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Advanced NMR, computational, and hybrid methods for metabolomics

代谢组学的先进 NMR、计算和混合方法

基本信息

批准号：
10625344
负责人：
Rafael Bruschweiler
金额：
$ 37.36万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-05-31
项目状态：
未结题

项目摘要

PROJECT SUMMARY Advanced NMR, computational, and hybrid methods for metabolomics The emergence of metabolomics promises to revolutionize the understanding of biological systems from a metabolite perspective with important implications for the diagnosis and treatment of disease. Despite recent progress in the profiling and quantitation of complex metabolomics mixtures, such as urine, serum, and cancer tissue, by NMR and mass spectrometry, important methodological challenges remain. The proposed research program aims at addressing some of these urgent challenges by developing new approaches to integrate NMR with cheminformatics and mass spectrometry for the de novo characterization of unknown metabolites and their chemical and structural motifs, by developing and maintaining advanced webservers and metabolomics databases (COLMAR) for the reliable, efficient, and user-friendly analysis of highly complex metabolomics spectroscopic data, by developing automated tools for the spectral deconvolution and quantitation of complex metabolomics mixtures with synergies for protein NMR applications, and by developing new hybrid approaches to bridge metabolomics with other Omics fields. The proposed research will promote the dissemination of metabolomics as a powerful, versatile, and manageable tool to the biomedical scientific community with synergistic benefits also for protein science. These activities are expected to lead to a better understanding of a broad range of biological questions from a metabolite perspective for the benefit of human health.

项目摘要代谢组学的先进NMR、计算和混合方法代谢组学的出现有望彻底改变对生物系统的理解从代谢物的角度来看，对疾病的诊断和治疗具有重要意义。尽管最近在复杂代谢组学混合物的分析和定量方面取得了进展，尿液、血清和癌组织的核磁共振和质谱分析，这是重要的方法学挑战保持。拟议的研究计划旨在通过以下方式解决其中一些紧迫的挑战：开发新的方法，将NMR与化学信息学和质谱学结合起来，未知代谢物及其化学和结构基序的从头表征，通过开发并维护先进的网络服务器和代谢组学数据库（科尔马），高效，用户友好的分析高度复杂的代谢组学光谱数据，通过开发用于复杂代谢组学混合物的光谱去卷积和定量的自动化工具，蛋白质NMR应用的协同作用，并通过开发新的混合方法，代谢组学与其他组学领域。拟议的研究将促进传播代谢组学作为一个强大的，多功能的，可管理的工具，以生物医学科学界，对蛋白质科学也有协同效益。预计这些活动将导致更好的从代谢物的角度理解广泛的生物学问题，人体健康