Systems Biology of Inflammation, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany.
Institute for Theoretical Biophysics, Humboldt University, Berlin, Germany.
Sci Adv. 2023 Sep 15;9(37):eadg7668. doi: 10.1126/sciadv.adg7668. Epub 2023 Sep 13.
Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.
免疫反应是由一系列多样化的相互作用的免疫细胞群体所严格调控的。除了分化成特定效应细胞类型等决策过程外，免疫细胞在炎症开始时就会启动增殖，形成两层复杂性。在这里，我们开发了一种通用的数学框架，用于对集体免疫细胞动力学进行数据驱动分析。我们确定了通用网络基元的定性和定量特性，并通过分析动力学转录组数据来指定分化动力学。此外，我们还针对急性和慢性淋巴细胞性脉络丛脑膜炎病毒感染小鼠中的 T 辅助 1 与滤泡辅助 T 细胞命运决定动力学，推导出了一个具体的、数据驱动的数学模型。该模型无需模型拟合，仅通过使用测量的响应时间分布，就能很好地重现重要的动力学特性。模型模拟预测了在急性和慢性感染情况下进行干预的不同机会窗口，这可能对优化靶向免疫疗法具有重要意义。
