Reconstructing the temporal landscape of gene regulation in aging

项目摘要/摘要 昼夜节律协调了大量的生化、生理和行为过程 一段24小时的时间。时钟电路，由一组核心时钟基因的振荡激活和抑制驱动， 存在于每个细胞中，并调节所有组织中近一半基因组的表达。丰富的EPI- 人口学证据将昼夜节律与人类健康联系起来。特别是，睡眠和昼夜节律的干扰 与神经变性有关，在那里它被认为是阿尔茨海默氏症的后果和风险因素 疾病(AD)。作为老年人最常见的痴呆症，阿尔茨海默病正迅速成为公共卫生危机。 据估计，美国有550万人受到SIS的影响(全球2400万人)，预计 接下来的20年。解决这一危机需要对疾病机制和身份fi阳离子有深刻的理解 诊断/治疗靶点。丰富的现有转录数据，再加上 用于时间重建和分析的复杂计算工具，提供了一个令人兴奋的机会 确定与衰老和阿尔茨海默病相关的基因表达振荡模式中的失调。 我们建议开发新的计算策略来破译基因表达的振荡模式 使用现有的非定时转录数据集，并将它们应用于AD研究的现有数据，以确定 与阿尔茨海默病相关的振荡模式的失调。我们的方法解决了许多分析问题- 长度，包括需要从未计时的样本重建时间信息；确保可概括性 和不同转录组fi技术的准确性；解决了未计时样本 可能不会持续一整天；以及识别复杂、非正弦节奏的变化。总而言之，这些研究 将揭示与衰老和阿尔茨海默病相关的基因表达的振荡模式，并将提供 对非计时数据进行昼夜节律分析的创新方法，可应用于其他表型。

PROJECT SUMMARY / ABSTRACT The circadian rhythm orchestrates a vast repertoire of biochemical, physiological, and behavioral processes with a 24-hour period. A clock circuit, driven by the oscillatory activation and repression of a set of core clock genes, is present in every cell and regulates the expression of nearly half the genome across all tissues. Abundant epi- demiological evidence links circadian regulation to human health. In particular, sleep and circadian disruption is associated with neurodegeneration, where it is thought to be both a consequence and a risk factor for Alzheimer's disease (AD). As the most common form of dementia in the elderly, AD is rapidly becoming a public health cri- sis, affecting an estimated 5.5M people in the United States (24M world-wide) and is expected to double over the next 20 years. Addressing this crisis requires a deep understanding of disease mechanisms and the identification of diagnostic/therapeutic targets. A wealth of existing transcriptomic data, coupled with the development of sophisticated computational tools for temporal reconstruction and analysis, provide an exciting opportunity to identify dysregulation in oscillatory patterns of gene expression associated with aging and AD. We propose to develop novel computational strategies to decipher the oscillatory patterns of gene expression using existing untimed transcriptomic datasets; and to apply them to existing data from studies of AD to identify dysregulation in the oscillatory patterns associated with AD. Our methods address a number of analytical chal- lenges, including the need to reconstruct temporal information from untimed samples; ensuring generalizability and accuracy across different transcriptomic profiling technologies; addressing the fact that untimed samples may not span the full day; and identifying changes in complex, non-sinusoidal rhythms. Together, these studies will reveal oscillatory patterns of gene expression associated with aging and Alzheimers disease, and will provide innovative new methods for the circadian analysis of untimed data that can be applied to other phenotypes.