Systems-Level Modeling for CRISPR-Based Metabolic Engineering.

The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.

Impact of predictive selection of LbCas12a CRISPR RNAs upon on- and off-target editing rates in soybean.

Clustered regularly interspaced short palindromic repeats (CRISPR) technology has revolutionized creating targeted genetic variation in crops. Although CRISPR enzymes have been reported to have high sequence-specificity, careful design of the editing reagents can also reduce unintended edits at highly homologous sites. This work details the first large-scale study of the heritability of on-target edits and the rate of edits at off-target sites in soybean (Glycine max), assaying ~700 T1 plants each resulting from transformation with LbCas12a constructs containing CRISPR RNAs (crRNAs) predicted to be either "unique" with no off-target sites or "promiscuous" with >10 potential off-targets in the soybean genome. Around 80% of the on-target edits observed in T0 plants were inherited in the T1 generation, and ~49% of the total observed on-target edits in T1 were not observed at T0, indicating continued activity of LbCas12a throughout the life cycle of the plant. In planta editing at off-target sites was observed for the Promiscuous but not the Unique crRNA. Examination of the edited off-target sites revealed that LbCas12a was highly tolerant to mismatches between the crRNA and target site in bases 21-23 relative to the start of the protospacer, but even a single mismatch in the first 20 nt drastically reduced the editing rate. In addition, edits at off-target sites have lower inheritance rates than on-target edits, suggesting that they occur later in the plant's lifecycle. Plants with a desired on-target edit and no off-target edits could be identified in the T1 generation for 100% of the T0 plants edited with the Unique crRNA compared with the 65% of T0 plants edited with the Promiscuous crRNA. This confirms that proper crRNA selection can reduce or eliminate off-target editing. Even when potential off-target sites are predicted, plants containing only the intended edits can still be identified and propagated.

Compartmentalized CRISPR Reactions (CCR) for High-Throughput Screening of Guide RNA Potency and Specificity.

CRISPR ribonucleoproteins (RNPs) use a variable segment in their guide RNA (gRNA) called a spacer to determine the DNA sequence at which the effector protein will exhibit nuclease activity and generate target-specific genetic mutations. However, nuclease activity with different gRNAs can vary considerably in a spacer sequence-dependent manner that can be difficult to predict. While computational tools are helpful in predicting a CRISPR effector's activity and/or potential for off-target mutagenesis with different gRNAs, individual gRNAs must still be validated in vitro prior to their use. Here, the study presents compartmentalized CRISPR reactions (CCR) for screening large numbers of spacer/target/off-target combinations simultaneously in vitro for both CRISPR effector activity and specificity by confining the complete CRISPR reaction of gRNA transcription, RNP formation, and CRISPR target cleavage within individual water-in-oil microemulsions. With CCR, large numbers of the candidate gRNAs (output by computational design tools) can be immediately validated in parallel, and the study shows that CCR can be used to screen hundreds of thousands of extended gRNA (x-gRNAs) variants that can completely block cleavage at off-target sequences while maintaining high levels of on-target activity. It is expected that CCR can help to streamline the gRNA generation and validation processes for applications in biological and biomedical research.

In Silico Design of gRNA for CRISPR/Cas9-Mediated Gene Knockout.

CRISPR/Cas9 stands as a revolutionary and versatile gene editing technology. At its core, the Cas9 DNA endonuclease is guided with precision by a specifically designed single-guide RNA (gRNA). This guidance system facilitates the introduction of double-stranded breaks (DSBs) within the DNA. Subsequent imprecise repairs, mainly through the non-homologous end-joining (NHEJ) pathway, yield insertions or deletions, resulting in frameshift mutations. These mutations are instrumental in achieving the successful knockout of the target gene. In this chapter, we describe all necessary steps to create and design a gRNA for a gene knockout to a target gene before to transfer it to a target plant.

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

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

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions.

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

CRISPR-Cas mediated genome engineering of cyanobacteria.

Over the past decade, several cyanobacterial strains have emerged as exciting model systems for unraveling important biological process like photosynthesis and nitrogen fixation. In parallel, novel strains are being developed as platforms for production of various value-added products. To meet either of these goals, synthetic biology tool development has been prioritized, and among many such tools, CRISPR-mediated genome editing tools distinctly hold the potential to revolutionize cyanobacterial research. This chapter reviews our current understanding of the existing and emerging CRISPR-based technologies and their potential application for advanced genome editing in cyanobacterial strains of interest. CRISPR-based tools have gained traction in cyanobacterial research for their ability to target the polyploid genomes in these organisms and generate fully segregated mutants in a remarkably short time. We discuss the native cyanobacterial CRISPR system and the promise they hold for use as precision tools for cyanobacterial genome editing. We elaborate the methodologies for the development of CRISPR-based markerless mutants in cyanobacteria as well as discuss strategies for large scale, regulated genome silencing with CRISPRi. We also highlight some of the emerging CRISPR tools that have shown promise in other prokaryotic and eukaryotic systems but are yet to be adapted for cyanobacterial research.

