| Autor: |
Suzanne Scott, Adrian Westhaus, Deborah Nazareth, Marti Cabanes-Creus, Renina Gale Navarro, Deborah Chandra, Erhua Zhu, Aravind Venkateswaran, Ian E. Alexander, Denis C. Bauer, Laurence O.W. Wilson, Leszek Lisowski |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Molecular Therapy: Methods & Clinical Development, Vol 32, Iss 4, Pp 101351- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2329-0501 |
| DOI: |
10.1016/j.omtm.2024.101351 |
| Popis: |
Gene therapies using recombinant adeno-associated virus (AAV) vectors have demonstrated considerable clinical success in the treatment of genetic disorders. Improved vectors with favorable tropism profiles, decreased immunogenicity, and enhanced manufacturability are poised to further improve the state of gene therapies. Such vectors can be identified through directed evolution, a process of subjecting a diverse capsid library to a selection pressure to identify individual variants with a desired trait. Currently, libraries that involve changes distributed throughout the AAV capsid coding region, such as DNA family shuffled libraries, are largely characterized using low-throughput Sanger sequencing of individual clones. However, improvements in long-read sequencing technologies have increased their applicability to capsid libraries and evaluation of the selection process. Here, we explore the application of Oxford Nanopore Technologies refined by a concatemeric consensus method for initial library characterization and monitoring selection of a shuffled AAV capsid library. Furthermore, we present AAVolve, a bioinformatic pipeline for processing long-read data from AAV-directed evolution experiments. Our approach allows high-throughput characterization of AAV capsids in a streamlined manner, facilitating deeper insights into library composition through multiple rounds of selection, and generalization through training of machine learning models. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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