The relationship between mutation frequency and replication strategy in positive-sense single-stranded RNA viruses

Authored by Daniel T Haydon, Gael Thebaud, Joel Chadoeuf, Marco J Morelli, John W McCauley

Date Published: 2010

DOI: 10.1098/rspb.2009.1247

Sponsors: French National Research Agency (ANR)

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

For positive-sense single-stranded RNA virus genomes, there is a trade-off between the mutually exclusive tasks of transcription, translation and encapsidation. The replication strategy that maximizes the intracellular growth rate of the virus requires iterative genome transcription from positive to negative, and back to positive sense. However, RNA viruses experience high mutation rates, and the proportion of genomes with lethal mutations increases with the number of replication cycles. Thus, intracellular mutant frequency will depend on the replication strategy. Introducing apparently realistic mutation rates into a model of viral replication demonstrates that strategies that maximize viral growth rate could result in an average of 26 mutations per genome by the time plausible numbers of positive strands have been generated, and that virus viability could be as low as 0.1 per cent. At high mutation rates or when a high proportion of mutations are deleterious, the optimal strategy shifts towards synthesizing more negative strands per positive strand, and in extremis towards a `stamping-machine' replication mode where all the encapsidated genomes come from only two transcriptional steps. We conclude that if viral mutation rates are as high as current estimates suggest, either mutation frequency must be considerably higher than generally anticipated and the proportion of viable viruses produced extremely small, or replication strategies cannot be optimized to maximize viral growth rate. Mechanistic models linking mutation frequency to replication mechanisms coupled with data generated through new deep-sequencing technologies could play an important role in improving the estimates of viral mutation rate.
Tags
Evolution fitness Rates Fidelity Translation Type-1 Genome Nucleotide substitutions Poliovirus rna Mutants