Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America.
Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia, United States of America.
PLoS Comput Biol. 2019 Jul 31;15(7):e1007172. doi: 10.1371/journal.pcbi.1007172. eCollection 2019 Jul.
In an inflammatory setting, macrophages can be polarized to an inflammatory M1 phenotype or to an anti-inflammatory M2 phenotype, as well as existing on a spectrum between these two extremes. Dysfunction of this phenotypic switch can result in a population imbalance that leads to chronic wounds or disease due to unresolved inflammation. Therapeutic interventions that target macrophages have therefore been proposed and implemented in diseases that feature chronic inflammation such as diabetes mellitus and atherosclerosis. We have developed a model for the sequential influx of immune cells in the peritoneal cavity in response to a bacterial stimulus that includes macrophage polarization, with the simplifying assumption that macrophages can be classified as M1 or M2. With this model, we were able to reproduce the expected timing of sequential influx of immune cells and mediators in a general inflammatory setting. We then fit this model to in vivo experimental data obtained from a mouse peritonitis model of inflammation, which is widely used to evaluate endogenous processes in response to an inflammatory stimulus. Model robustness is explored with local structural and practical identifiability of the proposed model a posteriori. Additionally, we perform sensitivity analysis that identifies the population of apoptotic neutrophils as a key driver of the inflammatory process. Finally, we simulate a selection of proposed therapies including points of intervention in the case of delayed neutrophil apoptosis, which our model predicts will result in a sustained inflammatory response. Our model can therefore provide hypothesis testing for therapeutic interventions that target macrophage phenotype and predict outcomes to be validated by subsequent experimentation.
在炎症环境中，巨噬细胞可以极化为炎症型 M1 表型或抗炎型 M2 表型，也可以存在于这两种极端之间的范围内。这种表型转换的功能障碍可能导致群体失衡，导致慢性伤口或疾病，原因是炎症未得到解决。因此，针对具有慢性炎症特征的疾病（如糖尿病和动脉粥样硬化）中存在的巨噬细胞，已经提出并实施了靶向巨噬细胞的治疗干预措施。我们已经开发了一种针对腹腔内免疫细胞顺序流入的模型，该模型响应细菌刺激，包括巨噬细胞极化，假设巨噬细胞可以分为 M1 或 M2。通过这个模型，我们能够在一般炎症环境中重现免疫细胞和介质顺序流入的预期时间。然后，我们将该模型拟合到来自炎症性腹膜炎小鼠模型的体内实验数据中，该模型广泛用于评估对炎症刺激的内源性过程。通过对所提出模型的局部结构和实际可识别性进行后验性探索，研究了模型的稳健性。此外，我们进行了敏感性分析，确定了凋亡中性粒细胞群体是炎症过程的关键驱动因素。最后，我们模拟了一系列拟议的治疗方法，包括在中性粒细胞凋亡延迟的情况下干预的关键点，我们的模型预测这将导致持续的炎症反应。因此，我们的模型可以为靶向巨噬细胞表型的治疗干预措施提供假设检验，并预测结果，以随后的实验进行验证。
