Discovery of discriminative LC-MS and 1H NMR metabolomics markers

Geoffrey T. Gipson

doi:10.17918/etd-2766

There is a growing trend to look for novel markers of altered phenotype that are not associated with existing biological knowledge. This exploratory approach has led to greater emphasis on generating and analyzing large amounts of data simultaneously. Discovery of metabolic markers through analysis of non-targeted, high- throughput data is a challenging, time-consuming process. Two of the most popular analytical techniques in metabolic profiling are 1H Nuclear Magnetic Resonance (NMR) spectroscopy and Liquid Chromatography (LC) -Mass Spectrometry (MS). There are many challenges associated with the interpretation of these complex metabolomic datasets and automated methods are critical for extracting biologically meaningful information from them. This work describes the development and application of several novel approaches for the analysis and interpretation of NMR and LC-MS data. A weighted, constrained least-squares algorithm which uses a linear mixture of reference standard data to model complex urine NMR spectra is discussed. This method was evaluated through applications on simulated and experimental datasets. The evaluation of this method suggests that the weighted least-squares approach is effective for identifying biochemical discriminators of varying physiological states. Next, a method for clustering MS instrumental artifacts and a stochastic local search algorithm for the automated assignment of large, complex MS-based metabolomic datasets is presented. Instrumental clusters, peaks grouped together by shared peak shape in the temporal domain, serve as a guide for the number of assignments necessary to completely explain a given dataset. Mass only assignments are then refined through the intersection of peak correlation pairs with a database of biochemically relevant interaction pairs. Further refinement is achieved through a stochastic local search optimization algorithm that selects individual assignments for each instrumental cluster. The algorithm works by choosing the peak assignment that maximally explains the connectivity of a given cluster. The findings indicate that this methodology provides a significant advantage over standard methods for the assignment of metabolites in an LC-MS dataset. Finally, a multi-platform (NMR, LC-MS, microarray) investigation of metabolic disturbances associated with the leptin receptor defective (db/db) mouse model of type 2 diabetes using the developed methodologies is described. Several urinary metabolites were found to be associated with diabetes and/or diabetes progression and confirmed in both NMR and LC-MS datasets. The confirmed metabolites were trimethylamine-noxide (TMAO), creatine, carnitine, and phenylalanine. Additionally, many metabolic markers were found by either NMR or LC-MS, but could not be found in both, due to instrumental limitations. This indicates that the combined use of NMR and LC-MS instrumentation provides complementary information that would be otherwise unattainable. Pathway analyses of urinary metabolites and liver, muscle, and adipose tissue transcripts from the db/db model were also performed. Metabolite and liver transcript levels associated with the TCA cycle and steroid processes were altered in db/db mice, as was gene expression in muscle and liver associated with fatty acid processing. The findings implicate a number of processes known to be associated with diabetes and reveal tissue specific responses to the condition. When studying metabolic disorders such as diabetes, platform integrated profiling of metabolite alterations in biofluids can provide important insight into the processes underlying the disease.

Discovery of discriminative LC-MS and 1H NMR metabolomics markers

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Discovery of discriminative LC-MS and 1H NMR metabolomics markers

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