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Updated: Apr 15, 2026

Assessing the Innate Sensing of HIV-1 Infected CD4+ T Cells by Plasmacytoid Dendritic Cells Using an Ex vivo Co-culture System.
Published on: September 1, 2015
Hybrid spreading mechanisms and T cell activation shape the dynamics of HIV-1 infection
Changwang Zhang1, Shi Zhou2, Elisabetta Groppelli3
1Department 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.
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