In vivo prime editing of a metabolic liver disease in miceDesirée Böck, Tanja Rothgangl, Lukas Villiger et al.|Science Translational Medicine|2022 Prime editing is a highly versatile CRISPR-based genome editing technology that works without DNA double-strand break formation. Despite rapid technological advances, in vivo application for the treatment of genetic diseases remains challenging. Here, we developed a size-reduced Sp Cas9 prime editor (PE) lacking the RNaseH domain (PE2 Δ RnH ) and an intein-split construct (PE2 p.1153) for adeno-associated virus–mediated delivery into the liver. Editing efficiencies reached 15% at the Dnmt1 locus and were further elevated to 58% by delivering unsplit PE2 Δ RnH via human adenoviral vector 5 (AdV). To provide proof of concept for correcting a genetic liver disease, we used the AdV approach for repairing the disease-causing Pah enu2 mutation in a mouse model of phenylketonuria (PKU) via prime editing. Average correction efficiencies of 11.1% (up to 17.4%) in neonates led to therapeutic reduction of blood phenylalanine, without inducing detectable off-target mutations or prolonged liver inflammation. Although the current in vivo prime editing approach for PKU has limitations for clinical application due to the requirement of high vector doses (7 × 10 14 vg/kg) and the induction of immune responses to the vector and the PE, further development of the technology may lead to curative therapies for PKU and other genetic liver diseases.
Predicting prime editing efficiency and product purity by deep learningMachine learning prediction of prime editing efficiency across diverse chromatin contextsNicolas Mathis, Ahmed Allam, András Tálas et al.|Nature Biotechnology|2024 The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-deficient and mismatch repair-proficient cell lines and in vivo in primary cells. With ePRIDICT, we further developed a model that quantifies how local chromatin environments impact prime editing rates. A machine learning model for prime editing efficiency prediction takes into account chromatin context.
Continuous directed evolution of a compact CjCas9 variant with broad PAM compatibilityDark-Field Microwells toward High-Throughput Direct miRNA Sensing with Gold NanoparticlesMicroRNA (miRNA) is a class of short RNA that is emerging as an ideal biomarker, as its expression level has been found to correlate with different types of diseases including diabetes and cancer. The detection of miRNA is highly beneficial for early diagnostics and disease monitoring. However, miRNA sensing remains difficult because of its small size and low expression levels. Common techniques such as quantitative real-time polymerase chain reaction (qRT-PCR), in situ hybridization and Northern blotting have been developed to quantify miRNA in a given sample. Nevertheless, these methods face common challenges in point-of-care practice as they either require complicated sample handling and expensive equipment, or suffer from low sensitivity. Here we present a new tool based on dark-field microwells to overcome these challenges in miRNA sensing. This miniaturized device enables the readout of a gold nanoparticle assay without the need of a dark-field microscope. We demonstrate the feasibility of the dark-field microwells to detect miRNA in both buffer solution and cell lysate. The dark-field microwells allow affordable miRNA sensing at a high throughput which make them a promising tool for point-of-care diagnostics.