Global migration of influenza A viruses in swine
Identifieur interne : 000379 ( Pmc/Checkpoint ); précédent : 000378; suivant : 000380Global migration of influenza A viruses in swine
Auteurs : Martha I. Nelson [États-Unis] ; Cécile Viboud [États-Unis] ; Amy L. Vincent [États-Unis] ; Marie R. Culhane [États-Unis] ; Susan E. Detmer [Canada] ; David E. Wentworth ; Andrew Rambaut [États-Unis, Royaume-Uni] ; Marc A. Suchard [États-Unis] ; Edward C. Holmes [Australie] ; Philippe Lemey [Belgique]Source :
- Nature communications [ 2041-1723 ] ; 2015.
Abstract
The complex and unresolved evolutionary origins of the 2009 H1N1 influenza pandemic exposed major gaps in our knowledge of the global spatial ecology and evolution of influenza A viruses in swine (swIAVs). Here we undertake an expansive phylogenetic analysis of swIAV sequence data and demonstrate that the global live swine trade strongly predicts the spatial dissemination of swIAVs, with Europe and North America acting as sources of viruses in Asian countries. In contrast, China has the world’s largest swine population but is not a major exporter of live swine, and is not an important source of swIAVs in neighboring Asian countries or globally. A meta-population simulation model incorporating trade data predicts that the global ecology of swIAVs is more complex than previously thought, and the US and China’s large swine populations are unlikely to be representative of swIAV diversity in their respective geographic regions, requiring independent surveillance efforts throughout Latin America and Asia.
Url:
DOI: 10.1038/ncomms7696
PubMed: 25813399
PubMed Central: 4380236
Affiliations:
- Australie, Belgique, Canada, Royaume-Uni, États-Unis
- Californie, Iowa, Maryland, Minnesota, Nouvelle-Galles du Sud, Écosse
- Sydney, Édimbourg
- Université d'Édimbourg, Université de Sydney
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PMC:4380236Le document en format XML
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<front><div type="abstract" xml:lang="en"><p id="P1">The complex and unresolved evolutionary origins of the 2009 H1N1 influenza pandemic exposed major gaps in our knowledge of the global spatial ecology and evolution of influenza A viruses in swine (swIAVs). Here we undertake an expansive phylogenetic analysis of swIAV sequence data and demonstrate that the global live swine trade strongly predicts the spatial dissemination of swIAVs, with Europe and North America acting as sources of viruses in Asian countries. In contrast, China has the world’s largest swine population but is not a major exporter of live swine, and is not an important source of swIAVs in neighboring Asian countries or globally. A meta-population simulation model incorporating trade data predicts that the global ecology of swIAVs is more complex than previously thought, and the US and China’s large swine populations are unlikely to be representative of swIAV diversity in their respective geographic regions, requiring independent surveillance efforts throughout Latin America and Asia.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101528555</journal-id>
<journal-id journal-id-type="pubmed-jr-id">37539</journal-id>
<journal-id journal-id-type="nlm-ta">Nat Commun</journal-id>
<journal-id journal-id-type="iso-abbrev">Nat Commun</journal-id>
<journal-title-group><journal-title>Nature communications</journal-title>
</journal-title-group>
<issn pub-type="epub">2041-1723</issn>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25813399</article-id>
<article-id pub-id-type="pmc">4380236</article-id>
<article-id pub-id-type="doi">10.1038/ncomms7696</article-id>
<article-id pub-id-type="manuscript">NIHMS666251</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Global migration of influenza A viruses in swine</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Nelson</surname>
<given-names>Martha I.</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
<xref rid="FN1" ref-type="author-notes">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Viboud</surname>
<given-names>Cécile</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Vincent</surname>
<given-names>Amy L.</given-names>
</name>
<xref ref-type="aff" rid="A2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Culhane</surname>
<given-names>Marie R.</given-names>
</name>
<xref ref-type="aff" rid="A3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Detmer</surname>
<given-names>Susan E.</given-names>
</name>
