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

基本信息

批准号：
10686795
负责人：
Jayajit Das
金额：
$ 36.33万
依托单位：
RESEARCH INST NATIONWIDE CHILDREN'S HOSP
依托单位国家：
美国
项目类别：
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/10686795
关键词：
Adaptive Immune System Address Affect Alleles Antigens B-Lymphocytes Binding Biochemical Reaction Biological Assay Calibration Cell Line Cell Surface Receptors Cells Complex Computer Models Cytoprotection Data Dengue Virus Dependence Ebola virus Electron Microscopy Event Face Family Flow Cytometry Glean HIV HLA Antigens Hepatitis C virus Histocompatibility Antigens Class I Human Immunoglobulins Immunology procedure Infection Information Theory Innate Immune System KLRD1 gene Killer Cells Kinetics Ligands Lymphocyte Measurement Methods Modeling NK Cell Activation Natural Killer Cells Nature Nonlinear Dynamics Parameter Estimation Peptide Fragments Peptide Library Peptides Physics Process Public Health RNA Virus Infections RNA Viruses Reaction Receptor Cell Regulation Replicon Resistance Role Signal Transduction Signaling Molecule Source Sum System T-Lymphocyte Vaccination Viral Virus Diseases Work Zika Virus adaptive immunity antagonist antigen binding 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

项目摘要

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.

开发模拟 NK 细胞针对 RNA 病毒感染反应的预测工具包涉及免疫细胞信号传导和激活的时空过程的数学模型（例如，适应性免疫的 T 细胞为复杂系统提供了新的机制见解。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点和不同点适应性免疫系统。 NK 细胞对全球重要的 RNA 病毒具有重要的抵抗力（例如 HCV、DENV、HIV、EBOV 和 ZIKV）感染。然而，旨在破译的定量模型 NK 细胞信号传导和激活的机制尚不完善，导致人们对 NK 细胞的了解不足许多关键结果与 NK 细胞对这些重要病毒病原体的反应有关。与细胞不同适应性免疫系统 NK 细胞不具有单一抗原特异性触发受体，但具有总信号来自激活和抑制受体以确定效应器功能是否启动。一个复杂的信号网络支撑这些受体：配体相互作用的传递。这个的层次感网络包括由细胞表面受体直接传输的信号，例如抑制性杀伤细胞免疫球蛋白样受体（KIR）和通过接头分子传递的信号。我们的工作重点是 KIR 和 NKG2 受体家族，因为它们是 NK 细胞保护免受全球侵害的关键组成部分重要的RNA病毒感染。收集 NK 细胞激活的机制具有挑战性由于配体的多样性，这些不同的受体：配体系统仅使用实验方法受体相互作用、非线性信号反应、KIR 和 NKG2 家族的重要时空变化受体聚类以及不同 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机械和数据驱动的计算机模型与台架实验探测表达特定 KIR 的 NK 细胞系和原代人 NK 细胞的激活 NKG2 家族受体受到源自全球重要 RNA 的新型肽库的刺激病毒（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成像。我们将追求三个目标：（1）开发定量工具包来分析 KIR 的肽调节和 NKG2 受体。 (2) NK细胞对全球重要RNA病毒（HCV、DENV）反应的定量模型体内感染。 (3) 定量测定 HLA 等位基因多样性在 NK 信号传导和激活。