Admin-Core

总体而言：我们的项目结合了遗传模型生物的显着优势、复杂的途径 绘图工具、高通量和精确的量子化学 (QM) 以及最先进的实验 测量。结果将是一种有效且具有成本效益的未知化合物新方法 代谢组学的鉴定，这是这个不断发展的医学科学领域面临的主要限制之一。 秀丽隐杆线虫对于这项研究有几个优势，包括超过 10,000 个可用的基因突变体， 完善的 CRISPR/Cas9 技术，以及超过 500 个野生线虫分离株，具有完整的 基因组。秀丽隐杆线虫基因的一半与人类疾病基因具有同源性，使这种模式生物成为 提高我们对人类疾病代谢途径的理解的杰出选择。我们将开发一个 用于样品制备的自动化管道，可重复测量数以万计的未知特征 UHPLC-MS/MS。我们将使用野生分离株进行代谢组范围的遗传关联研究（m- GWAS）和 SEM 路径，使用部分相关性来定位路径中的未知数。未知的相关性 将通过测量这些基因突变体的 UHPLC-MS/MS 数据来测试特定途径的代谢物 途径。分子式和路径信息将成为自动化量子力学的输入 计算所有可能的结构，这将用于准确计算 NMR 化学位移 与实验数据相符。正确的结构将通过与 2D NMR 数据进行比较来验证 相同的化合物。然后，经过验证的计算结构将用于改进基于 QM 的 MS/MS 使用实验 UHPLC-MS/MS 数据进行片段预测。行政核心（AC）将是 负责协调团队的所有站点和科学活动。爱迪生教授，整个博士 项目，将指导 AC 并将利用他在不同地点管理大型团队的经验。他已经做到了 这与跨越多个研究小组和大学的许多其他大型项目一起进行。爱迪生有 与该团队的每一位成员合作，并拥有涵盖大部分领域的科学背景和兴趣 项目。我们的团队成员都参与了与我们密切相关的大型项目，因此我们将 轻松与代谢组学界及其他领域建立新的联系。爱迪生教授是创始人 东南综合代谢组学中心主任，因此与各方进行了广泛的互动 NIH 共同基金代谢组学联盟第一阶段的成员。他也是创始董事会成员 北美代谢组学协会的成员，这将使与社区的进一步联系成为可能。博士。 帕纳戈斯将担任我们的项目经理，并将协调科学团队成员之间的定期互动。 行政核心将根据所有团队成员的意见优先考虑我们研究的途径和未知因素 和美国国立卫生研究院联盟。我们将为我们的实验和实验建立并维护一个中心网站和数据库。 计算数据。

Overall: Our project combines the significant advantages of a genetic model organism, sophisticated pathway mapping tools, high-throughput and accurate quantum chemistry (QM), and state-of-the-art experimental measurements. The result will be an efficient and cost-effective new approach for unknown compound identification in metabolomics, which is one of the major limitations facing this growing field of medical science. Caenorhabditis elegans has several advantages for this study, including over 10,000 available genetic mutants, well-developed CRISPR/Cas9 technology, and a panel of over 500 wild C. elegans isolates with complete genomes. Half of C. elegans genes have homologs to human disease genes, making this model organism an outstanding choice to improve our understanding of metabolic pathways in human disease. We will develop an automated pipeline for sample preparation to reproducibly measure tens of thousands of unknown features by UHPLC-MS/MS. We will use the wild isolates to conduct metabolome-wide genetic association studies (m- GWAS), and SEM-path to locate unknowns in pathways using partial correlations. The relevance of the unknown metabolites to specific pathways will be tested by measuring UHPLC-MS/MS data from genetic mutants of those pathways. Molecular formula and pathway information will be the inputs for automated quantum mechanical calculations of all possible structures, which will be used to accurately calculate NMR chemical shifts that will be matched to experimental data. The correct structures will be validated by comparing them with 2D NMR data of the same compound. The validated computed structures will then be used to improve QM-based MS/MS fragment prediction, using the experimental UHPLC-MS/MS data. The Administrative Core (AC) will be responsible for the coordination of all sites and scientific activities of the team. Prof. Edison, PD of the entire project, will direct the AC and will use his experience in managing large teams at different sites. He has done this with a number of other large projects spanning multiple research groups and universities. Edison has collaborated with every member of this team and has scientific background and interests covering most of the project. Our team members are all involved in large projects that are closely related to ours, and thus we will easily form new connections with the metabolomics community and beyond. Prof. Edison was the founding director of the Southeast Center for Integrated Metabolomics and thus has interacted extensively with all members of phase 1 of the NIH Common Fund Metabolomics Consortium. He also is a founding board member of the Metabolomics Association of North America, which will enable further connections to the community. Dr. Panagos will be our program manager and will coordinate regular interactions between scientific team members. The Administrative Core will prioritize pathways and unknowns for our study with the input of all team members and the NIH Consortium. We will establish and maintain a center website and database for our experimental and computational data.