Taking Multiple Infections of Cells and Recombination into Account Leads to Small Within-Host Effective-Population-Size Estimates of HIV-1

Whether HIV-1 evolution in infected individuals is dominated by deterministic or stochastic effects remains unclear because current estimates of the effective population size of HIV-1 in vivo , N e , are widely varying. Models assuming HIV-1 evolution to be neutral estimate N e ∼10 2 –10 4 , smaller than the inverse mutation rate of HIV-1 (∼10 5 ), implying the predominance of stochastic forces. In contrast, a model that includes selection estimates N e >10 5 , suggesting that deterministic forces would hold sway. The consequent uncertainty in the nature of HIV-1 evolution compromises our ability to describe disease progression and outcomes of therapy. We perform detailed bit-string simulations of viral evolution that consider large genome lengths and incorporate the key evolutionary processes underlying the genomic diversification of HIV-1 in infected individuals, namely, mutation, multiple infections of cells, recombination, selection, and epistatic interactions between multiple loci. Our simulations describe quantitatively the evolution of HIV-1 diversity and divergence in patients. From comparisons of our simulations with patient data, we estimate N e ∼10 3 –10 4 , implying predominantly stochastic evolution. Interestingly, we find that N e and the viral generation time are correlated with the disease progression time, presenting a route to a priori prediction of disease progression in patients. Further, we show that the previous estimate of N e >10 5 reduces as the frequencies of multiple infections of cells and recombination assumed increase. Our simulations with N e ∼10 3 –10 4 may be employed to estimate markers of disease progression and outcomes of therapy that depend on the evolution of viral diversity and divergence.