Graduate Thesis Or Dissertation
 

Comprehensive Analysis of The Brain Metabolome Using Mass Spectrometry in Conjunction with Data Analysis Techniques

Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/kp78gr00r

Descriptions

Attribute NameValues
Creator
Abstract
  • Metabolomics has recently gained momentum in biomolecule research and complements the genomics and proteomics research space. Metabolomics strives to detect, identify, and quantify all metabolites present in biological samples. In particular, biomolecular analysis using ultra-performance liquid chromatography combined with mass spectrometry (UPLC-MS) has become increasingly important for metabolomic analyses. Similarly, lipidomics is the quantitative analysis of lipids within a biological system. This dissertation focuses on the application of metabolomics and lipidomics with data analysis methods to study biomolecular changes in the brain to enable the discovery of biological pathways associated with disease states and injury. Specifically, analytical workflows were designed, developed, and applied for the extraction procedures to separate metabolites, lipids, and proteins from tissues and plasma samples. Different data analysis techniques were used and evaluated for data processing, visualization, and interpretation. In this thesis, the primary objective was to utilize UPLC coupled with High-Resolution Tandem Mass Spectrometry (UPLC-HRMS/MS) and advanced data analysis techniques to investigate the impact of the hAPP695SW transgene and associated amyloid-β accumulation on murine hippocampal biochemical pathways underlying Alzheimer's disease (AD) using a murine model of AD. Neurodegenerative diseases, such as AD, are characterized by the loss of brain function and structure, including neuronal death. In the brain, AD exhibits topological complexity, suggesting the need to analyze molecular mechanisms that govern disease susceptibility and progression in greater details. In this study, the metabolomic profile of hippocampal tissue from 20-month-old female Tg2576 mice expressing the familial AD-associated hAPP695SW transgene was compared to the metabolomic profile of their wild-type littermates. Comparing the two groups, 54 out of 180 annotated metabolites (30 higher and 24 lower) showed differences in abundance levels. These metabolites spanned the following compound classes: amino acids, nucleic acids, glycerophospholipids, ceramides, and fatty acids. Our findings allowed us to find several metabolic pathways, such as amino acid/peptide/amine metabolism, nucleic acid metabolism, metabolites linked to fatty acid oxidation and mitochondrial/peroxisomal function, metabolites associated with maintaining redox homeostasis, metabolites linked to neurotransmission and signaling, glycerophospholipid (GPL) metabolism, ceramide metabolism that was dysregulated in the Tg2576 mouse hippocampus. Overall, our findings pointed out that amyloid-β accumulation is associated with a shift to an excitatory-inhibitory imbalance in line with recent reports that neuronal hyperactivity and hyperexcitability are observed in the early stages of AD. In a related project, the hypothesis was tested that treatment of Tg2576 mice with an aqueous extract of Centella asiatica (CAW) causes metabolite changes in the hippocampus associated with the cognitive enhancement observed in behavioral tests. Specifically, a metabolomic examination of the hippocampus of Tg2576 mice was conducted using UPLC–HRMS/MS. In this chapter, different data analysis techniques, such as multivariate and univariate data analysis methods, were explored. As a result, the mice with neurocognitive deficits showed a greater Aβ plaque burden with an altered hippocampal metabolic profile characterized by higher levels of oxidized molecules. Also, there was a shift in phospholipid and ceramide metabolites. We also found improved cognitive function in WT mice after treatment with CAW. A strong correlation was found between elevated hippocampal metabolites and longer retention of fear memories, as 48 of 50 metabolites with the strongest correlations were positively related to contextual freezing. Longer retention of fear memories was associated with higher levels of glycosylated ceramides and proteinogenic amino acids. The increase in adjusted-contextual freezing was also significantly associated with higher levels of (lyso)phosphoethanolamines (LPE C22:2 and PE C18:0/C22:2), and diacylglycerol C16:0/C18:0. In the third project, a lipidomics analysis of the hippocampus and cortex of low-dose irradiated (1 Gy of X-rays) mice was conducted to support behavioral test results. The analytical strategy embarked on a sample processing workflow that allowed the extraction of lipids and proteins, allowing proteomics-driven explorations in a future project. In this project, lipids were detected and quantified by UPLC–HRMS/MS and annotated using exact mass information, fragment ion data, and retention times. Multiple data processing strategies were evaluated. The comparative study design allowed the discovery of several lipid species and a systematic evaluation of unsaturation content. The results showed a greater number of lipid alterations in the group subjected to the contextual fear conditioning test compared to the group that underwent the novel object recognition test. The long-term goal of this research is the integration of the current lipidomics finding with the future proteomic outputs for obtaining mechanistic insight into how fear and low-dose radiation impact learning and memory. In the last chapter, a targeted oxylipidomics analysis was developed and validated for the future identification of biomarkers associated with cognitive decline in Parkinson's disease (PD). PD is the second most prevalent neurodegenerative disease, and around 75% of PD patients show PD dementia (PDD) after ten years of diagnosis. It is crucial to discover biomarkers of PD in cognitively normal individuals to better predict cognitive decline and identify high-risk individuals. Studies showed a link between PD and disruptions in brain polyunsaturated fatty acid (PUFAs) metabolism and oxylipins. In this study, an UPLC-MS/MS method in Multiple Reaction Monitoring (MRM) mode was developed to detect and quantify 50 targeted oxylipins and five PUFAs in human plasma samples. The workflow was optimized and evaluated by comparing the concentration of the oxylipins to the reported data. The targeted oxylipin levels were consistent with previously reported literature values for the NIST (National Institute of Standards and Technology) human plasma standard. The analytes were quantitatively characterized with good linearity (R > 0.99), reproducibility (relative standard deviation (RSD) < 20 % for the majority of analytes), and recovery (80%-120% for all analytes). The optimized method was used to determine oxylipins and PUFAs levels in human plasma samples. In conclusion, this thesis summarizes the insights obtained from metabolomics and lipidomics studies using UPLC-HRMS/MS as the core analytical technique. Data analysis methods were essential for extracting molecular information to deepen our biochemical understanding of different neurodegenerative diseases. An innovative aspect of this thesis is the combination of MS-based “omics’ with behavioral studies and the application of correlation analysis to link molecular knowledge to the behavioral phenotype. The content of this thesis provides perspectives on the future of disease treatment by obtaining an in-depth understanding of biological pathways involved in disease progression by utilizing mass spectrometry-based metabolomics and lipidomics.
License
Resource Type
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Academic Affiliation
Rights Statement
Publisher
Peer Reviewed
Language

Relationships

Parents:

This work has no parents.

In Collection:

Items

Thumbnail Title Date Uploaded Visibility Actions
file details KhoraniMona_2023.pdf 2023-10-17 Public Download