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.

我的研究集中在一个叫代谢组学的科学领域。代谢组学涉及代谢组学的研究-在生物系统中发现的代谢产物的完整集合。代谢组学为代谢和生物化学提供了重要的见解。它带来了重要的发现，改变了我们对疾病、营养和生态的理解。这些发现正在导致新的药物，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（MS）和核磁共振（NMR）光谱，以尽可能快地全面表征尽可能多的代谢组。不幸的是，目前可用于代谢组学的工具被证明是不够的。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繁琐的手工工作。换句话说，代谢组学不是特别全面，也不是非常高的通量。代谢组学中的有限通量和全面性似乎是由于缺乏纯化合物标准品的适当参考光谱（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。对于这个NSERC的建议，我打算建立在我的实验室最近的发现，关于光谱预测和分析，使代谢组学更快，更全面，更有用的研究人员在植物，食品和环境科学。特别是，我们将结合联合收割机实验获得的数据（NMR和MS）在我的实验室收集了几个先进的计算技术，追求4个研究目标。第一个目标是在广泛的NMR和MS仪器上收集约500种天然产物的参考NMR和MS光谱。这些实验收集的数据将使我们能够追求剩下的3个计算目标。其中包括：1）开发快速、自动化的技术来对来自植物、食品和环境样品的NMR光谱进行去卷积; 2）使用基于规则的算法来提高可以预测天然产物的NMR谱的准确度，以使得能够更快地，更准确的化合物鉴定和3）创建基于规则的方法，以提高速度和准确性，其中“模块化”小分子的MS光谱在农业中很重要，生物和环境科学可以预测，从而提高他们的识别。这项工作将导致新的工具，使代谢组学更快，更便宜，更全面。它将允许代谢组学被更多的用户用于更广泛的学科。它还可以使代谢组学迁移到常规食品分析和环境监测中。