HIV Dynamics With Multiple Infections Of Cells And Recombination

Abstract

The ability to accelerate the accumulation of favorable combinations of mutations renders recombination a potent force underlying the emergence of forms of HIV that escape multi-drug therapy and specific host-immune responses. In this study, a mathematical model is developed that describes the dynamics of the emergence of recombinant forms of HIV following infection with diverse viral genomes. Mimicking recent in vitro experiments, target cells simultaneously exposed to two distinct, homozygous viral populations are considered and dynamical equations are constructed that predict the time-evolution of populations of uninfected, singly infected, and doubly infected cells, and homozygous, heterozygous, and recombinant viruses. Model predictions capture several recent experimental observations quantitatively and provide insights into the role of recombination in HIV dynamics. Comparisons of data from single round infection experiments with model predictions of the probability with which recombination accumulates distinct mutations present on the two genomic strands in a vision, indicates that »8 recombinational strand transfer events occur on average (95% confidence interval: 6-10) during reverse transcription of HIV in T cells. Model predictions of virus and cell dynamics describe the time-evolution and the relative prevalence of various infected cell subpopulations following the onset of infection observed experimentally. Remarkably, model predictions are in quantitative agreement with the experimental scaling relationship that the percentage of cells infected with recombinant genomes is proportional to the percentage of cells co-infected with the two genomes employed at the onset of infection. The model developed thus presents an accurate description of the influence of recombination on HIV dynamics in vitro. When distinctions between different viral genomes are ignored, the model reduces to the standard model of viral dynamics, which successfully predicts viral load changes in HIV patients undergoing therapy. The model developed may thus serve as a useful framework to predict the emergence of multi-drug resistant forms of HIV in infected individuals.