BEtarget: A versatile web-based tool to design guide RNAs for base editing in plants.

CRISPR-dependent base editors enable direct nucleotide conversion without the introduction of double-strand DNA break or donor DNA template, thus expanding the CRISPR toolbox for genetic manipulation. However, designing guide RNAs (gRNAs) for base editors to enable gene correction or inactivation is more complicated than using the CRISPR system for gene disruption. Here, we present a user-friendly web tool named BEtarget dedicated to the design of gRNA for base editing. It is currently supported by 46 plant reference genomes and 5 genomes of non-plant model organisms. BEtarget supports the design of gRNAs with different types of protospacer adjacent motifs (PAM) and integrates various functions, including automatic identification of open reading frame, prediction of potential off-target sites, annotation of codon change, and assessment of gRNA quality. Moreover, the program provides an interactive interface for users to selectively display information about the desired target sites. In brief, we have developed a flexible and versatile web-based tool to simplify complications associated with the design of base editing technology. BEtarget is freely accessible at https://skl.scau.edu.cn/betarget/.

Corrigendum: Strategies for Efficient Gene Editing in Protoplasts of Solanum tuberosum Theme: Determining gRNA Efficiency Design by Utilizing Protoplast (Research).

[This corrects the article DOI: 10.3389/fgeed.2021.795644.].

Systematic exploration of optimized base editing gRNA design and pleiotropic effects with BExplorer.

Base editing technology is being increasingly applied in genome engineering, but the current strategy for designing guide RNA (gRNA) relies substantially on empirical experience rather than a dependable and efficient in silico design. Furthermore, the pleiotropic effect of base editing on disease treatment remains unexplored, which prevents its further clinical usage. Here, we presented BExplorer, an integrated and comprehensive computational pipeline to optimize the design of gRNAs for 26 existing types of base editors in silico. Using BExplorer, we described its results for two types of mainstream base editors, BE3 and ABE7.10, and evaluated the pleiotropic effect of the corresponding base editing loci. BExplorer revealed 524 and 900 editable pathogenic Single Nucleotide Polymorphism (SNP) loci in the human genome together with the selected optimized gRNAs for BE3 and ABE7.10, respectively. In addition, the impact of 707 edited pathogenic SNP loci following base editing on 151 diseases was systematically explored by revealing their pleiotropic effects, indicating that base editing should be carefully utilized given the potential pleiotropic effects. Collectively, the systematic exploration of optimized base editing gRNA design and the corresponding pleiotropic effects with BExplorer provides a computational basis for applying base editing in disease treatment.

CROPSR: an automated platform for complex genome-wide CRISPR gRNA design and validation.

BACKGROUND: CRISPR/Cas9 technology has become an important tool to generate targeted, highly specific genome mutations. The technology has great potential for crop improvement, as crop genomes are tailored to optimize specific traits over generations of breeding. Many crops have highly complex and polyploid genomes, particularly those used for bioenergy or bioproducts. The majority of tools currently available for designing and evaluating gRNAs for CRISPR experiments were developed based on mammalian genomes that do not share the characteristics or design criteria for crop genomes. RESULTS: We have developed an open source tool for genome-wide design and evaluation of gRNA sequences for CRISPR experiments, CROPSR. The genome-wide approach provides a significant decrease in the time required to design a CRISPR experiment, including validation through PCR, at the expense of an overhead compute time required once per genome, at the first run. To better cater to the needs of crop geneticists, restrictions imposed by other packages on design and evaluation of gRNA sequences were lifted. A new machine learning model was developed to provide scores while avoiding situations in which the currently available tools sometimes failed to provide guides for repetitive, A/T-rich genomic regions. We show that our gRNA scoring model provides a significant increase in prediction accuracy over existing tools, even in non-crop genomes. CONCLUSIONS: CROPSR provides the scientific community with new methods and a new workflow for performing CRISPR/Cas9 knockout experiments. CROPSR reduces the challenges of working in crops, and helps speed gRNA sequence design, evaluation and validation. We hope that the new software will accelerate discovery and reduce the number of failed experiments.

