QUANTITATIVE ASSESSMENT OF BIOLOGICAL AGE AND ITS APPLICATIONS

生物年龄的定量评估及其应用

• 批准号: 10672456
• 负责人: Vadim N. Gladyshev
• 金额: $ 67.54万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10672456
• 关键词: Address; Age; Aging; Animals; Basic Science; Behavior Therapy; Biological; Biological Aging; Biological Markers; Biology; Biology of Aging; Blood specimen; Caloric Restriction; Categories; Cell Aging; Cell Culture Techniques; Cells; Chronology; Communities; Consensus; Cultured Cells; DNA Damage; DNA Methylation; Data; Development; Epigenetic Process; Exhibits; Future; Gene Expression; Generations; Genetic; Geroscience; Goals; Health; Human; In Vitro; Individual; Intervention; Link; Longevity; Measures; Methylation; Mitochondria; Modeling; Molecular; Monitor; Mus; Organ; Organism; Outcome; Phenotype; Physiological; Process; Reaction; Recommendation; Reporting; Research; Research Personnel; Resources; Sampling; Series; Signal Transduction; System; Testing; Therapeutic; Time; Tissues; Training; Validation; age related; cohort; dietary; dietary restriction; healthspan; in vivo; intervention effect; mimetics; mitochondrial dysfunction; molecular marker; mortality; mouse model; novel; novel therapeutics; pharmacologic; phenotypic data; practical application; prognostic; response; screening; senescence; tool

ABSTRACT Quantifying aging is a major goal in Geroscience research as the availability of a reliable marker of aging can facilitate understanding of the fundamental biology of aging, enable tracking of the aging process in different tissues and cell systems, and support identification and validation of interventions that extend lifespan and healthspan. Traditionally, aging has been monitored by following chronological age, mortality, age-related changes in gene expression, and/or other molecular features, however, there is currently no consensus on the best practices for quantitatively tracking progression through aging. The recent advent of biomarkers based on advanced omics approaches, such as DNA methylation, have provided some hope to support development of precise estimates of age, both in humans and mice. Nevertheless, the majority of such measures are trained as chronological age predictors, with little to no integration of biological, functional, or phenotypic data. Further, the modifiability of aging measures based on DNA methylation in response to lifespan and healthspan extending interventions is almost entirely unknown. We propose to address these challenges by developing a series of novel DNA methylation clocks by integrating information on phenotypic and functional aging, investigating links between DNA methylation and aging hallmarks, and evaluating DNA methylation responses to longevity interventions. We suggest that these clocks will offer a much-needed resource for the Geroscience community. We will develop these clocks using three general approaches. First, we will use cultured cells (MEFs) to induce or establish models of three well-known hallmarks of aging—cellular senescence, DNA damage, and mitochondrial dysregulation. We will then train epigenetic predictors of these hallmarks and validate them in vivo. We will also establish epigenetic alterations in response to novel and established longevity interventions. In doing so, we will develop biomarkers of intervention response that can be used to test mimetics, and/or optimize aging biomarkers. Finally, building on the highly characterized SLAM colony of C57Bl/6 and UM-HET3 animals, we will produce longitudinal methylation data across the lifespan that can be used to develop an epigenetic clock that can serve as a robust predictor of healthspan. We hypothesize that these new clocks will better capture biological age than chronological age trained clocks. Given that they were developed to capture different facets associated with the aging process, they can be combined to create a single aging measure that is more biologically informed and characterized compared to existing epigenetic clocks.

摘要 量化衰老是老年科学研究的一个主要目标，因为可靠的衰老标志物的可用性可以 促进对衰老的基本生物学的理解，使跟踪衰老过程中的不同 组织和细胞系统，并支持识别和验证延长寿命的干预措施， healthspan.传统上，通过以下方法监测衰老：实足年龄、死亡率、年龄相关的 基因表达和/或其他分子特征的变化，然而，目前还没有共识， 通过老化定量跟踪进展的最佳实践。最近出现的生物标志物的基础上， 先进的组学方法，如DNA甲基化，为支持 精确的年龄估计，无论是在人类还是小鼠。然而，大多数这类措施都经过培训， 实际年龄预测因子，几乎没有生物学、功能或表型数据的整合。此夕h 基于DNA甲基化的衰老指标对寿命和健康寿命延长的响应的可修改性 干预措施几乎完全未知。我们建议通过制定一系列 通过整合表型和功能性衰老的信息，研究 DNA甲基化和衰老标志之间的关系，以及评估DNA甲基化对长寿的反应， 干预措施。我们认为，这些时钟将提供一个急需的资源，为老年科学界。 我们将使用三种通用方法开发这些时钟。首先，我们将使用培养的细胞（MEFs）诱导 或者建立三个众所周知的衰老标志的模型-细胞衰老，DNA损伤， 线粒体失调然后，我们将训练这些标志的表观遗传预测因子，并在体内验证它们。 我们还将建立表观遗传改变，以应对新的和已建立的长寿干预措施。做 因此，我们将开发干预反应的生物标志物，可用于测试模拟物和/或优化老化 生物标志物。最后，基于C57 B1/6和UM-HET 3动物的高度表征的SLAM集落，我们将 产生整个生命周期的纵向甲基化数据，可用于开发表观遗传时钟， 可以作为健康寿命的可靠预测器。我们假设这些新的生物钟将更好地捕捉生物信息。 年龄比实际年龄训练的时钟。鉴于它们的开发是为了捕捉相关的不同方面 随着老化过程，它们可以结合起来，创造一个单一的老化措施，更生物信息 并与现有的表观遗传时钟进行了比较。