<xref ref-type="aff" rid="A4">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wentworth</surname>
<given-names>David E.</given-names>
</name>
<xref ref-type="aff" rid="A5">5</xref>
<xref rid="FN2" ref-type="author-notes">#</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Rambaut</surname>
<given-names>Andrew</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
<xref ref-type="aff" rid="A6">6</xref>
<xref ref-type="aff" rid="A7">7</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Suchard</surname>
<given-names>Marc A.</given-names>
</name>
<xref ref-type="aff" rid="A8">8</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Holmes</surname>
<given-names>Edward C.</given-names>
</name>
<xref ref-type="aff" rid="A9">9</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Lemey</surname>
<given-names>Philippe</given-names>
</name>
<xref ref-type="aff" rid="A10">10</xref>
</contrib>
</contrib-group>
<aff id="A1"><label>1</label>
Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892 USA</aff>
<aff id="A2"><label>2</label>
Virus and Prion Diseases of Livestock Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA 50010 USA</aff>
<aff id="A3"><label>3</label>
University of Minnesota Veterinary Diagnostic Laboratory, St. Paul, MN 55108 USA</aff>
<aff id="A4"><label>4</label>
Western College of Veterinary Medicine, University of Saskatchewan, Saskatchewan, Canada</aff>
<aff id="A5"><label>5</label>
J Craig Venter Institute, Rockville, MD USA 20850 USA</aff>
<aff id="A6"><label>6</label>
Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 FLT United Kingdom</aff>
<aff id="A7"><label>7</label>
Centre for Immunology, Infection and Evolution, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 FLT United Kingdom</aff>
<aff id="A8"><label>8</label>
Departments of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, and Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA 90095 USA</aff>
<aff id="A9"><label>9</label>
Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Biological Sciences and Sydney Medical School, University of Sydney, NSW 2006, Australia</aff>
<aff id="A10"><label>10</label>
Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium</aff>
<author-notes><corresp id="FN1"><label>*</label>
Corresponding author: Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, 16 Center Drive, Building 16, Room 202, Bethesda, MD 20892 USA. Tel: 1 301-402-5203; Fax: 1 301-496-8496; <email>nelsonma@mail.nih.gov</email>
</corresp>
<fn id="FN2" fn-type="present-address"><label>#</label>
<p>Current address: Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America</p>
</fn>
</author-notes>
<pub-date pub-type="nihms-submitted"><day>24</day>
<month>2</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="epub"><day>27</day>
<month>3</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>27</day>
<month>9</month>
<year>2015</year>
</pub-date>
<volume>6</volume>
<fpage>6696</fpage>
<lpage>6696</lpage>
<pmc-comment>elocation-id from pubmed: 10.1038/ncomms7696</pmc-comment>
<permissions><license xlink:href="http://www.nature.com/authors/editorial_policies/license.html#terms"><license-p>Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:<ext-link ext-link-type="uri" xlink:href="http://www.nature.com/authors/editorial_policies/license.html#terms">http://www.nature.com/authors/editorial_policies/license.html#terms</ext-link>
</license-p>
</license>
</permissions>
<abstract><p id="P1">The complex and unresolved evolutionary origins of the 2009 H1N1 influenza pandemic exposed major gaps in our knowledge of the global spatial ecology and evolution of influenza A viruses in swine (swIAVs). Here we undertake an expansive phylogenetic analysis of swIAV sequence data and demonstrate that the global live swine trade strongly predicts the spatial dissemination of swIAVs, with Europe and North America acting as sources of viruses in Asian countries. In contrast, China has the world’s largest swine population but is not a major exporter of live swine, and is not an important source of swIAVs in neighboring Asian countries or globally. A meta-population simulation model incorporating trade data predicts that the global ecology of swIAVs is more complex than previously thought, and the US and China’s large swine populations are unlikely to be representative of swIAV diversity in their respective geographic regions, requiring independent surveillance efforts throughout Latin America and Asia.</p>
</abstract>
</article-meta>
</front>
