Abstract
Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap.
Highlights
An influenza virus is a negative-stranded RNA virus that belongs to the Orthomyxoviridae family
The goal of this paper is to present a computational framework for influenza cartography construction which we call Matrix Completion-Multidimensional Scaling (MC-Multidimensional scaling (MDS))
If we arrange both antigens and antibodies in a hemagglutination inhibition (HI) matrix according to time, there is a banded structure, where most Type I data appear very close to the diagonal of the matrix; Type II data tend to be slightly off diagonal, while Type III data are more likely to occur in matrix entries that are significantly off diagonal (Figure 1)
Summary
An influenza virus is a negative-stranded RNA virus that belongs to the Orthomyxoviridae family. The influenza A viruses have 8 genomic segments (segment 1{8) with varying lengths from about 890 to 2,341 nucleotides which encode at least 11 proteins: PB2 by segment 1, PB1 and PB1-F2 by 2, PA by 3, haemagglutinin (HA) by 4, nucleoprotein (NP) by 5, neuraminidase (NA) by 6, matrix protein M1 and M2 by 7, and nonstructural protein NS1 and NS2 by 8. Among these proteins, the surface proteins HA and NA are involved in virus attachment and cell fusion. There is not yet an HA-NA nomenclature system in Influenza B and C viruses
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