Molecular and antigenic evolution of human influenza A/H3N2 viruses in Quebec, Canada, 2009–2011

Each year, influenza viruses cause epidemics by evading pre-existing humoral immunity through mutations in the major glycoproteins: the haemagglutinin (HA) and the neuraminidase (NA). In 2004, the antigenic evolution of HA of human influenza A (H3N2) viruses was mapped (Smith et al., Science 305, 371–376, 2004) from its introduction in humans in 1968 until 2003. The current study focused on the genetic evolution of NA and compared it with HA using the dataset of Smith and colleagues, updated to the epidemic of the 2009/2010 season. Phylogenetic trees and genetic maps were constructed to visualize the genetic evolution of NA and HA. The results revealed multiple reassortment events over the years. Overall rates of evolutionary change were lower for NA than for HA1 at the nucleotide level. Selection pressures were estimated, revealing an abundance of negatively selected sites and sparse positively selected sites. The differences found between the evolution of NA and HA1 warrant further analysis of the evolution of NA at the phenotypic level, as has been done previously for HA.

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Predictability of antigenic evolution for H3N2 human influenza A virus

Genes & Genetic Systems ◽

10.1266/ggs.88.225 ◽

2013 ◽

Vol 88 (4) ◽

pp. 225-232 ◽

Cited By ~ 11

Author(s):

Yoshiyuki Suzuki

Keyword(s):

Influenza A Virus ◽

Influenza A ◽

Human Influenza ◽

Antigenic Evolution

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Inference of Genotype–Phenotype Relationships in the Antigenic Evolution of Human Influenza A (H3N2) Viruses

PLoS Computational Biology ◽

10.1371/journal.pcbi.1002492 ◽

2012 ◽

Vol 8 (4) ◽

pp. e1002492 ◽

Cited By ~ 16

Author(s):

Lars Steinbrück ◽

Alice Carolyn McHardy

Keyword(s):

Influenza A ◽

Human Influenza ◽

Antigenic Evolution

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Predictability of the antigenic evolution of human influenza A H3 viruses

10.1101/770446 ◽

2019 ◽

Author(s):

R. Aguas ◽

N.M. Ferguson

Keyword(s):

Influenza A ◽

Simulation Algorithm ◽

Human Influenza ◽

Physiochemical Properties ◽

Machine Learning Methods ◽

Higher Dimensional ◽

Evolutionary Simulation ◽

Antigenic Evolution ◽

Inference Learning ◽

Viral Sequences

AbstractThe current influenza A antigenic evolution paradigm suggesting that antigenic evolution is highly constrained, with successful new viruses being near optimal at maximizing their antigenic distance from past strains. This begs the question of whether influenza’s antigenic evolution is fundamentally predictable, or if it takes place on a much higher dimensional antigenic space with multiple possible trajectories. We tackle this issue by building a genotype to phenotype map validated on historical hemagglutination inhibition assay data by using machine learning methods. This map uses amino acid physiochemical properties for inference, learning the expected antigenic distance given the differences in polarity and hydrophobicity observed across any two viral sequences, and is thus applicable to newly sampled viruses with previously unseen amino acids. This allows us to accurately blindly predict the antigenic relevance of soon to be vaccine viral strains. We couple the genotype to phenotype map with a molecular evolutionary simulation algorithm to explore the limits of influenza’s antigenic evolution and infer to what extent it is in fact predictable. Although we do uncover some canalization of antigenic trajectories, we find that multiple antigenic lineages are equally viable at any one point in time even though typically only one of those trajectories is actually realized.

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