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Mutational and fitness landscapes of an RNA virus revealed through population sequencing

2013; Nature Portfolio; Volume: 505; Issue: 7485 Linguagem: Inglês

10.1038/nature12861

1476-4687

Ashley Acevedo, Leonid Brodsky, Raul Andino,

RNA Research and Splicing

A new approach to accurately determine mutation frequencies with RNA virus populations called circular sequencing (CirSeq) has been developed; a study of the genetic composition of populations of poliovirus shows the fitness landscape for each nucleotide variant in an evolving RNA virus. Raul Andino and colleagues report a new approach to the determination of mutation frequencies within RNA virus populations. The new technique, circular sequencing (CirSeq), uses circularized genomic RNA fragments to generate tandem repeats that then serve as substrates for next-generation sequencing. The authors use CirSeq to track the genetic composition of populations of poliovirus replicating in human cells in culture and uncover the mutational landscape of the population. The extent of change in mutation frequency for each variant over the course of passage in cell culture is a measure of the relative fitness of each variant, thereby delineating the fitness landscape for an evolving RNA virus. RNA viruses exist as genetically diverse populations1. It is thought that diversity and genetic structure of viral populations determine the rapid adaptation observed in RNA viruses2 and hence their pathogenesis3. However, our understanding of the mechanisms underlying virus evolution has been limited by the inability to accurately describe the genetic structure of virus populations. Next-generation sequencing technologies generate data of sufficient depth to characterize virus populations, but are limited in their utility because most variants are present at very low frequencies and are thus indistinguishable from next-generation sequencing errors. Here we present an approach that reduces next-generation sequencing errors and allows the description of virus populations with unprecedented accuracy. Using this approach, we define the mutation rates of poliovirus and uncover the mutation landscape of the population. Furthermore, by monitoring changes in variant frequencies on serially passaged populations, we determined fitness values for thousands of mutations across the viral genome. Mapping of these fitness values onto three-dimensional structures of viral proteins offers a powerful approach for exploring structure–function relationships and potentially uncovering new functions. To our knowledge, our study provides the first single-nucleotide fitness landscape of an evolving RNA virus and establishes a general experimental platform for studying the genetic changes underlying the evolution of virus populations.