Tao Huan

As the most recently emerged “omics”, metabolomics grabbed attention in human health studies by measuring thousands of small-molecule metabolites in a wide range of biological samples. As the downstream products in the biological pathway, metabolites are regarded as the closest link to the phenotypes. Small stimuli in the human body will cause relatively huge changes in the level of metabolites. Liquid chromatography-mass spectrometry (LC-MS) is the mainstay in metabolomics research due to its high throughput, sensitivity, and reliable analysis of metabolites. Nevertheless, two of the main challenges in LC-MS based metabolomics are 1) how to apply metabolomics in studying human health and 2) apart from commonly used biological samples, including serum, plasma, and urine, how to develop a methodology of new biological samples that can be adapted to specific human health research. To address those challenges, in Chapter 2, I integrated metabolomics with metagenomics to examine human gut health. 13-species metagenomic signature was selected by random forest machine learning and achieved high diagnostic accuracy in differentiating hepatic decompensation in NAFLD-related cirrhosis. The signature was cross-validated by metabolomics. 32 metabolites and 15 metabolites from serum and feces, respectively, were found to be significantly linked to 13-discriminatory species, suggesting that the identified discriminatory species may play important roles in the progression from compensated to decompensated cirrhosis. This multi-omics study yields new avenues for identifying novel targets for therapy and microbial biomarkers of hepatic decompensation, a worldwide human disease. In Chapter 3, I integrated plasma metabolomics and proteomics to examine the health conditions of highly trained females and males following acute, severe-intensity exercise. Metabolomic and proteomic homeostasis were substantially perturbed. Through statistical analysis, some metabolites and proteins were found to be closely linked to high-intensity exercise. This multi-omics study was a powerful tool to study molecular responses to acute exercise and provided a new insight to exercise-bolstered human health. In Chapter 4, I developed a new methodology to track skin secretion. Our high-performance workflow was readily applied to a wide range of skin metabolomics research to gain a better understanding of the molecular signatures on skin that link to human health and disease.

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For this work, two areas of metabolomics were investigated relating to the fundamentals of the field and application to different experiments. The first chapter was an assessment and comparison of the MSMS spectra generated from different acquisition modes. The chosen acquisition modes were data-dependant acquisition (DDA), data-independent acquisition (DIA), and enhanced insource-fragmentation (eISF) at a range of collision energies. The data was obtained by performing untargeted metabolomics on a urine sample and a standard mixture solution through a LC-MS platform while also covering multiple ionization modes. The spectra from the three modes were compared against each other through several factors that relate to the various ways MSMS spectra are used in a metabolomics workflow. These comparisons involved investigating the spectral purity, quality of reference matching results, structural similarity, and de novo annotation performance. It was found that DDA performed the best with eISF and DIA following. It was seen that eISF performed on-par or slightly better than DIA at higher collision energies. This indicates that the collision energy used will have a notable impact on the performance of the mode. The second chapter involves the metabolomics of pancreatic cell samples. The purpose of this was to determine the metabolic profile between the control and treated groups. The control was regular cancer cells from the MiaPaCa2 cell line while the treated groups had specific genes knocked out. The investigation was performed to gain insight into which metabolic pathways the knocked-out genes were involved in. Using a LC-MS platform it was found that 12 metabolites showed significant intensity differences between the groups. A literature review of these compounds highlighted possible metabolomic pathways affected such as polyamine metabolism. The last chapter focuses on a lipidomics experiment that was performed on the bacteria Thermotoga maritima to investigate the lipid content of the bacterial membranes. The samples relating to each fraction were run through the same LC-MS platform as above. It was seen that there were three significantly different lipids apart from the fatty acid, phosphatidylethanolamine, and phosphatidylinositol lipid classes. These classes have all been shown to be involved in membrane stability and transport.

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