Peptide mimic for influenza vaccination using nonnatural combinatorial chemistry

Loading...
Thumbnail Image
File version

Version of Record (VoR)

Author(s)
Miles, John J
Tan, Mai Ping
Dolton, Garry
Edwards, Emily SJ
Galloway, Sarah AE
Laugel, Bruno
Clement, Mathew
Makinde, Julia
Ladell, Kristin
Matthews, Katherine K
Watkins, Thomas S
Tungatt, Katie
Wong, Yide
Lee, Han Siean
Clark, Richard J
Pentier, Johanne M
Attaf, Meriem
Lissina, Anya
Ager, Ann
Gallimore, Awen
Rizkallah, Pierre J
Gras, Stephanie
Rossjohn, Jamie
Burrows, Scott R
Cole, David K
Price, David A
Sewell, Andrew K
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2018
Size
File type(s)
Location
Abstract

Polypeptide vaccines effectively activate human T cells but suffer from poor biological stability, which confines both transport logistics and in vivo therapeutic activity. Synthetic biology has the potential to address these limitations through the generation of highly stable antigenic “mimics” using subunits that do not exist in the natural world. We developed a platform based on D–amino acid combinatorial chemistry and used this platform to reverse engineer a fully artificial CD8+ T cell agonist that mirrored the immunogenicity profile of a native epitope blueprint from influenza virus. This nonnatural peptide was highly stable in human serum and gastric acid, reflecting an intrinsic resistance to physical and enzymatic degradation. In vitro, the synthetic agonist stimulated and expanded an archetypal repertoire of polyfunctional human influenza virus–specific CD8+ T cells. In vivo, specific responses were elicited in naive humanized mice by subcutaneous vaccination, conferring protection from subsequent lethal influenza challenge. Moreover, the synthetic agonist was immunogenic after oral administration. This proof-of-concept study highlights the power of synthetic biology to expand the horizons of vaccine design and therapeutic delivery.

Journal Title

Journal of Clinical Investigation

Conference Title
Book Title
Edition
Volume

128

Issue

4

Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© The authors 2018. This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Item Access Status
Note
Access the data
Related item(s)
Subject

Biomedical and clinical sciences

Clinical sciences not elsewhere classified

Persistent link to this record
Citation
Collections