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How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response

BACKGROUND: Understanding what determines the between-host variability in infection dynamics is a key issue to better control the infection spread. In particular, pathogen clearance is desirable over rebounds for the health of the infected individual and its contact group. In this context, the Porci...

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Autores principales: Go, Natacha, Touzeau, Suzanne, Islam, Zeenath, Belloc, Catherine, Doeschl-Wilson, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352383/
https://www.ncbi.nlm.nih.gov/pubmed/30696429
http://dx.doi.org/10.1186/s12918-018-0666-7
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author Go, Natacha
Touzeau, Suzanne
Islam, Zeenath
Belloc, Catherine
Doeschl-Wilson, Andrea
author_facet Go, Natacha
Touzeau, Suzanne
Islam, Zeenath
Belloc, Catherine
Doeschl-Wilson, Andrea
author_sort Go, Natacha
collection PubMed
description BACKGROUND: Understanding what determines the between-host variability in infection dynamics is a key issue to better control the infection spread. In particular, pathogen clearance is desirable over rebounds for the health of the infected individual and its contact group. In this context, the Porcine Respiratory and Reproductive Syndrome virus (PRRSv) is of particular interest. Numerous studies have shown that pigs similarly infected with this highly ubiquitous virus elicit diverse response profiles. Whilst some manage to clear the virus within a few weeks, others experience prolonged infection with a rebound. Despite much speculation, the underlying mechanisms responsible for this undesirable rebound phenomenon remain unclear. RESULTS: We aimed at identifying immune mechanisms that can reproduce and explain the rebound patterns observed in PRRSv infection using a mathematical modelling approach of the within-host dynamics. As diverse mechanisms were found to influence PRRSv infection, we established a model that details the major mechanisms and their regulations at the between-cell scale. We developed an ABC-like optimisation method to fit our model to an extensive set of experimental data, consisting of non-rebounder and rebounder viremia profiles. We compared, between both profiles, the estimated parameter values, the resulting immune dynamics and the efficacies of the underlying immune mechanisms. Exploring the influence of these mechanisms, we showed that rebound was promoted by high apoptosis, high cell infection and low cytolysis by Cytotoxic T Lymphocytes, while increasing neutralisation was very efficient to prevent rebounds. CONCLUSIONS: Our paper provides an original model of the immune response and an appropriate systematic fitting method, whose interest extends beyond PRRS infection. It gives the first mechanistic explanation for emergence of rebounds during PRRSv infection. Moreover, results suggest that vaccines or genetic selection promoting strong neutralising and cytolytic responses, ideally associated with low apoptotic activity and cell permissiveness, would prevent rebound. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0666-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-63523832019-02-06 How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response Go, Natacha Touzeau, Suzanne Islam, Zeenath Belloc, Catherine Doeschl-Wilson, Andrea BMC Syst Biol Research Article BACKGROUND: Understanding what determines the between-host variability in infection dynamics is a key issue to better control the infection spread. In particular, pathogen clearance is desirable over rebounds for the health of the infected individual and its contact group. In this context, the Porcine Respiratory and Reproductive Syndrome virus (PRRSv) is of particular interest. Numerous studies have shown that pigs similarly infected with this highly ubiquitous virus elicit diverse response profiles. Whilst some manage to clear the virus within a few weeks, others experience prolonged infection with a rebound. Despite much speculation, the underlying mechanisms responsible for this undesirable rebound phenomenon remain unclear. RESULTS: We aimed at identifying immune mechanisms that can reproduce and explain the rebound patterns observed in PRRSv infection using a mathematical modelling approach of the within-host dynamics. As diverse mechanisms were found to influence PRRSv infection, we established a model that details the major mechanisms and their regulations at the between-cell scale. We developed an ABC-like optimisation method to fit our model to an extensive set of experimental data, consisting of non-rebounder and rebounder viremia profiles. We compared, between both profiles, the estimated parameter values, the resulting immune dynamics and the efficacies of the underlying immune mechanisms. Exploring the influence of these mechanisms, we showed that rebound was promoted by high apoptosis, high cell infection and low cytolysis by Cytotoxic T Lymphocytes, while increasing neutralisation was very efficient to prevent rebounds. CONCLUSIONS: Our paper provides an original model of the immune response and an appropriate systematic fitting method, whose interest extends beyond PRRS infection. It gives the first mechanistic explanation for emergence of rebounds during PRRSv infection. Moreover, results suggest that vaccines or genetic selection promoting strong neutralising and cytolytic responses, ideally associated with low apoptotic activity and cell permissiveness, would prevent rebound. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0666-7) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-29 /pmc/articles/PMC6352383/ /pubmed/30696429 http://dx.doi.org/10.1186/s12918-018-0666-7 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Go, Natacha
Touzeau, Suzanne
Islam, Zeenath
Belloc, Catherine
Doeschl-Wilson, Andrea
How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response
title How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response
title_full How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response
title_fullStr How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response
title_full_unstemmed How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response
title_short How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response
title_sort how to prevent viremia rebound? evidence from a prrsv data-supported model of immune response
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352383/
https://www.ncbi.nlm.nih.gov/pubmed/30696429
http://dx.doi.org/10.1186/s12918-018-0666-7
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