Department of Surgery, University of Pittsburgh, W944 Starzl Biomedical Sciences Tower, 200 Lothrop St, Pittsburgh, PA, 15213, USA.
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
Sci Rep. 2021 May 6;11(1):9703. doi: 10.1038/s41598-021-88936-8.
Systemic inflammation is complex and likely drives clinical outcomes in critical illness such as that which ensues following severe injury. We obtained time course data on multiple inflammatory mediators in the blood of blunt trauma patients. Using dynamic network analyses, we inferred a novel control architecture for systemic inflammation: a three-way switch comprising the chemokines MCP-1/CCL2, MIG/CXCL9, and IP-10/CXCL10. To test this hypothesis, we created a logical model comprising this putative architecture. This model predicted key qualitative features of systemic inflammation in patient sub-groups, as well as the different patterns of hospital discharge of moderately vs. severely injured patients. Thus, a rational transition from data to data-driven models to mechanistic models suggests a novel, chemokine-based mechanism for control of acute inflammation in humans and points to the potential utility of this workflow in defining novel features in other complex diseases.
系统性炎症是复杂的,可能会影响严重损伤等重症疾病的临床结果。我们获得了钝器创伤患者血液中多种炎症介质的时程数据。使用动态网络分析,我们推断出系统性炎症的一种新的控制架构:由趋化因子 MCP-1/CCL2、MIG/CXCL9 和 IP-10/CXCL10 组成的三向开关。为了验证这一假设,我们创建了一个包含这个假定结构的逻辑模型。该模型预测了患者亚组中系统性炎症的关键定性特征,以及中度和重度损伤患者的不同住院出院模式。因此,从数据到数据驱动模型再到机制模型的合理转变表明,一种新的、基于趋化因子的人类急性炎症控制机制,并指出该工作流程在定义其他复杂疾病的新特征方面具有潜在的应用价值。