AAV capsid engineering for enhancing gene transfer

Stanford University, Stanford CA

Investigators

Linked publications, trials & patents

Abstract

While it has taken a few decades, adeno-associated viral vectors (rAAV) have shown promise in clinical trials resulting in a handful of FDA approved drugs. While there is continued progress a number of limitations persist that hamper the field. For example, there is still an unpredictable discordance when new capsids are tested in animals vs humans as well as a large variation in dose response between individuals treated. In recent years, we have established that some of these differences are not due to DNA delivery into the cell and nucleus but differences in how the vector epigenome is created resulting in differential rates of vector-mediated transcription. The studies revealed that not only does the capsid protein influence the epigenetic state of the vector but that small variations in the capsid sequence can alter the epigenetic outcome and enhancing expression by two orders of magnitude with no or marginal changes in vector nuclear DNA copy number. In this proposal, we plan to study the molecular mechanisms involved in the process by identifying specific proteins and non-coding RNAs that associate with the capsid and vector genomes once in the nucleus. We will perform studies to establish the functionality of these host proteins in setting up the vector epigenome. We will use machine learning technologies to select for capsids that have improved properties for more concordant animal-human predictability and establish how the alterations in the selected capsids affects AAV-epigenomic states in transduced cells and in mice. Finally, we will design strategies to enhance the proximity of the epigenome favorable molecules into the capsid once in the nucleus. These studies will not only provide mechanistic knowledge of how AAV-delivered genomes become chromatinized but also establish new approaches to maximize expression in transduced tissues, and provide better parameters for predicting clinical efficacy from preclinical studies.