Honors College Thesis
 

Metabolomic Analysis of Oregon Coast Sediment Pore Waters using Ultrahigh Resolution Mass Spectrometry

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https://ir.library.oregonstate.edu/concern/honors_college_theses/00000598d

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  • One of the principal drivers of climate change is the concentration of greenhouse gases such as CO2 in the atmosphere. A large portion of this CO2 ends up in the waters off the continental coasts where it transforms into biomass. The major sink for this matter is the ocean sediments on continental margins where organic matter can be stored for thousands of years in extremely complex forms that are largely uncharacterized. Some of compounds remineralize into carbon dioxide and eventually re-enter the atmosphere, but others get locked into recalcitrant forms that are stored for millennia. Bacteria play a role in this cycle by transforming carbon from reactive forms to unreactive forms, but the mechanisms that this occurs by remains largely unclear. Progress in this field has largely been held back by the lack of available analytical tools to study the environment at such a small scale. New technologies such as Ultra High resolution mass spectrometry coupled with Liquid Chromatography gives us the ability to investigate these systems. Here, we investigated using 21T Fourier Transform Inductively Coupled Resonance Mass Spectrometry to conduct a metabolomic analysis of Oregon Coast sediment pore waters. This preliminary study has successfully predicted molecular formulas and classified compounds into groups of lipids, proteins, phytochemicals, carbohydrates, aminosugars, and nucleotides based on their stoichiometries. The method has allowed for elucidation of key differences between two distinct sediment regions (top of the sediments, and 4-8 cm into the sediments) efficiently using data plotting techniques such as van Krevelen diagrams and volcano plots. The study has captured the expected pattern of molecular weight distribution of DOM in the sediment pore waters, while also finding differences in oxidation states and compound class distributions that lead to additional research questions.
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