Gene regulation and social relationships across the life course in a nonhuman primate model

非人类灵长类动物模型中整个生命过程的基因调控和社会关系

• 批准号: 10491852
• 负责人: Jenny Tung
• 金额: $ 55.51万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10491852
• 关键词: Address; Adult; Affect; Aging; Aleurites; Alleles; Animal Model; Animals; Bacterial Infections; Biological; Biological Markers; Birth; Blood; Blood specimen; Brain; Cause of Death; Chromatin; Clinical; Complex; Coupling; DNA-Binding Proteins; Data; Disease; Ecosystem; Environment; Environmental Risk Factor; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Generations; Genes; Genetic; Genetic Variation; Genomics; Genotype; Glucocorticoids; Goals; Grain; Health; Heart Diseases; Human; Immune system; Individual; Infant; Infection; Innate Immune Response; Joints; Kenya; Life; Life Cycle Stages; Ligands; Light; Link; Loneliness; Longevity; Longitudinal Studies; Malignant Neoplasms; Measures; Mediating; Modeling; Molecular; Molecular Profiling; Morbidity - disease rate; Papio; Pathway interactions; Peripheral; Phenotype; Physiological; Population; Predisposition; Recording of previous events; Regulator Genes; Research Design; Risk; Risk Behaviors; Role; Sampling; Sex Chromatin; Signal Pathway; Silk; Skin; Social Behavior; Social Environment; Social isolation; Social status; Social support; Study Subject; Surveys; Testing; Time; Tissues; United States; Variant; Viral; Virus Diseases; Work; base; cohort; diet and exercise; disorder risk; experience; functional genomics; gene environment interaction; genome-wide analysis; genomic data; genomic signature; human model; immune function; immunoregulation; insight; mortality; mortality risk; nonhuman primate; pathogen; response; sample collection; social; social integration; social relationships; transcriptome sequencing

Project Summary Social support and social integration are some of the most robust predictors of morbidity and mortality identified to date. This relationship arises from increased susceptibility to several of the top causes of death in the United States, including major diseases of aging such as heart disease and cancer. Recent studies suggest that a signature of social relationships is also detectable in data on gene regulation, highlighting a potential pathway through which social ties get “under the skin” to influence health. However, despite abundant evidence that the health effects of social relationships begin early in life, no studies have related the full life course trajectory of social relationships to data on gene regulation, or used these data to investigate why some individuals appear more susceptible to social isolation than others. The goal of this study is to address these gaps by linking fine-grained, longitudinal data on social relationships to unbiased surveys of the molecular signature of social experience. To do so, we will leverage an established model for social relationships and health in natural animal populations, the baboons of the Amboseli ecosystem of Kenya. This population has been the subject of longitudinal study for up to 9 generations, revealing that social isolation predicts shortened lifespan in a manner highly analogous to humans. We propose to link annual measures of social relationships, from birth through adulthood, with gene expression and chromatin accessibility data collected both at baseline and following ex vivo challenge with bacterial and viral mimics. This strategy will allow us to investigate the types of social relationships that matter most, the timing of their effects on gene regulation, and their relevance for immune function, a primary contributor to variation in health during aging. Using these data, we will address three aims. First, we will characterize the gene regulatory signature of variation in social relationships across the life course. We will investigate the relative roles of early life, cumulative experience, and social relationships close to the time of biological sample collection, as well as the relative importance of social relationship quantity versus quality. Second, we will assess whether individuals vary in their sensitivity to social environments based on genotype, by identifying gene-social relationship interactions that affect gene expression. Finally, we will test the consequences of social relationship-associated gene regulation for immune defense and lifespan. In doing so, this work will shed important light on whether gene regulatory signatures of social relationships are likely to be mechanistically implicated in the link between social relationships and health, or instead serve as passive biomarkers. Together, our results will provide the most comprehensive window into the functional genomic signature of social relationships available to date. By revealing when, how, and for whom social relationships matter most, they will therefore address three questions of outstanding importance to understanding the role of the social environment in human health.

项目概要 社会支持和社会融合是发病率和死亡率最有力的预测因素 迄今为止，这种关系是由于对几种主要死亡原因的易感性增加而产生的。 美国最近的研究包括心脏病和癌症等主要衰老疾病。 表明社会关系的特征也可以在基因调控数据中检测到，这突显了 然而，尽管存在丰富的社会关系“深入皮肤”影响健康的潜在途径。 有证据表明社会关系对健康的影响从生命早期就开始了，但没有研究与整个生命有关 社会关系的轨迹轨迹与基因调控数据的关系，或使用这些数据来调查为什么某些 个人似乎比其他人更容易受到社会孤立。 本研究的目标是通过链接有关社会的细粒度纵向数据来解决这些差距 为此，我们将利用关系对社会经验的分子特征进行公正的调查。 自然动物种群中的社会关系和健康的既定模型，狒狒 肯尼亚安博塞利生态系统对该种群进行了长达 9 年的纵向研究。 几代人的研究表明，社会孤立会导致寿命缩短，其方式与 我们建议将人类从出生到成年的社会关系的年度衡量标准与基因联系起来。 在基线和离体攻击后收集的表达和染色质可及性数据 这种策略将使我们能够研究重要的社会关系类型。 最重要的是，它们对基因调控的影响的时间，以及它们与免疫功能的相关性，这是一个主要的因素。 导致衰老过程中健康变化的因素。 利用这些数据，我们将实现三个目标：首先，我们将描述基因调控特征。 我们将研究生命历程中社会关系的变化， 累积的经验，以及接近生物样本采集时的社会关系，以及 其次，我们将评估个人是否具有社会关系数量与质量的相对重要性。 通过识别基因与社会的关系，他们对基于基因型的社会环境的敏感性有所不同 最后，我们将测试与社会关系相关的后果。 在此过程中，这项工作将为免疫防御和寿命的基因调控提供重要线索。 社会关系的基因调控特征很可能机械地涉及到之间的联系 社会关系和健康，或者作为被动生物标志物，我们的结果将共同提供。 迄今为止最全面的社会关系功能基因组特征窗口。 揭示社会关系何时、如何以及对谁最重要，因此他们将解决三个问题 对于理解社会环境在人类健康中的作用至关重要的问题。