<floats-group><fig id="F1" orientation="portrait" position="float"><label>Fig. 1</label>
<caption><title>Modeled global swine distributions</title>
<p>Digital layers from Gridded Livestock of the World (GLW) (version 2.01)<sup><xref rid="R50" ref-type="bibr">50</xref>
</sup>
, downloaded from the publically available Livestock Geo-Wiki database (<ext-link ext-link-type="uri" xlink:href="http://www.livestock.geo-wiki.org">http://www.livestock.geo-wiki.org</ext-link>
) and manually edited in QGIS v.1.7.0. Swine densities are represented by black shading.</p>
</caption>
<graphic xlink:href="nihms666251f1"></graphic>
</fig>
<fig id="F2" orientation="portrait" position="float"><label>Fig. 2</label>
<caption><title>Inter-continental migration events of swIAVs</title>
<p>Circles represent the country of origin, based on the estimates summarized in the MCC tree, and are shaded accordingly. Lines represent the inferred time period of inter-continental transmission, within a level of uncertainty, inferred from the estimated date of ancestral nodes on the MCC tree. Triangles represent clades resulting from onwards transmission of the introduced viruses, are shaded by the country of destination, and extend as far forward in time as the most recently sampled virus. Numbers of introduction (1–18) correspond to the clade numbers on the phylogenies (<xref rid="F3" ref-type="fig">Fig. 3</xref>
and <xref ref-type="supplementary-material" rid="SD1">Supplementary Figs. 1–7</xref>
). The asterisks indicate that additional HA and NA swIAV sequence data was used to estimate the timing of introduction 18. Countries/regions are abbreviated as follows: CHN = China (including Hong Kong SAR and Taiwan), THA = Thailand, VNM = Vietnam, KOR = South Korea, JPN = Japan, MEX = Mexico, and EUR = Europe.</p>
</caption>
<graphic xlink:href="nihms666251f2"></graphic>
</fig>
<fig id="F3" orientation="portrait" position="float"><label>Fig. 3</label>
<caption><title>MCC trees of the NA lineages in swine</title>
<p>Time-scaled Bayesian MCC trees inferred for the NA segment for the three major swine virus lineages: (a) avian-origin Eurasian N1 swIAV lineage, the (b) classical N1 swIAV lineage, and the (c) multiple human seasonal virus-origin N2 swIAV lineages circulating in swine. Branches of human seasonal H3N2 influenza virus origin are shaded grey in (c), while branches associated with viruses from swine are shaded by country of origin: Argentina = brown; Canada = red; China (including Hong Kong SAR and Taiwan) = yellow; Europe = black; Japan = pink; Mexico = light blue; South Korea = green; Thailand = orange; USA = dark blue; Vietnam = purple. Posterior probabilities > 0.8 are included for key nodes, and international migration events that are supported by high posterior probabilities and long branch lengths are labeled according to <xref rid="F2" ref-type="fig">Fig. 2</xref>
.</p>
</caption>
<graphic xlink:href="nihms666251f3"></graphic>
</fig>
<fig id="F4" orientation="portrait" position="float"><label>Fig. 4</label>
<caption><title>Heat-map of swIAV migration between locations</title>
<p>Countries are listed in order of increasing geographical distance from Argentina (ARG). MEX = Mexico, USA = United States, CAN = Canada, EUR = Europe, JPN = Japan, CHN = China (including Hong Kong SAR and Taiwan), KOR = South Korea, THA = Thailand, VNM = Vietnam. The intensity of the color (red = high; white = low) reflects the number of ‘Markov jump’ counts inferred over the totality of phylogenies (all segments, all lineages) from one location to another (asymmetrical). Markov jump counts measure the number of inferred location state transitions, modelled by a continuous-time Markov chain process, that occur along the branches of the phylogeny. For clarity the heat-map has been divided into four sections representing (a) viral migration events within the Americas and between the Americas and Europe; (b) migrations from the Americas/Europe to Asia; (c) migrations from Asia to the Americas/Europe; and (d) migrations between Asian countries.</p>
</caption>
<graphic xlink:href="nihms666251f4"></graphic>
</fig>
<fig id="F5" orientation="portrait" position="float"><label>Fig. 5</label>
<caption><title>The support and contribution of swIAV diffusion predictors among 9 countries</title>
