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

描述（由申请人提供）：该提案旨在推进基因组研究的新统计方法，并揭示胚胎干细胞（ESC）移植后畸胎瘤发生和进展的复杂分子机制。本研究的生物学目的是探讨胚胎干细胞诱导巨噬细胞活化和畸胎瘤发生的机制和生物学途径，揭示人类MIF基因多态性与血清MIF浓度的关系，为选择安全的细胞移植患者提供依据。为了应对这些挑战，高通量基因组工具和新的统计方法将结合起来，以提高我们对巨噬细胞在畸胎瘤发生和发展中的作用的理解。该研究不仅关注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，在动物模型中的时间过程分析将导致识别从早期到晚期的畸胎瘤发展的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，新的统计技术被提出来回答以下重要问题：如何提高现有测试对具有稀疏性的大规模多重测试问题的能力;如何检测基因是否随时间过程表达;如何使用定量工具发现分子机制;如何控制一般依赖下的错误发现率;在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残差方差这些课题在压缩感知和计算机安全方面也有重要的应用。此外，提出了几种鲁棒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，从而选择安全的干细胞移植患者。该提案的一个重要特征是综合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。 公共卫生相关性：胚胎干细胞引起的畸胎瘤形成可能阻碍基于干细胞疗法的临床应用。这项研究将进一步提高我们对胚胎干细胞移植后畸胎瘤发生和发展的认识，并通过调控MIF表达和靶向M2巨噬细胞来抑制血管生成，从而有效减少畸胎瘤的形成，使干细胞移植安全。

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. PUBLIC HEALTH RELEVANCE:The teratoma formation caused by ESCs can hamper clinical applications of stem cell based therapies. Theproposed research will advance our knowledge on the initiation and progression of teratomas after ESCtransplantation and lead to effective reduction of teratoma formation by inhibition of angiogenesis viamanipulating MIF expression and targeting M2 macrophages, making stem cell transplantation safe.