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. 2015 Apr 2;11(4):e1004179.
doi: 10.1371/journal.pcbi.1004179. eCollection 2015 Apr.

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

Changwang Zhang  1 , Shi Zhou  2 , Elisabetta Groppelli  3 , Pierre Pellegrino  4 , Ian Williams  4 , Persephone Borrow  5 , Benjamin M Chain  3 , Clare Jolly  3
Affiliations

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

Changwang Zhang et al. PLoS Comput Biol. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The HIV-1 model reproduces the full course of HIV-1 infection.
(A) Diagrammatic representation of the model described by Equation 1. (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 1.(C) The density of quiescent, susceptible, latent and infected CD4+ T cells, and the density of free virions as function of time (in days).
Fig 2
Fig 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/μl) 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 S1 Table and S2 Table.
Fig 3
Fig 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 β 1 and cell-free infection rate β 2: (1) both use their default value, (2) β 1 uses its default value and β 2 = 0, (3) β 1 = 0 and β 2 uses its default value, (4) β 1 is twice its default value and β 2 = 0, (5) β 1 = 0 and β 2 is twice its default value.
Fig 4
Fig 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 (aN M/N in Equation 1), (2) fixed activation (aN M/N 0, where N 0 is the initial density of CD4+ T cells), and (3) doubled activation (2 × aN 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 (κII+0.1NNM, right y axis) as a function of time, respectively.
Fig 5
Fig 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/μl, (2) when N = 500 cells/μl; (3) when N = 350 cells/μl. The ‘prefect’ treatment here means both cell-to-cell infection and the cell-free infection are completely blocked (i.e. β 1 = 0, β 2 = 0 and the virus release rate g = 0) for 30 days.
Fig 6
Fig 6. Prediction of the time to AIDS, t A, for different treatment schemes.
We consider 30-day treatments starting (A) on the 3rd day and (B) when N = 500 cells/μl, respectively. All treatments block cell-free infection completely (β 2 = 0 and g = 0). The treatments also affect cell-to-cell infection (x axis) and / or manipulate the CD4+ T cell activation process (y axis, see Fig. 4A). The black squares represent treatments that reduced the density of infected cells, latent cells and free virions all below 10−12 per μl.
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