Inference of Genotype–Phenotype Relationships in the Antigenic Evolution of Human Influenza A (H3N2) Viruses
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{"title"=>"Inference of genotype-phenotype relationships in the antigenic evolution of human influenza A (H3N2) viruses", "type"=>"journal", "authors"=>[{"first_name"=>"Lars", "last_name"=>"Steinbrück", "scopus_author_id"=>"37098932800"}, {"first_name"=>"Alice Carolyn", "last_name"=>"McHardy", "scopus_author_id"=>"6602304583"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"22532796", "sgr"=>"84861119815", "doi"=>"10.1371/journal.pcbi.1002492", "scopus"=>"2-s2.0-84861119815", "pui"=>"364830893", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "issn"=>"1553734X"}, "id"=>"b82c47e5-195e-3f93-b994-8c1a31e8d588", "abstract"=>"Distinguishing mutations that determine an organism's phenotype from (near-) neutral 'hitchhikers' is a fundamental challenge in genome research, and is relevant for numerous medical and biotechnological applications. For human influenza viruses, recognizing changes in the antigenic phenotype and a strains' capability to evade pre-existing host immunity is important for the production of efficient vaccines. We have developed a method for inferring 'antigenic trees' for the major viral surface protein hemagglutinin. In the antigenic tree, antigenic weights are assigned to all tree branches, which allows us to resolve the antigenic impact of the associated amino acid changes. Our technique predicted antigenic distances with comparable accuracy to antigenic cartography. Additionally, it identified both known and novel sites, and amino acid changes with antigenic impact in the evolution of influenza A (H3N2) viruses from 1968 to 2003. The technique can also be applied for inference of 'phenotype trees' and genotype-phenotype relationships from other types of pairwise phenotype distances.", "link"=>"http://www.mendeley.com/research/inference-genotypephenotype-relationships-antigenic-evolution-human-influenza-h3n2-viruses", "reader_count"=>58, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>18, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>1, "Student > Master"=>5, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>18, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>1, "Student > Master"=>5, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Agricultural and Biological Sciences"=>26, "Business, Management and Accounting"=>1, "Veterinary Science and Veterinary Medicine"=>2, "Chemical Engineering"=>1, "Chemistry"=>2, "Computer Science"=>6, "Economics, Econometrics and Finance"=>1, "Engineering"=>3, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>1, "Medicine and Dentistry"=>4, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>26}, "Computer Science"=>{"Computer Science"=>6}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Saudi Arabia"=>1, "Canada"=>1, "United States"=>2, "Japan"=>2, "United Kingdom"=>2, "Moldova"=>1, "Germany"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/334707", "https://ndownloader.figshare.com/files/334835", "https://ndownloader.figshare.com/files/334962", "https://ndownloader.figshare.com/files/335004", "https://ndownloader.figshare.com/files/335073", "https://ndownloader.figshare.com/files/335274", "https://ndownloader.figshare.com/files/335340", "https://ndownloader.figshare.com/files/335395", "https://ndownloader.figshare.com/files/335450"], "description"=>"<div><p>Distinguishing mutations that determine an organism's phenotype from (near-) neutral ‘hitchhikers’ is a fundamental challenge in genome research, and is relevant for numerous medical and biotechnological applications. For human influenza viruses, recognizing changes in the antigenic phenotype and a strains' capability to evade pre-existing host immunity is important for the production of efficient vaccines. We have developed a method for inferring ‘antigenic trees’ for the major viral surface protein hemagglutinin. In the antigenic tree, antigenic weights are assigned to all tree branches, which allows us to resolve the antigenic impact of the associated amino acid changes. Our technique predicted antigenic distances with comparable accuracy to antigenic cartography. Additionally, it identified both known and novel sites, and amino acid changes with antigenic impact in the evolution of influenza A (H3N2) viruses from 1968 to 2003. The technique can also be applied for inference of ‘phenotype trees’ and genotype–phenotype relationships from other types of pairwise phenotype distances.</p> </div>", "links"=>[], "tags"=>["inference", "relationships", "antigenic", "influenza", "viruses"], "article_id"=>126136, "categories"=>["Biological Sciences", "Evolutionary Biology"], "users"=>["Lars Steinbrück", "Alice Carolyn McHardy"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002492.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002492.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002492.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002492.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002492.