AAVolve: Concatenated long-read deep sequencing enables whole capsid tracking during shuffled AAV library selection

Area of Science:

• Biotechnology
• Molecular Biology
• Bioinformatics

Background:

• Gene therapies utilizing adeno-associated virus (AAV) vectors show promise for genetic disorders.
• Enhancing AAV vector tropism, immunogenicity, and manufacturability is crucial for advancing gene therapies.
• Directed evolution identifies improved AAV variants by selecting from diverse capsid libraries.

Purpose of the Study:

• To explore the application of Oxford Nanopore Technologies with a concatemeric consensus method for characterizing shuffled AAV capsid libraries.
• To present AAVolve, a bioinformatics pipeline for processing long-read sequencing data from AAV-directed evolution experiments.
• To enable high-throughput characterization and deeper insights into AAV capsid libraries during selection.

Main Methods:

• Application of Oxford Nanopore Technologies for sequencing shuffled AAV capsid libraries.
• Utilizing a concatemeric consensus method for data refinement.
• Development and implementation of the AAVolve bioinformatics pipeline for long-read data processing.

Main Results:

• Demonstrated high-throughput characterization of AAV capsid libraries.
• Enabled streamlined monitoring of AAV-directed evolution selection processes.
• Facilitated deeper insights into library composition across multiple selection rounds.

Conclusions:

• Long-read sequencing, particularly Oxford Nanopore Technologies, significantly improves the characterization of AAV capsid libraries.
• The AAVolve pipeline offers a high-throughput and streamlined approach for analyzing AAV-directed evolution data.
• This methodology facilitates the identification of improved AAV vectors for gene therapy applications and enables machine learning model training.