The Role of the Glycerophosphocholine Remodelling in Alzheimer’s Disease

Abstract

Advances in high performance liquid chromatography-electrospray ionization-mass spectrometry made in proteomics and now applied to the emerging field of lipidomics has enabled the identification of lipid composition at the molecular level. These improvements have given fresh impetus to lipid research. Modulating lipid compositions has been suggested to represent a novel therapeutic target for intervention in Alzheimer’s disease. A better understanding of how metabolic alterations in the lipid landscape alter Alzheimer’s disease prognosis is required to realize this promise. To achieve this goal, further methodological improvement in lipidomic data acquisition and analysis are required as are comprehensive comparative analyses of lipid metabolism at the systems level in clinical samples and mouse models of human neurodegenerative disease. In this thesis, I present two new lipidomic bioinformatic tools Retention Time Standardization and Registration (RTStaR) and Visualization and Phospholipid Identification (VaLID) designed to facilitate analysis of high performance liquid chromatography-electrospray ionization-mass spectrometry lipidomic data. Using these tools and methodologies, I then comparatively profiled the glycerophosphocholine lipidome in the plasma of young adults, cognitively normal elderly with vascular impairment, mild cognitive impairment and late-onset Alzheimer’s disease patients and the entorhinal-hippocampal circuit of late-onset Alzheimer’s disease patients, TgCRND8 human amyloid beta precursor protein transgenic mice (Alzheimer’s disease mouse model), and across the lifespan of NonTg female littermates. Systems-level analyses identified aberrant glycerophosphocholine metabolic pathways systemically perturbed by age, disease, and amyloid beta biogenesis resulting in the regionally-specific accumulation of critical platelet-activating factor and, to a lesser extent, the lysoglycerophosphocholine, metabolites in brain that could be, in part, predicted by changes in plasma. Finally, using proteomic approaches I identified additional changes in lipid metabolic pathways associated with phenoconversion in the TgCRND8 mouse model of Alzheimer’s disease.