Dynamics of HIV-1 coinfection in different susceptible target cell populations during cell-free infection

J Theor Biol. 2018 Oct 14:455:39-46. doi: 10.1016/j.jtbi.2018.06.025. Epub 2018 Jul 7.


HIV-1 mutations rapidly accumulate through genetic recombination events, which require the infection of a single cell by two virions (coinfection). Accumulation of mutations in the viral population may lead to immune escape and high-level drug resistance. The existence of cell subpopulations characterized by different susceptibility to HIV-1 infection has been proposed as an important parameter driving coinfection (Dang et al., 2004). While the mechanism and the quantification of HIV-1 coinfection have been recently investigated by mathematical models, the detailed dynamics of this process during cell-free infection remains elusive. In this study, we constructed ordinary differential equations considering the heterogeneity of target cell populations during cell-free infection in cell culture, and reproduced the cell culture experimental data. Our mathematical analyses showed that the presence of two differently susceptible target cell subpopulations could explain our experimental datasets, while increasing the number of subpopulations did not improve the fitting. In addition, we quantitatively demonstrated that cells infected by multiple viruses mainly accumulated from one cell subpopulation under cell-free infection conditions. In particular, the frequency of infection events in the more susceptible subpopulation was 6.11-higher than that from the other subpopulation, and 98.3% of coinfected cells emerged from the more susceptible subpopulation. Our mathematical-experimental approach is able to extract such a quantitative information, and can be easily applied to other virus infections.

Keywords: Coinfection; HIV-1 infection; Mathematical model; Non-random infection.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Line
  • HIV Infections / metabolism*
  • HIV-1 / metabolism*
  • Humans
  • Models, Biological*