Untangling the effects of within-host and between-host natural selection in viruses with applications to evolution of...

The fitness of a pathogen can be characterized at multiple levels corresponding to different life cycle stages. Typically, epidemiological studies for measuring pathogen fitness compute a single selection coefficient for pathogen variants that encompasses the entire lifecycle. However, evolution towards greater fitness within hosts can potentially compromise transmissibility. To address this, we develop a population-genetic model and software for the inference of multi-level selection coefficients. Our approach builds on population-genetic models previously applied to macro-evolution, whereby speciation and extinction rates are state-dependent. We relate this state dependence to within- and between-host selection. Evolution of beneficial phenotypes within hosts may occur rapidly (higher 'speciation'), but reduce rates of dispersal between hosts (higher 'extinction'). This new coalescent-based method improves on existing multi-state speciation and extinction models by accounting for highly nonlinear population dynamics and variable sampling through time, making it suitable for epidemiology. We characterize the accuracy of this method using birth-death and coalescent simulations before analysing a real dataset of HIV-1 genetic sequences. We quantified the multi-level relative fitness underlying HIV-1 'co-receptor switching'. In accordance with previous observations, we estimate that the relative transmissibility of X4-tropic virus is 9%, while within-host switching to X4-tropism is approximately 7-fold higher than switching to R5-tropism, indicating a strong within-host and between-host trade-off of co-receptor usage.