Dataset Information

Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus.

ABSTRACT:

Motivation

Influenza virus antigenic variants continue to emerge and cause disease outbreaks. Time-consuming, costly and middle-throughput serologic methods using virus isolates are routinely used to identify influenza antigenic variants for vaccine strain selection. However, the resulting data are notoriously noisy and difficult to interpret and integrate because of variations in reagents, supplies and protocol implementation. A novel method without such limitations is needed for antigenic variant identification.

Results

We developed a Graph-Guided Multi-Task Sparse Learning (GG-MTSL) model that uses multi-sourced serologic data to learn antigenicity-associated mutations and infer antigenic variants. By applying GG-MTSL to influenza H3N2 hemagglutinin sequences, we showed the method enables rapid characterization of antigenic profiles and identification of antigenic variants in real time and on a large scale. Furthermore, sequences can be generated directly by using clinical samples, thus minimizing biases due to culture-adapted mutation during virus isolation.

Availability and implementation

MATLAB source codes developed for GG-MTSL are available through http://sysbio.cvm.msstate.edu/files/GG-MTSL/.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Han L

PROVIDER: S-EPMC6298058 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus.

Han Lei L Li Lei L Wen Feng F Zhong Lei L Zhang Tong T Wan Xiu-Feng XF

Bioinformatics (Oxford, England) 20190101 1

<h4>Motivation</h4>Influenza virus antigenic variants continue to emerge and cause disease outbreaks. Time-consuming, costly and middle-throughput serologic methods using virus isolates are routinely used to identify influenza antigenic variants for vaccine strain selection. However, the resulting data are notoriously noisy and difficult to interpret and integrate because of variations in reagents, supplies and protocol implementation. A novel method without such limitations is needed for antige ...[more]

PMID: 29878046

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Project description:BackgroundIn addition to causing the pandemic influenza outbreaks of 1918 and 2009, subtype H1N1 influenza A viruses (IAVs) have caused seasonal epidemics since 1977. Antigenic property of influenza viruses are determined by both protein sequence and N-linked glycosylation of influenza glycoproteins, especially hemagglutinin (HA). The currently available computational methods are only considered features in protein sequence but not N-linked glycosylation.ResultsA multi-task learning sparse group least absolute shrinkage and selection operator (LASSO) (MTL-SGL) regression method was developed and applied to derive two types of predominant features including protein sequence and N-linked glycosylation in hemagglutinin (HA) affecting variations in serologic data for human and swine H1N1 IAVs. Results suggested that mutations and changes in N-linked glycosylation sites are associated with the rise of antigenic variants of H1N1 IAVs. Furthermore, the implicated mutations are predominantly located at five reported antibody-binding sites, and within or close to the HA receptor binding site. All of the three N-linked glycosylation sites (i.e. sequons NCSV at HA 54, NHTV at HA 125, and NLSK at HA 160) identified by MTL-SGL to determine antigenic changes were experimentally validated in the H1N1 antigenic variants using mass spectrometry analyses. Compared with conventional sparse learning methods, MTL-SGL achieved a lower prediction error and higher accuracy, indicating that grouped features and MTL in the MTL-SGL method are not only able to handle serologic data generated from multiple reagents, supplies, and protocols, but also perform better in genetic sequence-based antigenic quantification.ConclusionsIn summary, the results of this study suggest that mutations and variations in N-glycosylation in HA caused antigenic variations in H1N1 IAVs and that the sequence-based antigenicity predictive model will be useful in understanding antigenic evolution of IAVs.

Project description:Influenza A H3N2 variant [A(H3N2)v] viruses, which have caused human infections in the United States in recent years, originated from human seasonal H3N2 viruses that were introduced into North American swine in the mid-1990s, but they are antigenically distinct from both the ancestral and current circulating H3N2 strains. A reference A(H3N2)v virus, A/Minnesota/11/2010 (MN/10), and a seasonal H3N2 strain, A/Beijing/32/1992 (BJ/92), were chosen to determine the molecular basis for the antigenic difference between A(H3N2)v and the ancestral viruses. Viruses containing wild-type and mutant MN/10 or BJ/92 hemagglutinins (HAs) were constructed and probed for reactivity with ferret antisera against MN/10 and BJ/92 in hemagglutination inhibition assays. Among the amino acids that differ between the MN/10 and BJ/92 HAs, those in antigenic site A had little impact on the antigenic phenotype. Within antigenic site B, mutations at residues 156, 158, 189, and 193 of MN/10 HA to those in BJ/92 switched the MN/10 antigenic phenotype to that of BJ/92. Mutations at residues 156, 157, 158, 189, and 193 of BJ/92 HA to amino acids present in MN/10 were necessary for BJ/92 to become antigenically similar to MN/10. The HA amino acid substitutions responsible for switching the antigenic phenotype also impacted HA binding to sialyl receptors that are usually present in the human respiratory tract. Our study demonstrates that antigenic site B residues play a critical role in determining both the unique antigenic phenotype and receptor specificity of A(H3N2)v viruses, a finding that may facilitate future surveillance and risk assessment of novel influenza viruses. IMPORTANCE:Influenza A H3N2 variant [A(H3N2)v] viruses have caused hundreds of human infections in multiple states in the United States since 2009. Most cases have been children who had contact with swine in agricultural fairs. These viruses originated from human seasonal H3N2 viruses that were introduced into the U.S. swine population in the mid-1990s, but they are different from both these ancestral viruses and current circulating human seasonal H3N2 strains in terms of their antigenic characteristics as measured by hemagglutination inhibition (HI) assay. In this study, we identified amino acids in antigenic site B of the surface glycoprotein hemagglutinin (HA) that explain the antigenic difference between A(H3N2)v and the ancestral H3N2 strains. These amino acid mutations also alter binding to minor human-type glycans, suggesting that host adaptation may contribute to the selection of antigenically distinct H3N2 variants which pose a threat to public health.

Dataset Information

Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus.

Similar Datasets

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