Using a mutual information-based site transition network to map the genetic evolution of influenza A/H3N2 virus
ABSTRACT Mapping the antigenic and genetic evolution pathways of influenza A is of critical importance in the vaccine development and drug design of influenza virus. In this article, we have analyzed more than 4000 A/H3N2 hemagglutinin (HA) sequences from 1968 to 2008 to model the evolutionary path of the influenza virus, which allows us to predict its future potential drifts with specific mutations.
The mutual information (MI) method was used to design a site transition network (STN) for each amino acid site in the A/H3N2 HA sequence. The STN network indicates that most of the dynamic interactions are positioned around the epitopes and the receptor binding domain regions, with strong preferences in both the mutation sites and amino acid types being mutated to. The network also shows that antigenic changes accumulate over time, with occasional large changes due to multiple co-occurring mutations at antigenic sites. Furthermore, the cluster analysis by subdividing the STN into several subnetworks reveals a more detailed view about the features of the antigenic change: the characteristic inner sites and the connecting inter-subnetwork sites are both responsible for the drifts. A novel five-step prediction algorithm based on the STN shows a reasonable accuracy in reproducing historical HA mutations. For example, our method can reproduce the 2003-2004 A/H3N2 mutations with approximately 70% accuracy. The method also predicts seven possible mutations for the next antigenic drift in the coming 2009-2010 season. The STN approach also agrees well with the phylogenetic tree and antigenic maps based on HA inhibition assays.
All code and data are available at http://ibi.zju.edu.cn/birdflu/.
- SourceAvailable from: Yoshiyuki Suzuki
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- "Since influenza A virus can evolve escape mutants rapidly , it was desirable to predict the antigenic evolution for developing effective vaccines. Although empirical methods have been proposed and reported to predict the antigenic evolution more or less accurately (Bush et al., 1999; Xia et al., 2009; He and Deem, 2010; Ito et al., 2011), they did not provide much insight into the effects of unobserved mutations and the mechanisms of antigenic evolution. In the present study, a theoretical method was introduced to predict the antigenic evolution by evaluating the effects of de novo mutations through estimating the antigenic distance. "
ABSTRACT: Influenza A virus continues to pose a threat to public health. Since this virus can evolve escape mutants rapidly, it is desirable to predict the antigenic evolution for developing effective vaccines. Although empirical methods have been proposed and reported to predict the antigenic evolution more or less accurately, they did not provide much insight into the effects of unobserved mutations and the mechanisms of antigenic evolution. Here a theoretical method was introduced to predict the antigenic evolution of H3N2 human influenza A virus by evaluating de novo mutations through estimating the antigenic distance. The antigenic distance defined with the hemagglutination inhibition (HI) titer was estimated with antigenic models taking into account the volume, isoelectric point, relative solvent accessibility, and distances from receptor-binding sites (RBS) and N-linked glycosylation sites (NGS) for amino acids in hemagglutinin 1 (HA1). When the best model with the optimized parameter values was used to predict the antigenic evolution for the dominant strains, the prediction accuracy was relatively low. However, there appeared to be an overall tendency that the amino acid sites with larger potential net effect on antigenicity were more likely to evolve and the amino acid changes with larger potential effect were more likely to take place, suggesting that natural selection may operate to enhance the antigenic evolution of H3N2 human influenza A virus.Genes & Genetic Systems 01/2013; 88(4):225-32. DOI:10.1266/ggs.88.225 · 0.87 Impact Factor
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- "Seasonal influenza epidemics are primarily caused by antigenic drift (that is, single-point mutations that are caused by the high mutation rate of influenza virus strains). Although single-point mutations occur at random, genetic changes can be predicted in advance . These predictions provide the opportunity to develop vaccines to prevent seasonal influenza and therefore also the risk of secondary bacterial infections. "
ABSTRACT: Seasonal and pandemic influenza are frequently complicated by bacterial infections, causing additional hospitalization and mortality. Secondary bacterial respiratory infection can be subdivided into combined viral/bacterial pneumonia and post-influenza pneumonia, which differ in their pathogenesis. During combined viral/bacterial infection, the virus, the bacterium and the host interact with each other. Post-influenza pneumonia may, at least in part, be due to resolution of inflammation caused by the primary viral infection. These mechanisms restore tissue homeostasis but greatly impair the host response against unrelated bacterial pathogens. In this review we summarize the underlying mechanisms leading to combined viral/bacterial infection or post-influenza pneumonia and highlight important considerations for effective treatment of bacterial pneumonia during and shortly after influenza.Critical care (London, England) 04/2010; 14(2):219. DOI:10.1186/cc8893
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ABSTRACT: The 2009 H1N1 influenza pandemic has attracted worldwide attention. The new virus first emerged in Mexico in April, 2009 was identified as a unique combination of a triple-reassortant swine influenza A virus, composed of genetic information from pigs, hu-mans, birds, and a Eurasian swine influenza virus. Several recent studies on the 2009 H1N1 virus util-ized small datasets to conduct analysis. With new se-quences available up to date, we were able to extend the previous research in three areas. The first was finding two networks of co-mutations that may po-tentially affect the current flu-drug binding sites on neuraminidase (NA), one of the two surface proteins of flu virus. The second was discovering a special stalk motif, which was dominant in the H5N1 strains in the past, in the 2009 H1N1 strains for the first time. Due to the high virulence of this motif, the second finding is significant in our current research on 2009 H1N1. The third was updating the phylogenetic an-alysis of current NA sequences of 2009 H1N1 and H5N1, which demonstrated that, in clear contrast to previous findings, the N1 sequences in 2009 are di-verse enough to cover different major branches of the phylogenetic tree of those in previous years. As the novel influenza A H1N1 virus continues to spread globally, our results highlighted the importance of performing timely analysis on the 2009 H1N1 virus.Journal of Biomedical Science and Engineering 01/2009; 02(07). DOI:10.4236/jbise.2009.27080