Proteomic Pathway Discovery in Cardiovascular Disease

 描述(由申请人提供)：尽管数十个蛋白质生物标志物已单独或在小组中与心血管风险相关，但它们的增量实用程度并不高。为了让新的生物标志物增加增量效用，它们必须提供不重叠的信息。然而，新的生物标记物通常出现在已被现有生物标记物覆盖的经过充分研究的途径中。因此，有必要考虑其他方法来识别心血管疾病的蛋白质生物标记物─，以提供新的生物信息，并最终实现更有效的预防干预。新兴的蛋白质组学技术开始允许对人类血浆样本进行系统的、公正的表征，尽管在基于人群的队列研究中几乎没有数据。为了解决先前研究的局限性，我们在实验室建立了一个高通量蛋白质组平台，使用化学修饰的单链DNA适配子测量1129个蛋白质。我们严格处理了分析内和分析间的可变性，获取数据的速度比基于质谱学的方法快10倍。在弗雷明翰心脏研究(FHS)的初步分析中，我们确认了已知生物标志物与弗雷明翰风险评分的既定相关性，并发现了许多新的关联。因此，我们建议将基于新适体的蛋白质组分析与FHS丰富的表型数据以及常见和罕见变异的遗传扫描相结合。我们将测试这一假设，即在表型良好的人群中进行蛋白质组学分析将阐明除已描述的那些疾病途径之外的其他疾病途径。在具体目标1中，我们将在横断面分析中确定新的心血管危险因素(BMI、收缩压、总/高密度脂蛋白胆固醇、糖尿病、吸烟和Framingham风险评分)的蛋白质组生物标志物。在具体目标2中，我们将在前瞻性分析中评估新的蛋白质组生物标记物是否预测未来心血管事件的风险。在具体目标3中，我们将描述与心血管风险相关的蛋白质的遗传决定因素。我们将分析AIMS 1和AIMS 2中确定的蛋白质的遗传决定因素，并检查决定蛋白质水平的遗传变异是否反过来与临床特征相关。这项多组学提案产生的所有主要数据将向科学界实时广泛提供。

 DESCRIPTION (provided by applicant): Although dozens of protein biomarkers have been associated individually or in panels with cardiovascular risk, their incremental utility has been modest. For new biomarkers to add incremental utility, they must provide non-overlapping information. However, new biomarkers typically emerge from well-studied pathways that are already covered by existing biomarkers. Thus, it is necessary to consider alternative approaches to identifying protein biomarkers of cardiovascular disease (CVD) ─ to provide novel biologic information and to ultimately enable more efficient targeting of preventive interventions. Emerging proteomic technologies are beginning to permit the systematic, unbiased characterization of human plasma samples, though few data exist in population-based cohort studies. To address limitations of prior studies, we have established a high-throughput proteomic platform in our laboratory that measures 1129 proteins using chemically modified single-stranded DNA aptamers. We have rigorously addressed intra- and inter-assay variability, acquiring data >10-fold faster than mass spectrometry based methods. In pilot analyses in the Framingham Heart Study (FHS), we confirmed established correlations of known biomarkers with the Framingham Risk Score and discovered many new associations. We therefore propose to integrate novel aptamer based proteomic analyses with the rich phenotypic data in FHS, as well as genetic scans for common and rare variants. We will test the hypothesis that proteomic profiling in well-phenotyped populations will illuminate additional disease pathways apart from those already described. In Specific Aim 1, we will identify novel proteomic biomarkers of cardiovascular risk factors (BMI, systolic blood pressure, total/HDL cholesterol, diabetes, smoking and the Framingham Risk Score) in cross-sectional analyses. In Specific Aim 2, we will assess whether novel proteomic biomarkers predict the risk of future CVD events in prospective analyses. In Specific Aim 3, we will characterize the genetic determinants of proteins associated with cardiovascular risk. We will analyze the genetic determinants of proteins identified in Aims 1 and 2, and examine whether genetic variants that determine protein levels are in turn associated with clinical traits. All of the primary data generated by this multi-omics proposal wil be made broadly available in real time to the scientific community.