A Computational Framework for Influenza Antigenic Cartography

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.

Within-Host Evolution Results in Antigenically Distinct GII.4 Noroviruses

“…. To further address this question, we used antigenic cartography, which provides easily interpretable measures and visualization of multidimensional antigenic relationships and has previously been used to study antigenic differences in influenza virus strains (40,49). This analysis provided further support for the idea that within-host changes in the virus can equal or exceed those differences seen across successive outbreak strains, with the antigenic space between P.D302 and both GII.4-2006b (D ϭ 9.91) and P.D1 (D ϭ 9.15) being greater than the average between the consecutive outbreak strains used in this study (average D ϭ 4.98; range, 2.11 to 12.11), and mirrors the global difference between early GII.4 isolates (1987, 1997, and 2002) and contemporary strains (2006b, 2009, and 2012).…”

“…Antigenic cartography. We utilized multidimensional-scaling (MDS) approaches as described and implemented within the AntigenMap 3D software (39,40). The EC 50 blockade titers of various sera against a panel of VLPs were normalized to the maximum blockade titer of each serum, as well as to the maximum overall blockade titer across sera (normalization method 1 in AntigenMap 3D).…”

“…Antigenic cartography. In order to further describe and visualize the differences between the antigenic properties of virus strains, we utilized the MDS approach known as antigenic cartography (39,40). Specifically, we used the pipeline described in AntigenMap 3D (39) (Fig.…”

Detection of Antigenic Variants of Subtype H3 Swine Influenza A Viruses from Clinical Samples

“…Antigenic maps were constructed using AntigenMap (http://sysbio.cvm.msstate. edu/AntigenMap) (33,34) HI assay results, polyPLA results suggested that subtype H3N2 swine IAVs did not react with the negative-control H1N1 virus and polyclonal antibodies.…”

“…The antigenic maps of H3N2 swine IAVs were constructed using AntigenMap (http://sysbio.cvm.msstate.edu/AntigenMap) and data derived from the HI assay or the polyPLA (33,34). The data entry with an HI titer of Ͻ1:10 or a ΔC T of Ͻ3.00 was determined as a low reactor for the data from HI or the polyPLA, respectively.…”