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An In vitro Model to Study Heterogeneity of Human Macrophage Differentiation and Polarization
Published on: June 12, 2013
A multiscale hybrid model for exploring the effect of Resolvin D1 on macrophage polarization during acute
Jeroen F Uleman1, Emiliano Mancini2, Rushd F M Al-Shama3
1Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Medical Neuroscience, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6500HB, Nijmegen, The Netherlands; Institute for Advanced Study, Amsterdam, The Netherlands; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Abstract:
Dysregulated inflammation underlies various diseases. Specialized pro-resolving mediators (SPMs) like Resolvin D1 (RvD1) have been shown to resolve inflammation and halt disease progression. Macrophages, key immune cells that drive inflammation, respond to the presence of RvD1 by polarizing to an anti-inflammatory type (M2). However, RvD1's mechanisms, roles, and utility are not fully understood. This paper introduces a gene-regulatory network (GRN) model that contains pathways for RvD1 and other SPMs and proinflammatory molecules like lipopolysaccharides. We couple this GRN model to a partial differential equation-agent-based hybrid model using a multiscale framework to simulate an acute inflammatory response with and without the presence of RvD1. We calibrate and validate the model using experimental data from two animal models. The model reproduces the dynamics of key immune components and the effects of RvD1 during acute inflammation. Our results suggest RvD1 can drive macrophage polarization through the G protein-coupled receptor 32 (GRP32) pathway. The presence of RvD1 leads to an earlier and increased M2 polarization, reduced neutrophil recruitment, and faster apoptotic neutrophil clearance. These results support a body of literature that suggests that RvD1 is a promising candidate for promoting the resolution of acute inflammation. We conclude that once calibrated and validated on human data, the model can identify critical sources of uncertainty, which could be further elucidated in biological experiments and assessed for clinical use.

