Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States.
Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States.
Elife. 2018 Oct 10;7:e37059. doi: 10.7554/eLife.37059.
The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling.
促炎型巨噬细胞活化在心血管疾病(CVD)中的作用非常复杂,适合采用网络方法进行研究。尽管蛋白质相互作用网络对于阐明包括 CVD 在内的复杂疾病的分子基础是不可或缺的工具,但由于其不针对任何特定的细胞类型、实验条件或疾病状态,因此其应用具有一定的局限性。我们通过将蛋白质相互作用网络与从激活的巨噬样细胞中进行的无偏蛋白质组学测量得出的共丰度网络相结合,为蛋白质相互作用网络赋予了特定条件的特异性。每种巨噬细胞表型都对蛋白质相互作用网络的特定区域有贡献。我们使用基于网络邻近性的优先级排序方法对组合网络进行分析,预测了潜在的巨噬细胞激活调节剂。随着共丰度边缘的加入,预测性能显著提高,而优先级候选物则捕获了炎症、免疫和 CVD 的特征。将新的网络拓扑结构与转录组学和蛋白质组学相结合,揭示了炎症的顶级候选驱动因素。体外功能丧失实验证明了这些蛋白质在促炎信号中的调节作用。