igRNA Prediction and Selection AI Models (igRNA-PS) for Bystander-less ABE Base Editing.

CRISPR derived base editing techniques tend to edit multiple bases in the targeted region, which impedes precise reversion of disease-associated single nucleotide variations (SNVs). We designed an imperfect gRNA (igRNA) editing strategy to achieve bystander-less single-base editing. To predict the performance and provide ready-to-use igRNAs, we employed a high-throughput method to edit 5000 loci, each with approximate 19 systematically designed ABE igRNAs. Through deep learning of the relationship of editing efficiency, original gRNA sequence and igRNA sequence, AI models were constructed and tested, designated igRNA Prediction and Selection AI models (igRNA-PS). The models have three functions, First, they can identify the major editing site from the bystanders on a gRNA protospacer with a near 90% accuracy. second, a modified single-base editing efficiency (SBE), considering both single-base editing efficiency and product purity, can be predicted for any given igRNAs. Third, for an editing locus, a set of 64 igRNAs derived from a gRNA can be generated, evaluated through igRNA-PS to select for the best performer, and provided to the user. In this work, we overcome one of the most significant obstacles of base editors, and provide a convenient and efficient approach for single-base bystander-less ABE base editing.

HMGN1 enhances CRISPR-directed dual-function A-to-G and C-to-G base editing.

C-to-G base editors have been successfully constructed recently, but limited work has been done on concurrent C-to-G and A-to-G base editing. In addition, there is also limited data on how chromatin-associated factors affect the base editing. Here, we test a series of chromatin-associated factors, and chromosomal protein HMGN1 was found to enhance the efficiency of both C-to-G and A-to-G base editing. By fusing HMGN1, GBE and ABE to Cas9, we develop a CRISPR-based dual-function A-to-G and C-to-G base editor (GGBE) which is capable of converting simultaneous A and C to G conversion with substantial editing efficiency. Accordingly, the HMGN1 role shown in this work and the resulting GGBE tool further broaden the genome manipulation capacity of CRISPR-directed base editors.

Obtaining the best igRNAs for bystander-less correction of all ABE-reversible pathogenic SNVs using high-throughput screening.

Imperfect -gRNA (igRNA) provides a simple strategy for single-base editing of a base editor. However, a significant number of igRNAs need to be generated and tested for each target locus to achieve efficient single-base reversion of pathogenic single nucleotide variations (SNVs), which hinders the direct application of this technology. To provide ready-to-use igRNAs for single-base and bystander-less correction of all the adenine base editor (ABE)-reversible pathogenic SNVs, we employed a high-throughput method to edit all 5,253 known ABE-reversible pathogenic SNVs, each with multiple systematically designed igRNAs, and two libraries of 96,000 igRNAs were tested. A total of 1,988 SNV loci could be single-base reversed by igRNA with a >30% efficiency. Among these 1,988 loci, 378 SNV loci exhibited an efficiency of more than 90%. At the same time, the bystander editing efficiency of 76.62% of the SNV loci was reduced to 0%, while remaining below 1% for another 18.93% of the loci. These ready-to-use igRNAs provided the best solutions for a substantial portion of the 4,657 pathogenic/likely pathogenic SNVs. In this work, we overcame one of the most significant obstacles of base editors and provide a ready-to-use platform for the genetic treatment of diseases caused by ABE-reversible SNVs.

Automated high-throughput genome editing platform with an AI learning in situ prediction model.

A great number of cell disease models with pathogenic SNVs are needed for the development of genome editing based therapeutics or broadly basic scientific research. However, the generation of traditional cell disease models is heavily dependent on large-scale manual operations, which is not only time-consuming, but also costly and error-prone. In this study, we devise an automated high-throughput platform, through which thousands of samples are automatically edited within a week, providing edited cells with high efficiency. Based on the large in situ genome editing data obtained by the automatic high-throughput platform, we develop a Chromatin Accessibility Enabled Learning Model (CAELM) to predict the performance of cytosine base editors (CBEs), both chromatin accessibility and the context-sequence are utilized to build the model, which accurately predicts the result of in situ base editing. This work is expected to accelerate the development of BE-based genetic therapies.