Integrated Chemoselective and Informatic Platform for Large-Scale Metabolomics

用于大规模代谢组学的集成化学选择性和信息平台

• 批准号: 8914844
• 负责人: Teresa Whei-Mei Fan
• 金额: $ 44.36万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-26 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8914844
• 关键词: A549; Aldehydes; Algorithms; Ammonium; Binding; Biochemical Pathway; Cell Extracts; Chemistry; Clinical; Computer software; Crude Extracts; Data; Data Analyses; Databases; Defect; Detection; Development; Diagnosis; Early Diagnosis; Enzymes; Fourier transform ion cyclotron resonance; Fumarate Hydratase; Future; Gene Expression; Genes; Glycols; Goals; Hereditary Leiomyomatosis and Renal Cell Cancer; Human; Human Cell Line; Informatics; Infusion procedures; Isomerism; Isotopes; Ketones; Label; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Mass Spectrum Analysis; Metabolic; Molecular; Molecular Profiling; Mus; Mutation; NMR Spectroscopy; Nature; Network-based; Oncogenic; Online Systems; Pathway interactions; Pattern; Plasma; Reaction; Regulation; Relative (related person); Renal carcinoma; Role; Sampling; Speed; Sulfhydryl Compounds; System; Technology; Tissues; Tracer; Transgenic Mice; adduct; base; cancer cell; chemical property; design; drug discovery; functional group; human disease; improved; ionization; malic enzyme; metabolomics; nano-electrospray; novel; outcome forecast; programs; protein expression; reconstruction; research study; software development; stable isotope; tandem mass spectrometry; tool; ultra high resolution

DESCRIPTION (provided by applicant): Recent advances in metabolomics technologies, especially those that combine the complementarily of mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) enables rapid analysis of thousands of peaks representing hundreds of metabolites. Yet, key barriers remain for large-scale metabolomics applications, most notably: even higher throughput with robust metabolite assignment and quantification; identification of unknown metabolites; analysis of low abundance/labile metabolites; and reconstruction of metabolic networks and regulation. Here, we propose an integrated approach that uses chemoselective (CS) tagging of key metabolite functional groups to boost the speed and accuracy of metabolite identification and enhance detection. Such tagged metabolites are inherently amenable for multiplexed analysis and reliable relative quantification via stable isotope encoding. This approach will accelerate analytical throughput while widening the classes of analytes, including metabolically enriched isotopologues, far beyond current limits. We will achieve our goals via the following specific aims: SA1. To develop CS probes for tagging metabolites by targeting functional groups: Hydrophobic quaternary ammonium (QA)-based CS probes will be developed for tagging carbonyl, amino, thiol, & diol functional groups (FG) in metabolites, optimized for direct infusion FT- ICR-MS detection & assignment; SA2. To develop a set of isotope-encoded CS probes for metabolite identification and multiplexed quantification by FT-ICR-MS and NMR: Developing 13C-encoded QA-CS probes will facilitate integrated structural analysis of metabolites by 13C edited 2D NMR and FT-ICR-MS, & multiplexed analysis by FT-ICR-MS; SA3. To develop web-based software for large-scale CS-adducted metabolite assignment & pathway reconstruction: Algorithm for robust automated MS assignment of metabolite & labeled isotopologues will be developed based on isotopologue cliques, FG profile, & molecular formula (MF). NMR- derived substructure & MF will be combined with MS data for semi-automated assignment of metabolite isotopomers & unknowns. Atom-resolved human metabolic database will be refined and tools developed for pathway reconstruction based on labeled metabolite profiles; SA4. To demonstrate the integrated CS profiling and biochemo-informatic approach in three basic and translational projects: Metabolic reprogramming driven by oncogenic gene defects or enzyme deletion in human lung and kidney cancers will be mapped using Stable Isotope-Resolved Metabolomics (SIRM) enhanced by newly developed CS tagging chemistry and automated assignment tools. Our long-term goal is to decipher human disease metabolic networks for drug discovery & early diagnosis.

描述（由申请人提供）：代谢组学技术的最新进展，特别是那些结合质谱（MS）和核磁共振波谱（NMR）互补的技术，可以快速分析代表数百种代谢物的数千个峰。然而，大规模代谢组学应用的关键障碍仍然存在，最明显的是：更高的通量与强大的代谢物分配和量化；未知代谢物的鉴定；低丰度/不稳定代谢物分析；以及代谢网络和调控的重建。在此，我们提出了一种综合方法，使用化学选择性（CS）标记关键代谢物功能群，以提高代谢物鉴定的速度和准确性，并提高检测。这种标记的代谢物本质上可通过稳定的同位素编码进行多路分析和可靠的相对定量。这种方法将加快分析通量，同时扩大分析物的种类，包括代谢富集的同位素，远远超出目前的限制。我们将通过以下具体目标实现我们的目标：开发基于疏水季铵（QA）的CS探针，用于标记代谢物中的羰基、氨基、硫醇和二醇官能团（FG），并优化为直接输注FT- ICR-MS检测和分配；SA2。开发一套同位素编码的CS探针，用于代谢物鉴定和FT-ICR-MS和NMR的多重定量：开发13C编码的QA-CS探针，将促进13C编辑的2D NMR和FT-ICR-MS对代谢物的综合结构分析，以及FT-ICR-MS的多重分析；SA3。开发基于网络的大规模cs -内合代谢物分配和途径重建软件：将基于同位素团、FG剖面和分子式（MF）开发代谢物和标记同位素的鲁棒自动MS分配算法。核磁共振衍生的亚结构和MF将与质谱数据相结合，用于代谢物同位素和未知物的半自动分配。原子解析的人类代谢数据库将被完善，基于标记代谢物谱的途径重建工具将被开发；SA4。为了在三个基础和转化项目中展示集成的CS分析和生物化学信息学方法：人类肺癌和肾癌中由致癌基因缺陷或酶缺失驱动的代谢重编程将使用稳定同位素分解代谢组学（SIRM）进行绘制，该方法由新开发的CS标记化学和自动分配工具增强。我们的长期目标是破译人类疾病代谢网络，用于药物发现和早期诊断。