Circulating microRNAs in Extracellular Vesicles, Air Particulate Pollution, and Lung Function in an Aging Cohort

 描述（由申请人提供）：生物标志物是细胞、生化或分子变化，可以在人体组织中轻松且非侵入性地测量，并且直接或间接地参与疾病途径。在过去的二十年里，科学研究一再表明，环境空气颗粒物污染，也称为颗粒物（PM），会引起肺部和全身炎症，以及肺功能下降和加速肺老化。反映风险暴露和对肺功能的临床前影响的生物标志物的有限可用性极大地限制了有效靶向预防的机会。为了解决这一差距，我们的长期目标是确定反映环境影响并预测肺功能受损风险的新型生物标志物。我们将利用最近的实验和临床证据对细胞外囊泡（EV）的作用-即，微小的膜结合囊泡，由人类细胞主动释放到血液中，以及它们的生物活性货物microRNA（miRNAs）作为炎症反应的新型运输工具。来自动物和人类研究的最新证据表明，PM诱导EV从肺和其他相关组织中的细胞释放到血流中。特别是，PM暴露导致EV封装的miRNA的释放，其是可以控制受体细胞中基因表达的关键生物活性分子。然而，迄今为止，在大型，良好的表型人群中没有数据可用于解决EV作为PM暴露与其对肺功能的不良影响之间联系的路径的一部分的潜在作用。在本提案中，我们利用了规范老化研究（NAS）队列（n=750）的独特性质，在该队列中，我们在20多年的随访中进行了多达7次访视，重复采集血液样本、暴露数据和肺功能测量。所有研究结果将在KORA（n=750）中进行独立验证，KORA是一个在研究设计、暴露水平和参与者特征方面与NAS非常相似的队列。我们假设，血液EV的数量、大小和miRNA负荷响应于环境PM的短期和/或长期暴露而改变（目标1）;并且EV数量、大小和miRNA负荷反映了在血液采样时和/或随后的连续访问时肺功能的降低（目标2）。我们将使用先进的统计建模来整合连接暴露、EV和肺功能受损的路径中的一组炎症标志物;我们将建立并公开提供关于血液EV及其miRNA起源的组织/细胞类型的参考数据集;我们将使用该数据集来估计将PM与肺功能受损联系起来的EV包封的miRNA的来源（探索性目标3）。在所有目标中，我们将使用PM化学成分的详细表征来捕获其排放源。由于我们可以利用NAS和KORA队列的大量现成可用的资源和数据，我们将能够以具有成本效益的方式概括在一个赠款周期内进行的二十多年随访的数据。

 DESCRIPTION (provided by applicant): Biomarkers are cellular, biochemical or molecular alterations that can be easily and non-invasively measured in human tissues and are directly or indirectly in the pathway of disease. Over the past two decades, scientific research has repeatedly shown that ambient air particulate pollution, also known as particulate matter (PM), induces lung and systemic inflammation, as well as reductions in lung function and accelerated lung aging. The limited availability of biomarkers that reflect at-risk exposures and preclinical effects on lung function dramatically limits opportunities for effective targeted prevention. To address this gap, our long-term goal is to identify novel biomarkers that reflect environmental influences and predict the risk of impaired lung function. We will leverage recent experimental and clinical evidence on the roles of Extracellular Vesicles (EVs)-i.e., tiny membrane-bound vesicles actively released by human cells into the bloodstream-and of their bioactive cargo of microRNAs (miRNAs) as novel conveyors of inflammatory responses. Recent evidence from animal and human studies has shown that PM induces the release of EVs into the bloodstream from cells in the lung and other relevant tissues. In particular, PM exposure causes release of EV-encapsulated miRNAs, which are key bioactive molecules that can control the expression of genes in recipient cells. Yet to date, no data in large, well-phenotyped populations are available to address the potential roles of EVs as part of the paths linking PM exposure to its adverse effects on lung function. In this proposal, we exploit the unique nature of the Normative Aging Study (NAS) cohort (n=750) in which we have repeated collections of blood samples, exposure data and lung function measures in up to 7 visits conducted over 20+ years of follow up. All findings will be independently validated in KORA (n=750), a cohort remarkably similar to the NAS for study design, exposure levels, and participants' characteristics. We hypothesize that the number, size, and miRNA cargo of blood EVs are modified in response to short- and/or long-term exposures to ambient PM (Aim 1); and that the EV number, size, and miRNA cargo reflect reduced lung function at the time of blood sampling and/or over subsequent serial visits (Aim 2). We will use advanced statistical modeling to integrate a panel of inflammation markers in the paths linking exposure, EVs, and impaired lung function; we will establish and make publicly available a reference dataset on the tissue/cell type of origin of blood EVs and of their miRNAs; we will use this dataset to estimate the sources of EV- encapsulated miRNAs linking PM to impaired lung function (Exploratory Aim 3). Across all aims, we will use detailed characterization of PM chemical components to capture their emission sources. As we can leverage a wealth of extant, ready-to-use resources and data from the NAS and KORA cohorts, we will be able to cost- effectively recapitulate data from more than two decades of follow up over a single grant cycle.