Identification and Interpretation of Chromatin Changes Associated with the Aging of Human Immune Cells

项目摘要/摘要 这项研究计划旨在弥合从临床样本生成基因组数据和从临床样本生成基因组数据之间的差距 我们能够推断和解释支持细胞功能和功能障碍的错综复杂的调控程序 人类细胞。UCAR实验室开发并应用计算解决方案来揭示复杂的监管 人类细胞中的程序，并解决以前无法解决的问题，这些问题与这些细胞中的干扰如何 这些项目会影响人类的健康和疾病。其目标是创建多功能、易于使用的计算工具 使用并与NGS数据日益复杂的复杂性保持一致。目前的重点是 衰老的免疫生物学利用了首席研究人员在计算机科学方面的广泛培训， 表观基因组学和衰老生物学。与杰克逊实验室的合作者和 康涅狄格大学健康中心的多项发现与病毒的基因组签名有关 人类免疫衰老，并产生了许多问题，这些问题构成了拟议研究的基础 计划，包括：1)哪些监管计划和监管互动因老龄化而中断 免疫细胞？2)男性和女性的年龄有什么不同？3)什么是假定的 健康老龄化的基因组/临床/免疫学标志物？为了解决这些问题，这项研究计划 将专注于开发机器学习和网络挖掘算法，以支持从 来源多样，因为无法推断复杂的监管相互作用和不同的监管要素 从单一数据类型。在这些工具的推动下，它将调查血液监管计划的动态- 通过与临床医生、免疫学家和 染色质科学家。这项研究将促进我们对免疫反应是如何 转录调控，将有助于设计干预措施，以促进老年人和 并将产生对基因组医学的不同领域有用的计算资源。

PROJECT SUMMARY/ABSTRACT This research program aims to bridge the gap between genomics data generation from clinical samples and our ability to infer and interpret intricate regulatory programs that underpin cell function and dysfunction in human cells. The Ucar laboratory develops and applies computational solutions to uncover complex regulatory programs in human cells and address previously inaccessible questions related to how disruptions in these programs affect human health and disease. The goal is to create computational tools that are versatile, easy to use and in keeping with the ever-increasing sophistication and complexity of NGS data. The current focus on the immunobiology of aging leverages the Principal Investigator's extensive training in computer science, epigenomics, and aging biology. Ongoing work with collaborators at The Jackson Laboratory and The University of Connecticut Health Center has led to multiple discoveries related to the genomic signatures of human immune aging, and has yielded numerous questions that form the basis for the proposed research program, including: 1) Which regulatory programs and regulatory interactions are disrupted with aging in which immune cells? 2) How do men and women age differently? 3) What are the putative genomic/clinical/immunological markers of healthy aging? To address these questions, this research program will focus on developing machine learning and network mining algorithms that enable integration of data from diverse sources, since complex regulatory interactions and diverse regulatory elements cannot be inferred from a single data type. Fueled by these tools, it will investigate the dynamics of regulatory programs in blood- derived human immune cells associated with aging through collaborations with clinicians, immunologists, and chromatin scientists. This research will advance our understanding of how immune responses are transcriptionally regulated, will facilitate the design of interventions to boost immune health in elderly and diseased individuals, and will yield computational resources useful to diverse areas of genomic medicine.