CRISPR genome editing using computational approaches: A survey.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of optimal gRNA and the selection of appropriate Cas enzymes. Numerous computational tools have been created in recent years to help researchers design the best gRNA for Clustered regularly interspaced short palindromic repeats researches. There are two approaches for designing an appropriate gRNA sequence (which targets our desired sites with high precision): experimental and predicting-based approaches. It is essential to reduce off-target sites when designing an optimal gRNA. Here we review both traditional and machine learning-based approaches for designing an appropriate gRNA sequence and predicting off-target sites. In this review, we summarize the key characteristics of all available tools (as far as possible) and compare them together. Machine learning-based tools and web servers are believed to become the most effective and reliable methods for predicting on-target and off-target activities of Clustered regularly interspaced short palindromic repeats in the future. However, these predictions are not so precise now and the performance of these algorithms -especially deep learning one's-depends on the amount of data used during training phase. So, as more features are discovered and incorporated into these models, predictions become more in line with experimental observations. We must concentrate on the creation of ideal gRNA and the choice of suitable Cas enzymes in order to make genome editing with Clustered regularly interspaced short palindromic repeats far more accurate and feasible.

Rational gRNA design based on transcription factor binding data.

The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system has become a standard tool in many genome engineering endeavors. The endonuclease-deficient version of Cas9 (dCas9) is also a powerful programmable tool for gene regulation. In this study, we made use of Saccharomyces cerevisiae transcription factor (TF) binding data to obtain a better understanding of the interplay between TF binding and binding of dCas9 fused to an activator domain, VPR. More specifically, we targeted dCas9-VPR toward binding sites of Gcr1-Gcr2 and Tye7 present in several promoters of genes encoding enzymes engaged in the central carbon metabolism. From our data, we observed an upregulation of gene expression when dCas9-VPR was targeted next to a TF binding motif, whereas a downregulation or no change was observed when dCas9 was bound on a TF motif. This suggests a steric competition between dCas9 and the specific TF. Integrating TF binding data, therefore, proved to be useful for designing guide RNAs for CRISPR interference or CRISPR activation applications.

Computational Design of gRNAs Targeting Genetic Variants Across HIV-1 Subtypes for CRISPR-Mediated Antiviral Therapy.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based HIV-1 genome editing has shown promising outcomes in in vitro and in vivo viral infection models. However, existing HIV-1 sequence variants have been shown to reduce CRISPR-mediated efficiency and induce viral escape. Two metrics, global patient coverage and global subtype coverage, were used to identify guide RNA (gRNA) sequences that account for this viral diversity from the perspectives of cross-patient and cross-subtype gRNA design, respectively. Computational evaluation using these parameters and over 3.6 million possible 20-bp sequences resulted in nine lead gRNAs, two of which were previously published. This analysis revealed the benefit and necessity of considering all sequence variants for gRNA design. Of the other seven identified novel gRNAs, two were of note as they targeted interesting functional regions. One was a gRNA predicted to induce structural disruption in the nucleocapsid binding site (Ψ), which holds the potential to stop HIV-1 replication during the viral genome packaging process. The other was a reverse transcriptase (RT)-targeting gRNA that was predicted to cleave the subdomain responsible for dNTP incorporation. CRISPR-mediated sequence edits were predicted to occur on critical residues where HIV-1 has been shown to develop resistance against antiretroviral therapy (ART), which may provide additional evolutionary pressure at the DNA level. Given these observations, consideration of broad-spectrum gRNAs and cross-subtype diversity for gRNA design is not only required for the development of generalizable CRISPR-based HIV-1 therapy, but also helps identify optimal target sites.

Strategies for Efficient Gene Editing in Protoplasts of Solanum tuberosum Theme: Determining gRNA Efficiency Design by Utilizing Protoplast (Research).

Potato, Solanum tuberosum is a highly diverse tetraploid crop. Elite cultivars are extremely heterozygous with a high prevalence of small length polymorphisms (indels) and single nucleotide polymorphisms (SNPs) within and between cultivars, which must be considered in CRISPR/Cas gene editing strategies and designs to obtain successful gene editing. In the present study, in-depth sequencing of the gene encoding glucan water dikinase (GWD) 1 and the downy mildew resistant 6 (DMR6-1) genes in the potato cultivars Saturna and Wotan, respectively, revealed both indels and a 1.3-2.8 higher SNP prevalence when compared to the heterozygous diploid RH genome sequence as expected for a tetraploid compared to a diploid. This complicates guide RNA (gRNA) and diagnostic PCR designs. At the same time, high editing efficiencies at the cell pool (protoplast) level are pivotal for achieving full allelic knock-out in tetraploids. Furthermore, high editing efficiencies reduce the downstream cumbersome and delicate ex-plant regeneration. Here, CRISPR/Cas ribonucleoprotein particles (RNPs) were delivered transiently to protoplasts by polyethylene glycol (PEG) mediated transformation. For each of GWD1 and the DMR6-1, 6-10 gRNAs were designed to target regions comprising the 5' and the 3' end of the two genes. Similar to other studies including several organisms, editing efficiency of the individual RNPs varied significantly, and some generated specific indel patterns. RNP's targeting the 5' end of GWD1 yielded significantly higher editing efficiency as compared to targeting the 3' end. For DMR6-1, such an effect was not seen. Simultaneously targeting each of the two target regions with two RNPs (multiplexing) yielded a clear positive synergistic effect on the total editing when targeting the 3' end of the GWD1 gene only. Multiplexing of the two genes, residing on different chromosomes, yielded no or a slightly negative effect on editing from the single or combined gRNA/RNPs. These initial findings may instigate much larger studies needed for facilitating and optimizing precision breeding in plants.

