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)~10²-104, smaller than the inverse mutation rate of HIV-1 (~105), implying the predominance of stochastic forces. In contrast, a model that includes selection estimates N(e)>105, 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)~10³-104, 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)>105 reduces as the frequencies of multiple infections of cells and recombination assumed increase. Our simulations with N(e)~10³-104 may be employed to estimate markers of disease progression and outcomes of therapy that depend on the evolution of viral diversity and divergence.