Date of Award
Fall 2025
Document Type
Open Access Dissertation
Department
Chemistry and Biochemistry
First Advisor
Qian Wang
Abstract
The increasing availability of large-scale datasets is transforming both molecular biology and clinical research, yet comprehensive proteome analysis remains hindered by the dominance of high-abundance proteins, which obscure detection of low-abundance species. This challenge is particularly critical in the study of post-translational modifications and plasma proteomes, where depth of coverage directly impacts biomarker discovery and mechanistic understanding of disease.
In this work, PolyTi resin was utilized for phosphoproteome enrichment to investigate mechanism of action and structure-activity relationship of green leaf volatiles (GLVs) in tomato cell suspension cultures. The enrichment of phosphoproteins revealed changes in the phosphoproteome in response to GLV treatment, providing novel insights into the molecular mechanisms underlying plant stress responses.
Surface-modified molybdenum disulfide nanoparticles (MMNs) were utilized to selectively enrich plasma proteins, and the physicochemical characteristics of the captured proteins were analyzed to elucidate the underlying enrichment mechanisms. Comparative plasma proteomic profiling between patients with peripheral artery disease and healthy controls revealed potential biomarker candidates. Additionally, these findings suggest a possible link between the disease and the Hippo signaling pathway, providing new insights into disease-associated molecular pathways.
Glycoprotein and phosphoprotein enrichment by MMNs was assessed as proof of concept for developing a multi-proteomics sample preparation platform. More glycoproteins were quantified in MMN-treated plasma compared to untreated plasma, in which almost no glycoproteins were detected. This supports the potential application of MMNs in multi-proteomic sample preparation platform.
In addition, large-scale data analysis revealed significant associations between risk factors across all five domains of social determinants of health (SDOH) and increased odds of mental health disorders. A comprehensive SDOH summary score was developed to aggregate these risk factors, demonstrating a strong correlation with the presence of mental health disorders. These findings suggest the potential utility of the SDOH summary score as a measure for assessing the risk of developing mental health disorders.
Together, these results demonstrate the power of proteomic and big data analyses in advancing our understanding of GLV signaling pathways in plants, elucidating molecular pathways related to PAD, identifying potential PAD biomarker candidates, and developing risk scores for predicting the likelihood of mental health disorders.
Rights
© 2025, Sasimonthakan Tanarsuwongkul
Recommended Citation
Tanarsuwongkul, S.(2025). Deciphering Stress and Diseases with Proteomics and Big Data Analyses. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/8606