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Hybrid spreading mechanisms and T cell activation shape the dynamics of HIV-1 infection.
- Source :
- PLoS Computational Biology, Vol 11, Iss 4, p e1004179 (2015)
- Publication Year :
- 2015
- Publisher :
- Public Library of Science (PLoS), 2015.
-
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.
- Subjects :
- Biology (General)
QH301-705.5
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X and 15537358
- Volume :
- 11
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.8e90892e37894ab487cb7972e287ff15
- Document Type :
- article
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1004179