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The Genetic Diversity of Influenza A Viruses in Wild Birds in Peru
research-article
Author(s):
Martha I. Nelson
1 ,
Simon Pollett
2
,
3
,
4
,
* ,
Bruno Ghersi
2 ,
Maria Silva
2 ,
Mark P. Simons
2 ,
Eliana Icochea
5 ,
Armando E. Gonzalez
5 ,
Karen Segovia
2 ,
Matthew R. Kasper
2 ,
Joel M. Montgomery
2 ,
Daniel G. Bausch
2
,
6
Publication date (Electronic):
19 January 2016
Journal:
PLoS ONE
Publisher:
Public Library of Science
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Abstract
Our understanding of the global ecology of avian influenza A viruses (AIVs) is impeded by historically low levels of viral surveillance in Latin America. Through sampling and whole-genome sequencing of 31 AIVs from wild birds in Peru, we identified 10 HA subtypes (H1-H4, H6-H7, H10-H13) and 8 NA subtypes (N1-N3, N5-N9). The majority of Peruvian AIVs were closely related to AIVs found in North America. However, unusual reassortants, including a H13 virus containing a PA segment related to extremely divergent Argentinian viruses, suggest that substantial AIV diversity circulates undetected throughout South America.
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Most cited references12
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Migratory flyway and geographical distance are barriers to the gene flow of influenza virus among North American birds.
Michelle Bales, David Spiro, Tommy Lam … (2012)
Despite the importance of migratory birds in the ecology and evolution of avian influenza virus (AIV), there is a lack of information on the patterns of AIV spread at the intra-continental scale. We applied a variety of statistical phylogeographic techniques to a plethora of viral genome sequence data to determine the strength, pattern and determinants of gene flow in AIV sampled from wild birds in North America. These analyses revealed a clear isolation-by-distance of AIV among sampling localities. In addition, we show that phylogeographic models incorporating information on the avian flyway of sampling proved a better fit to the observed sequence data than those specifying homogeneous or random rates of gene flow among localities. In sum, these data strongly suggest that the intra-continental spread of AIV by migratory birds is subject to major ecological barriers, including spatial distance and avian flyway. © 2011 Blackwell Publishing Ltd/CNRS.
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- Abstract: found
- Article: found
Influenza A Virus Migration and Persistence in North American Wild Birds
Justin Bahl, Scott Krauss, Denise Kühnert … (2013)
Introduction Migrating wild birds have been implicated in the spread and emergence of human and livestock influenza, including pandemic influenza and highly pathogenic H5N1 avian influenza [1]–[3]. Viral transmission between wild birds and domestic poultry has contributed to genomic reassortment and confounded disease control efforts [2], [4]. Subsequently, with the reintroduction of H5N1 to wild birds the virus has spread throughout Eurasia and Africa [5]–[9]. While it is contentious as to whether wild birds are the primary vectors spreading H5N1 viruses over long distances, there is little doubt that these animals play a role in confounding disease surveillance and control efforts. It is estimated worldwide that over 50 billion birds migrate annually between breeding and non-breeding areas [10]. Even though there is evidence that Anseriformes infected with influenza A virus have hampered migration, these hosts vector influenza viruses vast distances [11]–[12]. Disease transmissions between the millions of conspecific birds at congregating sites throughout the world contribute to the genetic variability and reassortment of influenza A viruses [13], [14]. It is not coincidental that these major breeding, feeding, and staging sites are also regions of high viral prevalence [14]–[21]. Recent efforts to assess invasive virological threats have focused on increased surveillance and early detection of introduced viral strains [22]–[24]. Influenza A viruses have transmitted between the Eurasian and North American wild Anseriformes and Charadriformes gene pools where birds from both continental regions commingle and therefore the threat posed by introduction of H5N1 to North America remains. However, once a virological threat has entered the North American bird population there is little information regarding how that virus may behave or diffuse between spatially distant migratory bird populations. The prediction of viral spread and disease emergence, as well as formulation of preparedness plans has generally been based on ad hoc approaches. This is largely due to