| Title |
Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation
|
|---|---|
| Published in |
BMC Bioinformatics, February 2018
|
| DOI | 10.1186/s12859-018-2042-4 |
| Pubmed ID | |
| Authors |
Christopher S. Anderson, Patrick R. McCall, Harry A. Stern, Hongmei Yang, David J. Topham |
| Abstract |
The ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to researchers. Antigenic mismatch between circulating strains and vaccine strains results in significantly decreased vaccine effectiveness. Furthermore, antigenic relatedness between the vaccine strain and the strains an individual was originally primed with can affect the cross-reactivity of the antibody response. Thus, understanding the antigenic relationships between influenza viruses that have circulated is important to both vaccinologists and immunologists. Here we develop a method of mapping antigenic relationships between influenza virus stains using a sequence-based antigenic distance approach (SBM). We used a modified version of the p-all-epitope sequence-based antigenic distance calculation, which determines the antigenic relatedness between strains using influenza hemagglutinin (HA) genetic coding sequence data and provide experimental validation of the p-all-epitope calculation. We calculated the antigenic distance between 4838 H1N1 viruses isolated from infected humans between 1918 and 2016. We demonstrate, for the first time, that sequence-based antigenic distances of H1N1 Influenza viruses can be accurately represented in 2-dimenstional antigenic cartography using classic multidimensional scaling. Additionally, the model correctly predicted decreases in cross-reactive antibody levels with 87% accuracy and was highly reproducible with even when small numbers of sequences were used. This work provides a highly accurate and precise bioinformatics tool that can be used to assess immune risk as well as design optimized vaccination strategies. SBM accurately estimated the antigenic relationship between strains using HA sequence data. Antigenic maps of H1N1 virus strains reveal that strains cluster antigenically similar to what has been reported for H3N2 viruses. Furthermore, we demonstrated that genetic variation differs across antigenic sites and discuss the implications. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 66 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 18% |
| Student > Ph. D. Student | 10 | 15% |
| Student > Master | 10 | 15% |
| Other | 5 | 8% |
| Student > Doctoral Student | 5 | 8% |
| Other | 12 | 18% |
| Unknown | 12 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 12 | 18% |
| Agricultural and Biological Sciences | 10 | 15% |
| Immunology and Microbiology | 9 | 14% |
| Medicine and Dentistry | 5 | 8% |
| Veterinary Science and Veterinary Medicine | 4 | 6% |
| Other | 10 | 15% |
| Unknown | 16 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 February 2018.
All research outputs
#14,838,259
of 23,023,224 outputs
Outputs from BMC Bioinformatics
#5,052
of 7,316 outputs
Outputs of similar age
#256,211
of 445,207 outputs
Outputs of similar age from BMC Bioinformatics
#61
of 94 outputs
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