Jana McGinnis1, Jennifer Laplante1, Matthew Shudt2, Kirsten St George3. 1. Laboratory of Viral Diseases, Wadsworth Center, New York State Department of Health, Albany, NY, USA. 2. The Applied Genomic Technologies Core, Wadsworth Center, New York State Department of Health, Albany, NY, USA. 3. Laboratory of Viral Diseases, Wadsworth Center, New York State Department of Health, Albany, NY, USA. Electronic address: Kirsten.St.George@health.ny.gov.
Abstract
BACKGROUND: The Wadsworth Center, New York State Department of Health (NYSDOH), conducts routine diagnosis and surveillance of influenza viruses. Whole genome sequencing (WGS) with next generation sequencing (NGS) was initiated to provide more rapid, detailed, thorough, and accurate analysis. OBJECTIVES: To optimize and implement a method for routine WGS of influenza A viruses. To use WGS to monitor influenza A viruses for reassortment, mutations associated with antiviral resistance and antigenicity changes, as well as those potentially affecting virulence and tropism. STUDY DESIGN: Multiple extraction and amplification methods were investigated and optimized for the production of template to be used for NGS. Additionally, software options were considered for data analysis. Initial WGS influenza projects have included the comparison of mixed population sequence data obtained with NGS, Sanger dideoxy sequencing, and pyrosequencing, the comparison of sequences obtained from paired primary/cultured samples, the analysis of sequence changes over several influenza seasons, and phylogenetic analysis. RESULTS: Procedures were optimized for extraction and amplification such that WGS could be successfully performed on both cultured isolates and primary specimens. Data is presented on 15 A/H1pdm09 and 44 A/H3N2 samples. Analysis of influenza A viruses identified and confirmed variant and mixed populations affecting antigenicity and antiviral susceptibility in both primary specimens and cultured isolates. CONCLUSIONS: An influenza A whole genome PCR method has been optimized for the reliable production of template for NGS. The WGS method has been successfully implemented for enhanced comprehensive surveillance and the generation of detailed clinical data on drug resistance and virulence. Data obtained with this method will also aid in future vaccine selection.
BACKGROUND: The Wadsworth Center, New York State Department of Health (NYSDOH), conducts routine diagnosis and surveillance of influenza viruses. Whole genome sequencing (WGS) with next generation sequencing (NGS) was initiated to provide more rapid, detailed, thorough, and accurate analysis. OBJECTIVES: To optimize and implement a method for routine WGS of influenza A viruses. To use WGS to monitor influenza A viruses for reassortment, mutations associated with antiviral resistance and antigenicity changes, as well as those potentially affecting virulence and tropism. STUDY DESIGN: Multiple extraction and amplification methods were investigated and optimized for the production of template to be used for NGS. Additionally, software options were considered for data analysis. Initial WGS influenza projects have included the comparison of mixed population sequence data obtained with NGS, Sanger dideoxy sequencing, and pyrosequencing, the comparison of sequences obtained from paired primary/cultured samples, the analysis of sequence changes over several influenza seasons, and phylogenetic analysis. RESULTS: Procedures were optimized for extraction and amplification such that WGS could be successfully performed on both cultured isolates and primary specimens. Data is presented on 15 A/H1pdm09 and 44 A/H3N2 samples. Analysis of influenza A viruses identified and confirmed variant and mixed populations affecting antigenicity and antiviral susceptibility in both primary specimens and cultured isolates. CONCLUSIONS: An influenza A whole genome PCR method has been optimized for the reliable production of template for NGS. The WGS method has been successfully implemented for enhanced comprehensive surveillance and the generation of detailed clinical data on drug resistance and virulence. Data obtained with this method will also aid in future vaccine selection.
Authors: Ruth E Timme; Errol Strain; Joseph D Baugher; Steven Davis; Narjol Gonzalez-Escalona; Maria Sanchez Leon; Marc W Allard; Eric W Brown; Sandra Tallent; Hugh Rand Journal: J Clin Microbiol Date: 2019-04-26 Impact factor: 5.948
Authors: Denise A Marston; Daniel L Horton; Javier Nunez; Richard J Ellis; Richard J Orton; Nicholas Johnson; Ashley C Banyard; Lorraine M McElhinney; Conrad M Freuling; Müge Fırat; Nil Ünal; Thomas Müller; Xavier de Lamballerie; Anthony R Fooks Journal: Virus Evol Date: 2017-12-13