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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=744864
• 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.

我的研究专注于一个名为代谢组学的科学领域。代谢组学涉及对代谢组的研究 - 在生物系统中发现的代谢物的完整收集。代谢组学为代谢和生物化学提供了重要的见解。这导致了重要的发现，这些发现正在改变我们对疾病，营养和生态学的理解。这些发现导致新药，更安全的食物和更好的环境监测。代谢组学使用高级技术，例如质谱（MS）和核磁共振（NMR）光谱法，以全面地表征尽可能多的代谢组。不幸的是，当前可用于代谢组学的工具证明是不足的。实际上，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％是可以识别的。此外，许多代谢组学方法都需要数周的繁琐的手动工作。换句话说，代谢组学不是特别全面，也不是很高的吞吐量。代谢组学的有限吞吐量和全面性似乎是由于缺乏纯复合标准的适当参考光谱（有助于复合鉴定）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创建或收集代谢组学社区所需的所有必要参考光谱。对于这个Nserc的建议，我打算建立在我的实验室中最新发现的基础上，在光谱预测和分析方面，使代谢组学更快，更全面，更有用，对植物，食品和环境科学领域的研究人员更有用。特别是，我们将结合我实验室中收集的实验获得的数据（NMR和MS）与几种高级计算技术来追求4个研究目标。第一个目标涉及在广泛的NMR和MS仪器中收集〜500个天然产品的参考NMR和MS光谱。这些实验收集的数据将使我们能够追求其余3个计算目标。其中包括：1）从植物，食品和环境样品中开发快速自动化的技术来解析NMR光谱； 2）使用基于规则的算法提高天然产品的NMR光谱可以预测可以更快，更准确的化合物识别和3）创建基于规则的方法，以提高“模块化”小分子在Agri-Bio和环境科学中重要的“模块化”小分子的速度和准确性，从而可以预测其认同，从而改善他们的认同。这项工作将导致新工具，该工具应该使代谢组学更快，更便宜，更全面。它将允许代谢组学用于更多的学科，以及迄今为止更多的用户。它还可以使代谢组学迁移到常规的食物分析和环境监测中。