Biology subjects

Kelkar, Y.

Publications and source records attributed to Kelkar, Y..

2 recordsLinked to original sources

bioRxiv2026

LNP Delivery of Prime Editors Enables Durable Correction of PAH R408W in Phenylketonuria Models

Phenylketonuria (PKU) is a monogenic metabolic disorder caused by mutations in the phenylalanine hydroxylase (PAH) gene, the most common of which is c.1222C>T (p.R408W). Current treatment options, including dietary restriction and enzyme substitution, can be effective but are often challenging for patients to maintain and do not address the underlying genetic defect. Gene editing represents a compelling one-time treatment approach for PKU. Herein, we demonstrate mRNA:LNP-mediated delivery of prime editors that enable therapeutically-relevant levels of correction of the R408W mutation in both in vitro and in vivo model systems. Further, we found that co-delivery of the DNA repair modulator RAD18 and engineering of the prime editor protein, enabled 2-fold and 3-fold increases in prime editing relative to PE3 respectively, resulting in ~50% correction efficiency in a humanized PKU mouse model. Collectively, this work identifies novel potential approaches to enhancing prime editing and establishes LNP-delivered prime editing as a mutation-specific, non-viral therapeutic strategy for PKU, as well as providing a framework for extending prime editing to other inherited liver diseases.

pharmacology and toxicology↗

bioRxiv2019

SUBSTRA: Supervised Bayesian Patient Stratification

Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals. We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection.

bioinformatics↗