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Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions

Influenza A viruses (IAV) are commonly used to infect animal cell cultures for research purposes and vaccine production. Their replication is influenced strongly by the multiplicity of infection (MOI), which ranges over several orders of magnitude depending on the respective application. So far, mat...

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Autores principales: Rüdiger, Daniel, Kupke, Sascha Young, Laske, Tanja, Zmora, Pawel, Reichl, Udo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396949/
https://www.ncbi.nlm.nih.gov/pubmed/30779733
http://dx.doi.org/10.1371/journal.pcbi.1006819
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author Rüdiger, Daniel
Kupke, Sascha Young
Laske, Tanja
Zmora, Pawel
Reichl, Udo
author_facet Rüdiger, Daniel
Kupke, Sascha Young
Laske, Tanja
Zmora, Pawel
Reichl, Udo
author_sort Rüdiger, Daniel
collection PubMed
description Influenza A viruses (IAV) are commonly used to infect animal cell cultures for research purposes and vaccine production. Their replication is influenced strongly by the multiplicity of infection (MOI), which ranges over several orders of magnitude depending on the respective application. So far, mathematical models of IAV replication have paid little attention to the impact of the MOI on infection dynamics and virus yields. To address this issue, we extended an existing model of IAV replication in adherent MDCK cells with kinetics that explicitly consider the time point of cell infection. This modification does not only enable the fitting of high MOI measurements, but also the successful prediction of viral release dynamics of low MOI experiments using the same set of parameters. Furthermore, this model allows the investigation of defective interfering particle (DIP) propagation in different MOI regimes. The key difference between high and low MOI conditions is the percentage of infectious virions among the total virus particle release. Simulation studies show that DIP interference at a high MOI is determined exclusively by the DIP content of the seed virus while, in low MOI conditions, it is predominantly controlled by the de novo generation of DIPs. Overall, the extended model provides an ideal framework for the prediction and optimization of cell culture-derived IAV manufacturing and the production of DIPs for therapeutic use.
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spelling pubmed-63969492019-03-09 Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions Rüdiger, Daniel Kupke, Sascha Young Laske, Tanja Zmora, Pawel Reichl, Udo PLoS Comput Biol Research Article Influenza A viruses (IAV) are commonly used to infect animal cell cultures for research purposes and vaccine production. Their replication is influenced strongly by the multiplicity of infection (MOI), which ranges over several orders of magnitude depending on the respective application. So far, mathematical models of IAV replication have paid little attention to the impact of the MOI on infection dynamics and virus yields. To address this issue, we extended an existing model of IAV replication in adherent MDCK cells with kinetics that explicitly consider the time point of cell infection. This modification does not only enable the fitting of high MOI measurements, but also the successful prediction of viral release dynamics of low MOI experiments using the same set of parameters. Furthermore, this model allows the investigation of defective interfering particle (DIP) propagation in different MOI regimes. The key difference between high and low MOI conditions is the percentage of infectious virions among the total virus particle release. Simulation studies show that DIP interference at a high MOI is determined exclusively by the DIP content of the seed virus while, in low MOI conditions, it is predominantly controlled by the de novo generation of DIPs. Overall, the extended model provides an ideal framework for the prediction and optimization of cell culture-derived IAV manufacturing and the production of DIPs for therapeutic use. Public Library of Science 2019-02-19 /pmc/articles/PMC6396949/ /pubmed/30779733 http://dx.doi.org/10.1371/journal.pcbi.1006819 Text en © 2019 Rüdiger et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rüdiger, Daniel
Kupke, Sascha Young
Laske, Tanja
Zmora, Pawel
Reichl, Udo
Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions
title Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions
title_full Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions
title_fullStr Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions
title_full_unstemmed Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions
title_short Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions
title_sort multiscale modeling of influenza a virus replication in cell cultures predicts infection dynamics for highly different infection conditions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396949/
https://www.ncbi.nlm.nih.gov/pubmed/30779733
http://dx.doi.org/10.1371/journal.pcbi.1006819
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