Quantitative Methods for Genome-wide Analysis of Macrophage Activation by ESCs

ESC 巨噬细胞激活的全基因组定量分析方法

• 批准号: 8325576
• 负责人: Jianqing Fan
• 金额: $ 36.25万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8325576
• 关键词: Angiogenesis Inhibition; Animal Model; Biological; Cell Therapy; Classification; Computer Security; Dependence; Development; Genes; Genetic; Genomics; Human; Individual; Knowledge; Lead; Macrophage Activation; Methods; Molecular; Pathway interactions; Patients; Research; Residual state; Serum; Signal Transduction; Staging; Statistical Methods; Stem cell transplant; Stem cells; Techniques; Teratoma; Testing; Time; Transplantation; clinical application; genome wide association study; genome-wide analysis; improved; macrophage; meetings; novel; statistics; tool

DESCRIPTION (provided by applicant):The proposal intends to advance both novel statistical methods for genomic research and discoveringcomplex molecular mechanisms on the teratoma initiation and progression after transplantation ofembryonic stem cells (ESCs). The biological aims are to investigate the mechanisms and biologic pathwaysof ESC-induced macrophage activation and teratoma development; and to unveil how the human MIFpolymorphisms associated with the serum MIF concentration in order to select patients for safe celltransplantation. To meet these challenges, high-throughput genomic tools and novel statistical methods willbe combined to enhance our understanding on the macrophage function on initiation and progression ofteratomas. The research will not only focus on identifying individual gene change on macrophages, but alsoanalyze underlying biologic pathways. In addition, time-course analysis in animal models will lead to identifygenes in different transition periods from early- to late-stage of teratoma development. These biologicalstudies prompt many new challenges in statistics. In particular, novel statistical techniques are proposed toanswer the following important questions: how to improve the power of existing tests for large-scale multipletesting problems with sparsity; how to detect whether genes have expressed over time course; how to usequantitative tools for discoveries of molecular mechanisms; how to control false discovery rate undergeneral dependence; what optimal detecting boundaries of signals are under arbitrary dependence; how toestimate residual variance in ultrahigh-dimensional regression. These topics have also importantimplications on compress sensing and computer security. In addition, several robust and effective methodsare proposed for ultrahigh-dimensional sparse regression, classification, and genetic networking. Thesenewly proposed methods will be applied to genome-wide association studies to identify single-nucleotidepolymorphisms associated with MIF expression in order to select patients for safe stem cell transplantation.An important feature of the proposal is the synthesis of PIs' extensive knowledge in statistics and cellbiology to gain better understanding of complicated biological problems.

描述（由申请人提供）：该提案旨在推进基因组研究的新颖统计方法，并发现胚胎干细胞（ESC）移植后畸胎瘤发生和进展的复杂分子机制。生物学目的是研究ESC诱导巨噬细胞活化和畸胎瘤发生的机制和生物学途径；并揭示人类MIF多态性与血清MIF浓度的关系，以便选择患者进行安全的细胞移植。为了应对这些挑战，高通量基因组工具和新颖的统计方法将结合起来，以增强我们对巨噬细胞在畸胎瘤发生和进展过程中功能的理解。该研究不仅侧重于识别巨噬细胞上的个体基因变化，还将分析潜在的生物学途径。此外，动物模型中的时程分析将有助于识别畸胎瘤发育早期到晚期不同过渡时期的基因。这些生物学研究给统计学带来了许多新的挑战。特别是，提出了新的统计技术来回答以下重要问题：如何提高稀疏性大规模多重测试问题的现有测试能力；如何检测基因是否随着时间的推移而表达；如何使用定量工具发现分子机制；一般依赖下如何控制错误发现率；任意依赖下信号的最佳检测边界是什么；如何估计超高维回归中的残差方差。这些主题对压缩传感和计算机安全也有重要影响。此外，还提出了几种稳健有效的方法用于超高维稀疏回归、分类和遗传网络。这些新提出的方法将应用于全基因组关联研究，以确定与MIF表达相关的单核苷酸多态性，以便选择患者进行安全的干细胞移植。该提案的一个重要特点是综合PI在统计学和细胞生物学方面的广泛知识，以更好地理解复杂的生物学问题。