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002492.s006", "https://dx.doi.org/10.1371/journal.pcbi.1002492.s007", "https://dx.doi.org/10.1371/journal.pcbi.1002492.s008", "https://dx.doi.org/10.1371/journal.pcbi.1002492.s009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Inference_of_Genotype_Phenotype_Relationships_in_the_Antigenic_Evolution_of_Human_Influenza_A_H3N2_Viruses/126136", "title"=>"Inference of Genotype–Phenotype Relationships in the Antigenic Evolution of Human Influenza A (H3N2) Viruses", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-04-19 01:42:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/650426"], "description"=>"<p>Branch lengths represent antigenic distances (maximum of up- and down-weights for each branch) inferred from a maximum likelihood tree of 258 hemagglutinin sequences of seasonal influenza A (H3N2) virus isolates and serological data. (A) Colored edges show antigenic type transitions, with internal branches with high average antigenic weights (≥1.0 antigenic units). Gray-blue edges represent high weight branches leading to a subtree with three isolates or less, representing low abundance types. (B) Isolates are color-coded by antigenic clusters according to Smith <i>et al.</i> (2004). Three isolates (A/Christchurch/4/85, A/Hong Kong/34/90 and A/Netherlands/172/96) are only present as antisera and were not assigned a cluster label.</p>", "links"=>[], "tags"=>["influenza"], "article_id"=>320934, "categories"=>["Biological Sciences", "Evolutionary Biology"], "users"=>["Lars Steinbrück", "Alice Carolyn McHardy"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002492.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Antigenic_tree_for_influenza_A_H3N2_viruses_/320934", "title"=>"Antigenic tree for influenza A (H3N2) viruses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-19 00:15:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/650490"], "description"=>"<p>For the two taxa <i>t<sub>2</sub></i> and <i>t<sub>4</sub></i>, no antiserum is present, and thus, <i>b<sub>3</sub></i> and <i>b<sub>6</sub></i> only have up-weights. A path from <i>t<sub>1</sub></i> to <i>t<sub>3</sub></i> would use the up-weights of branch <i>b<sub>1</sub></i> and <i>b<sub>2</sub></i>, and the down-weights of branch <i>b<sub>4</sub></i> and b<sub>5</sub>. Similarly, the path from <i>t<sub>2</sub></i> to <i>t<sub>1</sub></i> would use the up-weight of branch <i>b<sub>3</sub></i> and the down-weight of branch <i>b<sub>2</sub></i>. Notably, the path from <i>t<sub>1</sub></i> to <i>t<sub>1</sub></i>, namely the antigenic distance from antigen <i>t<sub>1</sub></i> to the antiserum raised against strain <i>t<sub>1</sub></i>, would use the up-weight and the down-weight of branch <i>b<sub>1</sub></i>.</p>", "links"=>[], "tags"=>["demonstrating"], "article_id"=>320988, "categories"=>["Biological Sciences", "Evolutionary Biology"], "users"=>["Lars Steinbrück", "Alice Carolyn McHardy"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002492.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_drawing_demonstrating_the_up_down_tree_concept_/320988", "title"=>"Schematic drawing demonstrating the up/down tree concept.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-19 00:16:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/650542"], "description"=>"<p>Branch amino acid changes indicate the corresponding branches, where changes in bold were also found by Smith <i>et al.</i> (2004), and weights give the respective up, down and average branch weights. Multiple branches that can be mapped to a single antigenic type are separated by dashed lines. Additional amino acid changes indicate branches that carry further mutations found to be cluster transition substitutions by Smith <i>et al.</i> (2004). For some branches, the down-weight was not defined, as no antiserum was in the respective subtree. Branches that can be mapped to multiple type transitions are shown at the first mapping only. Smith <i>et al.</i> (2004) present average distances between consecutive antigenic clusters, whereas average antigenic branch weights give a minimum distance between consecutive antigenic types. Note that on branches with multiple changes not all changes have to contribute to the antigenic weight, though their individual impacts could not be resolved with the dataset (unsampled viral isolates).</p>", "links"=>[], "tags"=>["branches", "antigenic", "weights", "types", "clusters", "Cartography", "isolates"], "article_id"=>321030, "categories"=>["Biological Sciences", "Evolutionary Biology"], "users"=>["Lars Steinbrück", "Alice Carolyn McHardy"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002492.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Internal_branches_with_high_average_antigenic_weights_8805_1_0_antigenic_units_and_according_antigenic_types_in_comparison_to_antigenic_clusters_identified_by_antigenic_cartography_branches_leading_to_three_or_less_isolates_are_excluded_/321030", "title"=>"Internal branches with high average antigenic weights (≥1.0 antigenic units) and according antigenic types in comparison to antigenic clusters identified by antigenic cartography (branches leading to three or less isolates are excluded).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-19 00:17:10"}

PMC Usage Stats | Further Information

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Relative Metric

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