Liquid chromatography and mass spectrometry based characterization to understand the role of neuropeptides in various physiological conditions and disease states

Neuropeptides are the largest and most diverse class of cell-cell signaling molecules in the brain. They are expressed and synthesized by neurons and endocrine organs, released upon stimulation and act by binding to specific cell surface receptors that initiate a cascade of downstream signaling mechanisms. Compelling evidence from several previous studies has demonstrated their role in several physiological functions such as appetite regulation, nociception, locomotion, reproduction, learning and memory. Given their important role as the molecular messengers of a biological system, there is a lot of interest in the accurate identification and characterization of these peptides. However, the task of characterizing them comes with several intrinsic challenges. First, these peptides undergo rapid post-mortem degradation during the extraction and analysis phase. Measuring depleted levels of peptides from a vast pool of ubiquitous peptides and degraded proteins requires unique sampling and analytical methods. Secondly, these peptides have widely different physio-chemical properties with varying degrees of hydrophobicity, mass to charge ratio and post-translational modifications. These properties mean that there is not a universal analytical approach that works for all peptide characterization approaches. Lastly, prior structural and sequential information of these peptides is necessary to characterize them using traditional immunohistochemistry-based approaches. Moreover, these traditional approaches suffer from lack of specificity and are not inherently multiplexed in nature. The primary objectives of my research were twofold : 1) Develop, implement and evaluate liquid chromatography (LC)-mass spectrometry (MS) based workflows for optimal neuropeptide characterization and 2) Understand the role of neuropeptides in various neuropathological disorders using these workflows. This dissertation is divided into two parts. In part 1, the primary focus is on analytical aspects of neuropeptide characterization, discussions about the pharmacological importance of neuropeptides and their measurement, the merits of various mass spectrometric instrumental platforms for neuropeptide characterization, the design of custom MS approaches for labile PTM characterization and machine learning based tools for peptide spectral classification. Next, the aforementioned tools and techniques were applied to elucidate the role of neuropeptides in chronic pain and chronic itch disorders. Findings from my works will lead to better characterization of neuropeptides and advance our knowledge and understanding about how these cell-cell signaling molecules play a decisive role on various neuropathological conditions.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemistry

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Anapindi, Krishna Dwaipayana Bharadwaj

Contributors dc:contributor

• Sweedler, Jonathan V
• Gillette, Martha U
• Rodriguez-Zas, Sandra
• Chan, Jefferson

• Liquid Chromatography, Mass Spectrometry, Neuropeptidomics

Statement dc:rights

• Copyright 2020 Krishna Dwaipayana Bharadwaj Anapindi

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/107958

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/107958