Human-Origin Influenza A(H3N2) Reassortant Viruses in Swine, Southeast Mexico

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Abstract

The genetic diversity of influenza A viruses circulating in swine in Mexico complicates control efforts in animals and presents a threat to humans, as shown by influenza A(H1N1)pdm09 virus. To describe evolution of swine influenza A viruses in Mexico and evaluate strains for vaccine development, we sequenced the genomes of 59 viruses and performed antigenic cartography on strains from 5 regions. We found that genetic and antigenic diversity were particularly high in southeast Mexico because of repeated introductions of viruses from humans and swine in other regions in Mexico. We identified novel reassortant H3N2 viruses with genome segments derived from 2 different viruses that were independently introduced from humans into swine: pandemic H1N1 viruses and seasonal H3N2 viruses. The Mexico swine viruses are antigenically distinct from US swine lineages. Protection against these viruses is unlikely to be afforded by US virus vaccines and would require development of new vaccines specifically targeting these diverse strains.

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Most cited references 19

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Marc Suchard, Andrew Rambaut (2009)

Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of protein-coding sequence evolution with a resulting increase in phylogenetic accuracy. Unfortunately, due to the high number of codon states, computational burden has largely thwarted phylogenetic reconstruction under codon models, particularly at the genomic-scale. Here, we describe novel algorithms and methods for evaluating phylogenies under arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the large number of processing cores to efficiently parallelize calculations even for large state-size models. We implement the approach in an existing Bayesian framework and apply the algorithms to estimating the phylogeny of 62 complete mitochondrial genomes of carnivores under a 60-state codon model. We see a near 90-fold speed increase over an optimized CPU-based computation and a >140-fold increase over the currently available implementation, making this the first practical use of codon models for phylogenetic inference over whole mitochondrial or microorganism genomes. Source code provided in BEAGLE: Broad-platform Evolutionary Analysis General Likelihood Evaluator, a cross-platform/processor library for phylogenetic likelihood computation (http://beagle-lib.googlecode.com/). We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (http://beast.bio.ed.ac.uk/).

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Counting labeled transitions in continuous-time Markov models of evolution.

Marc Suchard, Vladimir N. Minin (2008)

Counting processes that keep track of labeled changes to discrete evolutionary traits play critical roles in evolutionary hypothesis testing. If we assume that trait evolution can be described by a continuous-time Markov chain, then it suffices to study the process that counts labeled transitions of the chain. For a binary trait, we demonstrate that it is possible to obtain closed-form analytic solutions for the probability mass and probability generating functions of this evolutionary counting process. In the general, multi-state case we show how to compute moments of the counting process using an eigen decomposition of the infinitesimal generator, provided the latter is a diagonalizable matrix. We conclude with two examples that demonstrate the utility of our results.

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Long-term evolution and transmission dynamics of swine influenza A virus.

Dhanasekaran Vijaykrishna, Gavin J. D. Smith, Oliver G. Pybus … (2011)

Swine influenza A viruses (SwIV) cause significant economic losses in animal husbandry as well as instances of human disease and occasionally give rise to human pandemics, including that caused by the H1N1/2009 virus. The lack of systematic and longitudinal influenza surveillance in pigs has hampered attempts to reconstruct the origins of this pandemic. Most existing swine data were derived from opportunistic samples collected from diseased pigs in disparate geographical regions, not from prospective studies in defined locations, hence the evolutionary and transmission dynamics of SwIV are poorly understood. Here we quantify the epidemiological, genetic and antigenic dynamics of SwIV in Hong Kong using a data set of more than 650 SwIV isolates and more than 800 swine sera from 12 years of systematic surveillance in this region, supplemented with data stretching back 34 years. Intercontinental virus movement has led to reassortment and lineage replacement, creating an antigenically and genetically diverse virus population whose dynamics are quantitatively different from those previously observed for human influenza viruses. Our findings indicate that increased antigenic drift is associated with reassortment events and offer insights into the emergence of influenza viruses with epidemic potential in swine and humans.

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Author and article information

Journal

Journal ID (nlm-ta): Emerg Infect Dis

Journal ID (iso-abbrev): Emerging Infect. Dis

Journal ID (publisher-id): EID

Title: Emerging Infectious Diseases

Publisher: Centers for Disease Control and Prevention

ISSN (Print): 1080-6040

ISSN (Electronic): 1080-6059

Publication date (Print): April 2019

Volume: 25

Issue: 4

Pages: 691-700

Affiliations

[1]National Institutes of Health, Bethesda, Maryland, USA (M.I. Nelson, N.S. Trovão);

[2]National Animal Disease Center, Ames, Iowa, USA (C.K. Souza, A.L. Vincent);

[3]Icahn School of Medicine at Mount Sinai, New York, New York, USA (N.S. Trovão, I. Mena);

[4]University of Minnesota, Saint Paul, Minnesota, USA (A. Diaz, A. Rovira, M. Torremorell, D. Marthaler, M.R. Culhane)

Author notes

Address for correspondence: Martha I. Nelson, National Institutes of Health, 16 Center Dr, Bethesda, MD 20892, USA; email: nelsonma@ 123456mail.nih.gov

Article

Publisher ID: 18-0779

DOI: 10.3201/eid2504.180779

PMC ID: 6433011

PubMed ID: 30730827

SO-VID: 6b970b2a-e10b-40f9-a05c-5b48163aed48

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