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

项目摘要 生理过程是由多个相互作用的系统或网络执行的。随着年龄的增长，这些 网络失去正常运行的能力，导致失调-网络交互和连接 降级，因为节点不再能够响应来自另一个节点的信号。因此，个体差异在 老龄化的速度会导致疾病发病、功能衰退和预期寿命的差异。一直以来 这表明，延长人类健康寿命的唯一方法是减缓衰老过程。因此， 确定改变衰老速度的基因网络和环境成分(及其相互作用) 对改善人口健康至关重要。 拟议项目的目标是使用系统医学方法来识别遗传变异， 分子信号，以及与衰老速度差异相关的社会/行为因素。更确切地说， 在K99阶段，该项目的目标是为人类识别基于网络的遗传签名 健康跨度。为了实现这一研究目标，K99阶段将包括生物医学科学、高级 维度组学数据分析、网络分析和社会基因组学。此培训将在 加州大学洛杉矶分校大卫·格芬医学院统计遗传学和生物统计学系 指导Steve Horvath博士(生物信息学、数量遗传学、网络分析)、Steve Cole博士(社交 基因组学、计算模型、生化分析、分子遗传学)和Rita Effros博士( 衰老、免疫功能、端粒生物学)。在K99赛道培训的基础上，R00阶段的目标是 将：1)建立人类转录漂移(失调)的模型，并测试它是否与衰老有关- 相关疾病患病率或社会因素；以及2)确定与年龄相关的全基因组甲基化变化(在 在发病率和社会经济地位之间的联系中起中介作用。 衰老是一个高度复杂和多维的过程，受基因等多种因素的影响 社会/政治政策的变体。这种复杂性给研究人员带来了挑战，他们的目标是 以揭示生物衰老的调控因素。这一建议为这两种建模提供了一种真正新颖的方法 老化过程和检测改变它的因素。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老龄化速度的理解。 此外，这个项目将识别随着时间积累的衰老和转录漂移的分子信号， 并随后导致发病率和死亡率。了解分子和环境 生物衰老的调节剂对于开发有效的干预措施以减缓衰老过程和 延长健康预期寿命。这一知识还将改进与多重老龄化相关的风险评估 并通过确定高危人群来协助初级预防战略。

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.