Synergistic anti-HCV broadly neutralizing human monoclonal antibodies with independent mechanisms
Abstract
There is an urgent need for a vaccine to combat the hepatitis C virus (HCV) pandemic, and induction of broadly neutralizing monoclonal antibodies (bNAbs) against HCV is a major goal of vaccine development. Even within HCV genotype 1, no single bNAb effectively neutralizes all viral strains, so induction of multiple neutralizing monoclonal antibodies (NAbs) targeting distinct epitopes may be necessary for protective immunity. Therefore, identification of optimal NAb combinations and characterization of NAb interactions can guide vaccine development. We analyzed neutralization profiles of 12 human NAbs across diverse HCV strains, assigning the NAbs to two functionally distinct clusters. We then measured neutralizing breadth of 35 NAb combinations against genotype 1 isolates, with each combination including one NAb from each neutralization cluster. Many NAbs displayed complementary neutralizing breadth, forming combinations with greater neutralization across diverse strains than any individual bNAb. Remarkably, one of the most broadly neutralizing combinations of two NAbs, designated HEPC74/HEPC98, also displayed enhanced potency, with interactions matching the Bliss independence model, suggesting that these NAbs inhibit HCV infection through independent mechanisms. Subsequent experiments showed that HEPC74 primarily blocks HCV envelope protein binding to CD81, while HEPC98 primarily blocks binding to scavenger receptor B1 and heparan sulfate. Together, these data identify a critical vulnerability resulting from the reliance of HCV on multiple cell surface receptors, suggesting that vaccine induction of multiple NAbs with distinct neutralization profiles is likely to enhance the breadth and potency of the humoral immune response against HCV.
Additional Information
© 2017 National Academy of Sciences. Published under the PNAS license. Edited by Francis V. Chisari, The Scripps Research Institute, La Jolla, CA, and approved November 21, 2017 (received for review October 27, 2017). Published online before print December 18, 2017. We thank Dr. Robert Siliciano for useful discussions, Michelle Colbert for technical support, James Notaro for Python guidance, and Dr. Mansun Law and Dr. Steven Foung for the generous gift of monoclonal antibodies. We thank Tiffany Luong and the Caltech Protein Expression Center for help with sE2 expression. A.I.F. is a Cancer Research Institute Irvington Fellow supported by the Cancer Research Institute. This project received support from the US National Institutes of Health Grants K08 AI102761, U19 AI088791, and R01 AI127469. Author contributions: M.C.M. and J.R.B. designed research; M.C.M., V.J.K., L.N.W., and J.R.B. performed research; A.I.F. and J.E.C. contributed new reagents/analytic tools; M.C.M., V.J.K., L.N.W., S.C.R., J.E.C., and J.R.B. analyzed data; and M.C.M., A.I.F., S.C.R., J.E.C., and J.R.B. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1718441115/-/DCSupplemental.Attached Files
Published - PNAS-2018-Mankowski-E82-91.pdf
Supplemental Material - pnas.201718441SI.pdf
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Additional details
- PMCID
- PMC5776832
- Eprint ID
- 83953
- DOI
- 10.1073/pnas.1718441115
- Resolver ID
- CaltechAUTHORS:20171219-074244603
- Cancer Research Institute
- K08 AI102761
- NIH
- U19 AI088791
- NIH
- R01 AI127469
- NIH
- Created
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2017-12-19Created from EPrint's datestamp field
- Updated
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2022-03-18Created from EPrint's last_modified field