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      Ecological divergence of wild birds drives avian influenza spillover and global spread

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          Abstract

          The diversity of influenza A viruses (IAV) is primarily hosted by two highly divergent avian orders: Anseriformes (ducks, swans and geese) and Charadriiformes (gulls, terns and shorebirds). Studies of IAV have historically focused on Anseriformes, specifically dabbling ducks, overlooking the diversity of hosts in nature, including gull and goose species that have successfully adapted to human habitats. This study sought to address this imbalance by characterizing spillover dynamics and global transmission patterns of IAV over 10 years at greater taxonomic resolution than previously considered. Furthermore, the circulation of viral subtypes in birds that are either host-adapted (low pathogenic H13, H16) or host-generalist (highly pathogenic avian influenza—HPAI H5) provided a unique opportunity to test and extend models of viral evolution. Using Bayesian phylodynamic modelling we uncovered a complex transmission network that relied on ecologically divergent bird hosts. The generalist subtype, HPAI H5 was driven largely by wild geese and swans that acted as a source for wild ducks, gulls, land birds, and domestic geese. Gulls were responsible for moving HPAI H5 more rapidly than any other host, a finding that may reflect their long-distance, pelagic movements and their immuno-naïve status against this subtype. Wild ducks, long viewed as primary hosts for spillover, occupied an optimal space for viral transmission, contributing to geographic expansion and rapid dispersal of HPAI H5. Evidence of inter-hemispheric dispersal via both the Pacific and Atlantic Rims was detected, supporting surveillance at high latitudes along continental margins to achieve early detection. Both neutral (geographic expansion) and non-neutral (antigenic selection) evolutionary processes were found to shape subtype evolution which manifested as unique geographic hotspots for each subtype at the global scale. This study reveals how a diversity of avian hosts contribute to viral spread and spillover with the potential to improve surveillance in an era of rapid global change.

          Author summary

          Our study provides novel insights into the biology of influenza A virus (IAV), which is timely in view of the unusually large number of animal and human cases of highly pathogenic avian influenza (HPAI) H5 across Europe, Asia, Africa and North America. Currently we face challenges with predicting how the avian reservoir will influence IAV spread because the mechanisms by which different subtypes disperse are not well understood. Our study sought to address this knowledge gap by systematically comparing the evolutionary dynamics that drive IAV transmission across subtypes and bird hosts with the goal of identifying spillover pathways at the wild-domestic interface. By analyzing the evolution of IAV over 10 years at greater taxonomic resolution than previously considered, we uncovered a complex transmission network that relied on ecologically divergent bird hosts. Domestic birds were responsible for slow but steady range expansion of HPAI H5, while wild birds such as geese, swans, gulls and ducks contibuted to rapid but episodic dispersal via uniquely different pathways. By assessing how virus-host systems are coupled, findings from this study have the potential to refine and enhance global surveillance and outbreak prediction.

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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            RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

            RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak
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              Bayesian Phylogenetics with BEAUti and the BEAST 1.7

              Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: Methodology
                Role: InvestigationRole: Methodology
                Role: InvestigationRole: MethodologyRole: Project administration
                Role: InvestigationRole: Methodology
                Role: InvestigationRole: Project administration
                Role: InvestigationRole: Methodology
                Role: InvestigationRole: Methodology
                Role: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: Investigation
                Role: Funding acquisitionRole: InvestigationRole: Methodology
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                19 May 2022
                May 2022
                : 18
                : 5
                : e1010062
                Affiliations
                [1 ] Department of Biology, University of Massachusetts, Boston, Massachusetts, United States of America
                [2 ] Prince William Sound Science Center, Cordova, Alaska, United States of America
                [3 ] Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
                [4 ] U.S. Fish and Wildlife Service, Hadley, Massachusetts, United States of America
                [5 ] Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine Tufts University, North Grafton, Massachusetts, United States of America
                [6 ] College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America
                [7 ] Division of Water Supply Protection, Massachusetts Department of Conservation and Recreation, West Boylston, Massachusetts, United States of America
                [8 ] U.S. Geological Survey, National Wildlife Health Center, Madison, Wisconsin, United States of America
                MRC-University of Glasgow Centre for Virus Research, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-3213-6693
                https://orcid.org/0000-0001-9723-0184
                https://orcid.org/0000-0002-8630-4691
                https://orcid.org/0000-0002-6610-9047
                https://orcid.org/0000-0002-1795-3399
                https://orcid.org/0000-0003-4474-2076
                https://orcid.org/0000-0002-0206-2841
                https://orcid.org/0000-0001-7581-8526
                https://orcid.org/0000-0002-0515-9091
                https://orcid.org/0000-0002-5619-2609
                Article
                PPATHOGENS-D-21-02166
                10.1371/journal.ppat.1010062
                9119557
                35588106
                741e7604-2754-4f44-84bb-e385f93499fd

                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
                : 26 October 2021
                : 1 April 2022
                Page count
                Figures: 4, Tables: 0, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: HHSN272201400008C
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: HHSN272201400006C
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100012635, North Pacific Research Board;
                Award ID: 1411
                Award Recipient :
                Funding for this project was provided by the NIAID Centers of Excellence for Influenza Research and Surveillance (HHSN272201400008C (JR) & HHSN272201400006C (JH)) and the North Pacific Research Board (project no. 1411 (NH, MB, JR)). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Genetic sequences from influenza A virus surveillance from Alaska and Massachusetts are deposited in GenBank ( https://www.ncbi.nlm.nih.gov/genbank) under accession numbers listed in S1 and S2 Data.

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