Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data

Abstract Viruses experience selective pressure on the timing and order of events during infection to maximize the number of viable offspring they produce. Additionally, they may experience variability in cellular environments encountered, as individual eukaryotic cells can display variation in gene...

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Autores principales:	Ashley I. Teufel, Wu Liu, Jeremy A. Draghi, Craig E. Cameron, Claus O. Wilke
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1f3017db5ba54bf6bd7e42ad0a6cab05
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spelling	oai:doaj.org-article:1f3017db5ba54bf6bd7e42ad0a6cab052021-12-02T14:49:33ZModeling poliovirus replication dynamics from live time-lapse single-cell imaging data10.1038/s41598-021-87694-x2045-2322https://doaj.org/article/1f3017db5ba54bf6bd7e42ad0a6cab052021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87694-xhttps://doaj.org/toc/2045-2322Abstract Viruses experience selective pressure on the timing and order of events during infection to maximize the number of viable offspring they produce. Additionally, they may experience variability in cellular environments encountered, as individual eukaryotic cells can display variation in gene expression among cells. This leads to a dynamic phenotypic landscape that viruses must face to replicate. To examine replication dynamics displayed by viruses faced with this variable landscape, we have developed a method for fitting a stochastic mechanistic model of viral infection to time-lapse imaging data from high-throughput single-cell poliovirus infection experiments. The model’s mechanistic parameters provide estimates of several aspects associated with the virus’s intracellular dynamics. We examine distributions of parameter estimates and assess their variability to gain insight into the root causes of variability in viral growth dynamics. We also fit our model to experiments performed under various drug treatments and examine which parameters differ under these conditions. We find that parameters associated with translation and early stage viral replication processes are essential for the model to capture experimentally observed dynamics. In aggregate, our results suggest that differences in viral growth data generated under different treatments can largely be captured by steps that occur early in the replication process.Ashley I. TeufelWu LiuJeremy A. DraghiCraig E. CameronClaus O. WilkeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ashley I. Teufel Wu Liu Jeremy A. Draghi Craig E. Cameron Claus O. Wilke Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data
description	Abstract Viruses experience selective pressure on the timing and order of events during infection to maximize the number of viable offspring they produce. Additionally, they may experience variability in cellular environments encountered, as individual eukaryotic cells can display variation in gene expression among cells. This leads to a dynamic phenotypic landscape that viruses must face to replicate. To examine replication dynamics displayed by viruses faced with this variable landscape, we have developed a method for fitting a stochastic mechanistic model of viral infection to time-lapse imaging data from high-throughput single-cell poliovirus infection experiments. The model’s mechanistic parameters provide estimates of several aspects associated with the virus’s intracellular dynamics. We examine distributions of parameter estimates and assess their variability to gain insight into the root causes of variability in viral growth dynamics. We also fit our model to experiments performed under various drug treatments and examine which parameters differ under these conditions. We find that parameters associated with translation and early stage viral replication processes are essential for the model to capture experimentally observed dynamics. In aggregate, our results suggest that differences in viral growth data generated under different treatments can largely be captured by steps that occur early in the replication process.
format	article
author	Ashley I. Teufel Wu Liu Jeremy A. Draghi Craig E. Cameron Claus O. Wilke
author_facet	Ashley I. Teufel Wu Liu Jeremy A. Draghi Craig E. Cameron Claus O. Wilke
author_sort	Ashley I. Teufel
title	Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data
title_short	Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data
title_full	Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data
title_fullStr	Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data
title_full_unstemmed	Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data
title_sort	modeling poliovirus replication dynamics from live time-lapse single-cell imaging data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1f3017db5ba54bf6bd7e42ad0a6cab05
work_keys_str_mv	AT ashleyiteufel modelingpoliovirusreplicationdynamicsfromlivetimelapsesinglecellimagingdata AT wuliu modelingpoliovirusreplicationdynamicsfromlivetimelapsesinglecellimagingdata AT jeremyadraghi modelingpoliovirusreplicationdynamicsfromlivetimelapsesinglecellimagingdata AT craigecameron modelingpoliovirusreplicationdynamicsfromlivetimelapsesinglecellimagingdata AT clausowilke modelingpoliovirusreplicationdynamicsfromlivetimelapsesinglecellimagingdata
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Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data

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