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Title page for ETD etd-11032016-121024


Type of Document Master's Thesis
Author Wall, Martha Lynn
URN etd-11032016-121024
Title Novel stable isotope methods to quantify glucose metabolism
Degree Master of Science
Department Chemical and Physical Biology
Advisory Committee
Advisor Name Title
Jamey D. Young Committee Member
Owen P. McGuinness Committee Member
Keywords
  • gas chromatography
  • mass spectrometry
  • metabolic flux analysis
  • MATLAB
Date of Defense 2016-10-25
Availability unrestricted
Abstract
The development, validation, and application of novel methods to quantify glucose metabolism are presented in this thesis. The methodologies use stable isotope tracers, gas chromatography-mass spectrometry, and mathematical modeling to provide a dynamic understanding of metabolism at the systems level. All methods center on six fragments obtained from three distinct glucose derivatives that combine to provide accurate positional isotopic enrichment estimates. First, glucose was derivatized from plasma samples, and the six derivative fragments were measured using a custom MATLAB-based integration tool. The effects of measurement error on positional analysis were assessed, and a second interface tool for positional analysis was built and tested. Next, the method was applied to quantify the hypothesized G6PC2-mediated glucose cycling in isolated islets from WT and G6pc2 KO mice. Finally, the method was used to quantify flux through a metabolic network in vivo. Stable isotope tracers were infused into conscious mice, and plasma samples were collected. The plasma glucose was derviatized, and the mass isotopomer distributions for the six derivative fragments were measured. The measurements were fit to a model of hepatic glucose production to estimate metabolic fluxes in gluconeogenesis and the citric acid cycle. The result of this work is a well validated and paradigm-shifting method that can significantly advance knowledge of glucose metabolism. Improving the understanding of glucose metabolism at a systems level will impact the study of diseases that exhibit dysfunctional glucose metabolism, including diabetes and cancer.
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