Experimental Control of Macrophage Pro-Inflammatory Dynamics Using Predictive Models
| Autor: | Jun Ueda, Levi B. Wood, James E. Forsmo, Laura D. Weinstock, Alexis Wilkinson |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Histology lcsh:Biotechnology Cell Biomedical Engineering Macrophage polarization Bioengineering Stimulation Inflammation 02 engineering and technology Pathogenesis predictive model 03 medical and health sciences 0302 clinical medicine Immune system lcsh:TP248.13-248.65 medicine Macrophage Cytotoxic T cell Interferon gamma 030304 developmental biology Original Research system identification 0303 health sciences Chemistry Bioengineering and Biotechnology 021001 nanoscience & nanotechnology macrophages Cell biology 030104 developmental biology medicine.anatomical_structure inflammation 030220 oncology & carcinogenesis dynamic systems and control medicine.symptom trajectory planning 0210 nano-technology Biotechnology medicine.drug |
| Zdroj: | Frontiers in Bioengineering and Biotechnology, Vol 8 (2020) Frontiers in Bioengineering and Biotechnology |
| ISSN: | 2296-4185 |
| DOI: | 10.3389/fbioe.2020.00666 |
| Popis: | Macrophage activity is a major component of the healthy response to infection and injury that consists of tightly regulated early pro-inflammatory activation followed by anti-inflammatory and regenerative activity. In numerous diseases, however, macrophage polarization becomes dysregulated and can not only impair recovery, but can also promote further injury and pathogenesis, e.g. after injury, in diabetes, or in Alzheimer’s disease. Dysregulated macrophages may either fail to polarize or become chronically polarized, resulting in increased production of cytotoxic factors, diminished capacity to clear pathogens, or failure to promote tissue regeneration. In these cases, a method of predicting and dynamically controlling macrophage polarization will enable a new strategy for treating diverse inflammatory diseases. In this work, we developed a model-predictive control framework to temporally regulate macrophage polarization. Using RAW 264.7 macrophages as a model system, we enabled temporal control by identifying transfer function models relating the polarization marker iNOS to exogenous pro- and anti-inflammatory stimuli. These stimuli-to-iNOS response models were identified using linear autoregressive with exogenous input terms (ARX) equations and were coupled with nonlinear elements to account for experimentally identified supra-additive and hysteretic effects. Using this model architecture, we were able to reproduce experimentally observed temporal iNOS dynamics induced with lipopolysaccharides (LPS) and interferon gamma (IFN-γ). Moreover, the identified model enabled the design of time-varying input trajectories to experimentally sustain the duration and magnitude of iNOS expression. By designing transfer function models with the intent to predict cell behavior, we were able to predict and experimentally obtain temporal regulation of iNOS expression using LPS and IFN-γ from both naïve and non-naïve initial states. Moreover, our data driven models revealed decaying magnitude of iNOS response to LPS stimulation over time that could be recovered using combined treatment with both LPS and IFN-γ. Given the importance of dynamic tissue macrophage polarization and overall inflammatory regulation to a broad number of diseases, the temporal control methodology presented here will have numerous applications for regulating immune activity dynamics in chronic inflammatory diseases. |
| Databáze: | OpenAIRE |
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