A method to determine the duration of the eclipse phase for in vitro infection with a highly pathogenic SHIV strain

Kakizoe, Yusuke; Nakaoka, Shinji; Beauchemin, C.; Morita, Satoru; Mori, Hiromi; Igarashi, Tatsuhiko; Aihara, Kazuyuki; Miura, Tomoyuki; Iwami, Shingo

doi:10.1038/srep10371

Cited by 55 publications

(72 citation statements)

References 42 publications

(140 reference statements)

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“…The assumption of an exponentially distributed eclipse phase (Figure A, black) does not need to be made for modeling purposes, nor is it an experimentally validated assumption: it is simply the most straightforward. Careful and detailed in vitro influenza and SHIV infection experiments provide support for non‐exponentially distributed eclipse phases, finding that a gamma‐distributed time to productive infection describes the observed data far more accurately than an exponential distribution. A gamma distribution with an integer shape parameter, an Erlang distribution, for the time spent in the eclipse phase can be easily described as a series of ODEs:

\begin{matrix} \frac{italicdT}{italicdt} & = - β V T \\ \frac{d E_{1}}{italicdt} & = β V T - k n E_{1} \\ \frac{d E_{i}}{italicdt} & = k n (E_{i - 1} - E_{i}) for i = 2 \dots n \\ \frac{d I_{2}}{italicdt} & = k n E_{n} - δ I_{2} \\ \frac{dV}{dt} = p I_{2} - c V \end{matrix}

where cells in compartments E 1 , …, E n are in the eclipse phase, and the sum of the exponential distributions (the time spent in each compartment E 1 to E n ) provides a gamma distribution with integer shape parameter n , an Erlang distribution (Figure A, green).…”

Section: Modeling Acute Viral Dynamicsmentioning

confidence: 94%

Mathematical modeling of within‐host Zika virus dynamics

Best

Perelson

2018

Immunological Reviews

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Recent Zika virus outbreaks have been associated with severe outcomes, especially during pregnancy. A great deal of effort has been put toward understanding this virus, particularly the immune mechanisms responsible for rapid viral control in the majority of infections. Identifying and understanding the key mechanisms of immune control will provide the foundation for the development of effective vaccines and antiviral therapy. Here, we outline a mathematical modeling approach for analyzing the within-host dynamics of Zika virus, and we describe how these models can be used to understand key aspects of the viral life cycle and to predict antiviral efficacy.

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\begin{matrix} \frac{italicdT}{italicdt} & = - β V T \\ \frac{d E_{1}}{italicdt} & = β V T - k n E_{1} \\ \frac{d E_{i}}{italicdt} & = k n (E_{i - 1} - E_{i}) for i = 2 \dots n \\ \frac{d I_{2}}{italicdt} & = k n E_{n} - δ I_{2} \\ \frac{dV}{dt} = p I_{2} - c V \end{matrix}

Section: Modeling Acute Viral Dynamicsmentioning

confidence: 94%

Mathematical modeling of within‐host Zika virus dynamics

Best

Perelson

2018

Immunological Reviews

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“…Again, this difference in the value of R 0 * between SHIV-KS661 and -#64 implies that the highly pathogenic SHIV strain more efficiently causes systemic CD4 + T-cell depletion. In Additional file 5: Figure S2, we calculated the distribution of the basic reproduction number without the effects of removal, R 0 = β 50 p 50 T (0)/ δ ( c RNA + c 50 ), defined in our previous paper [14] and observed the same trend.…”

Section: Resultsmentioning

confidence: 65%

A highly pathogenic simian/human immunodeficiency virus effectively produces infectious virions compared with a less pathogenic virus in cell culture

