Mathematical models disentangle the role of IL-10 feedbacks in human monocytes upon proinflammatory activation Show others and affiliations
2023 (English) In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 299, no 10, article id 105205Article in journal (Refereed) Published
Abstract [en]
Inflammation is one of the vital mechanisms through which the immune system responds to harmful stimuli. During inflammation, pro and anti-inflammatory cytokines interplay to orchestrate fine-tuned, dynamic immune responses. The cytokine interplay governs switches in the inflammatory response and dictates the propagation and development of the inflammatory response. Molecular pathways underlying the interplay are complex, and time-resolved monitoring of mediators and cytokines is necessary as a basis to study them in detail. Our understanding can be advanced by mathematical models which enable to analyze the system of interactions and their dynamical interplay in detail. We, therefore, used a mathematical modeling approach to study the interplay between prominent pro and anti-inflammatory cytokines with a focus on tumor necrosis factor (TNF) and interleukin 10 (IL-10) in lipopolysaccharide (LPS)-primed primary human monocytes. Relevant time-resolved data were generated by experimentally adding or blocking IL-10 at different time points. The model was successfully trained and could predict independent validation data and was further used to perform simulations to disentangle the role of IL-10 feedbacks during an acute inflammatory event. We used the insight to obtain a reduced predictive model including only the necessary IL-10-mediated feedbacks. Finally, the validated reduced model was used to predict early IL-10 - TNF switches in the inflammatory response. Overall, we gained detailed insights into fine-tuning of inflammatory responses in human monocytes and present a model for further use in studying the complex and dynamic process of cytokine-regulated acute inflammation.
Place, publisher, year, edition, pages Elsevier, 2023. Vol. 299, no 10, article id 105205
Keywords [en]
NF‐kappa B (NF‐κB), computational biology, computer modeling, cytokine, endotoxin, human monocytes, inflammation, interleukin 10 (IL-10), lipopolysaccharide (LPS), mathematical modeling, ordinary differential equations (ODE), signal transduction, systems biology, tumor necrosis factor (TNF)
National Category
Immunology in the medical area
Identifiers URN: urn:nbn:se:oru:diva-108034 DOI: 10.1016/j.jbc.2023.105205 ISI: 001164667700001 PubMedID: 37660912 Scopus ID: 2-s2.0-85172191670 OAI: oai:DiVA.org:oru-108034 DiVA, id: diva2:1793862
Funder Knowledge Foundation, 20200017 Örebro University Swedish Research Council, 2018-05418; 2018-03319; 2019-03767 Swedish Foundation for Strategic Research, ITM17-0245 Knut and Alice Wallenberg Foundation, 2020.0182 Vinnova, 2020-04711 Swedish Heart Lung Foundation Åke Wiberg Foundation, M19-0449; M21-0030; M22-0027
Note The X-HiDE Consortium is funded by the Knowledge Foundation (20200017) and by strategic funding by Örebro University. G. C. acknowledges support from the Swedish Research Council (grant nos.: 2018-05418 and 2018-03319) , CENIIT (grant no.: 15.09) , the Swedish Foundation for Strategic Research (grant no.: ITM17-0245) , SciLifeLab National COVID-19 Research Program financed by the Knut and Alice Wallenberg Foundation (grant no.: 2020.0182) , the H2020 project PRECISE4Q (grant no.: 777107) , STRATIF-AI: the H-Europe project STRATIF-AI (grant no.: 101080875) , the Swedish Fund for Research Without Animal Experiments (grant no.: F2019-0010) , ELLIIT (grant no.: 2020-A12) , and VINNOVA (VisualSweden; grant no.: 2020-04711) . E. N. acknowledges support from the Swedish Research Council (grant no.: Dnr 2019-03767) , the Heart and Lung Foundation, CENIIT (grant no.: 20.08) , Ake Wibergs Stiftelse (grant nos.: M19-0449, M21-0030, and M22-0027) , and the Swedish Fund for Research Without Animal Experiments (grant no.: S2021-0008) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.r 03319) , CENIIT (grant no.: 15.09) , the Swedish Foundation for Strategic Research (grant no.: ITM17-0245) , SciLifeLab National COVID-19 Research Program financed by the Knut and Alice Wallenberg Foundation (grant no.: 2020.0182) , the H2020 project PRECISE4Q (grant no.: 777107) , STRATIF-AI: the H-Europe project STRATIF-AI (grant no.: 101080875) , the Swedish Fund for Research Without Animal Experiments (grant no.: F2019-0010) , ELLIIT (grant no.: 2020-A12) , and VINNOVA (VisualSweden; grant no.: 2020-04711) . E. N. acknowledges support from the Swedish Research Council (grant no.: Dnr 2019-03767) , the Heart and Lung Foundation, CENIIT (grant no.: 20.08) , Ake Wibergs Stiftelse (grant nos.: M19-0449, M21-0030, and M22-0027) , and the Swedish Fund for Research Without Animal Experiments (grant no.: S2021-0008) .
2023-09-042023-09-042024-10-25 Bibliographically approved
In thesis