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，尤其是那些互补的质谱（MS）和核磁共振光谱（NMR）的技术，可以快速分析代表数百个代谢物的数千个峰。然而，对于大规模代谢组学应用程序的主要障碍，最值得注意的是：更高的吞吐量，具有强大的代谢物分配和定量；鉴定未知代谢物；分析低丰度/不稳定代谢产物；代谢网络和调节的重建。在这里，我们提出了一种综合方法，该方法使用关键代谢物官能团的化学选择性（CS）标记来提高代谢物鉴定的速度和准确性并增强检测。这种标记的代谢物本质上可以通过稳定的同位素编码进行多重分析和可靠的相对定量。这种方法将加速分析吞吐量，同时扩大分析物的类别，包括代谢富集的同位素学，远远超出当前限制。我们将通过以下特定目标实现目标：SA1。为了开发用于通过靶向官能团标记代谢产物的CS探针：将开发基于疏水的Quaternary铵（QA）的CS探针，用于在代谢物中标记羰基，氨基，硫醇和二醇官能团（FG），在代谢物中进行了优化，可直接输注FT- ICR-MS检测和分配； SA2。为了开发一组同位素编码的CS探针，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探针将促进13C编辑的代谢物的综合结构分析，并通过13C编辑的2d NMR和FT-ICR-MS和FT-ICR-MS，以及通过FT-MS，以及通过FT-MSS通过FT-MSS进行了综合分析； SA3。为了开发用于大规模CS添加的代谢物分配和途径重建的基于Web的软件：将基于同位素群，FG，FG，＆Molecular Formula（MF）开发用于鲁棒自动化的MS分配算法。 NMR衍生的子结构和MF将与MS数据结合使用，用于代谢物同位素和未知数的半自动分配。原子分辨的人类代谢数据库将进行完善，并根据标记的代谢物概况开发用于途径重建的工具； 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.我们的长期目标是破译人类疾病代谢网络，以发现药物发现和早期诊断。