Adenine base editor-based correction of the cardiac pathogenic Lmna c.1621C > T mutation in murine hearts.

Abstract

Base editors are emerging as powerful tools to correct single-nucleotide variants and treat genetic diseases. In particular, the adenine base editors (ABEs) exhibit robust and accurate adenine-to-guanidine editing capacity and have entered the clinical stage for cardiovascular therapy. Despite the tremendous progress using ABEs to treat heart diseases, a standard technical route toward successful ABE-based therapy remains to be fully established. In this study, we harnessed adeno-associated virus (AAV) and a mouse model carrying the cardiomyopathy-causing Lmna c.1621C > T mutation to demonstrate key steps and concerns in designing a cardiac ABE experiment invivo. We found DeepABE as a reliable deep-learning-based model to predict ABE editing outcomes in the heart. Screening of sgRNAs for a Cas9 mutant with relieved protospacer adjacent motif (PAM) allowed the reduction of bystander editing. The ABE editing efficiency can be significantly enhanced by modifying the TadA and Cas9 variants, which are core components of ABEs. The ABE systems can be delivered into the heart via either dual AAV or all-in-one AAV vectors. Together, this study showcased crucial technical considerations in designing an ABE system for the heart and pointed out major challenges in further improvement of this new technology for gene therapy.