Hybrid spreading mechanisms and T cell activation shape the dynamics of HIV-1 infection

TL;DR

It is suggested that hybrid spreading is a fundamental feature of HIV infection, and a new mathematical model is provided that incorporates the ability of HIV-1 to use hybrid spreading mechanisms and provides the mathematical framework with which to evaluate future therapeutic strategies.

Abstract

HIV-1 can disseminate between susceptible cells by two mechanisms: cell-free infection following fluid-phase diffusion of virions and by highly-efficient direct cell-to-cell transmission at immune cell contacts. The contribution of this hybrid spreading mechanism, which is also a characteristic of some important computer worm outbreaks, to HIV-1 progression in vivo remains unknown. Here we present a new mathematical model that explicitly incorporates the ability of HIV-1 to use hybrid spreading mechanisms and evaluate the consequences for HIV-1 pathogenenesis. The model captures the major phases of the HIV-1 infection course of a cohort of treatment naive patients and also accurately predicts the results of the Short Pulse Anti-Retroviral Therapy at Seroconversion (SPARTAC) trial. Using this model we find that hybrid spreading is critical to seed and establish infection, and that cell-to-cell spread and increased CD4+ T cell activation are important for HIV-1 progression. Notably, the model predicts that cell-to-cell spread becomes increasingly effective as infection progresses and thus may present a considerable treatment barrier. Deriving predictions of various treatments' influence on HIV-1 progression highlights the importance of earlier intervention and suggests that treatments effectively targeting cell-to-cell HIV-1 spread can delay progression to AIDS. This study suggests that hybrid spreading is a fundamental feature of HIV infection, and provides the mathematical framework incorporating this feature with which to evaluate future therapeutic strategies.

Figures (6)

• Figure 1: The HIV-1 model reproduces the full course of HIV-1 infection.(A) Diagrammatic representation of the model described by equation (\ref{['eq:hiv-model2']}). (B) Numerical solutions of the model, plotting $N$, the density of all CD4$^+$ T cells (y axis on the left) and $V$, the density of free virions (y axis on the right in log scale) as a function of time (in days or weeks), respectively. The initial infection starts on day 0 and the cellular immune response starts on day 30. Parameter values are in Table \ref{['tab-pars']}.(C) The density of quiescent, susceptible, latent and infected CD4$^+$ T cells, and the density of free virions as function of time (in days).
• Figure 2: Model prediction for a cohort of treatment-naive HIV-1 patients.(A) Clinical data (circle and arrow) for all patients under study comparing against model prediction (diamond) for the time to AIDS ($t_A$), the quasi-steady density of CD4$^+$ T cells ($N_s$) and the quasi-steady density of free virions ($V_s$). An arrow represents that $t_A$ is greater than a particular value (represented by the connected circle) for a patient as his / her CD4$^+$ count did not reached AIDS level ($200\,cells/\mu l$) in the data.(B) Prediction (curve) of HIV progression course ($N$ and $log_{10}V$) for four typical patients, where clinical data are shown as dots.Full prediction results are shown in \ref{['tab-fit-pars']} and \ref{['tab-fit-vars']}.
• Figure 3: Two modes of HIV-1 infection. The density of CD4$^+$ T cells as a function of time for different values of cell-to-cell infection rate $\beta_1$ and cell-free infection rate $\beta_2$: (1) both use their default value, (2) $\beta_1$ uses its default value and $\beta_2=0$, (3) $\beta_1=0$ and $\beta_2$ uses its default value, (4) $\beta_1$ is twice its default value and $\beta_2=0$, (5) $\beta_1=0$ and $\beta_2$ is twice its default value.
• Figure 4: Progressive CD4$^+$ T cell activation drives progression to AIDS and increased cell-to-cell infection.(A) Progression of HIV-1 infection for different cell activation rates, including (1) normal activation ($a{N_M}/{N}$ in equation (\ref{['eq:hiv-model2']})), (2) fixed activation ($a{N_M}/{N_0}$, where $N_0$ is the initial density of CD4$^+$ T cells), and (3) doubled activation ( $2\times a{N_M}/{N}$ when $t>D$).(B) Numbers of newly infected cells in a day via cell-to-cell spreading and cell-free spreading, respectively. The inset shows the ratio of susceptible cells to all cells ($S/N$, left y axis) and the strength of immune response ($\kappa \frac{I}{I+0.1} \frac{N}{N_M}$, right y axis) as a function of time, respectively.
• Figure 5: Impact of treatment starting time on HIV progression. HIV progression for a 30-day 'perfect' treatment starting at three different times after the initial infection: (1) on the 3rd day when the density of all CD4$^+$ T cells is $N=725\,cells/\mu l$, (2) when $N=500\,cells/\mu l$; (3) when $N=350\,cells/\mu l$. The 'prefect' treatment here means both cell-to-cell infection and the cell-free infection are completely blocked (i.e. $\beta_1=0$, $\beta_2=0$ and the virus release rate $g=0$) for 30 days.