Michigan Compound Identification Development Cores (MCIDC)

密歇根化合物鉴定开发核心 (MCIDC)

• 批准号: 10183251
• 负责人: CHARLES ROBERT EVANS
• 金额: $ 84.57万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10183251
• 关键词: Address; Adoption; Biochemical Pathway; Biological; Biology; Blood; Cataloging; Chemicals; Clinical; Communication; Communities; Computer software; Data; Data Set; Databases; Detection; Development; Disease; Ensure; Environmental Exposure; Fractionation; Funding; Future; Goals; High Pressure Liquid Chromatography; Human; Hybrids; Isotopes; Libraries; Mass Fragmentography; Methods; Michigan; Molecular; Morphologic artifacts; Output; Pathway interactions; Productivity; Research; Research Design; Research Personnel; Resolution; Sampling; Scientist; Site; Spectrometry; Stable Isotope Labeling; Standardization; Structure; Surveys; Techniques; Technology; Testing; Time; Tissues; United States National Institutes of Health; Work; adduct; base; biomarker discovery; data repository; human disease; improved; improved outcome; in silico; in vivo; indexing; insight; instrumentation; interest; ion mobility; member; metabolomics; multimodality; novel; operation; programs; repository; tandem mass spectrometry; tool; ultra high pressure

Overall - Project Summary As a member of the NIH Common Funds Metabolomics Consortium, the Michigan Compound Identification Development Core (MCIDC) will using cutting-edge computational and experimental methods to systematically identify metabolites among the high proportion of features in untargeted metabolomics data which are presently considered unknown. In so doing, we will address a long-standing challenge in the field of metabolomics and enhance biological insights from extant and future metabolomics data. Our data will greatly contribute to platform-agnostic, rapidly-searchable metabolite databases, and the methods we develop will facilitate future compound identification efforts. We will achieve these goals by carrying out the following aims: Through the computational core of MCIDC, we will refine software currently operational in our lab that aids in annotation of features in untargeted metabolomics data as either primary features or as artifacts or degenerate features (e.g., isotopes, fragments, adducts, contaminants). This software will help prioritize identification efforts on primary features, while allowing artifacts and degenerate features to be indexed and rapidly removed from future data sets. We will implement a `hybrid search' approach that will allow unknown metabolite spectra to be searched against both in-silico and experimentally-derived spectra of compounds with similar structural motifs. We expect this approach will improve certainty of metabolite identification compared to in-silico spectra alone. We will contribute our data output to the National Metabolomics Data Repository and other databases. Through the experimental core of MCIDC, we will develop and implement novel and cutting-edge analytical technologies to aid in compound identification, and will systematically apply these techniques to unknown primary features in metabolomics data determined to be of high priority based on survey of public metabolomics databases. Techniques we will use to identify metabolites include high-resolution tandem mass spectrometry (MSn), ion mobility spectrometry, high-resolution chromatographic methods including ultra-high pressure liquid chromatography, sample pre-fractionation and multidimensional separations, in-vivo stable isotope labeling for structural elucidation, chemical derivatization, pre-concentration followed by NMR analysis, and (when necessary) synthesis and characterization of novel metabolite standards. Finally, through our administrative core, we will ensure coordinated operation between our own experimental and computational cores, and with other members of the NIH common funds metabolomics consortium. By coordinating between CIDC sites and prioritizing compound identification tasks as a group, we will maximize productivity and improve outcome of the metabolomics consortium efforts. By carrying out these aims, we anticipate that our CIDC will yield a lasting, unifying impact on interpretation of biological findings from the rich and growing datasets yielded by untargeted metabolomics.

总体 - 项目摘要 作为 NIH 共同基金代谢组学联盟的成员，密歇根化合物鉴定 开发核心（MCIDC）将使用尖端的计算和实验方法系统地 在非目标代谢组学数据的高比例特征中识别代谢物，这些特征是 目前认为未知。通过这样做，我们将解决该领域长期存在的挑战 代谢组学并增强对现有和未来代谢组学数据的生物学见解。我们的数据将大大 为与平台无关、可快速搜索的代谢物数据库做出贡献，我们开发的方法将 促进未来的化合物鉴定工作。我们将通过实现以下目标来实现这些目标： 通过 MCIDC 的计算核心，我们将改进实验室当前运行的软件，以帮助 将非目标代谢组学数据中的特征注释为主要特征、伪影或简并特征 特征（例如同位素、碎片、加合物、污染物）。该软件将有助于优先识别 致力于主要功能，同时允许对伪影和退化功能进行索引并快速删除 来自未来的数据集。我们将实施“混合搜索”方法，允许未知的代谢物光谱 可以根据具有相似结构的化合物的计算机和实验衍生光谱进行搜索 主题。与计算机光谱相比，我们预计这种方法将提高代谢物鉴定的确定性 独自的。我们将把我们的数据输出贡献给国家代谢组学数据存储库和其他数据库。 通过MCIDC的实验核心，我们将开发和实施新颖、前沿的分析方法 技术来帮助化合物鉴定，并将系统地将这些技术应用于未知的 根据公众调查确定代谢组学数据的主要特征是高度优先的 代谢组学数据库。我们将用于鉴定代谢物的技术包括高分辨率串联质量 光谱分析法 (MSn)、离子迁移光谱分析法、高分辨率色谱方法，包括超高分辨色谱法 压力液相色谱、样品预分级和多维分离、体内稳定 用于结构阐明、化学衍生、预浓缩和核磁共振分析的同位素标记， 以及（必要时）新型代谢物标准品的合成和表征。 最后，通过我们的行政核心，我们将确保我们自己的实验之间的协调运作。 和计算核心，以及 NIH 共同基金代谢组学联盟的其他成员。经过 通过协调 CIDC 站点并作为一个整体确定化合物鉴定任务的优先顺序，我们将最大限度地 生产力并改善代谢组学联盟努力的成果。 通过实现这些目标，我们预计我们的 CIDC 将对解释世界观产生持久、统一的影响。 来自非目标代谢组学产生的丰富且不断增长的数据集的生物学发现。