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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

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 (tA), the quasi-steady density of CD4+ T cells (Ns) and the quasi-steady density of free virions (Vs). An arrow represents that tA 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 log10V) for four typical patients, where clinical data are shown as dots.Full prediction results are shown in S1 Table and S2 Table.
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pcbi.1004179.g002: 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 (tA), the quasi-steady density of CD4+ T cells (Ns) and the quasi-steady density of free virions (Vs). An arrow represents that tA 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 log10V) for four typical patients, where clinical data are shown as dots.Full prediction results are shown in S1 Table and S2 Table.

Mentions: We use our model to theoretically reproduce the HIV-1 infection courses in the data. The values for HIV infectivity were fixed, as derived from the literature or our own observations (Table 1). Values for five parameters (Q0, S0, NM, κ and D) describing the characteristics of the immune response were chosen for each patient to minimise the error of the predicted quasi-stable level of T cell counts (Ns) and viral load (Vs), and the time of progression to AIDS (tA). All other values remained fixed at the default values in Table 1. The predicted progression results are compared against the actual measurements in Fig. 2 and S2 Table. The predicted Vs and tA for each patient were negatively correlated (correlation coefficient = −0.46), in agreement with the well-established relationship between these two clinical values. Remarkably, the model can fit all patients by modifying the five immune-relevant parameters over a narrow range. Furthermore, the parameter values which gave the best results for the patients (see S1 Table) are all very close to those in Table 1, which were derived independently from experimental measurements.


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)

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 (tA), the quasi-steady density of CD4+ T cells (Ns) and the quasi-steady density of free virions (Vs). An arrow represents that tA 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 log10V) for four typical patients, where clinical data are shown as dots.Full prediction results are shown in S1 Table and S2 Table.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004179.g002: 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 (tA), the quasi-steady density of CD4+ T cells (Ns) and the quasi-steady density of free virions (Vs). An arrow represents that tA 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 log10V) for four typical patients, where clinical data are shown as dots.Full prediction results are shown in S1 Table and S2 Table.
Mentions: We use our model to theoretically reproduce the HIV-1 infection courses in the data. The values for HIV infectivity were fixed, as derived from the literature or our own observations (Table 1). Values for five parameters (Q0, S0, NM, κ and D) describing the characteristics of the immune response were chosen for each patient to minimise the error of the predicted quasi-stable level of T cell counts (Ns) and viral load (Vs), and the time of progression to AIDS (tA). All other values remained fixed at the default values in Table 1. The predicted progression results are compared against the actual measurements in Fig. 2 and S2 Table. The predicted Vs and tA for each patient were negatively correlated (correlation coefficient = −0.46), in agreement with the well-established relationship between these two clinical values. Remarkably, the model can fit all patients by modifying the five immune-relevant parameters over a narrow range. Furthermore, the parameter values which gave the best results for the patients (see S1 Table) are all very close to those in Table 1, which were derived independently from experimental measurements.

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