In silico prediction and experimental confirmation of HA residues conferring enhanced human receptor specificity of H5N1 Influenza A viruses

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Ea
Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.
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Metadaten
Author:Sonja Schmier, Ahmed Mostafa, Thomas Haarmann, Norbert Bannert, John Ziebuhr, Veljko Veljkovic, Ursula Dietrich, Stephan Pleschka
URN:urn:nbn:de:hebis:30:3-504934
DOI:http://dx.doi.org/10.1038/srep11434
ISSN:2045-2322
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=26091504
Parent Title (English):Scientific reports
Publisher:Nature Publishing Group
Place of publication:London
Document Type:Article
Language:English
Year of Completion:2015
Date of first Publication:2015/06/19
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2019/07/15
Tag:Computational models; Influenza virus
Volume:5
Issue:Art. 11434
Pagenumber:16
First Page:1
Last Page:16
Note:
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
HeBIS PPN:452225833
Institutes:Georg-Speyer-Haus
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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