4.7 Article

Virus genome dynamics under different propagation pressures: reconstruction of whole genome haplotypes of west nile viruses from NGS data

Journal

BMC GENOMICS
Volume 16, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/s12864-015-1340-8

Keywords

West Nile virus; Quasispecies Reconstruction; Mutation-Selection Equilibrium; Cell Tropism

Funding

  1. Agricultural Research Council (ARC) Onderstepoort Veterinary Institute (OVI)
  2. AgriSETA

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Background: Extensive focus is placed on the comparative analyses of consensus genotypes in the study of West Nile virus (WNV) emergence. Few studies account for genetic change in the underlying WNV quasispecies population variants. These variants are not discernable in the consensus genome at the time of emergence, and the maintenance of mutation-selection equilibria of population variants is greatly underestimated. The emergence of lineage 1 WNV strains has been studied extensively, but recent epidemics caused by lineage 2 WNV strains in Hungary, Austria, Greece and Italy emphasizes the increasing importance of this lineage to public health. In this study we explored the quasispecies dynamics of minority variants that contribute to cell-tropism and host determination, i.e. the ability to infect different cell types or cells from different species from Next Generation Sequencing (NGS) data of a historic lineage 2 WNV strain. Results: Minority variants contributing to host cell membrane association persist in the viral population without contributing to the genetic change in the consensus genome. Minority variants are shown to maintain a stable mutation-selection equilibrium under positive selection, particularly in the capsid gene region. Conclusions: This study is the first to infer positive selection and the persistence of WNV haplotype variants that contribute to viral fitness without accompanying genetic change in the consensus genotype, documented solely from NGS sequence data. The approach used in this study streamlines the experimental design seeking viral minority variants accurately from NGS data whilst minimizing the influence of associated sequence error.

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