Iwanami

Kakizoe

Morita

et al. 2017

Theor Biol Med Model

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BackgroundThe host range of human immunodeficiency virus (HIV) is quite narrow. Therefore, analyzing HIV-1 pathogenesis in vivo has been limited owing to lack of appropriate animal model systems. To overcome this, chimeric simian and human immunodeficiency viruses (SHIVs) that encode HIV-1 Env and are infectious to macaques have been developed and used to investigate the pathogenicity of HIV-1 in vivo. So far, we have many SHIV strains that show different pathogenesis in macaque experiments. However, dynamic aspects of SHIV infection have not been well understood. To fully understand the dynamic properties of SHIVs, we focused on two representative strains—the highly pathogenic SHIV, SHIV-KS661, and the less pathogenic SHIV, SHIV-#64—and measured the time-course of experimental data in cell culture.MethodsWe infected HSC-F with SHIV-KS661 and -#64 and measured the concentration of Nef-negative (target) and Nef-positive (infected) HSC-F cells, the total viral load, and the infectious viral load daily for 9 days. The experiments were repeated at two different multiplicities of infection, and a previously developed mathematical model incorporating the infectious and non-infectious viruses was fitted to the full dataset of each strain simultaneously to characterize the infection dynamics of these two strains.Results and conclusionsWe quantified virological indices including virus burst sizes and basic reproduction number of both SHIV-KS661 and -#64. Comparing the burst size of total and infectious viruses (viral RNA copies and TCID50, respectively), we found that there was a statistically significant difference between the infectious virus burst size of SHIV-KS661 and -#64, while there was no significant difference between the total virus burst size. Furthermore, our analyses showed that the fraction of infectious virus among the produced SHIV-KS661 viruses, which is defined as the infectious viral load (TCID50/ml) divided by the total viral load (RNA copies/ml), is more than 10-fold higher than that of SHIV-#64 during overall infection (i.e., for 9 days). Taken together, we conclude that the highly pathogenic SHIV produces infectious virions more effectively than the less pathogenic SHIV in cell culture.Electronic supplementary materialThe online version of this article (doi:10.1186/s12976-017-0055-8) contains supplementary material, which is available to authorized users.

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“…In this article, a ranking of various distributions to the infected‐cell responses was performed, and the distribution with minimum AIC was selected as an input distribution for the kinetic parameter to simulate cell‐to‐cell variability using Monte‐Carlo simulations. The rationale behind choosing the list of PDFs presented in Table S1 is that the list contains most of the distributions that have been earlier used to define the variability in infection system and other biological system (Kakizoe et al, 2015; Leiva et al, 2015; Singer, MacLachlan, & Carpenter, 2001). We provide a summary of distributions (PDFs) that are fitted to various biological data and other natural/environmental data (Table S17).…”

Section: Discussionmentioning

confidence: 99%

“…The description of various PDFs considered is shown in Table S1. The rationale behind choosing the list of PDFs presented in Table S1 is that the list contains most of the distributions that have been earlier used to define the variability in infection system and biological system (Hu, Boritz, Wylie, & Douek, 2017; Kakizoe et al, 2015; Leiva et al, 2015; Singer et al, 2001). To find the most suitable PDF from the set of PDFs under consideration for l th dataset x ( l ) , we first calculate the log‐likelihood functions (Equation 2):

L_{m} true(x^{(l)}; θ_{m} true) = \sum_{j = 1}^{J_{l}} normallog p_{m} (x_{j}^{true(l true)} normal; θ_{m}) true(m = 1, 2, …, M true) .

…”

Section: Methodsmentioning

confidence: 99%

Statistical modeling of cell‐to‐cell variability in viral infection during passaging in suspension cell culture: Application in Monte‐Carlo simulation

Saxena

Suryateja

Upadhyay

et al. 2020

Biotech & Bioengineering

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Packaging during the passaging of viruses in cell cultures yields various phenotypes and is regulated by viral protein expression in infected cells. Although such a packaging mechanism has a profound effect in controlling the virus yield, little is known about the underlying statistical models followed by virus packaging and protein expression among cells infected with the virus. A predictive framework combining identification of the probability density function (PDF) based on log‐likelihood and using the PDF for Monte‐Carlo simulations is developed. The Birnbaum–Saunders distribution was found to be consistent with all three‐virus packaging levels, including nucleocapsids/occlusion‐derived virus (ODV), ODVs/polyhedra, and polyhedra/cell for both wild‐type and genetically modified AcMNPV. Next, it was demonstrated that PDF fitting could be used to compare two viruses having distinctly different genetic configurations. Finally, the identified PDF can be incorporated in RNA synthesis parameters for baculovirus infection to predict the cell‐to‐cell variability in protein expression using Monte‐Carlo simulations. The proposed tool can be used for the estimation of uncertainty in the kinetic parameter and prediction of cell‐to‐cell variability for other biological systems.

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A method to determine the duration of the eclipse phase for in vitro infection with a highly pathogenic SHIV strain

Cited by 55 publications

References 42 publications

Mathematical modeling of within‐host Zika virus dynamics

Mathematical modeling of within‐host Zika virus dynamics

A highly pathogenic simian/human immunodeficiency virus effectively produces infectious virions compared with a less pathogenic virus in cell culture

Statistical modeling of cell‐to‐cell variability in viral infection during passaging in suspension cell culture: Application in Monte‐Carlo simulation

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