Brief intro:
- Author: Shiwei Qiu, Lian Liu, Bin Xiang, Ziqin Jin, Yahong Li, Dong Li, Hanqing Hou, Kuan Li, Gege Wei, Jiangping Xie, Shang Li, Shuang Liu, Chunlai Chen, Xin Liang, Qianwen Sun & Wei Xiong
- Journal: Nature Biomedical Engineering
- Doi: https://www.doi.org/10.1038/s41551-026-01635-5
- Publication Date: 2026/3/23
Abstract
Frameshift mutations, responsible for >20% of Mendelian inherited diseases, pose substantial therapeutic challenges. Here we developed Template-Independent Genome Editing for Restoration (TIGER), a platform for the efficient and precise correction of frameshift mutations across various models. By identifying reproducible nucleotide-level factors that influence therapeutic efficacy across cells and tissues, we developed a scoring system for guide RNA (gRNA)–Cas9 outcomes. Approximately 75% of deletion and 50% of insertion mutations produced ≥30% in-frame products, sufficient for phenotypic restoration, with 38% and 65% achieving wild-type correction, respectively. To expand the applicability of TIGER across species and genome wide, we retrained the inDelphi algorithm to predict therapeutic gRNAs for single-nucleotide frameshifts. In a mouse model of deafness, delivery of SpCas9 and optimal gRNA via dual adeno-associated virus restored hearing thresholds to wild-type levels, with ~90% of in-frame edits being wild type. TIGER provides a robust and broadly applicable strategy for in vivo correction of inherited frameshift diseases.
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