HIV dynamics with multiple infections of target cells

Abstract

The high incidence of multiple infections of cells by HIV sets the stage for rapid HIV evolution by means of recombination. Yet how HIV dynamics proceeds with multiple infections remains poorly understood. Here, we present a mathematical model that describes the dynamics of viral, target cell, and multiply infected cell subpopulations during HIV infection. Model calculations reproduce several experimental observations and provide key insights into the influence of multiple infections on HIV dynamics. We find that the experimentally observed scaling law, that the number of cells coinfected with two distinctly labeled viruses is proportional to the square of the total number of infected cells, can be generalized so that the number of triply infected cells is proportional to the cube of the number of infected cells, etc. Despite the expectation from Poisson statistics, we find that this scaling relationship only holds under certain conditions, which we predict. We also find that multiple infections do not influence viral dynamics when the rate of viral production from infected cells is independent of the number of times the cells are infected, a regime expected when viral production is limited by cellular rather than viral factors. This result may explain why extant models, which ignore multiple infections, successfully describe viral dynamics in HIV patients. Inhibiting CD4 down-modulation increases the average number of infections per cell. Consequently, altering CD4 down-modulation may allow for an experimental determination of whether viral or cellular factors limit viral production.multiple infection ͉ recombination ͉ scaling ͉ CD4 down-modulation ͉ mathematical model

Extensive Recombination among Human Immunodeficiency Virus Type 1 Quasispecies Makes an Important Contribution to Viral Diversity in Individual Patients

“…To characterize the genome of clonal viruses, DNA was extracted from infected cell pellets, and genomic regions of Х1,300 bp in gag-pol and env were amplified from proviral DNA and sequenced. For the 127 clonal viruses evaluated (25,19,12,19,34, and 18 clones, respectively, for patients 1 to 6), base calls were unambiguous at all positions sequenced. In the course of the studies, proviral DNA from only two infected wells produced sequences in which ambiguous bases were identified, and these samples were excluded from analysis.…”

“…To the extent that the two RNA genomes are distinct, recombination generates viral DNA coding for genetic information different from that of the two parental strains (9,12,16,17). Numerous studies performed in vitro have documented the occurrence of recombination during HIV-1 replication (6,27,38,39,46,47,54).…”

A viral load-based cellular automata approach to modeling HIV dynamics and drug treatment

“…Much work has been done using ordinary differential equation (ODE) and partial differential equation (PDE) models (Arnaout et al, 2000;De Boer and Perelson, 1989;Dixit and Perelson, 2005;Kirschner, 1996;Wodarz and Nowak, 1998). These models have explained different aspects of the dynamics of the virus-immune system interaction.…”

Modeling viral coevolution: HIV multi-clonal persistence and competition dynamics

“…While the majority of HIV dynamics models assume that each CD4+ T cell is infected by a single HIV strain, Dixit and Perelson [30] have shown that with the progression of HIV infection multiple infections become very likely.…”