Admin-Core

总体而言：我们的项目结合了遗传模式生物的显著优势，复杂的途径 映射工具，高通量和准确的量子化学（QM），以及最先进的实验 测量.这一结果将是一种高效、低成本的未知化合物的新方法 代谢组学中的鉴定，这是医学科学这一不断发展的领域面临的主要限制之一。 秀丽隐杆线虫对这项研究有几个优势，包括超过10,000个可用的遗传突变体， 成熟的CRISPR/Cas9技术，以及一组超过500种野生C.线虫分离株， 基因组C的一半。线虫的基因与人类疾病基因有同源性，这使得这种模式生物成为 这是一个很好的选择，可以提高我们对人类疾病代谢途径的理解。我们将开发一个 用于样品制备的自动化流水线，可重复测量数万个未知特征， 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-1）。 GWAS）和SEM-路径，以使用偏相关来定位通路中的未知数。未知的相关性 将通过测量来自这些代谢物的遗传突变体的UHPLC-MS/MS数据来检测代谢物对特定途径的影响。 途径。分子式和途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， 与实验数据吻合。将通过将它们与以下化合物的2D NMR数据进行比较来验证正确的结构： 相同的化合物。验证的计算结构将用于改进基于质量管理的MS/MS 片段预测，使用实验UHPLC-MS/MS数据。行政核心（AC） 负责协调所有场地和团队的科学活动。Edison教授，整个 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。他所做 这与许多其他大型项目跨越多个研究小组和大学。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 很容易与代谢组学社区及其他领域建立新的联系。爱迪生教授是 东南综合代谢组学中心主任，因此与所有 NIH Common Fund Metabolomics Consortium的第一阶段成员。他也是创始董事会成员 北美代谢组学协会，这将使进一步连接到社区。博士 Panagos将是我们的项目经理，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，对我们研究的途径和未知因素进行优先排序 和NIH联盟。我们将建立和维护一个中心网站和数据库，为我们的实验和 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.