Reconstructing Cross-Species Ancestral Adeno-Associated
Viruses for Enhanced Gene Therapy Delivery
Name
xie-yxie25-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
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12.26 MB
Format
Adobe PDF
Checksum (MD5)
4738bf3c48849e5aeeb8ecbf353815b3
Author(s)
Xie, Yuxin
Advisor(s)
Feng, Guoping
Date Issued
September 2025
Publisher
Massachusetts Institute of Technology
Abstract
Adeno-associated viruses (AAV) are one of the most promising vectors for gene therapy because of their established safety, low immunogenicity, and capability to achieve sustained gene expression. However, many naturally occurring AAV variants have limitations in their potency, particularly in penetrating biological barriers like the blood-brain barrier (BBB). Additionally, their broad and nonspecific tropism can translate into suboptimal cross-species transduction efficiency and potential toxicity, complicating the clinical transition from animal model to humans. These challenges impede the use of naturally occurring AAVs for therapeutic gene delivery in many neurological disorders-such as autism spectrum disorders (ASD), Parkinson’s disease (PD), Huntington’s disease (HD)—as well as other systemic conditions like cystic fibrosis (CF). To overcome these barriers, we developed a computational framework based on ancestral sequence reconstruction (ASR) to engineer synthetic ancestral AAV capsids with the goal of enhanced targeting specificity and potency. We first validated this computational framework by replicating the previously engineered Anc80L65 capsid. Then, with 75 naturally occurring functional AAV sequences and additional experimentally screened variants exhibiting brain-targeting potency, we built an evolutionary framework. We applied multiple computational methods such as enhanced multiple sequence alignment, maximum-likelihood-based phylogenetic tree inference, and ancestral sequence reconstruction with Bayesian inference. With this methodology, we predicted several novel ancestral AAV capsid sequences at critical evolutionary nodes, particularly those representing functional transitions with potential improved blood-brain barrier penetration and CNS tropism. Our computational framework thus streamlines and accelerates the process of designing ancestral AAV variants with targeted gene therapy applications.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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