A High throughput, high-efficient AAV evolution system for tissue targeting AAV screening


Topic:

Pre-Clinical Research

Poster Number: P122

Author(s):

Chunyan He, PhD, Suzhou GenAssist Therapeutics Co., Ltd

Introduction
Many optimization methods of AAV capsids to enhance their specific tissue tropism and effective expression have been developed, such as direct evolution and rational design. These methods suffer from low screening efficiency due to low library abundance or poor underlying science. Only very few valuable capsids specific for muscle or CNS were obtained from these engineering systems. To overcome these issues, we developed an innovative evolution system for tissue specific AAV screening. Human genome contains all the information of the ligands to receptors on cell membrane. We hypothesize that human genome displaying AAV can gain the cell entry function of these ligands. Through appropriate screening systems, any tissue specific AAV capsid can be attained from this library.
Methods
Random peptides from human genome were inserted between amino acids 588 and 589 in AAV9 library capsid. The library was enriched in vitro and in vivo (mice and NHP) for several rounds. Capsid variants enriched relative to the virus library at the DNA and RNA level in various tissues were identified by target-NGS for each round. New motifs from the enriched sequences will be identified by a score-based approach. Leading candidates will be further optimized by combining motif-based machine learning with additional rational design to enhance the targeting properties and transduction efficiencies. Individual capsids with 3 unique barcodes will be further confirmed in NHP.
Results/Outlook
Approximately 8 x 1010 unique nucleotide sequences library was packaged into rAAVs. After several rounds of enrichment in iPSC-derived myotubes and muscles of animals (mice or NHP). Multiple new motifs have been enriched with RGD listed as one of TOP 10 motifs. Multiple top new motif-containing sequences exhibited 2-3 times higher specificity to muscles than RGD-containing sequences in mice. Motif-based machine learning was utilized to optimize tissue specificity of the sequences from in vitro and in vivo experiments. Leading candidate sequences specific to muscles are under investigation in DMD mice and the results will be available in Mar 2025. In summary, this innovative Human Originated Peptide Evolution (HOPE) AAV platform enables the findings of new motifs specific to different tissues (CNS, kidney, lung, et al), which can extremely expand the scope of AAV gene therapy in future.