a critical lack of knowledge of viral movements between different host populations [13]–[17]. The patterns of viral spread and subsequent risk posed by wild bird viruses therefore remain unpredictable. Methodological advances present an opportunity for large-scale assessment of spatiotemporal patterns of viral movement between migrating bird populations. In this study we identified 20 discrete regions in North America where influenza viruses have been systematically collected from wild birds to determine whether the viral population was structured according to host migratory flyways, and rates of gene flow between these populations. Avian influenza viruses were isolated annually throughout our surveillance in Alberta, Canada and Delaware Bay, USA and an additional 287 genomes were sequenced. Using full genome data we characterize the reassortment dynamics, spatial diffusion patterns and evolutionary genomics of influenza A viruses in North America collected over a 25-year period from migratory birds. Results Avian influenza H3 viruses were among the most frequently isolated influenza subtype from our surveillance in Alberta, Canada and Delaware Bay, USA [17]. We therefore randomly selected 200 H3 subtype isolates collected from 1976 to 2009 – plus an additional 100 influenza isolates of multiple subtypes – for full genome sequencing. Thirteen isolates could not be sequenced and a number of additional isolates were mixed samples containing multiple subtypes. As a result, 163 H3 subtype viruses and 124 isolates of other subtypes were sequenced. The newly sequenced H3-HA genes were analyzed with publically available H3-HA data to estimate the phylogenetic history (number of taxa (ntax) = 531). This large scale phylogeny of globally sampled H3 viruses from wild birds revealed three major lineages, two circulating in North America (Lineages I and II) and a third lineage that is a mix of North American and Eurasian isolates (Figure S1). All gene sequences that were of Eurasian origin were excluded from all further analysis in this study, including those that belonged to the mixed Eurasian/North American lineage. Comparative genomic analysis of H3 subtype viruses isolated from the Alberta and Delaware Bay sites was conducted to test AIV evolutionary dynamics in different hosts. In Alberta, where birds sampled were primarily juvenile Anseriformes [20] the H3-HA phylogeny showed that H3 viruses were recovered in almost every year (ntax = 94), with both Lineage I and II viruses present (Figure 1A). In contrast, in Delaware Bay, where only Charadriiformes were sampled, H3 viruses were detected in only 7 years (ntax = 69) from 24 years of surveillance (Figure 1B). In those years when H3 viruses were isolated in Delaware Bay, only a single clade was detected each sampling season and no co-circulation of these clades was apparent. While viral prevalence in Delaware Bay and Alberta are similar [17], Anseriformes host a representative diversity of AIV in North America. In contrast, Charadriiformes host limited viral diversity exhibiting local epidemic-like dynamics [25] suggesting Charadriformes in Delaware Bay are being infected from a currently undetected AIV population. 10.1371/journal.ppat.1003570.g001 Figure 1 A) H3-HA phylogenetic tree for isolates from Alberta. B) H3-HA phylogenetic tree for isolates from Delaware Bay. C) H3-HA phylogenetic tree for isolates from Alaska. D) Multidimensional scaling of tree-to-tree TMRCA estimates from Alberta. For reference, the space occupied by human H3N2 viruses from similar analysis is centered (grey circle). E) Multidimensional scaling of tree-to-tree patristic distance from Delaware Bay. F) Multidimensional scaling of tree-to-tree patristic distance from Alaska. We used multidimensional scaling of times of most recent common ancestor (tMRCAs) and patristic distances for each gene segment (excluding NA) to test differences in reassortment between populations (Figure 1C, D). In this analysis, the spread of each point cloud represents the statistical uncertainty in the phylogenetic history of each gene and we expect non-reassortant genes will have overlapping point clouds [26]. For both Alberta and Delaware Bay these analyses clearly indicate high levels of reassortment and that the evolutionary histories of the HA and internal genes are therefore partially independent, although the HA and PB1 from Delaware Bay show a higher level of similarity. To evaluate evolutionary dynamics and migration patterns of H3 subtype viruses throughout North America we identified viruses from avian hosts sampled in 20 defined discrete geographic regions excluding those sequences with recently introduced from Eurasia as described above (ntax = 437). The tMRCA of Lineages I and II was estimated to be ∼1942 (95% Bayesian Credibility Interval 1926–1962). The mechanism for maintenance of this deep divergence remains unknown, as viruses from both lineages have co-circulated in geographically overlapping host populations, primarily Anseriformes, throughout the entire surveillance period. One possibility is that this deep divergence is the product of (i) a very large host meta-population and (ii) relatively rare cross-species transmission rate when compared to annual seasonal epidemic dynamics leading to a lack of synchronicity of partial immunity across host species so that more than one lineage can effectively survive long periods of time. Although there was little evidence for geographic structuring of the virus population over extended periods, an obvious exception is a single lineage that has circulated for more than 10 years in birds sampled from Delaware Bay (Figure 2). 10.1371/journal.ppat.1003570.g002 Figure 2 Bayesian relaxed clock HA gene phylogenetic tree from all H3 wild bird isolates in North America. The two co-circulating North American lineages (I and II) are annotated to the right of the tree. Branches are colored according to ancestral state location estimated from geographical tip-state observations for all observed localities. Ancestral state reconstruction of virus geographic location suggests that the population of Lineage II was localized in southeast Alberta prior to migrating to other locations across all North American flyways (Figure 2). However, the apparent geographic isolation of viruses from Alberta may be an artifact as sampling in this location began 12 years before other sites. Furthermore, in Lineage I, where sampling was temporally and spatially more consistent, we found no evidence of localized ancestral populations. We next estimated rates of viral migration between discrete geographic locations treating each gene as an independent dataset to capitalize on the extra historical information generated by genetic reassortment. While each gene segment analysed supported lateral diffusion between migratory flyways over time, analysis of migration paths using single gene segments yielded contradictory answers (Figure S2, S3, S4, S5, S6, S7, S8). For example, the PB1 gene analysis highly supported migration events within the Pacific flyways, although none of the other gene segment analyses did (Figure S4). This is probably a reflection of the high rates of reassortment unlinking the evolutionary history of individual gene segments between subtypes. We further analyzed all publically available PA, PB1, PB2, NP and M sequence data from wild aquatic birds isolated between 1985–2009 in North America. The HA, NA and NS gene segments were not included in this analysis due to the deep divergence between the subtypes [16]. In this analysis we defined 16 geographic states and a 17th state termed “Other”, that maintained phylogenetic tree structure. The “Other” state included taxa isolated prior to 1998 where few geographic locations were sampled and locations where few isolates were encountered over the surveillance period [27]. This analysis included more than 1300 sequences for each gene. The migration pattern was jointly estimated from all gene datasets in a single analysis even though the taxon number and subtype between each gene dataset was not identical. The phylogenetic tree space was sampled independently for each dataset, but we assumed the migration parameters were linked. These parameters were estimated across all gene trees to elucidate the migration history of the avian influenza population in North American wild birds and showed similar levels of within versus between flyway migration rates (Figure 3). This was confirmed by statistical comparison of these rates, which showed no significant difference in diffusion patterns (mean within flyway rate>mean between flyway rate, Bayes factor (BF) = 0.968; mean between flyway rate>mean within flyway rate, BF = 1.033). 