Developing a predictive in silico toolkit for modeling NK cell responses against RNA virus infections

开发可预测的用于模拟NK细胞对RNA病毒感染的应答的电子工具包 涉及免疫细胞的信号传递和激活的时空过程的数学建模(例如， 获得性免疫的T细胞)提供了对复杂系统的新的机制洞察力。自然杀手 (NK)细胞是先天免疫系统的一部分，与淋巴细胞有重要的相似之处和不同之处 适应性免疫系统。NK细胞对全球重要的RNA病毒具有重要的抵抗力 (例如，丙型肝炎病毒、登革热病毒、艾滋病毒、EBOV和寨卡病毒)感染。然而，旨在破译的定量建模 NK细胞信号转导和激活的机制不发达，导致对 许多关键结果与NK细胞对这些重要病毒病原体的反应有关。与单元格不同 获得性免疫系统NK细胞没有单一的抗原特异性触发受体，而是总和信号 从激活和抑制受体获得，以确定是否启动效应器功能。一个 复杂的信号网络支持这些受体的传递：配体相互作用。它的层次感 网络包括由细胞表面受体直接传递的信号，例如抑制性杀伤细胞 免疫球蛋白样受体(KIR)和通过适配器分子传递的信号。我们的工作重点是 KIR和NKG2受体家族是全球NK细胞保护的重要组成部分 重要的RNA病毒感染。收集NK细胞激活的基础机制是具有挑战性的 这些不同的受体：配体系统由于配体的巨大多样性而仅使用实验方法- 受体相互作用、非线性信号反应、KIR和NKG2家族的非平凡时空变化 受体聚集，以及不同的人类白细胞抗原-多肽配体之间的相互作用。为了应对这一挑战，我们将 通过将空间分辨机制和数据驱动的硅胶模型与 台架实验探测NK细胞系和原代表达KIR和 NKG2-家族受体，由全球重要RNA来源的新多肽文库刺激 病毒(丙型肝炎病毒、登革热病毒、EBOV、ZIKAV)、丙型肝炎病毒和登革热病毒复制子，以及人工来源。In Silico模型 将植根于统计物理、统计学、信息论和非线性动力学，以及湿实验室 实验将基于活细胞成像、标准免疫分析、流式细胞术和共聚焦。 成像。我们将追求三个目标：(1)开发一个定量工具包来分析KIR和KIR的多肽调节 NKG2受体。(2)NK细胞对全球重要RNA病毒(丙型肝炎病毒，DENV)应答的定量模拟 体内感染。(3)定量检测人类白细胞抗原(HLA)等位基因多样性在NK信号传导中的作用 激活。

Developing a predictive in silico toolkit for modeling NK cell responses against RNA virus infections Mathematical modeling of spatiotemporal processes involved in signaling and activation of immune cells (e.g., T cells) of adaptive immunity have provided novel mechanistic insights into the complex system. Natural Killer (NK) cells are part of the innate immune system which share key similarities and differences with lymphocytes of the adaptive immune system. NK cells provide important resistance against globally important RNA virus (e.g., HCV, DENV, HIV, EBOV, and ZIKV) infections. However, quantitative modeling aimed at deciphering mechanisms that underlie NK cell signaling and activation is under-developed leading to poor understanding of many key results pertaining to NK cell responses to these important viral pathogens. Unlike cells of the adaptive immune system NK cells do not have a single antigen specific triggering receptor, but sum signals derived from activating and inhibitory receptors to determine whether or not effector functions are initiated. A complex signaling network underpins the transmission of these receptor:ligand interactions. The layering of this network includes signals transmitted directly by cell surface receptors, e.g., inhibitory killer cell immunoglobulin-like receptors (KIRs) and signals transmitted via adapter molecules. Our work has focused on the KIR and NKG2-family of receptors as these are a critical component of NK cell protection against globally important RNA virus infections. It is challenging to glean mechanisms that underlie activation of NK cells by these diverse receptor:ligand system using experimental approaches alone due to the large diversity of ligand- receptor interactions, nonlinear signaling reactions, non-trivial spatiotemporal changes in KIR and NKG2-family receptor clustering, and, interactions between different HLA-peptide ligands. To address this challenge we will develop an in silico toolkit by combining spatially resolved mechanistic and data-driven in silico models with bench experiments probing activation of NK cell lines and primary human NK cells expressing specific KIR and NKG2-family receptors that are stimulated by a novel library of peptides derived from globally important RNA viruses (HCV, DENV, EBOV, ZIKAV), HCV and DENV replicons, and, artificial sources. The in silico models will be rooted in statistical physics, statistics, information theory, and non-linear dynamics, and, the wet-lab experiments will be based on live-cell imaging, standard immune-assays, flow cytometry, and confocal imaging. We will pursue three aims:(1) Develop a quantitative toolkit to analyze peptide modulation of KIR and NKG2 receptors. (2) Quantitative modeling of NK cell response to globally important RNA virus (HCV, DENV) infections in vivo. (3) Quantitative determination of the roles of HLA allelic diversity in NK signaling and activation.