SynDesign: web-based prime editing guide RNA design and evaluation tool for saturation genome editing.

Saturation genome editing (SGE) enables in-depth functional evaluation of disease-associated genes and variants by generating all possible single nucleotide variants (SNVs) within a given coding region. Although prime editing can be employed for inducing these SNVs, designing efficient prime editing guide RNAs (pegRNAs) can be challenging and time-consuming. Here, we present SynDesign, an easy-to-use webtool for the design, evaluation, and construction precision pegRNA libraries for SGE with synonymous mutation markers. SynDesign offers a simple yet powerful interface that automates the generation of all feasible pegRNA designs for a target gene or variant of interest. The pegRNAs are selected using the state-of-the-art models to predict prime editing efficiencies for various prime editors and cell types. Top-scoring pegRNA designs are further enhanced using synonymous mutation markers which improve pegRNA efficiency by diffusing the cellular mismatch repair mechanism and serve as sequence markers for improved identification of intended edits following deep sequencing. SynDesign is expected to facilitate future research using SGE to investigate genes or variants of interest associated with human diseases. SynDesign is freely available at https://deepcrispr.info/SynDesign without a login process.

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.

Application of prime editing to the correction of mutations and phenotypes in adult mice with liver and eye diseases.

The use of prime editing-a gene-editing technique that induces small genetic changes without the need for donor DNA and without causing double strand breaks-to correct pathogenic mutations and phenotypes needs to be tested in animal models of human genetic diseases. Here we report the use of prime editors 2 and 3, delivered by hydrodynamic injection, in mice with the genetic liver disease hereditary tyrosinemia, and of prime editor 2, delivered by an adeno-associated virus vector, in mice with the genetic eye disease Leber congenital amaurosis. For each pathogenic mutation, we identified an optimal prime-editing guide RNA by using cells transduced with lentiviral libraries of guide-RNA-encoding sequences paired with the corresponding target sequences. The prime editors precisely corrected the disease-causing mutations and led to the amelioration of the disease phenotypes in the mice, without detectable off-target edits. Prime editing should be tested further in more animal models of genetic diseases.

Predicting the efficiency of prime editing guide RNAs in human cells.

Prime editing enables the introduction of virtually any small-sized genetic change without requiring donor DNA or double-strand breaks. However, evaluation of prime editing efficiency requires time-consuming experiments, and the factors that affect efficiency have not been extensively investigated. In this study, we performed high-throughput evaluation of prime editor 2 (PE2) activities in human cells using 54,836 pairs of prime editing guide RNAs (pegRNAs) and their target sequences. The resulting data sets allowed us to identify factors affecting PE2 efficiency and to develop three computational models to predict pegRNA efficiency. For a given target sequence, the computational models predict efficiencies of pegRNAs with different lengths of primer binding sites and reverse transcriptase templates for edits of various types and positions. Testing the accuracy of the predictions using test data sets that were not used for training, we found Spearman's correlations between 0.47 and 0.81. Our computational models and information about factors affecting PE2 efficiency will facilitate practical application of prime editing.