Circadian Organization and Disorder in Alzheimer's Disease

摘要 随着人口老龄化，阿尔茨海默病(AD)对公众健康的影响持续爆炸性增长。 日常生理和行为节律改变是阿尔茨海默病的显著特征。这些改变的活动节律 很难治疗，扰乱了患者和照顾者的生活。越来越多的证据表明，这些 变化不仅仅是症状。节律改变可能有助于AD的进展和发展。 许多重要的转录本、蛋白质和代谢物以每日周期振荡。了解这些 节律及其对阿尔茨海默病的影响，为发现新的治疗方法提供了可能性。 将昼夜节律生物学转化为阿尔茨海默氏症的治疗是有限的。每天显示哪些分子和途径 人类大脑的节律？这些节律是如何随着AD的变化而改变的？分子节律的变化 解释改变行为模式的原因？这些节律能被利用来治疗吗？ 要回答临床问题，我们需要人类数据。广告智库提供了无价的资源。但是大脑 银行几乎从来不提供病人死亡的一天中的时间，这使得使用这些数据来确定节律变得困难 研究。 我们开发了一个机器学习工具Cyclops(Cycle Order By Perioic Structure)来揭示 使用无序活检样本的分子节律。在评估大脑表达数据时，我们发现 独眼巨人可以正确地重建大脑样本中的节律，并正确预测死亡时间。 在这里，我们将订购来自对照受试者和AD患者的大脑皮层样本。我们将重建 这些人类大脑中的分子节律，识别AD患者的差异和已知药物的节律 靶点和AD疾病途径。 我们将分析已知死亡时间的样本子集，比较每个受试者的“内部 分子时间“和”时钟上的时间“。我们将检验这样一种假设，即对齐不良的患者 分子节律更有可能存在昼夜行为紊乱。我们将评估一项措施 转录节律强度，测试“较弱的”节律是否能预测行为或分子的错位。 AD会改变节律的产生吗？它会使静止的节律细胞和大脑区域失去同步性吗？使用数据 从同一受试者的多个大脑区域取样，我们将评估皮质内昼夜节律的同步性。 并将AD患者与对照组进行比较。利用单核测序数据，我们将探索AD的影响 关于细胞类型的特定节律及其同步性。最后，我们将检验重要AD的直接影响 导致分子时钟功能突变，测量孤立细胞的节律。 这项工作将促进我们对AD病理中昼夜节律的理解，澄清两者之间的关系 在行为和分子昼夜节律紊乱之间，并催化AD计时治疗的机会。

ABSTRACT With an aging population, the impact of Alzheimer's disease (AD) on public health continues to explode. Altered daily rhythms in physiology and behavior are prominent features of AD. These altered activity rhythms are difficult to treat, disrupting the lives of both patients and caregivers. Mounting evidence suggests that these changes are more than just symptoms. Altered rhythms may contribute to AD progression and development. Many important transcripts, proteins, and metabolites oscillate with a daily cycle. Understanding these rhythms, and their influence on AD, offers the potential to identify new therapies. The translation of circadian biology to Alzheimer's care is limited. Which molecules and pathways show daily rhythms in our human brains? How do those rhythms change with AD? Do changes in molecular rhythms explain changing behavioral patterns? Can these rhythms be exploited for therapeutic benefit? To answer clinical questions, we need human data. AD brain banks provide an invaluable resource. But brain banks almost never provide the time of day when patients died, making it difficult to use these data for rhythms research. We developed CYCLOPS (CYCLic Ordering by Periodic Structure), a machine-learning tool to uncover molecular rhythms using unordered biopsy samples. Evaluating brain expression data, we showed that CYCLOPS could correctly reconstruct rhythms in brain samples and correctly predict the time of death. Here we will order cortical brain samples from control subjects and patients with AD. We will reconstruct the molecular rhythms in these human brains, identifying differences in AD patients and rhythms in known drug targets and AD disease pathways. We will analyze a subset of samples where time of death is known, comparing each subject's “internal molecular time” with the “time on the clock.” We will test the hypothesis that patients with poorly aligned molecular rhythms are more likely to have circadian behavioral disturbance. We will evaluate a measure of transcriptional rhythm strength, testing if “weaker” rhythms predict behavioral or molecular misalignment. Does AD alter rhythm generation? Does it desynchronize still rhythmic cells and brain regions? Using data from multiple brain regions sampled from the same subjects, we will evaluate intracortical circadian synchrony and compare AD patients with controls. Using single-nucleus sequencing data, we will explore the effect of AD on cell type specific rhythms and their synchrony. Finally, we will test the direct influence of important AD causing mutations on molecular clock function, measuring rhythms in isolated cells. This work will advance our understanding of circadian rhythms in AD pathology, clarify the relationship between behavioral and molecular circadian disruption, and catalyze opportunities for AD chronotherapy.