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Dissertation/Thesis Abstract

Targeted Analogue Detection from Pestalotiopsis microspora Using Molecular Networking and Mass Defect Filtering: Towards the Development of new Ant-virulence Leads Against Methicillin Resistant Staphylococcus aureus
by Naphen, Cassandra Nichole, M.S., The University of North Carolina at Greensboro, 2018, 51; 10979446
Abstract (Summary)

Effective tools are needed to enable prioritization of lead molecules in natural products drug discovery research. As the field of mass spectrometry continues to advance, more people have access to high-resolution instruments, and more researchers rely on this technology for metabolite and compound class identification. To effectively access lead molecules from natural product mixtures, it is important to comprehensively assess large datasets in the most timely and cost-effective manner. For analyses based on liquid chromatography-mass spectrometry (LC-MS), several post-acquisition data analysis approaches can be employed to identify analogues of a lead molecule of interest. With this study, we sought to compare the effectiveness of mass defect filtering and molecular networking for this purpose. Secondary metabolite production by the ambuic acid-producing endophytic fungus Pestalotiopsis microspora was used as a test case for these analyses. Eight putative analogues of ambuic acid were identified using mass defect filtering and molecular networking analyses. Our results illustrate advantages and disadvantages of molecular networking and mass defect filtering and suggest that the combination of both data mining techniques may enable the most comprehensive evaluation of analogues.

Indexing (document details)
Advisor: Cech, Nadja B.
Commitee: Oberlies, Nicholas H., Croatt, Mitchell P.
School: The University of North Carolina at Greensboro
Department: College of Arts & Sciences: Chemistry and Biochemistry
School Location: United States -- North Carolina
Source: MAI 81/1(E), Masters Abstracts International
Subjects: Biochemistry, Health sciences, Pharmacology
Keywords: Antivirulence, Data mining, Drug discovery, Mass defect filtering, Mass spectrometry, Molecular networking
Publication Number: 10979446
ISBN: 9781085562935
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