The application of time domain processes for the improvement of data quality and enhanced pattern recognition in NMR based metabolomics

代谢组学是在系统生物学框架中使用的生命科学新兴方法之一，用于全球描述伴随给定基因修饰，药物干预或环境刺激的代谢变化。为此目的使用1H核磁共振（NMR）光谱学是特别有吸引力的，因为在每个样品的基础上相对便宜并且高通量。这使它成为在药物安全评估过程中监测毒理学变化的特别有用的功能性基因组工具，在药物评估过程中，药物的剂量通常远低于LD50水平（50%人群的致死剂量）。虽然溶液状态核磁共振光谱的获取可以自动化，但有几个问题阻碍了这些光谱的完全自动化分析。例如，从尿液和血浆等生物流体中获得的光谱的质量容易受到一些损害，特别是在药物中毒之后。来自药物代谢物的共振可能存在于光谱中，模糊了来自较低浓度内源性代谢物的共振。此外，血浆中的脂质颗粒如LDL、VLDL和HDL以及尿液中的蛋白质也可能模糊代谢物共振。因此，通常需要分析人员的干预，减少样品吞吐量并将变化引入模式识别分析，用于最终确定与给定毒理学干预相关的代谢概况。该项目旨在开发工具，使用用户友好和自动化的计算方法从核磁共振光谱中去除广泛的成分、相位畸变和基线偏移差异。基于连续小波变换（CWT）和贝叶斯建模框架的时域分析的使用将被研究，因为它们在我们之前的工作中已经显示出有希望的结果。特别是，连续小波变换的修改称为单一语音CWT将被检查，因为它允许有效地分离和减去不同线宽的共振。贝叶斯建模和可逆跳跃马尔可夫链蒙特卡罗模拟将有助于对信号中共振数的概率估计以及共振参数的估计，这对核磁共振实验结果的量化至关重要。上述技术的发展将使我们能够实现基于自动化时域的核磁共振谱改进和量化工具。最后，我们将与葛兰素史克公司的代谢分析小组合作，评估这些工具对药物毒性研究中常见的模式识别任务的影响。正在开发的工具最初将专门用于通过增强代谢组学/代谢组学研究来改进药物发现过程中的毒性预测，但也将在代谢组学和生物信息学中有更广泛的应用。因此，该项目将有可能协助广泛的代谢组学项目，包括制药、食品和医疗行业的项目。

Metabolomics is one of the emerging approaches in Life Sciences used in a Systems Biology framework to globally profile the changes in metabolism which accompany a given genetic modification, drug intervention or environmental stimulation. The use of 1H Nuclear Magnetic Resonance (NMR) spectroscopy for this purpose is particularly attractive being relatively cheap on a per sample basis as well as high-throughput. This has made it a particularly useful functional genomic tool for monitoring toxicology changes during the drug safety assessment process, where drugs are dosed typically at doses well below the LD50 level (the lethal dose for 50% of a population). While the acquisition of solution state NMR spectra can be automated, there are several issues that prevent the analysis of these spectra being fully automated. The quality of the spectra obtained from biofluids such as urine and blood plasma, for example, is prone to a number of impairments, especially following drug toxicity. Resonances from drug metabolites may be present in the spectra, obscuring the resonances from lower concentration endogenous metabolites. Furthermore, lipid particles such as LDL, VLDL and HDL in blood plasma and protein in urine may also obscure metabolite resonances. Therefore, the intervention of the analyst is normally required, decreasing sample throughput and introducing variation into the pattern recognition analysis which is used to final determine a metabolic profile associated with a given toxicological intervention. This project aims to develop tools to remove broad components, phase distortions and baseline offset differences from NMR spectra using computational methods that are user friendly and automated. The use of time-domain analysis based on Continuous Wavelet Transform (CWT) and Bayesian modelling framework will be investigated as they have shown promising results in our previous work. In particular, a modification of the Continuous Wavelet Transform known as a Single Voice CWT will be examined as it allows effective separation and subtraction of resonances with different line widths. Bayesian modelling and Reversible Jump Markov Chain Monte Carlo simulation will facilitate the probabilistic estimation of the number of resonances in the signal as well as the estimation of the parameters of the resonances which are critical for quantification of the NMR experiment results. Development of the aforementioned techniques will lead us to an automated time-domain based tool for NMR spectral improvement and quantification. Finally, in conjunction with the metabolic profiling group at GlaxoSmithKline, assessment of the effect of these tools on the pattern recognition tasks that are common in drug toxicity studies will be performed. The tools being developed will initially be tailored specifically to improving toxicity prediction in the drug discovery process through enhanced metabolomic/ metabonomic studies, but will also have a far broader application in metabolomics and bioinformatics. Thus, the project will potentially assist in a broad range of metabolomic projects including those in the pharmaceutical, food and medical industries.