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. 2012 Aug;29(8):2063-71.
doi: 10.1093/molbev/mss095. Epub 2012 Mar 16.

Detecting patches of protein sites of influenza A viruses under positive selection

Christina Tusche  1 , Lars Steinbrück, Alice C McHardy
Affiliations

Detecting patches of protein sites of influenza A viruses under positive selection

Christina Tusche et al. Mol Biol Evol. 2012 Aug.

Abstract

Influenza A viruses are single-stranded RNA viruses capable of evolving rapidly to adapt to environmental conditions. Examples include the establishment of a virus in a novel host or an adaptation to increasing immunity within the host population due to prior infection or vaccination against a circulating strain. Knowledge of the viral protein regions under positive selection is therefore crucial for surveillance. We have developed a method for detecting positively selected patches of sites on the surface of viral proteins, which we assume to be relevant for adaptive evolution. We measure positive selection based on dN/dS ratios of genetic changes inferred by considering the phylogenetic structure of the data and suggest a graph-cut algorithm to identify such regions. Our algorithm searches for dense and spatially distinct clusters of sites under positive selection on the protein surface. For the hemagglutinin protein of human influenza A viruses of the subtypes H3N2 and H1N1, our predicted sites significantly overlap with known antigenic and receptor-binding sites. From the structure and sequence data of the 2009 swine-origin influenza A/H1N1 hemagglutinin and PB2 protein, we identified regions that provide evidence of evolution under positive selection since introduction of the virus into the human population. The changes in PB2 overlap with sites reported to be associated with mammalian adaptation of the influenza A virus. Application of our technique to the protein structures of viruses of yet unknown adaptive behavior could identify further candidate regions that are important for host-virus interaction.

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Figures

F<sc>IG</sc>. 1.
FIG. 1.
Workflow for predicting patches under positive selection.
F<sc>IG</sc>. 2.
FIG. 2.
Schematic drawing of the graph-cut approach. The minimum cut minimizes the sum of weights of all edges cut by the line separating the positive and negative selection nodes. For a single node n, these are the lines shown in blue: the scaled distances to the nonselected neighbors in Nδ(n) and the connection to the other side (i.e., the negative) selection node with the weight P(n).
F<sc>IG</sc>. 3.
FIG. 3.
Overlap between selected epitope and avidity-changing sites. Venn diagram showing the overlap between subtype H1 residues in patches selected by the dN/dS graph-cut approach (red), the influenza A H1 epitope sites according to Caton et al. (1982) (blue), and avidity-changing sites according to Hensley et al. (2009) (green).
F<sc>IG</sc>. 4.
FIG. 4.
Patches under positive selection on HA. Patches on the HA protein structure of subtype H1 and H3 selected by the graph-cut algorithm. Patches are numbered according to tables 3 and 4.
F<sc>IG</sc>. 5.
FIG. 5.
Patches under positive selection on the HA and PB2 proteins of 2009 S-OIV. Patches on the 2009 swine-origin influenza A protein structures of HA and the c-terminal region of PB2, selected by the graph-cut algorithm. Patches are numbered according to tables 5 and 6.
F<sc>IG</sc>. 6.
FIG. 6.
Epitope sites not under positive selection. The histogram displays the ratio of residues within the corresponding P value intervals and demonstrates that many epitope sites feature insignificant P values resulting from an average dN/dS ratio. Epitopic sites are marked in red. The lower plot shows the distribution of the P values versus the dN/dS ratios for all residues of the H1 subtype.
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