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Systematic multi-trait AAV capsid engineering for efficient gene delivery

View ORCID ProfileFatma-Elzahraa Eid, View ORCID ProfileAlbert T. Chen, Ken Y. Chan, Qin Huang, Qingxia Zheng, View ORCID ProfileIsabelle G. Tobey, Simon Pacouret, Pamela P. Brauer, Casey Keyes, Megan Powell, Jencilin Johnston, Binhui Zhao, Kasper Lage, Alice F. Tarantal, View ORCID ProfileYujia A. Chan, View ORCID ProfileBenjamin E. Deverman
doi: https://doi.org/10.1101/2022.12.22.521680
Fatma-Elzahraa Eid
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
2Department of Systems and Computer Engineering, Al-Azhar University; Cairo, Egypt
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  • For correspondence: fatma@broadinstitute.org bdeverma@broadinstitute.org
Albert T. Chen
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Ken Y. Chan
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Qin Huang
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Qingxia Zheng
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Isabelle G. Tobey
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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  • ORCID record for Isabelle G. Tobey
Simon Pacouret
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Pamela P. Brauer
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Casey Keyes
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Megan Powell
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Jencilin Johnston
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Binhui Zhao
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Kasper Lage
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
3Department of Surgery, Massachusetts General Hospital; Boston, USA
4Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard; Cambridge, USA
5Institute of Biological Psychiatry, Mental Health Center St. Hans, Mental Health Services; Copenhagen, Denmark
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Alice F. Tarantal
6Departments of Pediatrics and Cell Biology and Human Anatomy, School of Medicine, and California National Primate Research Center, University of California, Davis; California, USA
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Yujia A. Chan
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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Benjamin E. Deverman
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge USA
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  • For correspondence: fatma@broadinstitute.org bdeverma@broadinstitute.org
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Abstract

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.

Competing Interest Statement

BED is a scientific founder and advisor at Apertura Gene Therapy and a scientific advisory board member at Tevard Biosciences. BED, FEE, and KYC are named inventors on patent applications filed by the Broad Institute of MIT and Harvard related to this study. Remaining authors declare that they have no competing interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 22, 2022.
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Systematic multi-trait AAV capsid engineering for efficient gene delivery
Fatma-Elzahraa Eid, Albert T. Chen, Ken Y. Chan, Qin Huang, Qingxia Zheng, Isabelle G. Tobey, Simon Pacouret, Pamela P. Brauer, Casey Keyes, Megan Powell, Jencilin Johnston, Binhui Zhao, Kasper Lage, Alice F. Tarantal, Yujia A. Chan, Benjamin E. Deverman
bioRxiv 2022.12.22.521680; doi: https://doi.org/10.1101/2022.12.22.521680
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Systematic multi-trait AAV capsid engineering for efficient gene delivery
Fatma-Elzahraa Eid, Albert T. Chen, Ken Y. Chan, Qin Huang, Qingxia Zheng, Isabelle G. Tobey, Simon Pacouret, Pamela P. Brauer, Casey Keyes, Megan Powell, Jencilin Johnston, Binhui Zhao, Kasper Lage, Alice F. Tarantal, Yujia A. Chan, Benjamin E. Deverman
bioRxiv 2022.12.22.521680; doi: https://doi.org/10.1101/2022.12.22.521680

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