Abstract

Mutational load is known to be of importance for the evolution of RNA viruses, the combination of a high mutation rate and large population size leading to an accumulation of deleterious mutations. However, while the effects of mutational load on global viral populations have been considered, its quantitative effects at the within-host scale of infection are less well understood. We here show that even on the rapid timescale of acute disease, mutational load has an effect on within-host viral adaptation, reducing the effective selection acting upon beneficial variants by ∼10 per cent. Furthermore, mutational load induces considerable stochasticity in the pattern of evolution, causing a more than five-fold uncertainty in the effective fitness of a transmitted beneficial variant. Our work aims to bridge the gap between classic models from population genetic theory and the biology of viral infection. In an advance on some previous models of mutational load, we replace the assumption of a constant variant fitness cost with an experimentally-derived distribution of fitness effects. Expanding previous frameworks for evolutionary simulation, we introduce the Wright-Fisher model with continuous mutation, which describes a continuum of possible modes of replication within a cell. Our results advance our understanding of adaptation in the context of strong selection and a high mutation rate. Despite viral populations having large absolute sizes, critical events in viral adaptation, including antigenic drift and the onset of drug resistance, arise through stochastic evolutionary processes.

Details

Title
Mutational load causes stochastic evolutionary outcomes in acute RNA viral infection
Author
Zhao, Lei 1 ; Abbasi, Ali B 1 ; Illingworth, Christopher J R 2 

 Department of Genetics, University of Cambridge, Cambridge, UK 
 Department of Genetics, University of Cambridge, Cambridge, UK; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK 
Publication year
2019
Publication date
Jan 2019
Publisher
Oxford University Press
e-ISSN
20571577
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3171773160
Copyright
© The Author(s) 2019. Published by Oxford University Press. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.