UC Davis

Metabolomics is a powerful tool in the field of natural compounds research, offering insights into the complex metabolic processes occurring within living organisms. In the context of natural compounds, metabolomics enables the identification and characterization of the diverse array of chemical entities produced by plants, animals, and microorganisms. This approach provides a holistic view of the dynamic nature of biological systems, unraveling the intricate interplay between genes, proteins, and metabolites, and facilitating the discovery and development of novel bioactive compounds for various applications, including medicine, agriculture, and biotechnology. This thesis explores the application of cutting-edge technologies in the identification and characterization of natural compounds, shedding light on their diverse chemical structures and potential biological activities. In Chapter one, a straightforward and potent method is presented for uncovering glycosides in food, specifically focusing on chocolate and tea, which are known to be rich in glycosides. Building upon the observation that glycosides and their corresponding aglycones fragment in almost identical patterns, a library of 3,027 standards was subjected to in-silico conjugation using thirteen different sugar combinations. This approach illuminated the vast realm of unknown glycosides present in food, revealing that virtually any compound can be conjugated at any point in time. Chapter two presents how a metabolomics approach enabled a combinatorial screen of 109 Family 1 Glycosyltransferases with 578 natural compounds. The experiments involved employing cell lysate as the source of enzymes, and substrates were grouped in sets of 40 to enable efficient high-throughput screening. The samples were processed in 96-well plates and subsequently analyzed through the utilization of LC-MS/MS technology. A comprehensive analysis resulted in the annotation of over 250 compounds, suggesting their potential substrate activity with at least one enzyme. This innovative approach not only facilitated the identification of enzymes capable of glycosylating aliphatic compounds but also revealed that Histidine is not a crucial residue for UGT1 activity. Chapter three discusses the effect of precursor ion intensity in the identification confidence of metabolites. We injected 200 metabolites in 13 different concentrations, by collecting negatives and positive mode data in 3 different collision energy, which resulted 15,600 spectra with different spectral quality. By comparing high- and low-quality spectra, we found that normalized entropy can be used for labeling low-quality spectra. Furthermore, our findings indicate that implementing denoising techniques on spectra can significantly enhance the reliability of compound identification, offering a novel dimension of data management for scientists in the field of mass spectrometry. Finally, Chapter four evaluates a direction that the field of metabolomics has been striving to embrace: the integration of targeted and untargeted metabolomics in a single LC-MSMS experiment. Previous endeavors have demonstrated success in conducting both targeted and untargeted experiments within a single injection. However, these efforts have been constrained by the limitations of instruments equipped with only a single detector, which restricts the ability to employ both approaches within a specified timeframe. This concept is made possible by the utilization of a Tribrid mass spectrometer, which incorporates two detectors concurrently. In our study, we effectively merged a Parallel Reaction Monitoring (PRM) analysis employing an Ion Trap with an untargeted analysis utilizing an Orbitrap in the IQ-X Tribrid Mass Spectrometer. By employing this method, we successfully identified low-abundance compounds that would have otherwise been overlooked by conventional untargeted analysis techniques.