ADDING ADDITIONAL FUNDS UNDER OPTION PERIOD 1, TASK AREAS 5 AND 6, FOR CORE YEAR 5.

在选项期 1、任务领域 5 和 6 下为核心第 5 年添加额外资金。

• 批准号: 10510183
• 负责人: ASHLIN BOLTON
• 金额: $ 1095.33万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-28 至 2022-09-27
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10510183
• 关键词: Address; Affinity; Antibodies; Area; Biochemical; Biological; Biological Assay; Biological Markers; Biological Models; Cardiometabolic Disease; Cardiovascular system; Cohort Studies; Collection; Communities; DNA; Data; Data Analyses; Diabetes prevention; Framingham Heart Study; Funding; Generations; Genetic study; Human; Institutes; Jackson Heart Study; Label; Laboratories; Mass Spectrum Analysis; Measures; Methods; National Heart, Lung, and Blood Institute; Nucleotides; Pathway interactions; Pilot Projects; Plasma; Population; Proteins; Proteomics; Protocols documentation; Request for Proposals; Research Personnel; Resources; Sampling; Services; Specificity; Technology; Time; Tissue Sample; Trans-Omics for Precision Medicine; Validation; Women' s Health; Work; aptamer; base; cohort; database of Genotypes and Phenotypes; experience; genetic information; interdisciplinary approach; meetings; metabolomics; next generation sequencing; novel; novel marker; population based; programs; response; trait

The Broad will provide both metabolomics and proteomics service for the TOPMed. Metabolomics. Metabolomic technologies provide the systematic biochemical characterization of human plasma and tissue samples. We have established a high-throughput metabolomics platform that measures over 600 known metabolites based on authentic standards, as well as thousands of LC-MS peaks that we are methodically working to characterize. Our methods integrate both targeted and non-targeted LC-MS approaches. We have rigorously addressed intra- and interassay variability, acquiring data at “population” level scale. In prior analyses in the Framingham Heart Study (FHS), the Diabetes Prevention Study, the Women’s Health Initiative and other cohorts, we have identified novel biomarkers of cardiometabolic disease and integrated genetic information and studies in model systems to identify novel biologic pathways. In response to Task Order Request for Proposals issued in 2017 for pilot metabolomics studies on 2000 MESA samples and in 2018 for CORE Year 3 metabolite profiling of approximately 8000 samples, we provided high quality targeted and nontargeted data delivered in a complete and timely manner, meeting deadlines in both cases. We are presently collaborating with MESA investigators to generate metabolomics data on approximately 8000 plasma samples for Data generation for CORE Year 4. Since the pilot studies, we have been responsive to TOPMed investigators working with metabolomics data and have supported and collaborated on data analysis. Based on these experiences, we seek to provide metabolomic capabilities in response to Task Order Request for Proposal (TORFP) #75N92020R0034; Centralized Omics REsource (CORE); Task Area 5: Metabolite Profiles using our high throughput, multidisciplinary approach. Proteomics Emerging proteomic technologies are beginning to permit the systematic, unbiased characterization of human plasma samples, though few data exist in large, population-based cohort studies. To address limitations of prior studies, we have established a high-throughput proteomic platform in our laboratory that measures 1,536 proteins using an extensive collection of nucleotide-labeled antibodies (Olink). The assay leverages a novel method referred to as a proximity extension assay (PEA) which can be quantified by Next Generation Sequencing (NGS). We have rigorously addressed intra- and inter-assay variability (both <13%) and acquired data >20-fold faster than mass spectrometry based methods with a level of specificity that appears to be better than existing high throughput affinity-based methods that use DNA aptamers. In new work in the Jackson Heart Study and other cohorts, we confirmed established relationships between known biomarkers and cardiovascular traits and discovered many new associations. Based on these experiences, we seek to provide proteomic capabilities in response to Task Order Request for Proposal (TORFP) #75N92020R0034; Centralized Omics REsource (CORE); Task Area 6: Proteomics using our high throughput, multidisciplinary approach. Our proposal reflects a longstanding collaborative effort between our laboratory and the Broad Institute. The present application will also couple discovery with novel “orthogonal” validation protocols using mass spectrometry. All of the data generated on our platform will be available for TOPMed investigators and will be made broadly available in real time to the scientific community via dbGap, as we have done previously. We believe this body of experience strongly. supports our plan to analyze ~8,000 samples and provide proteomic profiles as part of the NHLBI TOPMed program in the coming year.

Broad 将为 TOPMed 提供代谢组学和蛋白质组学服务。 代谢组学。 代谢组学技术提供了人类血浆和组织样本的系统生化特征。我们建立了高通量代谢组学平台 根据真实标准测量 600 多种已知代谢物，以及我们正在有条不紊地表征的数千个 LC-MS 峰。我们的方法将两者结合起来 靶向和非靶向 LC-MS 方法。我们严格解决了分析内和分析间的变异性，获取了“群体”水平的数据。在弗雷明汉心脏研究 (FHS)、糖尿病预防研究、妇女健康倡议和其他队列的先前分析中，我们已经确定了心脏代谢疾病的新生物标志物，并将遗传信息和研究整合到模型系统中，以确定新的生物途径。为了响应 2017 年发布的针对 2000 个 MESA 样本的试点代谢组学研究和 2018 年针对约 8000 个样本的 CORE 第 3 年代谢物分析的任务订单提案请求，我们提供了完整、及时的高质量靶向和非靶向数据，在这两种情况下均按时完成。我们目前正在与 MESA 研究人员合作，生成约 8000 个血浆样本的代谢组学数据，用于 CORE 第 4 年的数据生成。自试点研究以来，我们一直响应 TOPMed 研究人员处理代谢组学数据的要求，并支持和合作进行数据分析。 基于这些经验，我们寻求提供代谢组学能力，以响应任务订单提案请求 (TORFP) #75N92020R0034；集中组学资源（CORE）；任务领域 5：使用我们的高通量、多学科方法进行代谢物分析。 蛋白质组学 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、公正的表征，尽管大规模、基于人群的队列研究中存在很少的数据。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，使用广泛的集合来测量 1,536 种蛋白质 核苷酸标记抗体（Olink）。该测定利用了一种称为邻近延伸测定 (PEA) 的新方法，可以通过下一代测序进行量化 （NGS）。我们严格解决了测定内和测定间变异性（均 <13%），并且采集数据的速度比基于质谱的方法快 20 倍以上，且具有一定的特异性 这似乎比现有的使用 DNA 适体的基于高通量亲和力的方法更好。在杰克逊心脏研究和其他队列的新工作中，我们证实了已知生物标志物与心血管特征之间的关系，并发现了许多新的关联。 基于这些经验，我们寻求提供蛋白质组学能力，以响应任务订单提案请求 (TORFP) #75N92020R0034；集中组学资源（CORE）；任务领域 6：使用我们的高通量、多学科方法进行蛋白质组学。我们的提案反映了我们实验室和布罗德研究所之间的长期合作努力。本申请还将利用质谱法将发现与新颖的“正交”验证协议结合起来。我们平台上生成的所有数据都将可供 TOPMed 研究人员使用，并将通过 dbGap 向科学界广泛实时提供，正如我们之前所做的那样。我们坚信这些经验。 支持我们计划分析约 8,000 个样本并提供蛋白质组图谱，作为来年 NHLBI TOPMed 计划的一部分。