Molecular mechanisms and social constructs: How genes and environment regulate the rate of aging

分子机制和社会结构：基因和环境如何调节衰老速度

• 批准号: 9755278
• 负责人: Morgan Elyse Levine
• 金额: $ 23.53万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9755278
• 关键词: Achievement; Age; Aging; Animal Model; Attenuated; Behavioral; Biochemical; Bioinformatics; Biological Aging; Biology; Biology of Aging; Biometry; Blood; Cardiovascular Diseases; Cessation of life; Chronic; Complex; Computer Simulation; Data; Data Analyses; Deceleration; Development; Diabetes Mellitus; Disease; Environment; Environmental Risk Factor; Epigenetic Process; Genes; Genetic; Genetic Transcription; Goals; Human; Incidence; Individual; Information Networks; Intervention; Knowledge; Laws; Lead; Life Expectancy; Literature; Longevity; Lung diseases; Malignant Neoplasms; Maps; Measures; Mediating; Medical; Medicine; Mentorship; Methylation; Modeling; Molecular; Molecular Genetics; Morbidity - disease rate; NF-kappa B; Network-based; Neurodegenerative Disorders; Onset of illness; Ontology; Outcome; Outcome Measure; Pathway Analysis; Pathway interactions; Phase; Physiological Processes; Policies; Politics; Population; Predisposition; Prevalence; Prevention strategy; Primary Prevention; Process; Quantitative Genetics; Research; Research Personnel; Risk; Risk Assessment; Science; Signal Transduction; Smoker; Socioeconomic Status; System; Techniques; Testing; Time; Training; Work; age effect; age related; base; disorder risk; effective intervention; frailty; genetic profiling; genetic signature; genetic variant; genome wide methylation; healthspan; high dimensionality; immune function; improved; individual variation; insight; insulin signaling; low socioeconomic status; mTOR Signaling Pathway; medical schools; mortality; network models; never smoker; novel; novel strategies; population health; repaired; sarcopenia; social; social genomics; telomere

PROJECT ABSTRACT Physiological processes are carried out by multiple interacting systems, or networks. With aging, these networks lose the ability to function properly, causing dysregulation—network interactions and connectivity degrade as nodes are no longer able to respond to signals from one another. As a result, individual variations in the rate of aging give rise to differences in disease onset, functioning decline, and life expectancy. It has been suggested that the only way to extend healthy lifespan in humans is by slowing the aging process. Thus, identifying the gene networks and environmental components (and their interactions) which alter the rate of aging is essential for improving the health of the population. The goal of the proposed project is to use systems medicine approaches to identify genetic variants, molecular signals, and social/behavioral factors associated with differences in the rate of aging. More specifically, during the K99 phase, the aim of this project is to identify network-based genetic signatures for human healthspan. To accomplish this research goal, the K99 phase will involve training in biomedical sciences, high- dimensional omics data analysis, network analysis, and social genomics. This training will take place at the UCLA David Geffen School of Medicine in the departments of statistical genetics and biostatistics, under the mentorship of Dr. Steve Horvath (bioinformatics, quantitative genetics, network analysis), Dr. Steve Cole (social genomics, computational modeling, biochemical analyses, molecular genetics), and Dr. Rita Effros (biology of aging, immune function, telomere biology). Building on the training during the K99, the goal of the R00 phase will be to: 1) Model transcriptional drift (dysregulation) in humans and test whether it is associated with aging- related disease prevalence or social factors; and 2) Identify age-related genome-wide methylation changes (in blood) that mediate the association between morbidity and socioeconomic status. Aging is a highly complex and multidimensional process, which is influenced by factors ranging from gene variants to social/political policies. This complexity, has presented a challenge for researchers whose goals are to uncover the regulators of biological aging. This proposal presents a truly novel approach for both modeling the aging process and examining factors which alter it. The studies performed during the K99 and R00 phases will advance our understanding of how gene networks and social factors influence the pace of human aging. Additionally, this project will identify molecular signals of aging and transcriptional drift that accumulate with time, and which subsequently give rise to morbidity and mortality. Understanding the molecular and environmental regulators of biological aging is essential for developing effective interventions that slow the aging process and increase healthy life expectancy. This knowledge will also improve risk assessment in relation to multiple-aging related conditions, and aid primary prevention strategies by identifying at-risk groups.

项目摘要 生理过程由多个交互系统或网络进行。随着衰老，这些 网络失去了正常运行的能力，导致失调 - 网络交互和连接性 由于节点不再能够互相响应彼此的信号。结果，个人变化 衰老率会导致疾病发作，功能下降和预期寿命的差异。它一直 建议延长人类健康寿命的唯一方法是减慢衰老过程。那， 识别改变衰老率的基因网络和环境成分（及其相互作用） 对于改善人口的健康至关重要。 拟议项目的目的是使用系统医学方法来识别遗传变异， 分子信号以及与衰老率差异相关的社会/行为因素。更具体地说， 在K99阶段，该项目的目的是确定人类基于网络的遗传特征 HealthSpan。为了实现这一研究目标，K99阶段将涉及生物医学科学，高级培训 维度数学数据分析，网络分析和社会基因组学。该培训将在 加州大学洛杉矶分校的统计遗传学和生物统计学系的大卫·格芬医学院， 史蒂夫·霍尔瓦斯（Steve Horvath 基因组学，计算建模，生化分析，分子遗传学）和Rita Effros博士（生物学 衰老，免疫功能，端粒生物学）。在K99期间的训练中建立R00阶段的目标 将是：1）人类中的模型转录漂移（失调），并测试它是否与衰老有关 相关疾病患病率或社会因素； 2）确定与年龄相关的全基因组甲基化变化（在 血液）介导发病与社会经济地位之间的关联。 衰老是一个高度复杂且多维过程，它受基因范围从基因等因素的影响 社会/政治政策的变体。这种复杂性为研究人员提出了一个挑战，他们的目标是 发现生物衰老的调节剂。该提案为两种建模提供了一种真正新颖的方法 衰老过程和检查改变它的因素。在K99和R00阶段进行的研究 将促进我们对基因网络和社会因素如何影响人类衰老的步伐的理解。 此外，该项目将确定随时间累积的衰老和转录漂移的分子信号， 并随后引起发病率和死亡率。了解分子和环境 生物衰老的调节剂对于制定有效的干预措施至关重要，以减缓衰老过程和 增加健康的预期寿命。这些知识还将改善与多重衰老有关的风险评估 相关条件，以及通过识别高危群体来帮助初级预防策略。