10.1371/journal.ppat.1003570.g003 Figure 3 A) Mean migration rate per MCMC step within flyway migration rates vs Mean between flyway migration jointly estimated from all publically available PA, PB1, PB2, NP and M gene segments. B) Density distribution of mean within flyway and mean between flyway rates. Table 1 shows the mean migration rates for all statistically supported state transitions recovered from our analysis. The diffusion patterns recovered from this analysis show that when all subtypes, hosts and locations are considered there is extensive mixing of influenza A virus between populations (Figure 4). However, it is unlikely that this pattern can be generalized for individual subtypes. For example, analysis of H3-HA gene segments with the six other internal gene segments (excluding NA) showed greater within flyway migration compared to between flyway migration (Figure S2, S3, S4, S5, S6, S7, S8, S10). Surprisingly, we could not reject the null hypothesis that migration rates are unrelated to the distance between locations (Pearson correlation coefficient = −0.037; Mantel test of rates vs distance, p = 0.317, Figure S10). However, the large-scale spatial diffusion and persistence of AIV is facilitated by comingling of birds in congregation sites located where multiple flyways overlap, such as Alberta (Figure 4). Taken together these results suggest that the AIV population mixes extensively and rapidly despite large geographic separation between sampling locations. 10.1371/journal.ppat.1003570.g004 Figure 4 Patterns of viral migration jointly estimated across the 5 internal protein gene segments. Lines connecting discrete regions indicate statistically supported ancestral state changes and are thickened according to statistical support. There are five categories of support. The thinnest lines indicate 6≤BF 100 Alaska NW Alberta 1619 2.57 2.68 1 >100 New Brunswick Delaware Bay 1359 1.44 1.51 1 >100 British Columbia SE Alberta 713 1.37 1.41 1 >100 Ontario-Ohio Delaware Bay 715 1.19 1.24 0.77 29 Alaska New Brunswick 4797 1.01 1.11 0.6 13 Oregon California 556 0.93 0.97 1 >100 Quebec-NY State Texas 2665 0.87 0.87 0.61 13 SE Alberta Ontario-Ohio 2514 0.77 0.8 0.85 47 British Columbia Ontario-Ohio 3141 0.69 0.71 0.52 10 British Columbia California 1390 0.63 0.65 1 >100 Quebec-NY State Mississippi-Louisiana 2009 0.63 0.64 1 >100 Quebec-NY State Delaware Bay 858 0.57 0.59 1 >100 Ontario-Ohio Mississippi-Louisiana 1373 0.49 0.51 0.99 >100 Delaware Bay Mississippi-Louisiana 1432 0.4 0.42 1 >100 NW Alberta Quebec-NY State 3188 0.39 0.4 1 >100 Quebec-NY State New Brunswick 616 0.25 0.26 1 >100 British Columbia SW Alberta 749 0.18 0.19 1 >100 California Quebec-NY State 4029 0.13 0.14 1 >100 SW Alberta Ontario-Ohio 2447 0.12 0.13 0.74 25 ψ State Transition between the “Other” and Texas was supported once in our analysis (BF = 64, I = 88) likely due to the broad taxonomic sampling included in the “Other” state and phylogenetic uncertainty in estimating migration. † The indicator is the posterior probability of observing non-zero migration rates in the Bayesian sampled trees. * Bayes factor greater than 6 with indicator value greater than 0.50 was the minimum criteria for significance; 6≤BF 1300 sequences. While no whole genomes with Eurasian origins were evident in the datasets examined, numerous reassortant genes with recent Eurasian ancestry were detected. The neuraminidase (NA) gene was not included in the analysis due to the deep divergence between NA subtypes, while distribution of locations and time was sparse or inconsistent for individual NA genes. However, H3-HA gene sequences were sampled throughout North America and we therefore analyzed all H3-HA gene sequences isolated from wild aquatic birds (ntax = 437). We used time-stamped sequence data with a relaxed-clock Bayesian Markov chain Monte Carlo method as implemented in BEAST v1.6.2 and BEAST 2 for phylogenetic analysis [44], [45]. For all analyses we used the uncorrelated lognormal relaxed molecular clock to accommodate variation in molecular evolutionary rate amongst lineages, the SRD06 codon position model, with a different rate of nucleotide substitution for the 1st plus 2nd versus the 3rd codon position, and the HKY85 substitution model then applied to these codon divisions [46]. This analysis was conducted with a time-aware linear Bayesian skyride coalescent tree prior over the unknown tree space with relatively uninformative priors on all model parameters a normal prior on the mean skyride size (log units) of 11.0 (standard deviation 1.8) [47]. We performed three independent analyses of 50 million generations. These analyses were combined after the removal of an appropriate burn-in (10%–20% of the samples in most cases) with 5000 generations sampled from each run for a total of 15,000 trees and parameter estimates. We further compared relative genetic diversity and reassortment patterns of viral isolates from Alberta and Delaware Bay by estimating phylogenies as described above for these populations independently. Estimation of viral migration rates between discrete host populations using the internal gene sequences Analysis of migration paths using single gene segments yields answers that do not have to agree with each other, due to multiple factors such as sampling bias and/or reassortment. Therefore, we implemented one inclusive analysis of all genes in which each