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Liquid chromatography-mass spectrometry-based neuropeptide characterizations across neuronal models
Hou, Xiaowen
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https://hdl.handle.net/2142/31988
Description
- Title
- Liquid chromatography-mass spectrometry-based neuropeptide characterizations across neuronal models
- Author(s)
- Hou, Xiaowen
- Issue Date
- 2012-06-27T21:23:19Z
- Director of Research (if dissertation) or Advisor (if thesis)
- Sweedler, Jonathan V.
- Doctoral Committee Chair(s)
- Gillette, Rhanor
- Committee Member(s)
- Sweedler, Jonathan V.
- Cox, Charles L.
- Kelleher, Neil L.
- Department of Study
- School of Molecular & Cell Bio
- Discipline
- Biophysics & Computnl Biology
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- Ph.D.
- Degree Level
- Dissertation
- Keyword(s)
- Liquid chromatography
- Mass spectrometry
- Neuropeptide
- Neuropeptidomics
- Quantitative peptidomics
- Abstract
- Neuropeptides, as one largest category of cell-to-cell signaling molecules, play a regulatory role in a range of physiological and behavioral functions within an organism, such as reproduction, feeding, metabolism regulation, learning and memory. They act as neurotransmitters,neuromodulators, peptide hormones and trophic factors. They are differentially processed, modified, expressed and distributed, resulting in a wide range of chemical forms and quantities in different tissues and time points. Therefore, it is not an easy task to achieve a complete understanding of neuropeptides in the human brain. Fortunately, as there are a variety of well-conserved neurochemical pathways within simpler animal models, these studies can provide insights and address fundamental questions in the human brain. A number of model organisms that are commonly used in neuroscience have been explored. For example, the flatworm (Schmidtea mediterranea) provides understanding of regeneration and reproduction, while the comb jelly (Pleurobrachia pileus), as the earliest branching in extant animals, is an excellent model for investigating animal evolution. The ability to characterize peptides in a complex sample depends on the available of suitable tools. Mass spectrometry (MS), coupled to liquid chromatography (LC) and assisted by appropriate sampling protocols, has been successfully applied to unambiguously characterize neuropeptides and their post-translational modifications. No individual MS instrument and approach work well for a majority of samples. Therefore, we used multiple MS platforms with either electrospray ionization (ESI) or matrix-assisted laser desorption/ionization (MALDI) and selected several instruments to study peptide of interest in different animal models. We used and optimized three distinct LC-MS-based approaches. One is a non-targeted approach, which integrates LC-MS with bioinformatics, and analyzes the complement of peptides (termed peptidomics), with sample sizes ranging from whole animals to single organs or tissues. Another is a targeted approach, which focuses on specific peptides that have been implicated in a defined physiological or behavioral function. The third is a quantitative approach that compares the difference of peptide levels between two samples arising from different behavioral stages, application of drugs or chemicals, and arising through gene knockouts. We used these approaches combined with molecular and genomic information to advance our knowledge of neuropeptides. More specifically, we characterized the peptidomes in the flatworm, the comb jelly and the sea slug, from samples ranging from whole animals to tissues; we sequenced the human insulin ectopically expressed in the fruit fly, and we detected a putative alarin peptide in the human cell line which requires further verification; we confirmed peptide level changes as a function of genetic manipulations in the mouse tissues. Our knowledge of neuropeptides throughout the Metazoa has been enhanced based on these results.
- Graduation Semester
- 2012-05
- Permalink
- http://hdl.handle.net/2142/31988
- Copyright and License Information
- Copyright 2012 Xiaowen Hou
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