Date of Award
Open Access Dissertation
Chemistry and Biochemistry
Phosphorylation is the most common post-translational modification that occurs within the cell and is intricately involved in many biological processes. It is important to understand the roles that protein kinases and phosphatases play by mapping protein phosphorylation as many disease states stem from aberrant signaling events. With the advent of modern mass spectrometry (MS), researchers are now provided with more complex phosphorylation patterns from thousands of proteins in each mass spectrometric analysis. Further developments in quantification techniques, such as stable isotope labeling with amino acids in cell culture (SILAC) and label-free quantitation, have allowed researchers to reliably quantify changes to these proteins in response to different treatments. A main challenge in MS-based phosphoproteomics is the need for phosphopeptide enrichment prior to MS analysis which can be labor-intensive and is plagued by irreproducibility. In the Wang lab, we have developed an automated phosphopeptide enrichment protocol that addresses many of the issues normally associated with conventional phosphopeptide enrichment workflows. Our established method outperforms conventional manual phosphopeptide enrichment methods in terms of phosphopeptide identifications and specificity. We have applied our enrichment method to two different projects, one focused on understanding thymidylate synthase inhibition in colorectal cancer cells and the second focused on understanding how tomato cells respond to green leaf volatiles.
Additionally, extracellular vesicles are a promising biomarker discovery tool that over the past 10 years researchers have used proteomic studies to identify potential biomarkers for disease diagnosis. One major problem is that the current extraction techniques are cumbersome and time consuming. To address these issues, we applied an IMCStip-based automated workflow for the purification of extracellular vesicles from urine samples. Further work is needed to optimize the extraction process, but this work highlights the potential for automating the extraction workflow aiding in EV-based biomarker discovery.
Mullis, B. T.(2021). Automating and Implementing Complex, Multistep Sample Preparation Processes to Generate Reproducible Proteomic and Phosphoproteomic Data. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/6303
Available for download on Monday, May 15, 2023