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

Engineering adeno-associated viral vectors for CRISPR/Cas based in vivo therapeutic genome editing

The recent approval of the first gene editing therapy for sickle cell disease and transfusion-dependent beta-thalassemia by the U.S. Food and Drug Administration (FDA) demonstrates the immense potential of CRISPR (clustered regularly interspaced short palindromic repeats) technologies to treat patients with genetic disorders that were previously considered incurable. While significant advancements have been made with ex vivo gene editing approaches, the development of in vivo CRISPR/Cas gene editing therapies has not progressed as rapidly due to significant challenges in achieving highly efficient and specific in vivo delivery. Adeno-associated viral (AAV) vectors have shown great promise in clinical trials as vehicles for delivering therapeutic transgenes and other cargos but currently face multiple limitations for effective delivery of gene editing machineries. This review elucidates these challenges and highlights the latest engineering strategies aimed at improving the efficiency, specificity, and safety profiles of AAV-packaged CRISPR/Cas systems (AAV-CRISPR) to enhance their clinical utility.

Nickase fidelity drives EvolvR-mediated diversification in mammalian cells

In vivo genetic diversifiers have previously enabled efficient searches of genetic variant fitness landscapes for continuous directed evolution. However, existing genomic diversification modalities for mammalian genomic loci exclusively rely on deaminases to generate transition mutations within target loci, forfeiting access to most missense mutations. Here, we engineer CRISPR-guided error-prone DNA polymerases (EvolvR) to diversify all four nucleotides within genomic loci in mammalian cells. We demonstrate that EvolvR generates both transition and transversion mutations throughout a mutation window of at least 40 bp and implement EvolvR to evolve previously unreported drug-resistant MAP2K1 variants via substitutions not achievable with deaminases. Moreover, we discover that the nickase's mismatch tolerance limits EvolvR's mutation window and substitution biases in a gRNA-specific fashion. To compensate for gRNA-to-gRNA variability in mutagenesis, we maximize the number of gRNA target sequences by incorporating a PAM-flexible nickase into EvolvR. Finally, we find a strong correlation between predicted free energy changes underlying R-loop formation and EvolvR's performance using a given gRNA. The EvolvR system diversifies all four nucleotides to enable the evolution of mammalian cells, while nuclease and gRNA-specific properties underlying nickase fidelity can be engineered to further enhance EvolvR's mutation rates.

Highly parallel profiling of the activities and specificities of Cas12a variants in human cells

Several Cas12a variants have been developed to broaden its targeting range, improve the gene editing specificity or the efficiency. However, selecting the appropriate Cas12a among the many orthologs for a given target sequence remains difficult. Here, we perform high-throughput analyses to evaluate the activity and compatibility with specific PAMs of 24 Cas12a variants and develop deep learning models for these Cas12a variants to predict gene editing activities at target sequences of interest. Furthermore, we reveal and enhance the truncation in the integrated tag sequence that may hinder off-targeting detection for Cas12a by GUIDE-seq. This enhanced system, which we term enGUIDE-seq, is used to evaluate and compare the off-targeting and translocations of these Cas12a variants.

Recent advances in therapeutic gene-editing technologies

The advent of gene-editing technologies, particularly CRISPR-based systems, has revolutionized the landscape of biomedical research and gene therapy. Ongoing research in gene editing has led to the rapid iteration of CRISPR technologies, such as base and prime editors, enabling precise nucleotide changes without the need for generating harmful double-strand breaks (DSBs). Furthermore, innovations such as CRISPR fusion systems with DNA recombinases, DNA polymerases, and DNA ligases have expanded the size limitations for edited sequences, opening new avenues for therapeutic development. Beyond the CRISPR system, mobile genetic elements (MGEs) and epigenetic editors are emerging as efficient alternatives for precise large insertions or stable gene manipulation in mammalian cells. These advances collectively set the stage for next-generation gene therapy development. This review highlights recent developments of genetic and epigenetic editing tools and explores preclinical innovations poised to advance the field.

Improving adenine base editing precision by enlarging the recognition domain of CRISPR-Cas9

Domain expansion contributes to diversification of RNA-guided-endonucleases including Cas9. However, it remains unclear how REC domain expansion could benefit Cas9. In this study, we identify an insertion spot that is compatible with large REC insertion and succeeds in enlarging the non-catalytic REC domain of Streptococcus pyogenes Cas9. The natural-evolution-like giant SpCas9 (GS-Cas9) is created and shows substantially improved editing precision. We further discover that enlarging the REC domain could enable regulation of the N-terminal adenine deaminase TadA8e tethered to the Cas9 scaffold, which contributes to substantially reducing unexpected editing and improving the precision of the adenine base editor ABE8e. We provide proof of concept for evolution-inspired expansion of Cas9 and offer an alternative solution for optimizing gene editors. Our study also indicates a vast potential for engineering the topological malleability of RNA-guided endonucleases and base editors.

Integrating machine learning and genome editing for crop improvement

Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.

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.

Accurate top protein variant discovery via low-N pick-and-validate machine learning

A strategy to obtain the greatest number of best-performing variants with least amount of experimental effort over the vast combinatorial mutational landscape would have enormous utility in boosting resource producibility for protein engineering. Toward this goal, we present a simple and effective machine learning-based strategy that outperforms other state-of-the-art methods. Our strategy integrates zero-shot prediction and multi-round sampling to direct active learning via experimenting with only a few predicted top variants. We find that four rounds of low-N pick-and-validate sampling of 12 variants for machine learning yielded the best accuracy of up to 92.6% in selecting the true top 1% variants in combinatorial mutant libraries, whereas two rounds of 24 variants can also be used. We demonstrate our strategy in successfully discovering high-performance protein variants from diverse families including the CRISPR-based genome editors, supporting its generalizable application for solving protein engineering tasks. A record of this paper's transparent peer review process is included in the supplemental information.

CRISPR enables sustainable cereal production for a greener future

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become the most important tool for targeted genome editing in many plant and animal species over the past decade. The CRISPR/Cas9 technology has also sparked a flood of applications and technical advancements in genome editing in the key cereal crops, including rice, wheat, maize, and barley. Here, we review advanced uses of CRISPR/Cas9 and derived systems in genome editing of cereal crops to enhance a variety of agronomically important features. We also highlight new technological advances for delivering preassembled Cas9-gRNA ribonucleoprotein (RNP)-editing systems, multiplex editing, gain-of-function strategies, the use of artificial intelligence (AI)-based tools, and combining CRISPR with novel speed breeding (SB) and vernalization strategies.