network will automatically learn an appropriate feature representation for the sgRNA sequence and combine it with the positional information for self-attention learning of the Transformer. Finally, a regression score is generated by predicting biological features. Experiments on seven public datasets illustrate that TransCrispr outperforms state-of-the-art methods in terms of prediction accuracy and generalization ability.

Optimized prime editing in monocot plants using PlantPegDesigner and engineered plant prime editors (ePPEs)

Prime editors (PEs), which can install desired base edits without donor DNA or double-strand breaks, have been used in plants and can, in principle, accelerate crop improvement and breeding. However, their editing efficiency in plants is generally low. Optimizing the prime editing guide RNA (pegRNA) by designing the sequence on the basis of melting temperature, using dual-pegRNAs and engineering PEs have all been shown to enhance PE efficiency. In addition, an automated pegRNA design platform, PlantPegDesigner, has been developed on the basis of rice prime editing experimental data. In this protocol, we present detailed protocols for designing and optimizing pegRNAs using PlantPegDesigner, constructing engineered plant PE vectors with enhanced editing efficiency for prime editing, evaluating prime editing efficiencies using a reporter system and comparing the effectiveness and byproducts of PEs by deep amplicon sequencing. Using this protocol, researchers can construct optimized pegRNAs for prime editing in 4-7 d and obtain prime-edited rice or wheat plants within 3 months.

The PROTECTOR strategy employs dCas orthologs to sterically shield off-target sites from CRISPR/Cas activity

Off-target mutagenesis of CRISPR/Cas systems must be solved to facilitate safe gene therapy. Here, we report a novel approach, termed "PROTECTOR", to shield known off-target sites by directing the binding of an orthologous nuclease-dead Cas protein to the off-target site to sterically interfere with Cas activity. We show that this method reduces off-target mutation rates of two well-studied guide RNAs without compromising on-target activity and that it can be used in combination with high-fidelity Cas enzymes to further reduce off-target editing. This expands the suite of off-target mitigation strategies and offers an ability to protect off-target sites even when their sequences are fully identical to target sites.

Practical Application of the 3Rs in Rodent Transgenesis

The principles of the 3Rs (replace, reduce, refine), as originally published by Russell and Burch, are internationally acclaimed guidelines for meeting ethical and welfare standards in animal experimentation. Genome manipulation is a standard technique in biomedical research and beyond. The goal of this chapter is to give practical advice on the implementation of the 3Rs in laboratories generating genetically modified rodents. We cover 3R aspects from the planning phase through operations of the transgenic unit to the final genome-manipulated animals. The focus of our chapter is on an easy-to-use, concise protocol that is close to a checklist. While we focus on mice, the proposed methodological concepts can be easily adapted for the manipulation of other sentient animals.

Expanding the Scope of Bacterial CRISPR Activation with PAM-Flexible dCas9 Variants

CRISPR-Cas transcriptional tools have been widely applied for programmable regulation of complex biological networks. In comparison to eukaryotic systems, bacterial CRISPR activation (CRISPRa) has stringent target site requirements for effective gene activation. While genes may not always have an NGG protospacer adjacent motif (PAM) at the appropriate position, PAM-flexible dCas9 variants can expand the range of targetable sites. Here we systematically evaluate a panel of PAM-flexible dCas9 variants for their ability to activate bacterial genes. We observe that dxCas9-NG provides a high dynamic range of gene activation for sites with NGN PAMs while dSpRY permits modest activity across almost any PAM. Similar trends were observed for heterologous and endogenous promoters. For all variants tested, improved PAM-flexibility comes with the trade-off that CRISPRi-mediated gene repression becomes less effective. Weaker CRISPR interference (CRISPRi) gene repression can be partially rescued by expressing multiple sgRNAs to target many sites in the gene of interest. Our work provides a framework to choose the most effective dCas9 variant for a given set of gene targets, which will further expand the utility of CRISPRa/i gene regulation in bacterial systems.

Constructing next-generation CRISPR-Cas tools from structural blueprints

Clustered regularly interspaced short palindromic repeats - CRISPR-associated protein (CRISPR-Cas) systems are a critical component of the bacterial adaptive immune response. Since the discovery that they can be reengineered as programmable RNA-guided nucleases, there has been significant interest in using these systems to perform diverse and precise genetic manipulations. Here, we outline recent advances in the mechanistic understanding of CRISPR-Cas9, how these findings have been leveraged in the rational redesign of Cas9 variants with altered activities, and how these novel tools can be exploited for biotechnology and therapeutics. We also discuss the potential of the ubiquitous, yet often-overlooked, multisubunit CRISPR effector complexes for large-scale genomic deletions. Furthermore, we highlight how future structural studies will bolster these technologies.