Nicolas Mathis

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.

Abstract Cancer‐associated fibroblasts (CAFs) are key regulators of tumorigenesis and promising targets for next‐generation therapies. We discovered that cancer cell‐derived activin A reprograms fibroblasts into pro‐tumorigenic CAFs. Mechanistically, this occurs via Smad2‐mediated transcriptional regulation of the formin mDia2, which directly promotes filopodia formation and cell migration. mDia2 also induces expression of CAF marker genes through prevention of p53 nuclear accumulation, resulting in the production of a pro‐tumorigenic matrisome and secretome. The translational relevance of this finding is reflected by activin A overexpression in tumor cells and of mDia2 in the stroma of skin cancer and other malignancies and the correlation of high activin A/mDia2 levels with poor patient survival. Blockade of this signaling axis using inhibitors of activin, activin receptors, or mDia2 suppressed cancer cell malignancy and squamous carcinogenesis in 3D organotypic cultures, ex vivo, and in vivo , providing a rationale for pharmacological inhibition of activin A‐mDia2 signaling in stratified cancer patients.

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.