Selected Grantee Publications
Pre-Challenge Gut Microbial Signature Predicts RhCMV/SIV Vaccine Efficacy in Rhesus Macaques
Brochu et al., Microbiology Spectrum. 2025.
https://journals.asm.org/doi/10.1128/spectrum.01285-24
Rhesus cytomegalovirus–based simian immunodeficiency virus (RhCMV/SIV) vaccines provide protection against SIV challenge in approximately 60% of vaccinated rhesus macaques. This study assessed the role that gut microbiota play in SIV vaccine efficacy by analyzing the microbiomes of rhesus macaques before and after immunization using novel compositional data analysis techniques and machine-learning strategies. Researchers identified a gut microbial signature that predicted vaccine protection outcomes and correlated with early biomarker changes in the blood (i.e., host immune response to vaccination). This study indicates that the gut microbiome might play a role in vaccine-induced immunity. Supported by ORIP (P51OD011092).
Systematic Multi-trait AAV Capsid Engineering for Efficient Gene Delivery
Eid et al., Nature Communications. 2024.
https://doi.org/10.1038/s41467-024-50555-y
Engineering novel functions into proteins while retaining desired traits is a key challenge for developers of viral vectors, antibodies, and inhibitors of medical and industrial value. In this study, investigators developed Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait adeno-associated virus (AAV) capsids. Fit4Function was used to generate reproducible screening data from a capsid library that samples the entire manufacturable sequence space. The Fit4Function data were used to train accurate sequence-to-function models, which were combined to develop a library of capsid candidates. Compared to AAV9, top candidates from the Fit4Function capsid library exhibited comparable production yields; more efficient murine liver transduction; up to 1,000-fold greater human hepatocyte transduction; and increased enrichment in a screen for liver transduction in macaques. The Fit4Function strategy enables prediction of peptide-modified AAV capsid traits across species and is a critical step toward assembling an ML atlas that predicts AAV capsid performance across dozens of traits. Supported by ORIP (P51OD011107, U42OD027094), NIDDK, NIMH, and NINDS.