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: 8687656
• 负责人: Teresa Whei-Mei Fan
• 金额: $ 43.94万
• 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/8687656
• 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，特别是那些将质谱（MS）和核磁共振波谱（NMR）相结合的技术，能够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仍然存在关键障碍，最值得注意的是：通过强大的代谢物分配和量化实现更高的通量；鉴定未知代谢物；低丰度/不稳定代谢物的分析；以及代谢网络和调节的重建。在这里，我们提出了一种综合方法，使用关键代谢物功能组的化学选择性（CS）标记来提高代谢物识别的速度和准确性并增强检测。这种标记的代谢物本质上适合多重分析和通过稳定同位素编码进行可靠的相对定量。这种方法将加速分析通量，同时拓宽分析物的类别，包括代谢富集的同位素体，远远超出目前的限制。我们将通过以下具体目标来实现我们的目标：SA1。开发用于通过靶向官能团标记代谢物的 CS 探针：将开发基于疏水季铵 (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)，并针对直接输注 FT-ICR 进行优化-MS检测和分配； SA2。开发一套同位素编码的 CS 探针，用于通过 FT-ICR-MS 和 NMR 进行代谢物鉴定和多重定量：开发 13C 编码的 QA-CS 探针将有助于通过 13C 编辑的 2D NMR 和 FT-ICR 对代谢物进行综合结构分析MS 和 FT-ICR-MS 多重分析； SA3。开发基于网络的软件，用于大规模 CS 加合代谢物分配和通路重建：将基于同位素体团、FG 谱和分子式 (MF) 开发代谢物和标记同位素体的强大自动 MS 分配算法。 NMR 衍生的子结构和 MF 将与 MS 数据相结合，以半自动分配代谢物同位素异构体和未知物。将完善原子解析的人类代谢数据库，并开发基于标记代谢物谱的途径重建工具； SA4。展示三个基础和转化项目中的综合 CS 分析和生物化学信息学方法：将使用新开发的稳定同位素解析代谢组学 (SIRM) 来绘制由人类肺癌和肾癌中致癌基因缺陷或酶缺失驱动的代谢重编程 (SIRM) CS 标记化学和自动分配工具。我们的长期目标是破译人类疾病代谢网络以进行药物发现和早期诊断。