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Systems-level identification of key regulators deciding immune cell state

决定免疫细胞状态的关键调节因子的系统级识别

基本信息

批准号：
10583462
负责人：
Wei Wang
金额：
$ 70.03万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10583462
关键词：
ATAC-seq Area Autoimmunity Biological Biological Response Modifiers CD8-Positive T-Lymphocytes Cell Differentiation process Cell Lineage Cell physiology Cells Chromatin Code Computer Analysis Data Development Enzymes Foundations Genes Genetic Genome Genomics Human Human body Immune Immune system Immunotherapy Individual Infection Malignant Neoplasms Measurement Metabolic Metabolic Pathway Methods MicroRNAs Modeling Multiomic Data Mus Noise Performance Population Publishing Regulation Research Resistance Role Signal Transduction Signaling Protein Specific qualifier value Specificity Surveys System Systems Biology Tai Ji Techniques Technology Testing Therapeutic Vaccines Virus Work cell type clinical application comparative computerized tools deep learning model design differential expression immune system function immunoengineering immunoregulation improved insight multiple omics novel novel therapeutics pathogen patient response programs protein protein interaction success therapeutically effective tool transcription factor transcriptome transcriptome sequencing tumor

项目摘要

Abstract Understanding the mechanisms and identifying regulators of immune cell development and differentiation are critical for developing new and more effective therapeutics. The fast advancement of genomic technologies provides an unprecedented opportunity to study the immune cells at the system level. The noise in the high throughput measurements and the complexity of multiple omics data make it an urgent need for developing novel and powerful systems biology approach for integrative analysis to reveal the underlying regulatory mechanisms for immune system. We propose a new method to integrate multiomics data at the genetic network level for identification of key regulators deciding the cell state and cell fate (Specific Aim 1). We will apply the method to systematically uncover the regulatory mechanisms in the mouse immune system by analyzing 86 immune cell populations (Specific Aim 2). We will also perform comparative analysis of the human and mouse immune cells to reveal the conserved regulatory code for immune cell specification (Specific Aim 3). We will rigorously assess the performance of the computational analysis, experimentally confirm the importance of the identified key regulators and investigate their roles in regulating immune cell functions. Once complete, the proposed work will provide not only a general tool for integrative analysis of multiomics data but also specific mechanistic insights for understanding the regulation of immune system functions.

摘要了解免疫细胞发育的机制和识别调节因子，分化对于开发新的和更有效的治疗剂至关重要。快速基因组技术的进步为研究人类基因组提供了前所未有的机会。免疫细胞在系统水平。高吞吐量测量中的噪声和多组学数据的复杂性使其迫切需要开发新颖而强大的系统生物学方法进行综合分析，以揭示潜在的调节免疫系统的机制。我们提出了一种新的方法来整合多组学数据，遗传网络水平，用于鉴定决定细胞状态和细胞命运的关键调节因子（具体目标1）。我们将应用该方法系统地揭示调控机制通过分析86个免疫细胞群体，在小鼠免疫系统中进行了研究（特异性目标2）。我们将还对人类和小鼠的免疫细胞进行了比较分析，以揭示免疫细胞规范的保守调节代码（具体目标3）。我们将严格评估性能的计算分析，实验证实的重要性，确定的关键调节因子，并研究它们在调节免疫细胞功能中的作用。一旦完成后，拟议的工作不仅将提供一个综合分析的一般工具，多组学数据，但也有具体的机制见解，了解调控免疫系统功能。