Number of infection events per cell during HIV-1 cell-free infection

• Published in: Scientific reports Vol. 7; no. 1; pp. 6559 - 11
• Main Authors: Ito, Yusuke, Remion, Azaria, Tauzin, Alexandra, Ejima, Keisuke, Nakaoka, Shinji, Iwasa, Yoh, Iwami, Shingo, Mammano, Fabrizio
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.07.2017; Nature Publishing Group; Nature Portfolio
• Subjects: 13; 13/31; 14; 631/114/2415; 631/326/596/1787; Binomial distribution; CD4-Positive T-Lymphocytes; CD4-Positive T-Lymphocytes - virology; Cell culture; Cell Line; Genomes; Heterogeneity; HIV-1; HIV-1 - growth & development; Humanities and Social Sciences; Humans; Infections; Life Sciences; Mathematical models; Models, Theoretical; multidisciplinary; Recombination; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-03954-9

HIV-1 accumulates changes in its genome through both recombination and mutation during the course of infection. For recombination to occur, a single cell must be infected by two HIV strains. These coinfection events were experimentally demonstrated to occur more frequently than would be expected for independent infection events and do not follow a random distribution. Previous mathematical modeling approaches demonstrated that differences in target cell susceptibility can explain the non-randomness, both in the context of direct cell-to-cell transmission, and in the context of free virus transmission (Q. Dang et al ., Proc. Natl. Acad. Sci. USA 101:632-7, 2004: K. M. Law et al ., Cell reports 15:2711-83, 2016). Here, we build on these notions and provide a more detailed and extensive quantitative framework. We developed a novel mathematical model explicitly considering the heterogeneity of target cells and analysed datasets of cell-free HIV-1 single and double infection experiments in cell culture. Particularly, in contrast to the previous studies, we took into account the different susceptibility of the target cells as a continuous distribution. Interestingly, we showed that the number of infection events per cell during cell-free HIV-1 infection follows a negative-binomial distribution, and our model reproduces these datasets.