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Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach
Computer simulations of mathematical models open up the possibility of assessing hypotheses generated by experiments on pathogen immune evasion in human whole-blood infection assays. We apply an interdisciplinary systems biology approach in which virtual infection models implemented for the dissecti...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016326/ https://www.ncbi.nlm.nih.gov/pubmed/33793643 http://dx.doi.org/10.1371/journal.pone.0249372 |
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author | Lehnert, Teresa Prauße, Maria T. E. Hünniger, Kerstin Praetorius, Jan-Philipp Kurzai, Oliver Figge, Marc Thilo |
author_facet | Lehnert, Teresa Prauße, Maria T. E. Hünniger, Kerstin Praetorius, Jan-Philipp Kurzai, Oliver Figge, Marc Thilo |
author_sort | Lehnert, Teresa |
collection | PubMed |
description | Computer simulations of mathematical models open up the possibility of assessing hypotheses generated by experiments on pathogen immune evasion in human whole-blood infection assays. We apply an interdisciplinary systems biology approach in which virtual infection models implemented for the dissection of specific immune mechanisms are combined with experimental studies to validate or falsify the respective hypotheses. Focusing on the assessment of mechanisms that enable pathogens to evade the immune response in the early time course of a whole-blood infection, the least-square error (LSE) as a measure for the quantitative agreement between the theoretical and experimental kinetics is combined with the Akaike information criterion (AIC) as a measure for the model quality depending on its complexity. In particular, we compare mathematical models with three different types of pathogen immune evasion as well as all their combinations: (i) spontaneous immune evasion, (ii) evasion mediated by immune cells, and (iii) pre-existence of an immune-evasive pathogen subpopulation. For example, by testing theoretical predictions in subsequent imaging experiments, we demonstrate that the simple hypothesis of having a subpopulation of pre-existing immune-evasive pathogens can be ruled out. Furthermore, in this study we extend our previous whole-blood infection assays for the two fungal pathogens Candida albicans and C. glabrata by the bacterial pathogen Staphylococcus aureus and calibrated the model predictions to the time-resolved experimental data for each pathogen. Our quantitative assessment generally reveals that models with a lower number of parameters are not only scored with better AIC values, but also exhibit lower values for the LSE. Furthermore, we describe in detail model-specific and pathogen-specific patterns in the kinetics of cell populations that may be measured in future experiments to distinguish and pinpoint the underlying immune mechanisms. |
format | Online Article Text |
id | pubmed-8016326 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-80163262021-04-08 Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach Lehnert, Teresa Prauße, Maria T. E. Hünniger, Kerstin Praetorius, Jan-Philipp Kurzai, Oliver Figge, Marc Thilo PLoS One Research Article Computer simulations of mathematical models open up the possibility of assessing hypotheses generated by experiments on pathogen immune evasion in human whole-blood infection assays. We apply an interdisciplinary systems biology approach in which virtual infection models implemented for the dissection of specific immune mechanisms are combined with experimental studies to validate or falsify the respective hypotheses. Focusing on the assessment of mechanisms that enable pathogens to evade the immune response in the early time course of a whole-blood infection, the least-square error (LSE) as a measure for the quantitative agreement between the theoretical and experimental kinetics is combined with the Akaike information criterion (AIC) as a measure for the model quality depending on its complexity. In particular, we compare mathematical models with three different types of pathogen immune evasion as well as all their combinations: (i) spontaneous immune evasion, (ii) evasion mediated by immune cells, and (iii) pre-existence of an immune-evasive pathogen subpopulation. For example, by testing theoretical predictions in subsequent imaging experiments, we demonstrate that the simple hypothesis of having a subpopulation of pre-existing immune-evasive pathogens can be ruled out. Furthermore, in this study we extend our previous whole-blood infection assays for the two fungal pathogens Candida albicans and C. glabrata by the bacterial pathogen Staphylococcus aureus and calibrated the model predictions to the time-resolved experimental data for each pathogen. Our quantitative assessment generally reveals that models with a lower number of parameters are not only scored with better AIC values, but also exhibit lower values for the LSE. Furthermore, we describe in detail model-specific and pathogen-specific patterns in the kinetics of cell populations that may be measured in future experiments to distinguish and pinpoint the underlying immune mechanisms. Public Library of Science 2021-04-01 /pmc/articles/PMC8016326/ /pubmed/33793643 http://dx.doi.org/10.1371/journal.pone.0249372 Text en © 2021 Lehnert 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 Lehnert, Teresa Prauße, Maria T. E. Hünniger, Kerstin Praetorius, Jan-Philipp Kurzai, Oliver Figge, Marc Thilo Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach |
title | Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach |
title_full | Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach |
title_fullStr | Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach |
title_full_unstemmed | Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach |
title_short | Comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach |
title_sort | comparative assessment of immune evasion mechanisms in human whole-blood infection assays by a systems biology approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016326/ https://www.ncbi.nlm.nih.gov/pubmed/33793643 http://dx.doi.org/10.1371/journal.pone.0249372 |
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