Journal
JOURNAL OF THEORETICAL BIOLOGY
Volume 455, Issue -, Pages 39-46Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jtbi.2018.06.025
Keywords
HIV-1 infection; Mathematical model; Coinfection; Non-random infection
Categories
Funding
- Agence Nationale de Recherches sur le Sida et les Hepatites Virales (ANRS)
- Sidaction
- JST PRESTO
- CREST program
- Japan Society for the Promotion of Science (JSPS) KAKENHI [16H04845, 16K13777, 15KT0107, 26287025]
- Ministry of Education, Culture, Science, Sports, and Technology (MEXT) of Japan [16H06429, 16K21723, 17H05819, 18H05103]
- J-PRIDE [17fm0208006h0001, 17fm0208019h0101, 17fm0208014h0001]
- Program on the Innovative Development
- Application of New Drugs for Hepatitis B, AMED [17fk0310114h0101, 18fk0210036j0001]
- Mitsui Life Social Welfare Foundation
- Shin-Nihon of Advanced Medical Research
- GSK Japan Research Grant 2016
- Mochida Memorial Foundation for Medical and Pharmaceutical Research
- Suzuken Memorial Foundation
- SEI Group CSR Foundation
- Life Science Foundation of Japan
- SECOM Science and Technology Foundation
- Center for Clinical and Translational Research of Kyushu University Hospital
- Kyushu University-initiated venture business seed development program (GAP Fund)
- Japan Prize Foundation
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HIV-1 mutations rapidly accumulate through genetic recombination events, which require the infection of a single cell by two virions (coinfection). Accumulation of mutations in the viral population may lead to immune escape and high-level drug resistance. The existence of cell subpopulations characterized by different susceptibility to HIV-1 infection has been proposed as an important parameter driving coinfection (Dang et al., 2004). While the mechanism and the quantification of HIV-1 coinfection have been recently investigated by mathematical models, the detailed dynamics of this process during cell-free infection remains elusive. In this study, we constructed ordinary differential equations considering the heterogeneity of target cell populations during cell-free infection in cell culture, and reproduced the cell culture experimental data. Our mathematical analyses showed that the presence of two differently susceptible target cell subpopulations could explain our experimental datasets, while increasing the number of subpopulations did not improve the fitting. In addition, we quantitatively demonstrated that cells infected by multiple viruses mainly accumulated from one cell subpopulation under cell-free infection conditions. In particular, the frequency of infection events in the more susceptible subpopulation was 6.11-higher than that from the other subpopulation, and 98.3% of coinfected cells emerged from the more susceptible sub population. Our mathematical-experimental approach is able to extract such a quantitative information, and can be easily applied to other virus infections. (C) 2018 Elsevier Ltd. All rights reserved.
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