Methodology Development and Application of Untargeted Metabolomics

• Main Authors: Ching Lo, 羅靖
• Other Authors: 徐丞志
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/yzqpyp

碩士 === 國立臺灣大學 === 化學研究所 === 107 === Metabolomics is the study of investigating the small molecules composition of biological samples. Compared to proteomics or genomics, the information metabolomics provides is more related to phenotype. Due to the high diversity of metabolite structures, metabolites act as many different roles in organisms, including energy preserver, building blocks of biomolecules, hormone, neurotransmitter, coenzyme, etc. Biochemists have been devoted to understanding the whole metabolome and the interaction between metabolites and enzymes in the past decade. Unlike targeted metabolomics, untargeted metabolomics utilizes MS/MS comparison with online databases to identified regulated metabolites in an unbiased fashion. We used liquid chromatography-tandem mass spectrometry (LC-MS/MS) based method to study the metabolome in tumor-derived exosomes and ApoE knockout rats to find small molecule biomarkers for organ-tropic metastasis and potential drugs for cardiovascular disease. We further developed a data-independent method, Selected MARgins acquisiTion (SMART), for untargeted metabolomic featuring designed isolation windows. SMART improved MS/MS spectrum deconvolution and resulted in better metabolite identification. Recently, tumor-derived exosomes received tremendous attention because of its crucial role in cancer progression and metastasis. Tumor-derived exosomes were a rich source of biomarkers, some protein and microRNA biomarkers were discovered and even translated into clinical use. However, few studies focused on metabolite composition or biomarkers in exosomes. We performed nanoUPLC-MS/MS-based untargeted metabolomics to analyze the differences between exosomes secreted from cancerous cell lines. We’ve found some metabolites which were significantly changed and could be potential biomarkers for tumor metastasis. As tumors conduct cross-organ communication by secreting exosomes to the circulation system, gut microbiota also tends to secrete metabolites to the circulation system and affect the host’s metabolism. Evidence showed that gut microbiota is associated with several metabolic pathways and disease progression, including the lipid metabolism pathway and cardiovascular disease. To survey the effect of gut microbiota on cardiovascular disease, we applied untargeted metabolomics to study the differences between germ-free and specific-pathogen-free ApoE-/- rats. We have discovered some microbiota-derived metabolites related to cardiovascular disease. Finally, we developed a novel data-independent acquisition (DIA) method named SMART. When using the SMART method, we generated a set of unequal width isolation windows according to the LC-MS peak distribution. Consequently, we could simplify MS/MS spectra and improve peaks deconvolution, and resulted in a more comprehensive and convincing metabolites identification. In this study, we applied untargeted metabolomics on different biological systems to discover tumor-metastasis-associated biomarkers and cardiovascular-disease-related metabolites. Furthermore, we developed a new DIA method, SMART, featuring designed isolation windows and improve MS/MS data deconvolution.