Best, A., Jubrail, J., Boots, M. et al. (2 more authors) (2020) A mathematical model shows macrophages delay staphylococcus aureus replication, but limitations in microbicidal capacity restrict bacterial clearance. Journal of Theoretical Biology, 497. 110256. ISSN 0022-5193
Abstract
S. aureus is a leading cause of bacterial infection. Macrophages, the first line of defence in the human immune response, phagocytose and kill S. aureus but the pathogen can evade these responses. Therefore, the exact role of macrophages is incompletely defined. We develop a mathematical model of macrophage - S. aureus dynamics, built on recent experimental data. We demonstrate that, while macrophages may not clear infection, they significantly delay its growth and potentially buy time for recruitment of further cells. We find that macrophage killing is a major obstacle to controlling infection and ingestion capacity also limits the response. We find bistability such that the infection can be limited at low doses. Our combination of experimental data, mathematical analysis and model fitting provide important insights in to the early stages of S. aureus infections, showing macrophages play an important role limiting bacterial replication but can be overwhelmed with large inocula.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/). | ||||||
Keywords: | S. aureus; Macrophage; Infection; Phagocytosis | ||||||
Dates: |
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Institution: | The University of Sheffield | ||||||
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) The University of Sheffield > Sheffield Teaching Hospitals |
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Funding Information: |
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Depositing User: | Symplectic Sheffield | ||||||
Date Deposited: | 04 May 2020 09:15 | ||||||
Last Modified: | 07 Jul 2021 10:58 | ||||||
Status: | Published online | ||||||
Publisher: | Elsevier BV | ||||||
Refereed: | Yes | ||||||
Identification Number: | https://doi.org/10.1016/j.jtbi.2020.110256 |