Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908; and.
Department of Cellular and Integrative Physiology, University of Nebraska Medical Center and Research Service, Nebraska-Western Iowa Health Care System, Omaha, NE 68198.
J Immunol. 2021 Feb 15;206(4):883-891. doi: 10.4049/jimmunol.1901444. Epub 2021 Jan 6.
Macrophages are subject to a wide range of cytokine and pathogen signals in vivo, which contribute to differential activation and modulation of inflammation. Understanding the response to multiple, often-conflicting cues that macrophages experience requires a network perspective. In this study, we integrate data from literature curation and mRNA expression profiles obtained from wild type C57/BL6J mice macrophages to develop a large-scale computational model of the macrophage signaling network. In response to stimulation across all pairs of nine cytokine inputs, the model predicted activation along the classic M1-M2 polarization axis but also a second axis of macrophage activation that distinguishes unstimulated macrophages from a mixed phenotype induced by conflicting cues. Along this second axis, combinations of conflicting stimuli, IL-4 with LPS, IFN-γ, IFN-β, or TNF-α, produced mutual inhibition of several signaling pathways, e.g., NF-κB and STAT6, but also mutual activation of the PI3K signaling module. In response to combined IFN-γ and IL-4, the model predicted genes whose expression was mutually inhibited, e.g., or and , or mutually enhanced, e.g., and , validated by independent experimental data. Knockdown simulations further predicted network mechanisms underlying functional cross-talk, such as mutual STAT3/STAT6-mediated enhancement of Il4rα expression. In summary, the computational model predicts that network cross-talk mediates a broadened spectrum of macrophage activation in response to mixed pro- and anti-inflammatory cytokine cues, making it useful for modeling in vivo scenarios.
巨噬细胞在体内受到广泛的细胞因子和病原体信号的影响，这些信号导致炎症的不同激活和调节。理解巨噬细胞所经历的多种、常常相互冲突的信号的反应需要网络视角。在这项研究中，我们整合了文献整理和从野生型 C57/BL6J 小鼠巨噬细胞获得的 mRNA 表达谱的数据，以开发一个大规模的巨噬细胞信号网络计算模型。在对所有九种细胞因子输入的两两刺激的反应中，该模型预测了沿着经典的 M1-M2 极化轴的激活，但也预测了第二个巨噬细胞激活轴，该轴将未受刺激的巨噬细胞与由相互冲突的信号诱导的混合表型区分开来。沿着这个第二个轴，冲突刺激的组合，如 IL-4 与 LPS、IFN-γ、IFN-β 或 TNF-α，对几个信号通路产生了相互抑制，如 NF-κB 和 STAT6，但也对 PI3K 信号模块产生了相互激活。对联合 IFN-γ 和 IL-4 的反应，模型预测了表达相互抑制的基因，如 或 和 ，或相互增强的基因，如 和 ，这被独立的实验数据所验证。敲低模拟进一步预测了网络交叉对话的机制，如相互的 STAT3/STAT6 介导的 Il4rα 表达增强。总之，该计算模型预测，网络交叉对话介导了巨噬细胞对混合促炎和抗炎细胞因子信号的更广泛的激活谱，使其在模拟体内情景方面非常有用。