gene is treated as an independent dataset, but shares the migration parameters with all other genes. In order to estimate migration patterns for a single subtype as well as an average migration pattern of the entire AIV gene pool we devised two datasets. The first dataset focused on seven gene segments from H3 influenza A (excluding NA) as this was the most commonly isolated subtype throughout the surveillance period in both Alberta and Delaware Bay. Secondly, we analyzed all publically available PB1, PB2, PA, NP, M gene segments (excluding recent introductions from Eurasia) to estimate the viral migration patterns across the entire population of birds regardless of subtype. HA, NA and NS genes were not included due to the deep divergence between subtypes. This latter analysis resulted in a dataset of more than 1300 sequences for each of the five genes included. While the phylogeny and substitution rates were separate for each gene, based on a joint migration process a single migration matrix was estimated. We used a reversible continuous-time Markov chain model to estimate the migration rates between geographical regions and the general patterns of avian influenza A virus circulation in different populations [48]. In these analyses we used a constant-population coalescent process prior over the phylogenies and uncorrelated lognormal relaxed molecular clocks. Here we identified 16 discrete geographic regions, based on observed sampling locations, estimated from a 5′×5′ latitude-longitude square (Supporting Data Files; File S1, Table S2, S3, Figure S12), plus an additional character state containing taxa isolated prior to 1998 and locations with fewer than four sequences isolated. We selected discrete geographic sites based on the grid instead of assigning taxa to discrete flyways as these vary to a large degree between potential host populations and overlap between geographic zones. By defining the discrete characters in such a manner we were able to group a number of sampling sites and establish a parameter limit that could be addressed by the data available. A limitation of this approach is that migration rates between locations less than 400 km could not be detected. The ancestral states were mapped onto the internal nodes of phylogenetic trees sampled during the Bayesian analysis (Supporting Data Files; Figures S2, S3, S4, S5, S6, S7, S8). Given the large number of states, a Bayesian stochastic search variable selection (BSSVS) was employed to reduce the number of parameters to those with significantly non-zero transition rates [48]. The BSSVS explores and efficiently reduces the state space by employing a binary indicator (I) [48]. From the BSSVS results, a Bayes factor (BF) test can be applied to assess the support for individual transitions between discrete geographic states. The BF was deemed statistically significant where I>0.5 and the BF>6 from the combined independent analyses. Therefore our minimal critical cutoff for statistical supports were 6≤BF 100 indicating decisive support [48]–[50]. Within flyway rate estimates were compared with between flyway rate estimates to determine if migration of the viral population was structured by flyway. The Pearson correlation coefficient and the Mantel statistical test of correlation (100000 permutations) were conducted to test correlation between migration rate and distance between sites. Statistical comparison of genomic phylogenies for reassortment We used multidimensional scaling plots to visually assess the strength of reassortment in Alberta and Delaware Bay. In this analysis the tree-to-tree variation in branch lengths is visualized as a cloud of points where the centroid of the cloud represents the mean from the 500 trees used in the analysis. Here we assume that gene segments with similar evolutionary histories will occupy the similar locations in the 2-dimensional Euclidean space where the cloud of points should overlap. We used two metrics to assess the degree of reassortment of the influenza A virus populations in the two discrete sampling regions: the time to the most recent common ancestor (tMRCA) or patristic distances calculated from a posterior distribution of trees. From a posterior distribution of phylogenetic trees we estimated the tMRCA for influenza A viruses sampled in each location from each gene during each year and computed the correlation coefficient of the tMRCAs between each pair of trees. This method of tree to tree comparisons has been applied to seasonal influenza A viruses [26] where the uncertainty of the phylogenetic history in the Bayesian posterior sampling of trees for each influenza A gene segments was compared using the tMRCA estimated for annual seasonal