The role of cell-to-cell transmission in HIV infection: insights from a mathematical modeling approach

Author:

Rong Sophia Y.1,Guo Ting23,Smith J. Tyler3,Wang Xia43

Affiliation:

1. Buchholz High School, Gainesville, FL 32606, USA

2. Aliyun School of Big Data, Changzhou University, Changzhou 213164, China

3. Department of Mathematics, University of Florida, Gainesville, FL 32611, USA

4. School of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China

Abstract

<abstract><p>HIV infection remains a serious global public health problem. Although current drug treatment is effective and can reduce plasma viral loads below the level of detection, it cannot eradicate the virus. The reasons for the low virus persistence despite long-term therapy have not been fully elucidated. In addition, multiple HIV infection, i.e., infection of a cell by multiple viruses, is common and can facilitate viral recombination and mutations, evading the immune system and conferring resistance to drug treatment. The mechanisms for multiple HIV infection formation and their respective contributions remain unclear. To answer these questions, we developed a mathematical modeling framework that encompasses cell-free viral infection and cell-to-cell spread. We fit sub-models that only have one transmission route and the full model containing both to the multi-infection data from HIV-infected patients, and show that the multi-infection data can only be reproduced if these two transmission routes are both considered. Computer simulations with the best-fitting parameter values indicate that cell-to-cell spread leads to the majority of multiple infection and also accounts for the majority of overall infection. Sensitivity analysis shows that cell-to-cell spread has reduced susceptibility to treatment and may explain low HIV persistence. Taken together, this work indicates that cell-to-cell spread plays a crucial role in the development of HIV multi-infection and low HIV persistence despite long-term therapy, and therefore has important implications for understanding HIV pathogenesis and developing more effective treatment strategies to control or even eliminate the disease.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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