Self-assembling influenza nanoparticle vaccines elicit broadly neutralizing H1N1 antibodies View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-07

AUTHORS

Masaru Kanekiyo, Chih-Jen Wei, Hadi M. Yassine, Patrick M. McTamney, Jeffrey C. Boyington, James R. R. Whittle, Srinivas S. Rao, Wing-Pui Kong, Lingshu Wang, Gary J. Nabel

ABSTRACT

Influenza viruses pose a significant threat to the public and are a burden on global health systems. Each year, influenza vaccines must be rapidly produced to match circulating viruses, a process constrained by dated technology and vulnerable to unexpected strains emerging from humans and animal reservoirs. Here we use knowledge of protein structure to design self-assembling nanoparticles that elicit broader and more potent immunity than traditional influenza vaccines. The viral haemagglutinin was genetically fused to ferritin, a protein that naturally forms nanoparticles composed of 24 identical polypeptides. Haemagglutinin was inserted at the interface of adjacent subunits so that it spontaneously assembled and generated eight trimeric viral spikes on its surface. Immunization with this influenza nanoparticle vaccine elicited haemagglutination inhibition antibody titres more than tenfold higher than those from the licensed inactivated vaccine. Furthermore, it elicited neutralizing antibodies to two highly conserved vulnerable haemagglutinin structures that are targets of universal vaccines: the stem and the receptor binding site on the head. Antibodies elicited by a 1999 haemagglutinin-nanoparticle vaccine neutralized H1N1 viruses from 1934 to 2007 and protected ferrets from an unmatched 2007 H1N1 virus challenge. This structure-based, self-assembling synthetic nanoparticle vaccine improves the potency and breadth of influenza virus immunity, and it provides a foundation for building broader vaccine protection against emerging influenza viruses and other pathogens. More... »

PAGES

102

Journal

TITLE

Nature

ISSUE

7456

VOLUME

499

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nature12202

DOI

http://dx.doi.org/10.1038/nature12202

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049251875

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23698367


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/nature12202'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/nature12202'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nature12202'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nature12202'


 

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