2.50
Hdl Handle:
http://hdl.handle.net/10033/347045
Title:
Computational prediction of vaccine strains for human influenza A (H3N2) viruses.
Authors:
Steinbrück, L; Klingen, T R; McHardy, A C
Abstract:
Human influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. Importance: Human influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.
Affiliation:
Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.
Citation:
Computational prediction of vaccine strains for human influenza A (H3N2) viruses. 2014, 88 (20):12123-32 J. Virol.
Journal:
Journal of virology
Issue Date:
Oct-2014
URI:
http://hdl.handle.net/10033/347045
DOI:
10.1128/JVI.01861-14
PubMed ID:
25122778
Type:
Article
Language:
en
ISSN:
1098-5514
Appears in Collections:
publications of the research group bioinformatics in infection research ([BRICS] BIFO)

Full metadata record

DC FieldValue Language
dc.contributor.authorSteinbrück, Len
dc.contributor.authorKlingen, T Ren
dc.contributor.authorMcHardy, A Cen
dc.date.accessioned2015-03-24T13:03:14Zen
dc.date.available2015-03-24T13:03:14Zen
dc.date.issued2014-10en
dc.identifier.citationComputational prediction of vaccine strains for human influenza A (H3N2) viruses. 2014, 88 (20):12123-32 J. Virol.en
dc.identifier.issn1098-5514en
dc.identifier.pmid25122778en
dc.identifier.doi10.1128/JVI.01861-14en
dc.identifier.urihttp://hdl.handle.net/10033/347045en
dc.description.abstractHuman influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. Importance: Human influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.en
dc.language.isoenen
dc.subject.meshAntigens, Viralen
dc.subject.meshComputational Biologyen
dc.subject.meshInfluenza A Virus, H3N2 Subtypeen
dc.subject.meshInfluenza Vaccinesen
dc.titleComputational prediction of vaccine strains for human influenza A (H3N2) viruses.en
dc.typeArticleen
dc.contributor.departmentHelmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.en
dc.identifier.journalJournal of virologyen

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