Cargando…
Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections
The immune system has to fight off hundreds of microbial invaders every day, such as the human-pathogenic fungus Aspergillus fumigatus. The fungal conidia can reach the lower respiratory tract, swell and form hyphae within six hours causing life-threatening invasive aspergillosis. Invading pathogens...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086013/ https://www.ncbi.nlm.nih.gov/pubmed/37037824 http://dx.doi.org/10.1038/s41540-023-00272-x |
_version_ | 1785022053206720512 |
---|---|
author | Saffer, Christoph Timme, Sandra Rudolph, Paul Figge, Marc Thilo |
author_facet | Saffer, Christoph Timme, Sandra Rudolph, Paul Figge, Marc Thilo |
author_sort | Saffer, Christoph |
collection | PubMed |
description | The immune system has to fight off hundreds of microbial invaders every day, such as the human-pathogenic fungus Aspergillus fumigatus. The fungal conidia can reach the lower respiratory tract, swell and form hyphae within six hours causing life-threatening invasive aspergillosis. Invading pathogens are continuously recognized and eliminated by alveolar macrophages (AM). Their number plays an essential role, but remains controversial with measurements varying by a factor greater than ten for the human lung. We here investigate the impact of the AM number on the clearance of A. fumigatus conidia in humans and mice using analytical and numerical modeling approaches. A three-dimensional to-scale hybrid agent-based model (hABM) of the human and murine alveolus allowed us to simulate millions of virtual infection scenarios, and to gain quantitative insights into the infection dynamics for varying AM numbers and infection doses. Since hABM simulations are computationally expensive, we derived and trained an analytical surrogate infection model on the large dataset of numerical simulations. This enables reducing the number of hABM simulations while still providing (i) accurate and immediate predictions on infection progression, (ii) quantitative hypotheses on the infection dynamics under healthy and immunocompromised conditions, and (iii) optimal AM numbers for combating A. fumigatus infections in humans and mice. |
format | Online Article Text |
id | pubmed-10086013 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100860132023-04-12 Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections Saffer, Christoph Timme, Sandra Rudolph, Paul Figge, Marc Thilo NPJ Syst Biol Appl Article The immune system has to fight off hundreds of microbial invaders every day, such as the human-pathogenic fungus Aspergillus fumigatus. The fungal conidia can reach the lower respiratory tract, swell and form hyphae within six hours causing life-threatening invasive aspergillosis. Invading pathogens are continuously recognized and eliminated by alveolar macrophages (AM). Their number plays an essential role, but remains controversial with measurements varying by a factor greater than ten for the human lung. We here investigate the impact of the AM number on the clearance of A. fumigatus conidia in humans and mice using analytical and numerical modeling approaches. A three-dimensional to-scale hybrid agent-based model (hABM) of the human and murine alveolus allowed us to simulate millions of virtual infection scenarios, and to gain quantitative insights into the infection dynamics for varying AM numbers and infection doses. Since hABM simulations are computationally expensive, we derived and trained an analytical surrogate infection model on the large dataset of numerical simulations. This enables reducing the number of hABM simulations while still providing (i) accurate and immediate predictions on infection progression, (ii) quantitative hypotheses on the infection dynamics under healthy and immunocompromised conditions, and (iii) optimal AM numbers for combating A. fumigatus infections in humans and mice. Nature Publishing Group UK 2023-04-10 /pmc/articles/PMC10086013/ /pubmed/37037824 http://dx.doi.org/10.1038/s41540-023-00272-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Saffer, Christoph Timme, Sandra Rudolph, Paul Figge, Marc Thilo Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections |
title | Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections |
title_full | Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections |
title_fullStr | Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections |
title_full_unstemmed | Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections |
title_short | Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections |
title_sort | surrogate infection model predicts optimal alveolar macrophage number for clearance of aspergillus fumigatus infections |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086013/ https://www.ncbi.nlm.nih.gov/pubmed/37037824 http://dx.doi.org/10.1038/s41540-023-00272-x |
work_keys_str_mv | AT safferchristoph surrogateinfectionmodelpredictsoptimalalveolarmacrophagenumberforclearanceofaspergillusfumigatusinfections AT timmesandra surrogateinfectionmodelpredictsoptimalalveolarmacrophagenumberforclearanceofaspergillusfumigatusinfections AT rudolphpaul surrogateinfectionmodelpredictsoptimalalveolarmacrophagenumberforclearanceofaspergillusfumigatusinfections AT figgemarcthilo surrogateinfectionmodelpredictsoptimalalveolarmacrophagenumberforclearanceofaspergillusfumigatusinfections |