Biology subjects

Eid, F.-E.

Publications and source records attributed to Eid, F.-E..

4 recordsLinked to original sources

bioRxiv2023

An AAV capsid reprogrammed to bind human Transferrin Receptor mediates brain-wide gene delivery

Developing vehicles that efficiently deliver genes throughout the human central nervous system (CNS) will broaden the range of treatable genetic diseases. We engineered an AAV capsid, BI-hTFR1, that binds human Transferrin Receptor (TfR1), a protein expressed on the blood-brain barrier (BBB). BI-hTFR1 was actively transported across a human brain endothelial cell layer and, relative to AAV9, provided 40-50 times greater reporter expression in the CNS of human TFRC knock-in mice. The enhanced tropism was CNS-specific and absent in wild type mice. When used to deliver GBA1, mutations of which cause Gaucher disease and are linked to Parkinsons disease, BI-hTFR1 substantially increased brain and cerebrospinal fluid glucocerebrosidase activity compared to AAV9. These findings establish BI-hTFR1 as a promising vector for human CNS gene therapy.

neuroscience↗

bioRxiv2022

Systematic multi-trait AAV capsid engineering for efficient gene delivery

Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids. By leveraging a capsid library that evenly samples the manufacturable sequence space, reproducible screening data are generated to train accurate sequence-to-function models. Combining six models, we designed a multi-trait (liver-targeted, manufacturable) capsid library and validated 89% of library variants on all six predetermined criteria. Furthermore, the models, trained only on mouse in vivo and human in vitro Fit4Function data, accurately predicted AAV capsid variant biodistribution in macaque. Top candidates exhibited high production yields, efficient murine liver transduction, up to 1000-fold greater human hepatocyte transduction, and increased enrichment, relative to AAV9, in a screen for liver transduction in macaques. The Fit4Function strategy ultimately makes it possible to predict cross-species traits of peptide-modified AAV capsids and is a critical step toward assembling an ML atlas that predicts AAV capsid performance across dozens of traits.

synthetic biology↗

bioRxiv2022

Targeting AAV vectors to the CNS via de novo engineered capsid-receptor interactions

Viruses have evolved the ability to bind and enter cells through interactions with a wide variety of host cell macromolecules. Here, we screened for AAV capsids that bind two host cell proteins expressed on the mouse blood-brain barrier, LY6A or the related protein LY6C1. Introducing interactions with either protein target generated hundreds of capsids with dramatically enhanced central nervous system (CNS) tropisms. In contrast to the AAV-PHP.B capsid family, which interacts with LY6A and only exhibits its enhanced CNS tropism in a subset of mouse strains, the capsids that engage LY6C1 maintain their CNS tropism in BALB/cJ mice. Compared to conventional in vivo screens for CNS cell transducing capsids, a single round of protein target binding screening recovered significantly more capsids with enhanced performance that were validated in subsequent in vivo screens. Moreover, the initial screening round generated reproducible and quantitative target binding data that enabled the efficient machine learning-guided generation of more diverse targetspecific capsids. This work demonstrates that AAV capsids can be directly targeted to specific proteins to generate potent gene delivery vectors with known mechanisms of action and predictable tropisms.

synthetic biology↗

bioRxiv2020

Systematic auditing is essential to debiasing machine learning in biology

Representational biases that are common in biological data can inflate prediction performance and confound our understanding of how and what machine learning (ML) models learn from large complicated datasets. However, auditing for these biases is not a common practice in ML in the life sciences. Here, we devise a systematic auditing framework and harness it to audit three different ML applications of significant therapeutic interest: prediction frameworks of protein-protein interactions, drug-target bioactivity, and MHC-peptide binding. Through this, we identify unrecognized biases that hinder the ML process and result in low model generalizability. Ultimately, we show that, when there is insufficient signal in the training data, ML models are likely to learn primarily from representational biases.

systems biology↗