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

Zhang C, Zhou S, Groppelli E, Pellegrino P, Williams I, Borrow P, Chain BM, Jolly C - PLoS Comput. Biol. (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University College London, London, United Kingdom; Security Science Doctoral Research Training Centre, University College London, London, United Kingdom; School of Computer Science, National University of Defense Technology, Changsha, China.

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.

No MeSH data available.


Related in: MedlinePlus

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).
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pcbi.1004179.g001: 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).

Mentions: We here introduce a model of HIV-1 infection as depicted in Fig. 1A. We consider four distinct CD4+ T cell states: activated, uninfected susceptible (S) cells; activated and productively infected (I) cells; quiescent, uninfected (Q) cells; and quiescent latently (L) infected cells. The total CD4+ cell density (N) changes with time and is given by the sum of these four terms, i.e. N(t) = Q(t) + S(t) + I(t) + L(t). The model can be described by an Ordinary Differential Equation (ODE) system (Equation 1) and is illustrated in Fig. 1A.dQdt=-γQ+rSS-aNMNQ+bdSdt=-γSS-rSS+aNMNQ-cISNθβ1-SVβ2+pNM-NNMSdIdt=-γII-rII+aNMNL+cISNθβ1+SVβ2-κII+0.1NNMIdLdt=-γL+rII-aNMNLdVdt=-γVV+gI(1)The density variables (Q, S, I, L, V) and parameters are defined in Table 1. The densities are measured as numbers of cells or virions in a μl of blood/extracellular fluid. We set a density variable to zero when it drops to below 10−12/μl, accounting for the fact that when density of cells or virions drops to such low level, there is a high probability that it would die out (density becomes zero). The default value of parameters, shown in Table 1, are taken from the literature or estimated from clinical and experimentally observed data. The killing coefficient κ equals to its value in Table 1 when t ≥ D; otherwise κ = 0 when t < D.


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

Zhang C, Zhou S, Groppelli E, Pellegrino P, Williams I, Borrow P, Chain BM, Jolly C - PLoS Comput. Biol. (2015)

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).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4383537&req=5

pcbi.1004179.g001: 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).
Mentions: We here introduce a model of HIV-1 infection as depicted in Fig. 1A. We consider four distinct CD4+ T cell states: activated, uninfected susceptible (S) cells; activated and productively infected (I) cells; quiescent, uninfected (Q) cells; and quiescent latently (L) infected cells. The total CD4+ cell density (N) changes with time and is given by the sum of these four terms, i.e. N(t) = Q(t) + S(t) + I(t) + L(t). The model can be described by an Ordinary Differential Equation (ODE) system (Equation 1) and is illustrated in Fig. 1A.dQdt=-γQ+rSS-aNMNQ+bdSdt=-γSS-rSS+aNMNQ-cISNθβ1-SVβ2+pNM-NNMSdIdt=-γII-rII+aNMNL+cISNθβ1+SVβ2-κII+0.1NNMIdLdt=-γL+rII-aNMNLdVdt=-γVV+gI(1)The density variables (Q, S, I, L, V) and parameters are defined in Table 1. The densities are measured as numbers of cells or virions in a μl of blood/extracellular fluid. We set a density variable to zero when it drops to below 10−12/μl, accounting for the fact that when density of cells or virions drops to such low level, there is a high probability that it would die out (density becomes zero). The default value of parameters, shown in Table 1, are taken from the literature or estimated from clinical and experimentally observed data. The killing coefficient κ equals to its value in Table 1 when t ≥ D; otherwise κ = 0 when t < D.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University College London, London, United Kingdom; Security Science Doctoral Research Training Centre, University College London, London, United Kingdom; School of Computer Science, National University of Defense Technology, Changsha, China.

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.

No MeSH data available.


Related in: MedlinePlus