Functional Genomics Core

核心摘要 本PPG应用程序的总体目标是比较和对比抑制性药物的作用机制。 受体PD 1和LAG 3在耐受性和自身免疫、癌症和慢性炎症的背景下对T细胞起作用。 感染在整个研究中使用的一个主要方法是应用全基因组 转录谱分析这个功能基因组学和计算生物学核心的目的是 管理、分析和整合这些数据集，并确定参与 通过PD 1和/或LAG 3进行免疫调节。该核心还将应用基于网络的计算方法， 定义由PD 1调节的中心重要的“枢纽”基因、生物学途径和细胞网络，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 PD 1和LAG 3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： 目的1：对个体表达谱进行宿主、标准化、预处理和分析 转录谱数据集。这个目标将提供数据托管，一套统一的方法，以质量 控制和规范化全基因组转录数据集以及项目特定的分析， 转录差异。自身反应性和耗竭的CD 4 + T细胞数据集，Treg参与 自身免疫和肿瘤反应，以及抗病毒和抗肿瘤耗尽的CD 8 + T细胞，其中PD 1和LAG 3 将被生成并整合，以获得有关基因和途径的信息 由PD 1和LAG 3控制。 目的2：整合转录数据集的分析，并使用基于网络的计算 建模以定义涉及PD 1和/或LAG 3生物学的重要通路。首先 Aim将整合来自不同项目的转录数据集，以定义 自身免疫、癌症和慢性感染中的PD 1和LAG 3通路其次，我们将使用加权基因 共表达网络分析（WGCNA），以确定参与的中心重要的“枢纽”基因和途径， PD 1和LAG 3的功能和协同作用。 目的3：生成和验证逆转录病毒方法，以直接测试功能性预测 基因组分析本目标将在MigR 1或相关骨架上生成并验证逆转录病毒（RV） 允许在参与自身免疫的CD 4+或CD 8 + T细胞中过表达（或敲低）目的基因 或对肿瘤或慢性病毒感染的反应。这一办法将导致联合国系统内的高度协同作用。 PPG有助于将精力集中在至关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.