Hybrid Spreading Mechanisms and T Cell Activation Shape the Dynamics of HIV-1 Infection
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.
- Publication:
-
PLoS Computational Biology
- Pub Date:
- April 2015
- DOI:
- 10.1371/journal.pcbi.1004179
- arXiv:
- arXiv:1503.08992
- Bibcode:
- 2015PLSCB..11E4179Z
- Keywords:
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- Quantitative Biology - Populations and Evolution;
- Computer Science - Artificial Intelligence;
- Computer Science - Computational Engineering;
- Finance;
- and Science;
- Physics - Biological Physics;
- Quantitative Biology - Cell Behavior
- E-Print:
- PLOS Computational Biology. 2015 Apr 2