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Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics
The viral population of HIV-1, like many pathogens that cause systemic infection, is structured and differentiated within the body. The dynamics of cellular immune trafficking through the blood and within compartments of the body has also received wide attention. Despite these advances, mathematical...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051940/ https://www.ncbi.nlm.nih.gov/pubmed/27706164 http://dx.doi.org/10.1371/journal.pbio.1002567 |
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author | Lythgoe, Katrina A. Blanquart, François Pellis, Lorenzo Fraser, Christophe |
author_facet | Lythgoe, Katrina A. Blanquart, François Pellis, Lorenzo Fraser, Christophe |
author_sort | Lythgoe, Katrina A. |
collection | PubMed |
description | The viral population of HIV-1, like many pathogens that cause systemic infection, is structured and differentiated within the body. The dynamics of cellular immune trafficking through the blood and within compartments of the body has also received wide attention. Despite these advances, mathematical models, which are widely used to interpret and predict viral and immune dynamics in infection, typically treat the infected host as a well-mixed homogeneous environment. Here, we present mathematical, analytical, and computational results that demonstrate that consideration of the spatial structure of the viral population within the host radically alters predictions of previous models. We study the dynamics of virus replication and cytotoxic T lymphocytes (CTLs) within a metapopulation of spatially segregated patches, representing T cell areas connected by circulating blood and lymph. The dynamics of the system depend critically on the interaction between CTLs and infected cells at the within-patch level. We show that for a wide range of parameters, the system admits an unexpected outcome called the shifting-mosaic steady state. In this state, the whole body’s viral population is stable over time, but the equilibrium results from an underlying, highly dynamic process of local infection and clearance within T-cell centers. Notably, and in contrast to previous models, this new model can explain the large differences in set-point viral load (SPVL) observed between patients and their distribution, as well as the relatively low proportion of cells infected at any one time, and alters the predicted determinants of viral load variation. |
format | Online Article Text |
id | pubmed-5051940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50519402016-10-27 Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics Lythgoe, Katrina A. Blanquart, François Pellis, Lorenzo Fraser, Christophe PLoS Biol Research Article The viral population of HIV-1, like many pathogens that cause systemic infection, is structured and differentiated within the body. The dynamics of cellular immune trafficking through the blood and within compartments of the body has also received wide attention. Despite these advances, mathematical models, which are widely used to interpret and predict viral and immune dynamics in infection, typically treat the infected host as a well-mixed homogeneous environment. Here, we present mathematical, analytical, and computational results that demonstrate that consideration of the spatial structure of the viral population within the host radically alters predictions of previous models. We study the dynamics of virus replication and cytotoxic T lymphocytes (CTLs) within a metapopulation of spatially segregated patches, representing T cell areas connected by circulating blood and lymph. The dynamics of the system depend critically on the interaction between CTLs and infected cells at the within-patch level. We show that for a wide range of parameters, the system admits an unexpected outcome called the shifting-mosaic steady state. In this state, the whole body’s viral population is stable over time, but the equilibrium results from an underlying, highly dynamic process of local infection and clearance within T-cell centers. Notably, and in contrast to previous models, this new model can explain the large differences in set-point viral load (SPVL) observed between patients and their distribution, as well as the relatively low proportion of cells infected at any one time, and alters the predicted determinants of viral load variation. Public Library of Science 2016-10-05 /pmc/articles/PMC5051940/ /pubmed/27706164 http://dx.doi.org/10.1371/journal.pbio.1002567 Text en © 2016 Lythgoe 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 Lythgoe, Katrina A. Blanquart, François Pellis, Lorenzo Fraser, Christophe Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics |
title | Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics |
title_full | Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics |
title_fullStr | Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics |
title_full_unstemmed | Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics |
title_short | Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics |
title_sort | large variations in hiv-1 viral load explained by shifting-mosaic metapopulation dynamics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051940/ https://www.ncbi.nlm.nih.gov/pubmed/27706164 http://dx.doi.org/10.1371/journal.pbio.1002567 |
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