CRISPR Guide RNA Design Guidelines for Efficient Genome Editing.

The simple applicability and facile target programming of the CRISPR/Cas9-system abolish the major boundaries of previous genome editing tools, making it the tool of choice for generating site-specific genome alterations. Its versatility and efficacy have been demonstrated in various organisms; however, accurately predicting guide RNA efficiencies remains an organism-independent challenge. Thus, designing optimal guide RNAs is essential to maximize the experimental outcome. Here, we summarize the current knowledge for guide RNA design and highlight discrepancies between different experimental systems.

crispRdesignR: A Versatile Guide RNA Design Package in R for CRISPR/Cas9 Applications.

The success of CRISPR/Cas9 gene editing applications relies on the efficiency of the single guide RNA (sgRNA) used in conjunction with the Cas9 protein. Current sgRNA design software vary in the details they provide on sgRNA sequence efficiency and usually limit organism choice to a list of developer-selected species. The crispRdesignR package aims to address these limitations by providing comprehensive sequence features of the generated sgRNAs in a single program, which allows users to predict sgRNA efficiency and design sgRNA sequences for systems that currently do not have optimized efficiency scoring methods. crispRdesignR reports extensive information on all designed sgRNA sequences with robust off-target calling and annotation and can be run in a user-friendly graphical interface. The crispRdesignR package is implemented in R and has fully editable code for specialized purposes including sgRNA design in user-provided genomes. The package is platform independent and extendable, with its source code and documentation freely available at https://github.com/dylanbeeber/crispRdesignR.

Development of a DNA double-strand break-free base editing tool in Corynebacterium glutamicum for genome editing and metabolic engineering.

As a traditional amino acid producing bacterium, Corynebacterium glutamicum is a platform strain for production of various fine chemicals. Based on the CRISPR (Clustered regularly interspaced short palindromic repeats)-Cas9 system, gene editing tools that enable base conversion in the genome of C. glutamicum have been developed. However, some problems such as genomic instability caused by DNA double-strand break (DSB) and off-target effects need to be solved. In this study, a DSB-free single nucleotide genome editing system was developed by construction of a bi-directional base conversion tool TadA-dCas9-AID. This system includes cytosine base editors (CBEs): activation-induced cytidine deaminase (AID) and adenine deaminase (ABEs): tRNA adenosine deaminase (TadA), which can specifically target the gene through a 20-nt single guide RNA (sgRNA) and achieve the base conversion of C-T, C-G and A-G in the 28-bp editing window upstream of protospacer adjacent motif. Finally, as a proof-of-concept demonstration, the system was used to construct a mutant library of zwf gene in C. glutamicum S9114 genome to improve the production of a typical nutraceutical N-acetylglucosamine (GlcNAc). The GlcNAc titer of the mutant strain K293R was increased by 31.9% to 9.1 â€‹g/L in shake flask. Here, the developed bases conversion tool TadA-dCas9-AID does not need DNA double-strand break and homologous template, and is effective for genome editing and metabolic engineering in C. glutamicum.

CGD: Comprehensive guide designer for CRISPR-Cas systems.

The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas systems, including dead Cas9 (dCas9), Cas9, and Cas12a, have revolutionized genome engineering in mammalian somatic cells. Although computational tools that assess the target sites of CRISPR-Cas systems are inevitably important for designing efficient guide RNAs (gRNAs), they exhibit generalization issues in selecting features and do not provide optimal results in a comprehensive manner. Here, we introduce a Comprehensive Guide Designer (CGD) for four different CRISPR systems, which utilizes the machine learning algorithm, Elastic Net Logistic Regression (ENLOR), to autonomously generalize the models. CGD contains specific models trained with public datasets generated by CRISPRi, CRISPRa, CRISPR-Cas9, and CRISPR-Cas12a (designated as CGDi, CGDa, CGD9, and CGD12a, respectively) in an unbiased manner. The trained CGD models were benchmarked to other regression-based machine learning models, such as ElasticNet Linear Regression (ENLR), Random Forest and Boruta (RFB), and Extreme Gradient Boosting (Xgboost) with inbuilt feature selection. Evaluation with independent test datasets showed that CGD models outperformed the pre-existing methods in predicting the efficacy of gRNAs. All CGD source codes and datasets are available at GitHub (https://github.com/vipinmenon1989/CGD), and the CGD webserver can be accessed at http://big.hanyang.ac.kr:2195/CGD.