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Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution

BACKGROUND: Host resistance and viral pathogenicity are determined by molecular interactions that are part of the evolutionary arms race between viruses and their hosts. Viruses are obligate intracellular parasites and entry to the host cell is the first step of any virus infection. Commonly, viruse...

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Autores principales: Shin, Jeewoen, MacCarthy, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080737/
https://www.ncbi.nlm.nih.gov/pubmed/27784264
http://dx.doi.org/10.1186/s12862-016-0804-z
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author Shin, Jeewoen
MacCarthy, Thomas
author_facet Shin, Jeewoen
MacCarthy, Thomas
author_sort Shin, Jeewoen
collection PubMed
description BACKGROUND: Host resistance and viral pathogenicity are determined by molecular interactions that are part of the evolutionary arms race between viruses and their hosts. Viruses are obligate intracellular parasites and entry to the host cell is the first step of any virus infection. Commonly, viruses enter host cells by binding cell surface receptors. We adopt a computational modeling approach to study the evolution of the first infection step, where we consider two possible levels of resistance mechanism: at the level of the binding interaction between the host receptor and a virus binding protein, and at the level of receptor protein expression where we use a standard gene regulatory network model. At the population level we adopted the Susceptible-Infected-Susceptible (SIS) model. We used our multi-scale model to understand what conditions might determine the balance between use of resistance mechanisms at the two different levels. RESULTS: We explored a range of different conditions (model parameters) that affect host evolutionary dynamics and, in particular, the balance between the use of different resistance mechanisms. These conditions include the complexity of the receptor binding protein-protein interaction, selection pressure on the host population (pathogenicity), and the number of expressed cell-surface receptors. In particular, we found that as the receptor binding complexity (understood as the number of amino acids involved in the interaction between the virus entry protein and the host receptor) increases, viruses tend to become specialists and target one specific receptor. At the same time, on the host side, the potential for resistance shifts from the changes at the level of receptor binding (protein-protein) interaction towards changes at the level of gene regulation, suggesting a mechanism for increased biological complexity. CONCLUSIONS: Host resistance and viral pathogenicity depend on quite different evolutionary conditions. Viruses may evolve cell entry strategies that use small receptor binding regions, represented by low complexity binding in our model. Our modeling results suggest that if the virus adopts a strategy based on binding to low complexity sites on the host receptor, the host will select a defense strategy at the protein (receptor) level, rather than at the level of the regulatory network - a virus-host strategy that appears to have been selected most often in nature. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12862-016-0804-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-50807372016-10-31 Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution Shin, Jeewoen MacCarthy, Thomas BMC Evol Biol Research Article BACKGROUND: Host resistance and viral pathogenicity are determined by molecular interactions that are part of the evolutionary arms race between viruses and their hosts. Viruses are obligate intracellular parasites and entry to the host cell is the first step of any virus infection. Commonly, viruses enter host cells by binding cell surface receptors. We adopt a computational modeling approach to study the evolution of the first infection step, where we consider two possible levels of resistance mechanism: at the level of the binding interaction between the host receptor and a virus binding protein, and at the level of receptor protein expression where we use a standard gene regulatory network model. At the population level we adopted the Susceptible-Infected-Susceptible (SIS) model. We used our multi-scale model to understand what conditions might determine the balance between use of resistance mechanisms at the two different levels. RESULTS: We explored a range of different conditions (model parameters) that affect host evolutionary dynamics and, in particular, the balance between the use of different resistance mechanisms. These conditions include the complexity of the receptor binding protein-protein interaction, selection pressure on the host population (pathogenicity), and the number of expressed cell-surface receptors. In particular, we found that as the receptor binding complexity (understood as the number of amino acids involved in the interaction between the virus entry protein and the host receptor) increases, viruses tend to become specialists and target one specific receptor. At the same time, on the host side, the potential for resistance shifts from the changes at the level of receptor binding (protein-protein) interaction towards changes at the level of gene regulation, suggesting a mechanism for increased biological complexity. CONCLUSIONS: Host resistance and viral pathogenicity depend on quite different evolutionary conditions. Viruses may evolve cell entry strategies that use small receptor binding regions, represented by low complexity binding in our model. Our modeling results suggest that if the virus adopts a strategy based on binding to low complexity sites on the host receptor, the host will select a defense strategy at the protein (receptor) level, rather than at the level of the regulatory network - a virus-host strategy that appears to have been selected most often in nature. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12862-016-0804-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-26 /pmc/articles/PMC5080737/ /pubmed/27784264 http://dx.doi.org/10.1186/s12862-016-0804-z Text en © The Author(s). 2016 Open AccessThis 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
Shin, Jeewoen
MacCarthy, Thomas
Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution
title Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution
title_full Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution
title_fullStr Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution
title_full_unstemmed Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution
title_short Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution
title_sort potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080737/
https://www.ncbi.nlm.nih.gov/pubmed/27784264
http://dx.doi.org/10.1186/s12862-016-0804-z
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