<p>Twelve predictors were considered: geographical distance (km), volume of live swine trade, 1996–2012 (USD), swine population size for the years 1969–2010, the total number of imports of live swine during 1969–2010, the total number of swine exports during 1969–2010, the percent change in swine population size from 1969–2010, and the number of sequences available from a given country for our analysis. ‘O’ refers to the swine population of origin, and ‘d’ refers to the swine population of destination. Support for each predictor is represented by an inclusion probability that is estimated as the posterior expectation for the indicator variable associated with each predictor (<bold>E</bold>
[<italic>δ</italic>
]). The contribution of each predictor is represented by the mean and credible intervals of the GLM coefficients (<italic>β</italic>
) on a log scale conditional on the predictor being included in the model (<italic>β</italic>
|<italic>δ</italic>
=1). See <xref ref-type="supplementary-material" rid="SD1">Supplementary Fig. 8</xref>
for MP and NS results.</p>
</caption>
<graphic xlink:href="nihms666251f5"></graphic>
</fig>
<fig id="F6" orientation="portrait" position="float"><label>Fig. 6</label>
<caption><title>Maps of simulated spread of influenza viruses via live swine trade flows</title>
<p>Simulated spread of an influenza virus from 5 seed countries (shaded in black) to 146 countries for which live swine trade is available from the United Nations Commodity Trade Statistics Database (available at <ext-link ext-link-type="uri" xlink:href="http://comtrade.un.org">http://comtrade.un.org</ext-link>
) (a–e). Probability of an outbreak in the invaded country is shaded from white (probability of 0) to red (probability of 1). The probability of co-invasion by both a virus seeded in North America (Canada and the United States) and Europe also is shaded from white (probability of 0) to red (probability of 1) (f). Arrows represent the direction of viral dissemination for countries with a probability of an outbreak > 0.25 (see <xref ref-type="supplementary-material" rid="SD1">Supplementary Table 5</xref>
for a complete list of all outbreak probabilities by country).</p>
</caption>
<graphic xlink:href="nihms666251f6"></graphic>
</fig>
</floats-group>
</pmc>
<affiliations><list><country><li>Australie</li>
<li>Belgique</li>
<li>Canada</li>
<li>Royaume-Uni</li>
<li>États-Unis</li>
</country>
<region><li>Californie</li>
<li>Iowa</li>
<li>Maryland</li>
<li>Minnesota</li>
<li>Nouvelle-Galles du Sud</li>
<li>Écosse</li>
</region>
<settlement><li>Sydney</li>
<li>Édimbourg</li>
</settlement>
<orgName><li>Université d'Édimbourg</li>
<li>Université de Sydney</li>
</orgName>
</list>
<tree><noCountry><name sortKey="Wentworth, David E" sort="Wentworth, David E" uniqKey="Wentworth D" first="David E." last="Wentworth">David E. Wentworth</name>
</noCountry>
<country name="États-Unis"><region name="Maryland"><name sortKey="Nelson, Martha I" sort="Nelson, Martha I" uniqKey="Nelson M" first="Martha I." last="Nelson">Martha I. Nelson</name>
</region>
<name sortKey="Culhane, Marie R" sort="Culhane, Marie R" uniqKey="Culhane M" first="Marie R." last="Culhane">Marie R. Culhane</name>
<name sortKey="Rambaut, Andrew" sort="Rambaut, Andrew" uniqKey="Rambaut A" first="Andrew" last="Rambaut">Andrew Rambaut</name>
<name sortKey="Suchard, Marc A" sort="Suchard, Marc A" uniqKey="Suchard M" first="Marc A." last="Suchard">Marc A. Suchard</name>
<name sortKey="Viboud, Cecile" sort="Viboud, Cecile" uniqKey="Viboud C" first="Cécile" last="Viboud">Cécile Viboud</name>
<name sortKey="Vincent, Amy L" sort="Vincent, Amy L" uniqKey="Vincent A" first="Amy L." last="Vincent">Amy L. Vincent</name>
</country>
<country name="Canada"><noRegion><name sortKey="Detmer, Susan E" sort="Detmer, Susan E" uniqKey="Detmer S" first="Susan E." last="Detmer">Susan E. Detmer</name>
</noRegion>
</country>
<country name="Royaume-Uni"><region name="Écosse"><name sortKey="Rambaut, Andrew" sort="Rambaut, Andrew" uniqKey="Rambaut A" first="Andrew" last="Rambaut">Andrew Rambaut</name>
</region>
<name sortKey="Rambaut, Andrew" sort="Rambaut, Andrew" uniqKey="Rambaut A" first="Andrew" last="Rambaut">Andrew Rambaut</name>
</country>
<country name="Australie"><region name="Nouvelle-Galles du Sud"><name sortKey="Holmes, Edward C" sort="Holmes, Edward C" uniqKey="Holmes E" first="Edward C." last="Holmes">Edward C. Holmes</name>
</region>
</country>
<country name="Belgique"><noRegion><name sortKey="Lemey, Philippe" sort="Lemey, Philippe" uniqKey="Lemey P" first="Philippe" last="Lemey">Philippe Lemey</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
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