A novel method to identify regulators of biological aging 
based on high-throughput sequencing of epigenetic clocks.

项目摘要 基于DNA胞嘧啶甲基化的表观遗传时钟(DNAME)是目前最强大的生物标志物 哺乳动物的衰老。他们可以准确地估计不同生物样本的年龄，并对 被认为可以延缓衰老的干预措施，如卡路里限制或雷帕霉素治疗。尽管他们 随着表观遗传时钟在该领域日益普遍，人们对其背后的生物学了解甚少。同样，他们的 作为测试药物干预和发现新的衰老相关基因的读数的潜力尚未 没有实现。大规模使用DNAME时钟的主要限制之一是当前方法的成本。通常 用来分析表观遗传年龄的技术，如甲基化芯片和RRBS，测量了数百 数以千计的CPG，每个样品的价格是数百美元。经济、有针对性的方法，可以 测量几乎任何表观遗传时钟都是非常必要的，以使更大的实验能够以表观遗传年龄作为 初级读数。 对于这个建议的F99阶段，我开发了一种新的方法，称为基于标记的索引 用于甲基化测序(Time-Seq)，实现数十到数百个目标DNAME时钟测序 同时采集的样本。我已经证明，Time-Seq能够测量不同的表观遗传学时钟 扩展和降低成本的范围为1-2个数量级。我计划通过比较来验证该方法 传统方法和建立新的表观遗传年龄预测和生物标记物，训练与年龄相关的脆弱性 指标。 在K00阶段，我将使用老化的小鼠肌肉作为一种生物驱动表观遗传学时钟的研究 模特。我将采取一种多组学的方法，使用时间序列和现有的基因组方法来测试 这一假说认为，发育基因上可遗传的沉默丧失在一定程度上推动了时钟的滴答作响。 这一建议将为老龄领域提供一种亟需的方法，以实现经济高效和高效率的 吞吐量表观遗传时钟分析，以及基于脆弱性的新生物标记物，可用于学术研究 研究和临床背景。最终，该方法将帮助我们理解如何以及为什么可以构建 DNAME时钟和它们告诉我们的关于衰老过程中表观基因组功能障碍的信息。

Project Summary Epigenetic clocks based on DNA cytosine methylation (DNAme) are currently the most robust biomarkers of aging in mammals. They can accurately estimate the age of diverse biosamples and are responsive to interventions–such as caloric restriction or rapamycin treatment—that are thought to slow aging. Despite their increasing ubiquity in the field, the biology underlying epigenetic clocks is poorly understood. Likewise, their potential as a readout to test pharmaceutical interventions and discover new aging-associated genes is yet unrealized. One of the main limitations to using DNAme clocks at scale is the cost of current methods. Commonly used technologies to assay epigenetic age, such as methylation chip and RRBS, measure hundreds of thousands of CpGs and cost hundreds of dollars per sample. An economical, targeted approach that can measure virtually any epigenetic clock is badly needed to enable larger experiments with epigenetic age as the primary readout. For the F99 phase of this proposal, I have developed a new method called Tagmentation-based Indexing for Methylation SEquencing (TIME-Seq) that enables targeted DNAme clock sequencing of dozens to hundreds of samples simultaneously. I have shown that TIME-Seq is capable of measuring diverse epigenetic clocks at a range of scales and decreases costs 1-2 orders of magnitude. I plan to validate the method via comparison to conventional methods and build novel epigenetic age predictors and biomarkers trained on age-related frailty metrics. In the K00 phase, I will investigate the biology driving epigenetic clocks using aging mouse muscle as a model. I will take a multiomic approach, using TIME-Seq and established genomic methods, to test the hypothesis that heritable loss of silencing at developmental loci, in part, drives ticking of the clocks. This proposal will provide the aging field with a much-needed method for cost-effective and high- throughput epigenetic clock assay, as well as novel biomarkers based on frailty, which can be used in academic research and a clinical context. Ultimately, the method will help us understand how and why it is possible to build DNAme clocks and what they tell us about epigenomic dysfunction during aging.