Novel Approaches to Spectral Prediction and Spectral Deconvolution for Metabolomics

代谢组学光谱预测和光谱反卷积的新方法

• 批准号: RGPIN-2019-05538
• 负责人: Wishart, David
• 金额: $ 5.76万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=692066
• 关键词: Novel; Approaches; Spectral; Prediction; Deconvolution

My research is focused on a field of science called metabolomics. Metabolomics involves the study of the metabolome the complete collection of metabolites found in biological systems. Metabolomics provides important insights into metabolism and biochemistry. It has led to important discoveries that are changing our understanding of disease, nutrition and ecology. These discoveries are leading to new drugs, safer foods and better environmental monitoring. Metabolomics uses advanced technologies such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy to comprehensively characterize as much of the metabolome, as rapidly as possible. Unfortunately, the current tools available for metabolomics are proving inadequate. Indeed, less than 2% of the 10,000+ signals collected in many MS-based metabolomics studies are identifiable. Furthermore, many metabolomics methods require weeks of tedious, manual work. In other words, metabolomics is not particularly comprehensive nor is it very high throughput. The limited throughput and comprehensiveness in metabolomics appears to be due to a lack of appropriate reference spectra of pure compound standards (to aid in compound identification) and the lack of automated tools for using these reference spectra to the observed spectra of biological mixtures. Unfortunately, there are insufficient resources to experimentally create or collect all the necessary reference spectra needed for the metabolomics community. For this NSERC proposal I intend to build on recent discoveries made in my lab, with regard to spectral prediction and analysis, to make metabolomics faster, more comprehensive and more useful to researchers in plant, food and environmental science. In particular, we will combine experimentally acquired data (NMR and MS) collected in my lab with several advanced computational techniques to pursue 4 research objectives. The first objective involves collecting reference NMR and MS spectra for a targeted set of ~500 natural products over a wide range of NMR and MS instruments. These experimentally collected data will allow us to pursue the remaining 3 computational objectives. These include: 1) developing fast, automated techniques to deconvolve NMR spectra from plants, foods and environmental samples; 2) using rule-based algorithms to improve the accuracy with which NMR spectra of natural products can be predicted to enable faster, more accurate compound identification and 3) creating rule-based methods to improve the speed and accuracy with which MS spectra of “modular” small molecules important in agri-bio and environmental science can be predicted, thereby improving their identification. This work will lead to new tools that should make metabolomics faster, cheaper and more comprehensive. It will allow metabolomics to be used in a wider number of disciplines, by far more users. It may also enable the migration of metabolomics into routine food analysis and environmental monitoring.

我的研究集中在一个叫做代谢组学的科学领域。代谢组学涉及对代谢组的研究，即在生物系统中发现的代谢物的完整集合。代谢组学为新陈代谢和生物化学提供了重要的见解。它带来了重要的发现，正在改变我们对疾病、营养和生态学的理解。这些发现正在催生新的药物、更安全的食品和更好的环境监测。代谢组学使用先进的技术，如质谱学(MS)和核磁共振(核磁共振)光谱，以尽可能快的速度对尽可能多的代谢体进行全面的表征。不幸的是，目前可用于代谢组学的工具被证明是不充分的。事实上，在许多基于MS的代谢组学研究中收集的10,000多个信号中，只有不到2%是可识别的。此外，许多代谢组学方法需要数周乏味的手工工作。换句话说，代谢组学并不是特别全面，也不是很高的吞吐量。代谢组学的吞吐量和全面性有限，似乎是因为缺乏纯化合物标准的适当参考光谱(以帮助化合物鉴定)，以及缺乏将这些参考光谱用于观察到的生物混合物光谱的自动化工具。不幸的是，没有足够的资源来实验性地创建或收集代谢组学社区所需的所有必要的参考光谱。对于NSERC的这项建议，我打算以我实验室在光谱预测和分析方面的最新发现为基础，使代谢组学更快、更全面，对植物、食品和环境科学的研究人员更有用。特别是，我们将结合在我的实验室收集的实验数据(核磁共振和质谱学)和几种先进的计算技术来追求4个研究目标。第一个目标涉及通过广泛的核磁共振和质谱仪收集一组目标为~500种天然产物的参考核磁共振和质谱图。这些通过实验收集的数据将使我们能够实现剩下的3个计算目标。这些措施包括：1)开发快速、自动化的技术来分解植物、食品和环境样品的核磁共振谱；2)使用基于规则的算法来提高预测天然产物的核磁共振谱的准确性，以实现更快、更准确的化合物鉴定；以及3)创建基于规则的方法来提高对农业生物和环境科学中重要的“模块化”小分子的MS谱进行预测的速度和精度，从而改进它们的识别。这项工作将带来新的工具，使代谢组学更快、更便宜、更全面。它将允许代谢组学在更多的学科中使用，到目前为止有更多的用户。它还可能使代谢组学转移到常规的食品分析和环境监测中。