Dissection of cell-cell communication networks in chronic viral infection by kinetic immune-cell monitoring and mathematical modeling

哺乳动物的免疫系统依赖于许多异质细胞的协调作用，这些细胞紧密地共同控制着免疫反应。虽然对病原体的无效反应会对整个生物体造成直接的风险，但免疫系统的过度反应也是如此，它可以直接诱发致命的感染性休克，以及慢性过敏或自身免疫性疾病，给患者带来终身的后果。在过去的几十年里，人们对免疫调节途径有了大量的了解，并确定了由40多种细胞因子和越来越多的免疫细胞亚型组成的细胞间通信网络。越来越多的证据表明，这些细胞类型和调节网络在慢性炎症条件下进化，朝着控制良好的持续炎症状态发展。然而，对这些过程的定量理解才刚刚开始出现，靶向免疫细胞通信网络的药物（“生物疗法”，如TNF α阻滞剂）的作用仍然不完全清楚。在这里，我们提出了一个深入的，跨学科的研究T细胞分化，增殖和细胞间通讯感染淋巴细胞性脉络丛脑膜炎病毒（LCMV）小鼠。这一成熟的急性和慢性炎症模型系统为研究体内急性期（感染后4至8天）、急性到慢性过渡期（感染后10至20天）和长时间持续炎症（超过50天）的T细胞表型和细胞间通讯网络提供了机会。单细胞表型实验（单细胞rna测序和多色流式细胞术）以及定量数据分析和数学建模将量化和合理化慢性炎症中细胞-细胞通信网络的进化。在这里，基于数据的模型将允许控制电路的功能评估，如反馈和前馈回路，并将使系统的计算机微扰研究模拟药物的作用。数据分析和建模的结果将在研究中使用已建立的技术通过体内功能分析进行测试。综上所述，拟议的研究将使用最先进的单细胞和数据分析技术，旨在对慢性炎症中的免疫反应进行更深入、定量的了解。这些见解可能有助于开发和优化针对自身免疫性疾病的靶向联合疗法。

The mammalian immune system depends on the coordinated action of many heterogeneous cells, which together tightly control immune responses. While ineffective responses to pathogens cause immediate risks for the whole organism, the same is true for overreactions of the immune system, which can induce directly lethal septic shock as well as chronic allergies or autoimmune diseases with life-long consequences for patients. The last decades brought about an enormous knowledge on immune-regulatory pathways, and identified cell-to-cell communication networks consisting of more than 40 cytokine species and a growing number of immune cell subtypes. Accumulating evidence suggests that these cell types and regulatory networks evolve under conditions of chronic inflammation, towards a well-controlled persistent state of inflammation. However, a quantitative understanding of these processes is only beginning to emerge, and the effects of drugs targeting immune cell communication networks (‘biological therapies’ such as TNF alpha blockers) remain incompletely understood.Here, we propose an in-depth, interdisciplinary study of T cell differentiation, proliferation and cell-to-cell communication in mice infected with lymphocytic choriomeningitis virus (LCMV). That well-established model system for both acute and chronic inflammation offers the opportunity to study T cell phenotypes and cell-cell communication networks in vivo, in the acute phase (~days 4 to 8 after infection), the acute-to-chronic transition phase (~days 10-20 after infection) and over long time-intervals of persistent inflammation (more than 50 days). Single-cell phenotyping experiments (single-cell RNA-sequencing and multicolor flow cytometry) along with quantitative data analysis and mathematical modeling will quantify and rationalize the evolution of cell-cell communication networks in chronic inflammation. Here, the data-based models will allow for functional assessment of control circuits such as feedback and feedforward loops, and will enable systematic in silico perturbation studies simulating the effects of drugs. Results from data analysis and modeling will be tested within the study by functional in vivo assays using already established technology. Taken together, the proposed study will use state-of-the art single-cell and data analysis techniques, aiming for a deeper, quantitative understanding of immune responses in chronic inflammation. Such insights will likely contribute to the development and optimization of targeted combination therapies for autoimmune diseases.