A Multi-omics approach to Environment and Depression in Parkinsons disease (MOOD-PD)

项目摘要 我们建议使用代谢和多组学标记物来阐明长期接触杀虫剂如何影响 农村老年人和帕金森病患者抑郁和焦虑的发生 (PD)和抑郁。抑郁症和焦虑症是老年人最常见的精神障碍类型。 在长期护理环境中，抑郁症的患病率高达25%，这对共病的医疗保健使用有很大贡献 疾病1-5。严重抑郁发作和临床焦虑在帕金森病患者中的发生率要高得多，甚至在很久以前 帕金森病诊断1、2.提高我们对抑郁症和焦虑症可改变的危险因素和机制的理解 对老年人来说，这是一个紧迫的公共卫生问题。在过去的二十年里，我们的团队产生了一个独特的数据 资源，现在使我们有机会调查共同和重要的 暴露于环境中的抑郁和焦虑(杀虫剂)，并通过 多维生物网络。在之前的资助下，我们已经收集了终生抑郁/焦虑的诊断和 大量人群中抑郁和焦虑症状的治疗历史以及当前(和随访)状况- 加州中央山谷居民帕金森病的病例对照研究，包括500多名帕金森病患者 被密切关注了十多年。在这些人中，多达38%的帕金森病患者和27%的没有 帕金森病患者在一生中的任何时候都会被诊断为抑郁症或焦虑症。对于所有研究参与者，我们都有 基因组(Illumina全球筛选阵列，660K标记)数据可用，我们额外生成了约800个 表观基因组(Illumina Infinium450K DNA平台；全基因组DNA甲基化)和非靶向代谢数据(在 300名参与者的两个时间点)。我们开发了一个基于纵向地理信息系统(GIS)的评估 与所有农业农药的类型、日期和地点有关的国家强制信息的农药暴露 自1974年以来，加州记录了对土地使用地图和研究参与者的住所和工作场所的申请。 此外，我们还收集了大量关于职业、家庭和园艺使用杀虫剂的信息。生物学 过程，包括对慢性毒物暴露或疾病过程的生物(代谢、表观遗传)反应 动态的，但也取决于遗传易感性。在这里，我们建议结合一个强大的系统生物学分析 询问表观遗传学和代谢学数据以识别特定农药特征的方法 暴露(有机磷、拟除虫菊酯、新烟碱)和与帕金森病抑郁相关的外周疾病过程。 我们的数据使我们能够高效地进行调查多维网络的高风险先导研究 使用有监督的机器学习方法识别不同生物系统中的慢性响应模式 可与暴露和/或疾病过程相关联的分子层。将我们的多组数据组合成 多维网络将填补我们目前关于分子机制方面的空白 帕金森病中的抑郁和焦虑障碍，这是一种在老龄化社会中日益重要的主要神经退行性疾病。

Project Abstract We propose to use metabolic and multi-omic markers to elucidate how chronic, long-term pesticide exposures affect the occurrence of depression and anxiety in elderly living in a rural environment and those who develop Parkinson’s disease (PD) and depression. Depression and anxiety disorders are the most common types of mental disorders in older adults. Depression prevalence is as high as 25% in long-term care settings and strongly contributes to health care use for comorbid illnesses1-5. Major depressive episodes and clinical anxiety occur at a much higher rate among PD patients even well before PD diagnosis1,2. Improving our understanding of modifiable risk factors and mechanisms involved in depression and anxiety amongst the elderly is an urgent public health matter. Over the past two decades, our team has generated a unique data resource that now provides us with the opportunity to investigate the contributions of common and important environmental exposures (pesticides) to depression and anxiety and to explore disease processes through multidimensional biologic networks. With prior funding, we have collected lifetime depression/anxiety diagnosis and treatment histories as well as current (and follow-up) status of depressive and anxiety symptoms in a large population- based case control study of PD among residents of the California Central Valley, including more than 500 PD patients we closely followed for over a decade. Among these individuals, as many as 38% of PD patients and 27% of elderly without PD reported a diagnosis of depression or anxiety disorder at any time during their life. For all study participants we have genome (Illumina Global Screening Array, 660K markers) data available, and for ~800 additionally we generated epigenome (Illumina Infinium 450K DNA platform; genome-wide DNA methylation) and untargeted metabolomic data (at two time points for 300 participants). We developed a longitudinal geographic information system (GIS) based assessment for pesticide exposures that links state-mandated information on type, date, and location of all agricultural pesticide applications in California recorded since 1974 to land use maps and study participants’ residences and work places. Additionally, we collected extensive information on occupational, home and gardening use of pesticides. Biologic processes, including biologic (metabolic, epigenetic) responses to chronic toxicant exposures or disease processes are dynamic but also dependent on genetic susceptibilities. Here, we propose to combine a powerful systems biology analytic approach to interrogate epigenetic and metabolomic data anchored in genetics to identify signatures for specific pesticide exposures (organophosphates, pyrethroids, neonicotinoids) and peripheral disease processes related to depression in PD. Our data uniquely position us to efficiently conduct a high-risk pilot study that investigates multidimensional networks using supervised machine learning methods to identify chronic response patterns in biologic systems across different molecular layers that can be linked to exposure and/or disease processes. Assembling our multi-omic data into multidimensional networks will address gaps in our current knowledge concerning molecular mechanisms contributing to depression and anxiety disorders in PD, a major neurodegenerative disorder of growing importance in aging societies.