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      Highly Pathogenic Avian Influenza Virus (H5N1) Clade 2.3.4.4b Introduced by Wild Birds, China, 2021

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          Abstract

          Highly pathogenic avian influenza (HPAI) subtype H5N1 clade 2.3.4.4b virus has spread globally, causing unprecedented large-scale avian influenza outbreaks since 2020. In 2021, we isolated 17 highly pathogenic avian influenza H5N1 viruses from wild birds in China. To determine virus origin, we genetically analyzed 1,529 clade 2.3.4.4b H5N1 viruses reported globally since October 2020 and found that they formed 35 genotypes. The 17 viruses belonged to genotypes G07, which originated from eastern Asia, and G10, which originated from Russia. The viruses were moderately pathogenic in mice but were highly lethal in ducks. The viruses were in the same antigenic cluster as the current vaccine strain (H5-Re14) used in China. In chickens, the H5/H7 trivalent vaccine provided complete protection against clade 2.3.4.4b H5N1 virus challenge. Our data indicate that vaccination is an effective strategy for preventing and controlling the globally prevalent clade 2.3.4.4b H5N1 virus.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          Kazutaka Katoh, Daron Standley (2013)
          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            MEGA11: Molecular Evolutionary Genetics Analysis Version 11

            Koichiro Tamura, Glen Stecher, Sudhir Kumar (2021)
            The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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              Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10

              Marc Suchard, Philippe Lemey, Guy Baele … (2018)
              Abstract The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.
              Article 0 comments Cited 1046 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Emerg Infect Dis
                Journal ID (iso-abbrev): Emerg 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): July 2023
                Volume: 29
                Issue: 7
                Pages: 1367-1375
                Affiliations
                [1]State Key Laboratory for Animal Didease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China (J. Tian, X. Bai, M. Li, X. Zeng, X. Song, Z. Zhao, G. Tian, L. Liu, Y. Guan, Y Li, H. Chen);
                [2]Preventive and Control Center for Animal Disease of Heilongjiang Province, Harbin (J. Xu, P. Li, M. Wang)
                Author notes
                Address for correspondence: Yanbing Li and Hualan Chen, Harbin Veterinary Research Institute, CAAS, 678 Haping Rd, Harbin 150069, China; email: liyanbing@ 123456caas.cn and chenhualan@ 123456caas.cn
                Article
                Publisher ID: 22-1149
                DOI: 10.3201/eid2907.221149
                PMC ID: 10310395
                PubMed ID: 37347504
                SO-VID: f6d9d24e-ca35-461f-8695-72564be92cbf
                Copyright statement: Copyright @ 2023
                License:

                Emerging Infectious Diseases is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.

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                Subject: Research
                Subject: Research
                Subject: Highly Pathogenic Avian Influenza Virus (H5N1) Clade 2.3.4.4b Introduced by Wild Birds, China, 2021

                ScienceOpen disciplines: Infectious disease & Microbiology
                Keywords: influenza,influenza virus,clade 2.3.4.4b,h5n1,wild birds,phylogeny,virulence,antigenic property,protective efficacy,viruses,china
                Data availability:
                ScienceOpen disciplines: Infectious disease & Microbiology
                Keywords: influenza, influenza virus, clade 2.3.4.4b, h5n1, wild birds, phylogeny, virulence, antigenic property, protective efficacy, viruses, china

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