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Antigenic evolution of viruses in host populations.

ABSTRACT: To escape immune recognition in previously infected hosts, viruses evolve genetically in immunologically important regions. The host's immune system responds by generating new memory cells recognizing the mutated viral strains. Despite recent advances in data collection and analysis, it remains conceptually unclear how epidemiology, immune response, and evolutionary factors interact to produce the observed speed of evolution and the incidence of infection. Here we establish a general and simple relationship between long-term cross-immunity, genetic diversity, speed of evolution, and incidence. We develop an analytic method fusing the standard epidemiological susceptible-infected-recovered approach and the modern virus evolution theory. The model includes the factors of strain selection due to immune memory cells, random genetic drift, and clonal interference effects. We predict that the distribution of recovered individuals in memory serotypes creates a moving fitness landscape for the circulating strains which drives antigenic escape. The fitness slope (effective selection coefficient) is proportional to the reproductive number in the absence of immunity R0 and inversely proportional to the cross-immunity distance a, defined as the genetic distance of a virus strain from a previously infecting strain conferring 50% decrease in infection probability. Analysis predicts that the evolution rate increases linearly with the fitness slope and logarithmically with the genomic mutation rate and the host population size. Fitting our analytic model to data obtained for influenza A H3N2 and H1N1, we predict the annual infection incidence within a previously estimated range, (4-7)%, and the antigenic mutation rate of Ub = (5 - 8) ? 10(-4) per transmission event per genome. Our prediction of the cross-immunity distance of a = (14 - 15) aminoacid substitutions agrees with independent data for equine influenza.

SUBMITTER: Rouzine IM

PROVIDER: S-EPMC6173453 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Antigenic evolution of viruses in host populations.

Rouzine Igor M IM Rozhnova Ganna G

PLoS pathogens 20180912 9

To escape immune recognition in previously infected hosts, viruses evolve genetically in immunologically important regions. The host's immune system responds by generating new memory cells recognizing the mutated viral strains. Despite recent advances in data collection and analysis, it remains conceptually unclear how epidemiology, immune response, and evolutionary factors interact to produce the observed speed of evolution and the incidence of infection. Here we establish a general and simple ...[more]

PMID: 30208108

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Project description:BACKGROUND: In pandemic and epidemic forms, avian and human influenza viruses often cause significant damage to human society and economics. Gradually accumulated mutations on hemagglutinin (HA) cause immunologically distinct circulating strains, which lead to the antigenic drift (named as antigenic variants). The "antigenic variants" often requires a new vaccine to be formulated before each annual epidemic. Mapping the genetic evolution to the antigenic drift of influenza viruses is an emergent issue to public health and vaccine development RESULTS: We developed a method for identifying antigenic critical amino acid positions, rules, and co-mutated positions for antigenic variants. The information gain (IG) and the entropy are used to measure the score of an amino acid position on hemagglutinin (HA) for discriminating between antigenic variants and similar viruses. A position with high IG and entropy implied that this position is highly correlated to an antigenic drift. Nineteen positions with high IG and high genetic diversity are identified as antigenic critical positions on the HA proteins. Most of these antigenic critical positions are located on five epitopes or on the surface based on the HA structure. Based on IG values and entropies of these 19 positions on the HA, the decision tree was applied to create a rule-based model and to identify rules for predicting antigenic variants of a given two HA sequences which are often a vaccine strain and a circulating strain. The predicting accuracies of this model on two sets, which consist of a training set (181 hemagglutination inhibition (HI) assays) and an independent test set (31,878 HI assays), are 91.2% and 96.2% respectively. CONCLUSION: Our method is able to identify critical positions, rules, and co-mutated positions on HA for predicting the antigenic variants. The information gains and the entropies of HA positions provide insight to the antigenic drift and co-evolution positions for influenza seasons. We believe that our method is robust and is potential useful for studying influenza virus evolution and vaccine development.

Project description:BACKGROUND: The World Health Organization (WHO) organizes consultations in February and September of each year, spearheaded by an advisory group of experts to analyze influenza surveillance data generated by the WHO Global Influenza Surveillance and Response System (GISRS). The purpose of these consultations is to recommend the composition on influenza virus vaccines for the northern and southern hemispheres, respectively. The latest news of influenza viruses is made available to the public and updated on the WHO website. Although WHO discloses the manner in which it has made the recommendation, usually by considering epidemiological and clinical information to analyze the antigenic and genetic characteristics of seasonal influenza viruses, most individuals do not possess an understanding of antigenic drift and when it occurs. FINDINGS: We have constructed a web server, named Fluctrl, and implemented a pipeline whereby HA sequence data is downloaded from the Influenza Virus Resource at NCBI along with their isolation information including isolation year and location, which are parsed and managed in MySQL database. By analyzing the frequency of each amino acid residue of the HA1 domain expressed by the viruses on annual basis, users are able to obtain evolutionary dynamics of human influenza viruses corresponding with epidemics. Users are able to upload and analyze their HA1 sequences for generating evolutionary dynamics. In addition, a distribution of amino acid residues at a particular site is represented geographically to trace the location where antigenic variants are seeded. CONCLUSIONS: Fluctrl is constructed for monitoring the antigenic evolution of human influenza A viruses. This tool is intended to inform the general public how and when influenza viruses evade the human body's immunity. Furthermore, leveraging the geographic information, the original locations of emerging influenza viruses can be traced. Fluctrl is freely accessible at http://sb.nhri.org.tw/fluctrl.

Dataset Information

Antigenic evolution of viruses in host populations.

Publications

Antigenic evolution of viruses in host populations.

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