Meinel DM, Heinzinger S, Eberle U, Ackermann N, Schönberger K, Sing A. Whole genome sequencing identifies influenza A H3N2 transmission and offers superior resolution to classical typing methods.
Infection 2018;
46:69-76. [PMID:
29086356 DOI:
10.1007/s15010-017-1091-3]
[Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 10/24/2017] [Indexed: 02/06/2023]
Abstract
OBJECTIVES
Influenza with its annual epidemic waves is a major cause of morbidity and mortality worldwide. However, only little whole genome data are available regarding the molecular epidemiology promoting our understanding of viral spread in human populations.
METHODS
We implemented a RT-PCR strategy starting from patient material to generate influenza A whole genome sequences for molecular epidemiological surveillance. Samples were obtained within the Bavarian Influenza Sentinel. The complete influenza virus genome was amplified by a one-tube multiplex RT-PCR and sequenced on an Illumina MiSeq.
RESULTS
We report whole genomic sequences for 50 influenza A H3N2 viruses, which was the predominating virus in the season 2014/15, directly from patient specimens. The dataset included random samples from Bavaria (Germany) throughout the influenza season and samples from three suspected transmission clusters. We identified the outbreak samples based on sequence identity. Whole genome sequencing (WGS) was superior in resolution compared to analysis of single segments or partial segment analysis. Additionally, we detected manifestation of substantial amounts of viral quasispecies in several patients, carrying mutations varying from the dominant virus in each patient.
CONCLUSION
Our rapid whole genome sequencing approach for influenza A virus shows that WGS can effectively be used to detect and understand outbreaks in large communities. Additionally, the genomic data provide in-depth details about the circulating virus within one season.
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