Cell-to-cell infection by HIV contributes over half of virus infection
Our reply to the comment on “Cell-to-cell infection by HIV contributes over half of virus infection” by D. Wodarz et al.
Shingo Iwami (1,2,3) and Kei Sato (4,3)
(1) Mathematical Biology Laboratory, Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Fukuoka 8128581, Japan.
(2) PRESTO, JST, Kawaguchi, Saitama 3320012, Japan.
(3) CREST, JST, Kawaguchi, Saitama 3320012, Japan.
(4) Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Kyoto 6068507, Japan.
As we mentioned in the discussion section of our recent publication “Cell-to-cell infection by HIV contributes over half of virus infection” [1], our results are basically consistent with the report from Komarova et al. [2]. However, to our knowledge, our experimental data and mathematical approach are more suitable and robust for analyzing HIV-1 cell-to-cell infection in terms of the following issues:
First, our experimental dataset includes 3 virological parameters: “the number of uninfected cells”, “the number of infected cells”, and “viral load” from the
both static and shaking cell cultures. It is particularly noteworthy that we measured the detailed time-course of experimental data even after the peak of the infected cells and viral load is reached. Compared to our experiments, Komarova et al. [2] have measured and used only a single virological parameter - the percentage of infected cells. This method obviously limits the accuracy and robustness of parameter estimation, especially death rate of infected cells (also see below).
Second, in Komarova et al. [2], the clearance rate of viruses is much larger than the death rate of infected cells. This value was only validated in vivo [3], but not in in vitro cell culture [4-9]. This means the quasi-equilibrium approximation, which is used in Komarova et al. [2], is not validated for modeling and analyzing their dataset. In contrast, we did not use any "unrealistic" parameters in our mathematical model. Therefore, we think we could estimate all parameters in our model using the complete experimental data (also see above). Furthermore, Komarova et al. assumed a value for the death rate of infected cells because they could not estimate it from their dataset (although they performed sensitivity analysis for their fixed parameters).
Third, in addition to parameter estimation, we derived the basic reproduction number, the generation time, and the Malthus coefficient and quantified them. We would like to emphasize that these issues were not investigated in Komarova et al. [2]. Our indices fully characterize the benefit of cell-to-cell infection.
Fourth, using Bayesian inference, we estimated parameter values while considering uncertainty. This is very important for the current theoretical studies. However, Komarova et al. [2] did not consider any uncertainty and did not address this issue.
Taken together, although our conclusion is similar to that of Komarova et al. [2], we believe with confidence that our findings establish the importance of cell-to-cell infection and associate parameters in a robust way as explained above. We introduced our novel model, relying on a carefully designed experiment, to accurately extract the quantitative information that underlies HIV-1 infection. Therefore, we think our paper can complement some incomplete analyses by Komarova et al. [2].
Finally, we thank Wodarz et al. for providing some good suggestions for future study – the use of mutant viruses and antibodies to exclusively block cell-free infection would provide new insights for a detailed and quantitative understanding of the efficacy of cell-free and cell-to-cell transmission of HIV.
References
(1) Iwami S, Takeuchi JS, Nakaoka S, Mammano F, Clavel F, et al. (2015)
Cell-to-cell infection by HIV contributes over half of virus infection. eLife 2015; 4:e08150.
(2) Komarova NL, Anghelina D, Voznesensky I, Trinite B, Levy DN, et al. (2013) Relative contribution of free-virus and synaptic transmission to the spread of HIV-1 through target cell populations. Biol Lett 9: 20121049.
(3) Nowak M, May RM (2000) Virus Dynamics: Mathematical Principles of Immunology and Virology: Mathematical Principles of Immunology and Virology: Oxford University Press.
(4) Iwami S, Holder BP, Beauchemin CA, Morita S, Tada T, et al. (2012) Quantification system for the viral dynamics of a highly pathogenic simian/human immunodeficiency virus based on an in vitro experiment and a mathematical model. Retrovirology 9: 18.
(5) Iwami S, Sato K, De Boer RJ, Aihara K, Miura T, et al. (2012) Identifying viral parameters from in vitro cell cultures. Front Microbiol 3: 319.
(6) Ikeda H, Godinho-Santos A, Rato S, Vanwalscappel B, Clavel F, et al. (2015) Quantifying the Antiviral Effect of IFN on HIV-1 Replication in Cell Culture. Sci Rep 5: 11761.
(7) Iwami S, Sato K, Morita S, Inaba H, Kobayashi T, et al. (2015) Pandemic HIV-1 Vpu overcomes intrinsic herd immunity mediated by tetherin. Sci Rep 5: 12256.
(8) Kakizoe Y, Morita S, Nakaoka S, Takeuchi Y, Sato K, et al. (2015) A conservation law for virus infection kinetics in vitro. J Theor Biol 376: 39-47.
(9) Kakizoe Y, Nakaoka S, Beauchemin CA, Morita S, Mori H, et al. (2015) A method to determine the duration of the eclipse phase for in vitro infection with a highly pathogenic SHIV strain. Sci Rep 5: 10371.
