Treatment of a metabolic liver disease by in vivo genome base editing in adult mice.
Lukas VilligerHiu Man Grisch-ChanHelen LindsayFemke RingnaldaChiara B PoglianoGabriella AllegriRalph FingerhutJohannes HäberleJoao MatosMark D RobinsonBeat ThönyGerald SchwankPublished in: Nature medicine (2018)
CRISPR-Cas-based genome editing holds great promise for targeting genetic disorders, including inborn errors of hepatocyte metabolism. Precise correction of disease-causing mutations in adult tissues in vivo, however, is challenging. It requires repair of Cas9-induced double-stranded DNA (dsDNA) breaks by homology-directed mechanisms, which are highly inefficient in nondividing cells. Here we corrected the disease phenotype of adult phenylalanine hydroxylase (Pah)enu2 mice, a model for the human autosomal recessive liver disease phenylketonuria (PKU)1, using recently developed CRISPR-Cas-associated base editors2-4. These systems enable conversion of C∙G to T∙A base pairs and vice versa, independent of dsDNA break formation and homology-directed repair (HDR). We engineered and validated an intein-split base editor, which allows splitting of the fusion protein into two parts, thereby circumventing the limited cargo capacity of adeno-associated virus (AAV) vectors. Intravenous injection of AAV-base editor systems resulted in Pahenu2 gene correction rates that restored physiological blood phenylalanine (L-Phe) levels below 120 µmol/l [5]. We observed mRNA correction rates up to 63%, restoration of phenylalanine hydroxylase (PAH) enzyme activity, and reversion of the light fur phenotype in Pahenu2 mice. Our findings suggest that targeting genetic diseases in vivo using AAV-mediated delivery of base-editing agents is feasible, demonstrating potential for therapeutic application.
Keyphrases
- crispr cas
- genome editing
- gene therapy
- genome wide
- high fat diet induced
- copy number
- induced apoptosis
- young adults
- emergency department
- machine learning
- metabolic syndrome
- binding protein
- single molecule
- childhood cancer
- deep learning
- cell death
- circulating tumor
- polycyclic aromatic hydrocarbons
- climate change
- artificial intelligence
- diabetic rats
- skeletal muscle