Metabolic Patterns in 1H NMR Spectra of Biofluids

描述(申请人提供)：核磁共振(核磁共振)被广泛用于研究生物样品的代谢状态。它的非侵入性和检测多种化合物的能力使其能够在一段时间内以高度详细的方式跟踪复杂的生化过程，从而使其用于“代谢组学”或表征生命系统对病理生理刺激或基因修饰的代谢物反应。它从数百个相关的样本中产生具有大量共振的光谱。模式识别技术在这里的使用受到两个因素的限制：1)由温度、pH等的微小差异引起的频率的微小系统变化；2)使用主成分分析(PCA)来识别数据集中变化的数学分量，而不是动态变化的真实物理来源。频率的变化导致光谱只有几百个点，而使用主成分分析来识别数据集中的潜在模式使找到有物理意义的新陈代谢模式变得困难。在这本R33中，我们通过对高分辨率核磁共振数据进行稳健的预处理，然后应用贝叶斯频谱分解(BSD)和约束非负矩阵分解(CNMF)来揭示描述系统变化的潜在代谢模式，从而解决了这些问题。有五个主要的具体目标 具体目标I：开发一种半自动的尿液高分辨~1H核磁共振谱的预处理程序，以提高谱质量，使PR程序能够识别任何潜在的生物化学相关的谱型。 具体目标二：实施贝叶斯频谱分解(BSD)，作为一种实用、易用的频谱分析程序。 具体目标三.实现约束非负矩阵分解(CNMF)作为一种实用的、易于使用的频谱分析程序 具体目标IV.在Linux集群上将BSD实现为并行代码。 应用BSD和cNMF分析大鼠和小鼠毒理学研究中获得的一系列尿液的核磁共振谱。

DESCRIPTION (provided by applicant): 1H nuclear magnetic resonance (NMR) is widely used to investigate the metabolic state of biological samples. Its non-invasive nature and ability to detect multiple compounds allow it to follow complex biochemical processes over time at a high level of detail leading to its use in "metabolomics" or characterization of the metabolite response of living systems to pathophysiological stimuli or genetic modification. It generates spectra with a large number of resonances from hundreds of related samples. The use of pattern recognition techniques here have been restricted by two factors: 1) small systematic variations in frequency caused by small differences in temperature, pH, etc. and 2) use of principal component analysis (PCA) for the identification of the mathematical components of the variation in the dataset, rather then true physical sources for dynamic changes. The variations in frequency have led to "binning" the spectra to only a few hundred points while the use of PCA to identify underlying patterns in the datasets makes finding physically meaningful metabolic patterns hard. In this R33 we address these problems by robust pretreatment of high resolution NMR data and then applying Bayesian Spectral Decomposition (BSD) and constrained Non-negative Matrix Factorization (cNMF) to uncover the underlying metabolic patterns that describe the change in the system. There are five main Specific Aims Specific Aim I: Develop a semi-automated preprocessing procedure for series of high resolution 1H NMR spectra of urine to improve spectral quality to enable PR procedures to identify any underlying biochemically relevant spectral patterns. Specific Aim II: Implement Bayesian Spectral Decomposition (BSD) as a practical easy-to-use spectral analysis procedure. Specific Aim III. Implement constrained Non-negative Matrix Factorization (cNMF) as a practical easy-to-use spectral analysis procedure Specific Aim IV. Implement BSD as parallel code on a Linux cluster. Specific Aim V. Apply BSD and cNMF to analyze a series of NMR spectra of urine acquired from toxicology studies of rats and mice that have been preprocessed by the techniques of Specific Aim I.