Automated metabolite identification and quantification using J-resolved NMR spectroscopy

我们将开发免费的、可公开访问的软件，这些软件可以通过网络进行评估，以帮助测量生物样本中自然产生的化学物质（称为代谢物）的数量。具体来说，该软件将分析使用一种称为核磁共振（NMR）光谱学的工具收集的数据，并将明确确定存在哪些代谢物并测量其浓度。这种被称为代谢组学的研究最近继基因组学和蛋白质组学的成功之后发展成为一个重要的国际研究领域。代谢组学研究从大学的基础研究到工业应用研究，包括广泛的应用，如帮助科学家研究除草剂如何改变植物的生物化学；描述医院中健康患者和患病患者的代谢差异；研究环境中的动物如何应对污染。迄今为止，大多数代谢组学研究都比较了两种或两种以上生物样本中代谢物的总体模式或指纹图谱。尽管这可以提供这些样本之间是否存在总体代谢差异的重要信息，但它对这些样本之间的确切差异提供的信息很少，特别是样品之间个体代谢物的数量如何变化。我们提出的方法是向前迈出的重要一步，因为它们将使代谢物的明确鉴定和准确定量成为可能。这很重要，而且实际上是必要的，原因如下：-它允许对生物过程的机制有新的认识；例如，了解疾病的机制可以帮助开发新药，并使患者对这些药物的反应得以监测。-它允许代谢物的变化与基因表达（转录组学）和蛋白质水平（蛋白质组学）的变化相结合/这是系统生物学的目标/提供更完整的生物有机体描述。-它允许生物样本根据其组成部分真正表征，而不仅仅是整体代谢指纹。这反过来又允许NMR数据与使用其他技术获得的测量结果进行比较。我们研究的具体目标是开发计算机软件，可以自动分析从核磁共振光谱仪收集的代谢物数据，特别是从二维（2D） j分辨率核磁共振数据集。这种类型的核磁共振实验在代谢组学中迅速普及。由于测量结果以2D格式显示，代谢物产生的所有数百个峰都显着分散，最大限度地减少了它们的重叠（这是传统一维NMR实验中的一个主要问题，因为不可能对代谢物进行明确的鉴定和量化）。我们的新分析将产生一份代谢物及其浓度的清单。除了这项工作，我们还将构建一个包含300种纯代谢物的二维j分辨率NMR数据集的库。这一重要资源对我们的分析方法至关重要，将受到国际代谢组学界的广泛重视（参见伦敦帝国学院和加州大学戴维斯分校的来信），并将得到尽可能广泛的传播，因为它将被存放在世界上最大的代谢物光谱公共数据库中（参见美国麦迪逊-威斯康辛大学的来信）。这个核磁共振代谢物库也将被安置在伯明翰，因为它形成了分析二维核磁共振数据的软件包的关键组成部分。因此，我们建议在伯明翰实现一个数据库来存储NMR库。最终的结果将是一个用户友好的软件包，它将免费提供给科学界，并将大大受益。

We will develop free, publicly accessible software that can be assessed via the web to help measure the amounts of naturally occurring chemicals (called metabolites) in a biological sample. Specifically, the software will analyse data collected using a tool called nuclear magnetic resonance (NMR) spectroscopy, and will determine unambiguously which metabolites are present and will measure their concentrations. This type of study / called metabolomics / has recently grown into a major international field of research following on from the success of genomics and proteomics. Metabolomic studies span from basic research in universities to applied studies in industry, including wide ranging applications such as helping scientists to investigate how herbicides alter the biochemistry of plants; characterising the differences between the metabolism of healthy and diseased patients in a hospital; and investigating how animals in the environment cope with pollution. To date, most metabolomics studies have compared the overall patterns or fingerprints of the metabolites between two or more types of biological samples. Although this can provide important information as to whether overall metabolic differences exist between these samples, it provides very little insight into precisely how these samples differ, in particular how the amount of the individual metabolites vary between the samples. Our proposed methods are an important step forward as they will enable the unambiguous identification and accurate quantification of metabolites. This is important and in fact necessary for several reasons: - It allows new insight into the mechanism of biological processes; e.g., understanding the mechanism of a disease can help to develop new drugs and enable the response of patients to these drugs to be monitored. - It allows changes in the metabolites to be integrated with changes in gene expression (transcriptomics) and protein levels (proteomics) / which is a goal of systems biology / to provide a more complete description of a biological organism. - It allows a biological sample to be truly characterised in terms of its component parts, not just an overall metabolic fingerprint. This in turn allows the NMR data to be compared with measurements obtained using other techniques. The specific objectives of our study are to develop computer software that can automatically analyse metabolite data collected from an NMR spectrometer, specifically from a 2-dimensional (2D) J-resolved NMR dataset. This type of NMR experiment is rapidly gaining in popularity in metabolomics. Since the measurements are displayed in a 2D format, all the hundreds of peaks that arise from the metabolites are spread out significantly, minimising their overlap (which is a major problem in traditional one-dimensional NMR experiments for which unambiguous metabolite identification and quantification is impossible). Our new analysis will produce a list of the metabolites present and their concentrations. Alongside this work we will construct a library of 2D J-resolved NMR datasets of 300 pure metabolites. This important resource is crucial for our analysis method, will be widely valued by the international metabolomics community (see letters from Imperial College London and the University of California-Davis), and will receive the widest possible dissemination as it will be housed in the largest public database of metabolite spectra in the world (see letter from University of Madison-Wisconsin, US). This NMR metabolite library will also be housed at Birmingham, since it forms a critical component of the software package for analysing the 2D NMR data. Therefore we propose to implement a database at Birmingham to store the NMR library. The final outcome will be a user-friendly software package that will be freely available to, and will significantly benefit, the scientific community.