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

开发模拟 NK 细胞针对 RNA 病毒感染反应的预测工具包

• 批准号: 10246263
• 负责人: Jayajit Das
• 金额: $ 36.49万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-05 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246263
• 关键词: Adaptive Immune System; Address; Affect; Alleles; Antigens; B-Lymphocytes; Biochemical Reaction; Biological Assay; CD94 Antigen; Cell Line; Cell Surface Receptors; Cells; Complex; Computer Models; Cytoprotection; Data; Dengue Infection; Dependence; Ebola virus; Electron Microscopy; Event; Face; Family; Flow Cytometry; Glean; HIV; HLA Antigens; Hepatitis B e Antigens; Hepatitis C virus; Histocompatibility Antigens Class I; Human; Immunoglobulins; Immunology procedure; Information Theory; Innate Immune System; KLRD1 gene; Killer Cells; Kinetics; Ligands; Lymphocyte; Measurement; Methods; Modeling; NK Cell Activation; Natural Increases; Natural Killer Cells; Nature; Nonlinear Dynamics; Peptide Fragments; Peptide Library; Peptides; Physics; Plant Roots; Process; Public Health; RNA Virus Infections; RNA Viruses; Reaction; Receptor Cell; Receptor Signaling; Regulation; Replicon; Resistance; Role; Signal Transduction; Signaling Molecule; Source; Sum; System; T-Lymphocyte; Vaccination; Viral; Virus Diseases; Work; ZIKV infection; Zika Virus; adaptive immunity; antagonist; antigen binding; base; comparative; confocal imaging; data-driven model; design; experimental study; global health; human subject; immune activation; in silico; in vivo; insight; laboratory experiment; live cell imaging; mathematical model; novel; pathogenic virus; receptor; response; simulation; spatiotemporal; statistics; synergism; transmission process

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.

开发用于模拟针对RNA病毒感染的NK细胞应答的预测性计算机工具包 涉及免疫细胞的信号传导和激活的时空过程的数学建模（例如， T细胞）的适应性免疫提供了新的机制的见解复杂的系统。自然杀伤 (NK)细胞是先天免疫系统的一部分，与淋巴细胞有着关键的相似性和差异 适应性免疫系统。NK细胞对全球重要的RNA病毒提供重要的抵抗力 (e.g., HCV、DENV、HIV、EBOV和ZIKV）感染。然而，旨在破译 NK细胞信号传导和活化的基础机制尚不成熟，导致对NK细胞信号传导和活化的理解不足。 关于NK细胞对这些重要病毒病原体的应答的许多关键结果。与细胞不同， 适应性免疫系统NK细胞不具有单一的抗原特异性触发受体，而是总和信号 从激活和抑制性受体衍生，以确定是否启动效应器功能。一 复杂的信号网络支持这些受体：配体相互作用的传递。这种分层 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）和通过衔接分子传递的信号。我们的工作重点是 KIR和NKG 2-受体家族，因为它们是NK细胞保护全球免受 重要的RNA病毒感染收集NK细胞活化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单独使用实验方法， 受体相互作用，非线性信号传导反应，KIR和NKG 2家族的非平凡时空变化 受体聚集，以及不同HLA-肽配体之间的相互作用。为了应对这一挑战，我们将 通过将空间解析的机械和数据驱动的计算机模型与 探索NK细胞系和表达特异性KIR的原代人NK细胞的活化的实验室实验， NKG 2家族受体受源自全球重要RNA的新型肽库刺激 病毒（HCV，DENV，EBOV，ZIKAV），HCV和DENV复制子，以及人工来源。计算机模型 将植根于统计物理学，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、标准免疫测定、流式细胞术和共聚焦显微镜。 显像我们将追求三个目标：（1）开发一个定量工具包来分析KIR的肽调节， NKG 2受体。(2)NK细胞对全球重要RNA病毒（HCV、DENV）应答的定量建模 体内感染。(3)定量测定HLA等位基因多样性在NK信号传导中的作用， activation.