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，密歇根州化合物鉴定 开发核心（MCIDC）将使用先进的计算和实验方法， 在非靶向代谢组学数据中的高比例特征中识别代谢物， 目前被视为未知。在这样做时，我们将解决在以下领域的一个长期挑战： 代谢组学和增强从现存和未来的代谢组学数据的生物学见解。我们的数据将大大 有助于平台无关，快速搜索代谢物数据库，我们开发的方法将 为今后的化合物识别工作提供便利。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，我们将改进我们实验室目前运行的软件， 将非目标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）。这个软件将帮助优先识别 在主要特征上的努力，同时允许对伪像和退化特征进行索引并快速移除 未来的数据集。我们将实施一种“混合搜索”方法， 根据具有相似结构的化合物的计算机模拟和实验衍生光谱进行搜索 图案我们预计，与计算机光谱相比，这种方法将提高代谢物鉴别的确定性 一个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的和尖端的分析， 技术，以帮助化合物鉴定，并将系统地应用这些技术，以未知的 代谢组学数据的主要特征根据公众调查确定为高优先级 代谢组学数据库。我们将使用的技术，以确定代谢物，包括高分辨率串联质谱 质谱法（MSn）、离子迁移谱法、高分辨率色谱法（包括超高分辨率色谱法）、质谱法等。 压力液相色谱法，样品预分馏和多维分离，体内稳定 同位素标记用于结构解析、化学衍生、预浓缩，然后进行NMR分析， 和（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作， 和计算核心，以及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，并作为一个团队优先考虑化合物识别任务，我们将最大限度地 生产力和改善结果的代谢组学联盟的努力。 通过实现这些目标，我们预计，我们的CIDC将产生持久的，统一的影响，解释 非靶向代谢组学产生的丰富和不断增长的数据集的生物学发现。

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.