Reconstructing the temporal landscape of gene regulation in aging

项目总结/摘要 昼夜节律协调了大量的生化、生理和行为过程， 24小时内一个时钟电路，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。丰富的肾上腺素- 生物学证据将昼夜节律调节与人类健康联系起来。特别是睡眠和昼夜节律紊乱 与神经退行性疾病有关，它被认为是阿尔茨海默氏症的后果和风险因素 疾病（AD）。作为老年痴呆症中最常见的形式，AD正迅速成为公共卫生危机。 sis，影响了美国估计550万人（全球2400万人），预计将在2015年翻一番。 未来20年解决这一危机需要深入了解疾病机制， 诊断/治疗目标。大量现有的转录组学数据，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， 鉴定与衰老和AD相关基因表达振荡模式的失调。 我们建议开发新的计算策略来破译基因表达的振荡模式 使用现有的不定时转录组数据集;并将其应用于AD研究的现有数据，以确定 与AD相关的振荡模式失调。我们的方法解决了一些分析挑战- 包括需要从不定时的样本中重建时间信息;确保可推广性 不同转录组学分析技术的准确性;解决了不定时样本 可能不会持续一整天;以及识别复杂的非正弦节律的变化。这些研究一起 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 创新的新方法，用于非定时数据的昼夜节律分析，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.