Browsing by Subject "Metabolomics"

Gliomas are tumors of the central nervous system for which improvements in treatment are critically needed. Mutations in IDH1 and IDH2, which encode the cytosolic and mitochondrial NADP+-dependent isocitrate dehydrogenases, respectively, are frequent in gliomas. Here, we summarize recent literature concerning gliomas, the normal cellular functions of IDH1/2, the epidemiology of IDH1/2 mutations, and the understanding of the function of IDH1/2 mutations in cancer. We then show in vitro using liquid chromatography-mass spectrometry that a function of many IDH1/2 mutations is to produce 2-hydroxyglutarate. Next, we use a mass spectrometry based platform to characterize metabolic changes in a glioma cell line expressing IDH1/2 mutants and show that the IDH mutants are associated with lowered N-acetylated amino acids both in this cell line model and in primary tumor tissue. Finally, we develop and characterize a Drosophila melanogaster (fruit fly) model of IDH1/2-mutated cancer by expressing the mutated Drosophila homolog of IDH1 in fly tissues using the UAS-Gal4 binary expression system. These results delineate downstream molecular players that likely play a role in IDH1/2-mutated cancer and provide a model organism for interrogation of genetic networks that interact with IDH1/2 mutation. These findings refine our understanding of glioma pathogenesis and may inform the design of new glioma therapies.

High-throughput molecular profiling is being increasingly applied to identify novel biomarkers and mechanisms of health and disease. One such application is the use of mass spectrometry-based metabolomic profiling in cardiovascular disease (CVD), whose underlying pathophysiology and risk prediction models are incompletely understood. Two general approaches have been taken in these applications: targeted and non-targeted profiling. The targeted approach identifies and quantifies select known or potential biomarkers in CVD, often via isotope-labeled internal standards. The non-targeted approach attempts to profile the full spectrum of the metabolome, with identification of metabolites aided by existing spectral libraries. In contrast to many successful applications of targeted metabolomics to CVD, early applications of non-targeted profiling have resulted in several pitfalls due to lack of rigor in study design, immature technologic platforms, and challenges in metabolite identification and quantification both at the experimental and computational level. These pitfalls highlight the importance of experimental design and method development in non-targeted metabolomic profiling. The overall goal of this dissertation is to improve methods in non-targeted metabolomic profiling both at the experimental and computational level, and apply these improved methods to CVD human studies. Specifically, this dissertation aims to: 1) identify and modify factors that could influence metabolite identification and quantification in GC-MS based non-targeted profiling at the experimental level; (2) apply emerging methods for metabolite identification at the computational level to generate hypotheses for unknowns; and (3) apply metabolomic profiling to studies in human cardiovascular disease, using the refined methods from the first two aims.

For the first aim, we sought to identify and modify factors in GC-MS-based metabolomic profiling of human plasma that could influence metabolite identification and quantification at the experimental level. Our experimental design included two studies: 1) the limiting-dilution study, which investigated the effects of laboratory preparation and analysis on analyte identification and quantification, and 2) the concentration-specific study, which compared the optimal plasma extract volume established in the first study with the volume used in the current institutional protocol. We tested and confirmed our hypothesis that contaminants, concentration, intra- and inter-experiment variability are major factors influencing metabolite identification and quantification. In addition, we established methods for improved metabolite identification and quantification, which were summarized into recommendations for experimental design of GC-MS-based profiling of human plasma.

For the second aim, we applied emerging methods for metabolite identification level to generate hypotheses for unknowns at the computational level. Specifically, we tested and confirmed our hypothesis that integrating genomic, transcriptomic, and metabolomic data could generate hypotheses for unknowns. Combining the strengths of multiple omics platforms and metabolomic databases, we were able to generate hypotheses for three unknown metabolites implicated in CVD at the computational level.

For the third aim, we applied metabolomic profiling to two studies of CVD and tested the hypothesis that application of the refined methods developed in the first two aims of this dissertation are useful in CVD biomarker and mechanism discovery. In one study, we used heart failure with preserved ejection fraction (HFpEF) as a model to demonstrate the power of targeted metabolomic profiling in testing existing hypotheses of CVD biomarkers and mechanisms. In a second study, we used incident CVD events as a model to 1) apply the refined methods from the first two aims of this dissertation, and 2) demonstrate the power of non-targeted metabolomic profiling in generating novel hypotheses of CVD biomarkers and mechanisms.

This dissertation contributes to research in metabolomics and CVD in several ways. The most significant contribution is the set of recommendations for experimental design in non-targeted metabolomics, which has been incorporated into the workflow of non-targeted profiling at the Duke Molecular Physiology Institute for future studies. Additional contributions include the following: hypotheses for three unknowns implicated in incident CVD events, and novel biomarkers and mechanisms implicated in HFpEF and CVD. Future directions from this dissertation include the following: 1) application of the same principles to method development and validation of metabolomic profiling using other analytical technologies; 2) experimental validation of the hypotheses for unknowns generated by this dissertation; and 3) functional validation of the biomarkers and mechanisms implicated in CVD at the experimental level.

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