Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
Immunol Rev. 2018 Sep;285(1):147-167. doi: 10.1111/imr.12671.
Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host-directed therapies.
针对病原体的免疫反应非常复杂，在许多疾病中尚未得到很好的理解，对于持续性病原体感染更是如此。免疫系统通过平衡促炎和抗炎细胞及信号来实现长期控制感染，同时防止过度活跃的炎症反应导致广泛的组织损伤，这是一种机制。这种平衡是动态的，免疫系统会对来自宿主和病原体的信号做出反应，通过持续反馈在多个尺度上维持稳定状态。使用传统研究策略来确定随时间推移介导这种平衡的信号、细胞、细胞因子和其他免疫反应因素一直很困难。基于来自传统系统的数据的计算模型研究可以确定这种平衡如何有助于免疫。在这里，我们提供了来自实验和数学/计算研究的证据，以支持在持续性和其他感染情况下存在动态平衡的概念。我们主要关注目前全球因传染病而导致死亡的主要原因——结核病，并为其他感染提供了证据。更好地了解动态平衡的免疫反应有助于制定治疗策略，这些策略既利用药物，也利用宿主导向疗法。
