Functional Genomics Core

核心摘要 这个PPG应用程序的总体目标是比较和对比抑制 受体PD1和LAG3在耐受和自身免疫、癌症和慢性疾病的背景下作用于T细胞 感染。在整个研究中使用的一个主要方法是在全基因组范围内应用 转录特征分析。这个功能基因组学和计算生物学核心的目的是 管理、分析和集成这些数据集，并定义关键基因和途径 PD1和/或LAG3的免疫调节作用。该核心还将应用基于网络的计算方法来 定义核心重要的“中枢”基因、生物通路和细胞网络受PD1和/或 LAG3.因此，核心C将应用集成的计算和生物信息学方法来定义生物 PD1和LAG3在自身免疫、抗肿瘤和抗病毒T细胞中的作用这三个目标是： 目标1：托管、标准化、前处理并提供个体的表情图谱分析 转录特征分析数据集。这一目标将提供数据托管，一套统一的质量方法 控制和标准化全基因组转录数据集以及特定于项目的分析 转录差异。自身反应和耗尽的CD4+T细胞的数据集，Treg参与 自身免疫和肿瘤反应以及抗病毒和抗肿瘤耗尽的CD8+T细胞，其中PD1和LAG3 将被产生并整合以获得关于基因和途径的信息 由PD1和LAG3控制。 目标2：整合转录数据集的分析并使用基于网络的计算 建模以确定参与PD1和/或LAG3生物学的中心重要通路。首先，这是 AIM将整合来自不同项目的转录数据集，以定义 PD1和LAG3通路在自身免疫、癌症和慢性感染中的作用第二，我们将使用加权基因 共表达网络分析(WGCNA)定义核心重要的“中枢”基因和参与的途径 PD1和LAG3的功能和协同作用。 目标3：生成和验证逆转录病毒方法，以直接测试功能预测 基因组分析。这一目标将在MigR1或相关主干上生成和验证逆转录病毒(RV 允许参与自身免疫的CD4+或CD8+T细胞过度表达(或敲除)感兴趣的基因 或对肿瘤或慢性病毒感染的反应。这一方法将在 PPG和帮助集中精力集中在中心重要的基因和途径上。

CORE SUMMARY The overall goal of this PPG application is to compare and contrast the mechanisms by which the inhibitory receptors PD1 and LAG3 operate on T cells in the context of tolerance and autoimmunity, cancer, and chronic infection. One major approach to be used throughout the studies is the application of genome-wide transcriptional profiling. The purpose of this Functional Genomics and Computational Biology Core is to manage, analyze and integrate these datasets and define key genes and pathways involved in immunoregulation by PD1 and/or LAG3. This Core will also apply network-based computational approaches to define centrally important “hub” genes, biological pathways and cellular networks regulated by PD1 and/or LAG3. Thus, Core C will apply integrated computational and bioinformatics approaches to define biological effects of PD1 and LAG3 in autoimmune, antitumor and antiviral T cells. The three Aims are: AIM 1: To host, normalize, pre-process and provide expression profiling analysis of individual transcriptional profiling datasets. This Aim will provide data hosting, a uniform set of approaches to quality control and normalize genome-wide transcriptional datasets as well as Project-specific analysis of transcriptional differences. Datasets for autoreactive and exhausted CD4+ T cells, Treg involved in autoimmunity and tumor responses, and antiviral and antitumor exhausted CD8+ T cells where PD1 and LAG3 have been disrupted will be generated and integrated to obtain information about genes and pathways controlled by PD1 and LAG3. AIM 2: To integrate analysis of transcriptional datasets and use network-based computational modeling to define centrally important pathways involved in the biology of PD1 and/or LAG3. First, this Aim will integrate transcriptional datasets from different Projects to define common and unique features of the PD1 and LAG3 pathways in autoimmunity, cancer, and chronic infection. Second, we will use Weighted Gene Co-expression Network Analysis (WGCNA) to define centrally important “hub” genes and pathways involved in PD1 and LAG3 function and synergy. AIM 3: To generate and validate retroviral approaches to directly test predictions from functional genomic analyses. This Aim will generate and validate retroviruses (RV) on the MigR1 or related backbones to allow overexpression (or knockdown) of genes of interest in CD4+ or CD8+ T cells involved in autoimmunity or responses to tumors or chronic viral infection. This approach will lead to a high degree of synergy within the PPG and help to a focus efforts on centrally important genes and pathways.