Structure-guided evolution of antigenically distinct adeno-associated virus variants for immune evasion.

Published

Journal Article

Preexisting neutralizing antibodies (NAbs) against adeno-associated viruses (AAVs) pose a major, unresolved challenge that restricts patient enrollment in gene therapy clinical trials using recombinant AAV vectors. Structural studies suggest that despite a high degree of sequence variability, antibody recognition sites or antigenic hotspots on AAVs and other related parvoviruses might be evolutionarily conserved. To test this hypothesis, we developed a structure-guided evolution approach that does not require selective pressure exerted by NAbs. This strategy yielded highly divergent antigenic footprints that do not exist in natural AAV isolates. Specifically, synthetic variants obtained by evolving murine antigenic epitopes on an AAV serotype 1 capsid template can evade NAbs without compromising titer, transduction efficiency, or tissue tropism. One lead AAV variant generated by combining multiple evolved antigenic sites effectively evades polyclonal anti-AAV1 neutralizing sera from immunized mice and rhesus macaques. Furthermore, this variant displays robust immune evasion in nonhuman primate and human serum samples at dilution factors as high as 1:5, currently mandated by several clinical trials. Our results provide evidence that antibody recognition of AAV capsids is conserved across species. This approach can be applied to any AAV strain to evade NAbs in prospective patients for human gene therapy.

Full Text

Duke Authors

Cited Authors

  • Tse, LV; Klinc, KA; Madigan, VJ; Castellanos Rivera, RM; Wells, LF; Havlik, LP; Smith, JK; Agbandje-McKenna, M; Asokan, A

Published Date

  • June 2017

Published In

Volume / Issue

  • 114 / 24

Start / End Page

  • E4812 - E4821

PubMed ID

  • 28559317

Pubmed Central ID

  • 28559317

Electronic International Standard Serial Number (EISSN)

  • 1091-6490

International Standard Serial Number (ISSN)

  • 0027-8424

Digital Object Identifier (DOI)

  • 10.1073/pnas.1704766114

Language

  • eng