Dataset Information

Whole Genome Sequencing of A(H3N2) Influenza Viruses Reveals Variants Associated with Severity during the 2016⁻2017 Season.

ABSTRACT: Influenza viruses cause a remarkable disease burden and significant morbidity and mortality worldwide, and these impacts vary between seasons. To understand the mechanisms associated with these differences, a comprehensive approach is needed to characterize the impact of influenza genomic traits on the burden of disease. During 2016⁻2017, a year with severe A(H3N2), we sequenced 176 A(H3N2) influenza genomes using next generation sequencing (NGS) for routine surveillance of circulating influenza viruses collected via the French national influenza community-based surveillance network or from patients hospitalized in the intensive care units of the University Hospitals of Lyon, France. Taking into account confounding factors, sequencing and clinical data were used to identify genomic variants and quasispecies associated with influenza severity or vaccine failure. Several amino acid substitutions significantly associated with clinical traits were found, including NA V263I and NS1 K196E which were associated with severity and co-occurred only in viruses from the 3c.2a1 clade. Additionally, we observed that intra-host diversity as a whole and on a specific set of gene segments increased with severity. These results support the use of whole genome sequencing as a tool for the identification of genetic traits associated with severe influenza in the context of influenza surveillance.

SUBMITTER: Simon B

PROVIDER: S-EPMC6410005 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Seasonal influenza epidemics are associated with high mortality and morbidity in the human population. Influenza surveillance is critical for providing information to national influenza programmes and for making vaccine composition predictions. Vaccination prevents viral infections, but rapid influenza evolution results in emerging mutants that differ antigenically from vaccine strains. Current influenza surveillance relies on Sanger sequencing of the haemagglutinin (HA) gene. Its classification according to World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC) guidelines is based on combining certain genotypic amino acid mutations and phylogenetic analysis. Next-generation sequencing technologies enable a shift to whole-genome sequencing (WGS) for influenza surveillance, but this requires laboratory workflow adaptations and advanced bioinformatics workflows. In this study, 253 influenza A(H3N2) positive clinical specimens from the 2016-2017 Belgian season underwent WGS using the Illumina MiSeq system. HA-based classification according to WHO/ECDC guidelines did not allow classification of all samples. A new approach, considering the whole genome, was investigated based on using powerful phylogenomic tools including beast and Nextstrain, which substantially improved phylogenetic classification. Moreover, Bayesian inference via beast facilitated reassortment detection by both manual inspection and computational methods, detecting intra-subtype reassortants at an estimated rate of 15 %. Real-time analysis (i.e. as an outbreak is ongoing) via Nextstrain allowed positioning of the Belgian isolates into the globally circulating context. Finally, integration of patient data with phylogenetic groups and reassortment status allowed detection of several associations that would have been missed when solely considering HA, such as hospitalized patients being more likely to be infected with A(H3N2) reassortants, and the possibility to link several phylogenetic groups to disease severity indicators could be relevant for epidemiological monitoring. Our study demonstrates that WGS offers multiple advantages for influenza monitoring in (inter)national influenza surveillance, and proposes an improved methodology. This allows leveraging all information contained in influenza genomes, and allows for more accurate genetic characterization and reassortment detection.

Dataset Information

Whole Genome Sequencing of A(H3N2) Influenza Viruses Reveals Variants Associated with Severity during the 2016⁻2017 Season.

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