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Michigan Compound Identification Development Cores (MCIDC)

密歇根化合物鉴定开发核心 (MCIDC)

基本信息

批准号：
10257642
负责人：
CHARLES ROBERT EVANS
金额：
$ 5万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10257642
关键词：
Address Adoption Biochemical Pathway Biological Biology Blood Cataloging Catalogs 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 warehouse 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

项目摘要

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.

整体-项目摘要