Collaborative Research: Development of bioinformatic methods for studying gene expression network inflammation and neuronal regeneration

将开发新的生物信息学和统计学方法来研究包括脊髓在内的创伤性中枢神经系统，这种系统会引发炎症反应，从而产生实质性的继发性组织损伤并抑制神经元再生。人类脊髓核心损伤的抗炎治疗和治疗必须基于对参与炎症反应的细胞的类型、损伤后出现的时间和它们的行为性质的了解。然而，炎症性级联反应及其与神经发生的关系是复杂的，包括脊髓损伤在内的中枢神经系统损伤的后果尚不清楚。侵袭性巨噬细胞和驻留的小胶质细胞是单核细胞系的两种主要细胞类型，它们在炎症过程中发挥着重要作用。由于缺乏特异性标记物，损伤脊髓内活化的小胶质细胞侵袭巨噬细胞的功能作用并不是很清楚。我们建议使用微阵列技术来研究不同时间点小胶质细胞和巨噬细胞的表达谱，以及炎症和神经发生之间的表达网络。提出了一种新的方法来标记小胶质细胞/巨噬细胞缺失小鼠的特定细胞群。将开发新的统计技术来直接解决我们生物学研究中的挑战。这些措施包括消除Affymetrix数据的强度效应，识别重要基因并确定随时间推移的基因表达模式，识别一小群区分入侵的巨噬细胞和激活的脊髓小胶质细胞的基因等等。它们涉及高维特征空间中的统计估计、检验、变量选择、分类和网络建模。将通过开发新的统计方法来应对与高维相关的挑战，从而面对这些新出现的问题。同时，研究人员还打算通过渐近分析和模拟研究提供对这些问题及其相关方法的基本理解。该提案的一个显著特点是结合了我们在统计学和分子生物学方面的专业知识，以更好地了解脊髓和中枢神经系统损伤中的分子紊乱。我们的目标包括识别在脊髓损伤中揭示驻留的小胶质细胞和侵袭的巨噬细胞的不同功能特征的基因，以及选择在神经元对炎症的特定反应的特定方面唯一改变的基因。这些方法不仅可以帮助我们了解脊髓损伤炎症反应的分子机制，而且有可能识别脊髓损伤后治疗干预的靶向基因。此外，我们开发的尖端统计技术和生物信息学工具可以应用于其他生物学和统计研究。该项目还将通过与学生密切合作，以研究助学金的形式资助他们，创建数据集，并为该提案资助的每一项主要研究工作开发公开可用的计算机代码(两者均可通过网络获得)，从而将研究与教育结合起来。各种研究成果和实例将被简化并在本科生和研究生课程中教授，特别是用于指导本科高级论文和博士论文。博士后研究员和代表性不足的群体将接受培训，作为我们研究调查的一部分。结果将通过在研讨会、会议、专业协会会议和互联网上的陈述广泛传播。

New bioinformatic and statistical methods will be developed to study traumatic central nervous system including spinal cord, which provokes an inflammatory response that generates substantial secondary tissue damage and inhibits neuronal regeneration. Anti-inflammatory treatment of human spinal core injury and itstiming must be based on knowledge of the types of cells participating in the inflammatory response, the time after injury when they appear and the nature of their actions. However, inflammatory cascades and relationship with neurogenesis are complicated and consequence of central nervous system injury including spinal cord injury is poorly understood. Invading macrophages and resident microglia cells, the two major cell types that are from monocytic lineage play major role in the inflammatory process. Due to the lack of specific markers, the functional roles of invading macrophages from activated microglia within injuredspinal cord are not largely unknown. We propose to use microarray techniques to investigate expression profiles on microglia and macrophages in different time points and the expression network between inflammation and neurogenesis. New methods are proposed to label defined cell populations in microglia/macrophage deleted mice. New statistical techniques will be developed to address directly thechallenges from our biological studies. These include removing intensity effect of the Affymetrix data, identifying significant genes and determining gene expressions patterns over time, identifying a small group of genes that differentiate invading macrophages from activated microglia in the spinal cord, among others. They involve the statistical estimation, testing, variable selection, classification and network modeling in high-dimensional feature spaces. These emerging problems will be confronted via developing new statistical methods to address the challenges associated with high-dimensionality. At the same time, the investigators also intend to provide fundamental understanding, via asymptotic analysis and simulation studies, to these problems and their associated methodologies. A distinguished feature of the proposal is the combination the strengths of our expertise in statistics and molecular biology to gain better understanding of moleculardisturbances in spinal cord and central nervous system injury.The proposal investigates molecular disturbances in spinal cord and central nervous system injury. Our aims include identifying genes that reveal the distinct functional profiles of resident microglia and invading macrophages in spinal cord injury, and selecting the genes that are uniquely altered during specific facets of the neuronal response to inflammation. These approaches can not only help us understand the molecular mechanisms of inflammatory responses in spinal cord injury, but also potentially identify genes to be targeted for therapeutic intervention following spinal core injury. In addition, our developed cutting-edging statistical techniques and bioinformatic tools can be applied to other biological and statistical researches. The project will also integrate research and education by working closely with students and funding them in the form of research assistantships, creating datasets, and developing publicly available computer code (both made available through the web) for each of the main research endeavors funded by this proposal. Various research findings and examples will be simplified and taught in both undergraduate and graduate courses.In particular, they will be used in supervising undergraduate senior theses and Ph.D. theses. Postdoctoral fellows and underrepresented groups will be trained as a part of our research investigation. The results will be disseminated broadly through presentations at seminars, conferences, professional association meetings, and internet.