influenza A virus outbreaks in two geographic locations. In our data sets there was a sparseness of sampling through time, especially in Delaware Bay. Therefore we encountered high levels of uncertainty where no clear pattern was discernable and zero distances between trees resulted in computational errors by using the tMRCA to estimate phylogenetic uncertainty between gene trees. To overcome this we computed the correlation matrix of the pairwise tree distances. Here we calculated the correlation coefficient for each pair of trees using the patristic distances between every taxon, where the patristic distance is the sum of branch lengths between two nodes. The dissimilarity matrix was obtained by calculating one minus the correlation matrix. Ethics statement All animal experiments were performed following Protocol Number 081 approved on August 19, 2011 by the St. Jude Children's Research Hospital Institutional Animal Care and Use Committee in compliance with the Guide for the Care and Use of Laboratory Animals, 8th Ed. These guidelines were established by the Institute of Laboratory Animal Resources and approved by the Governing Board of the U.S. National Research Council. Supporting Information Figure S1 Neighbor joining phylogenetic tree produced from an HKY85 nucleotide substitution model optimized distance matrix from all available H3-HA data, including sequences generated in this study. The major lineages; Oceania, Eurasia, and North American Lineages I and II are indicated to the right of the tree. Bootstrap supports for these major lineages are indicated on the tree. The scale bar indicates nucleotide substitutions/site. (PDF) Click here for additional data file. Figure S2 H3 Hemagglutinin gene tree nexus file. Temporally structured maximum clade credibility phylogenetic tree showing the mixing of avian influenza A virus isolated from North American wild birds for each individual gene dataset. Ancestral state changes recovered from the discrete phylogeographic analyses are indicated by color changes at tree nodes. Purple bars on nodes indicated 95% confidence intervals of date estimates. Trees with taxon labels and node annotations can be viewed in FigTree (available from http://tree.bio.ed.ac.uk/software/figtree/). Also applies to figures S3, S4, S5, S6, S7, S8. (TREE) Click here for additional data file. Figure S3 PB2 gene tree nexus file. (TREE) Click here for additional data file. Figure S4 PB1 gene tree nexus file. (TREE) Click here for additional data file. Figure S5 PA gene tree nexus file. (TREE) Click here for additional data file. Figure S6 NP gene tree nexus file. (TREE) Click here for additional data file. Figure S7 M gene tree nexus file. (TREE) Click here for additional data file. Figure S8 NS gene tree nexus file. (TREE) Click here for additional data file. Figure S9 A) Mean migration rate per MCMC step within flyway migration rates vs Mean between flyway migration jointly estimated from a subsampled dataset of Figure S9 including 20 isolates per year and all H3 sequences available; B) Density distribution of mean within flyway and mean between flyway rates. (PDF) Click here for additional data file. Figure S10 Relationship of migration rate and distance. A) Mean statistically supported rates vs distance between discrete migration sites; B) Median statistically supported rates vs distance between discrete migration sites; C) All Mean migration rates vs distance between discrete migration sites; D) All Median rate indicator vs distance between discrete migration sites. (PDF) Click here for additional data file. Figure S11 Interactive Google Earth Supplementary Data. GenBank Accession numbers and specific location of virus sampling for all sequences used in this study in the 5° Latitude by 5° Longitude square used to define the discrete character for ancestral state reconstruction. (KML) Click here for additional data file. Figure S12 PB2 gene tree nexus file used to estimate joint migration. Interactive Tree files. Temporally structured maximum clade credibility phylogenetic tree with all available data used to jointly estimate the migration patterns summarized in Figure 4. Ancestral state changes recovered from the discrete phylogeographic analyses are indicated by color changes at tree nodes. Purple bars on nodes indicated 95% confidence intervals of date estimates. Trees with taxon labels and node annotations can be viewed in FigTree (available from http://tree.bio.ed.ac.uk/software/figtree/). Also applies to figures S13, S14, S15, S16. (TREE) Click here for additional data file. Figure S13 PB1 gene tree nexus file used to estimate joint migration. (TREE) Click here for additional data file. Figure S14 PA gene tree nexus