Michigan Compound Identification Development Cores (MCIDC)

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来系统地 在非靶向代谢组学数据的高比例特征中识别代谢物 目前被认为是未知的。通过这样做，我们将解决在以下领域的长期挑战 代谢组学并从现有和未来的代谢组学数据中增强生物学洞察力。我们的数据将极大地 为平台无关、可快速搜索的代谢物数据库做出贡献，我们开发的方法将 促进今后的化合物鉴定工作。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，我们将改进目前在我们实验室运行的软件，以帮助 将非靶向代谢组学数据中的特征注释为主要特征或伪影或退化 特征(例如，同位素、碎片、加合物、污染物)。该软件将帮助确定识别的优先顺序 在主要要素上的努力，同时允许对人工产物和退化要素进行索引和快速移除 来自未来的数据集。我们将实施一种混合搜索方法，允许未知的代谢物光谱 对具有相似结构的化合物的电子光谱和实验衍生光谱进行搜索 图案。我们期望这种方法将提高与硅光谱相比的代谢物鉴定的确定性。 独自一人。我们将把我们的数据输出贡献给国家代谢组学数据库和其他数据库。 通过MCIDC的实验核心，我们将开发和实施新颖和尖端的分析 有助于化合物鉴定的技术，并将系统地将这些技术应用于未知 代谢组学数据中的主要特征根据公众调查被确定为高度优先 代谢组学数据库。我们将用来鉴定代谢物的技术包括高分辨率串联质量 光谱(MSN)、离子迁移率光谱、高分辨率色谱方法，包括超高 加压液相色谱、样品预分离和多维分离，体内稳定 用于结构鉴定、化学衍生化、预浓缩和核磁共振分析的同位素标记， 以及(必要时)新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行 和计算核心，以及与NIH共同基金代谢组学联盟的其他成员。通过 在CIDC站点之间进行协调，并将化合物鉴定任务作为一个小组进行优先排序，我们将最大限度地 代谢组学联盟努力提高生产率和改善结果。 通过实现这些目标，我们预计我们的CIDC将对解释产生持久、统一的影响 从非靶向代谢组学产生的丰富和不断增长的数据集中获得的生物学发现。

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.