file used to estimate joint migration. (TREE) Click here for additional data file. Figure S15 NP gene tree nexus file used to estimate joint migration. (TREE) Click here for additional data file. Figure S16 M gene tree nexus file used to estimate joint migration. (TREE) Click here for additional data file. File S1 BEAST2 executable xml file detailing the parameters for the joint estimation of the single migration rate matrix from independently generated phylogenies (BEAST2 available from http://beast2.cs.auckland.ac.nz/index.php/Main_Page). (TXT) Click here for additional data file. Table S1 Host Avifauna most frequently infected with influenza A virus summarized from the Centers of Excellence for Influenza Research and Surveillance North American wild bird surveillance efforts reporting from 2007. (DOC) Click here for additional data file. Table S2 GenBank Accession numbers, isolation date and location of virus sampling for additional sequences from public databases used in this study. (DOC) Click here for additional data file. Table S3 Associated geographic metadata and exact date of sampling of newly sequenced avian influenza A viruses. (DOC) Click here for additional data file. Table S4 Number of taxa included per protein coding region to estimate average migration dynamics between discrete regions. (DOC) Click here for additional data file. Text S1 Supplementary information describing flyways and bird behavior. (DOC) Click here for additional data file.
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- Abstract: found
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Extensive Geographic Mosaicism in Avian Influenza Viruses from Gulls in the Northern Hemisphere
Michelle Wille, Gregory Robertson, Hugh Whitney … (2011)
Due to limited interaction of migratory birds between Eurasia and America, two independent avian influenza virus (AIV) gene pools have evolved. There is evidence of low frequency reassortment between these regions, which has major implications in global AIV dynamics. Indeed, all currently circulating lineages of the PB1 and PA segments in North America are of Eurasian origin. Large-scale analyses of intercontinental reassortment have shown that viruses isolated from Charadriiformes (gulls, terns, and shorebirds) are the major contributor of these outsider events. To clarify the role of gulls in AIV dynamics, specifically in movement of genes between geographic regions, we have sequenced six gull AIV isolated in Alaska and analyzed these along with 142 other available gull virus sequences. Basic investigations of host species and the locations and times of isolation reveal biases in the available sequence information. Despite these biases, our analyses reveal a high frequency of geographic reassortment in gull viruses isolated in America. This intercontinental gene mixing is not found in the viruses isolated from gulls in Eurasia. This study demonstrates that gulls are important as vectors for geographically reassorted viruses, particularly in America, and that more surveillance effort should be placed on this group of birds.
Author and article information
Contributors
Jonas Waldenström: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, CA USA
)
ISSN
(Electronic):
1932-6203
Publication date
(Electronic):
19
January
2016
Publication date Collection: 2016
Volume: 11
Issue: 1
Electronic Location Identifier: e0146059
Affiliations
[1
]Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United
States of America
[2
]Virology and Emerging Infections Department, United States Naval Medical Research
Unit No. 6, Callao, Peru
[3
]Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney,
New South Wales, Australia
[4
]Department of Epidemiology and Biostatistics, University of California San Francisco,
San Francisco, California, United States of America
Author notes
Conceived and designed the experiments: MN BG EI AG MK DB JM. Performed the experiments: MN BG MPS KS. Analyzed the data: MN SP. Contributed reagents/materials/analysis tools: MS KS. Wrote the paper: MN BG EI AG MK DB JM MS MPS KS.
* E-mail:
spollett@
123456med.usyd.edu.au
Article
Publisher ID: PONE-D-15-39885DOI: 10.1371/journal.pone.0146059
PMC ID: 4718589
PubMed ID: 26784331
SO-VID: 2904196d-203a-4b8d-bcb4-02a19d290ec4
License:
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
History
Date
received
: 17
September
2015
Date
accepted
: 11
December
2015
Page count
Figures: 2,
Tables: 0,
Pages: 10
Funding
This work was supported by Global Emerging Infections Surveillance (GEIS) Work unit
no.: 847705 82000 25GB B0016. The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Categories
Subject:
Research Article
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