In this dissertation, I will review the history and general strategies for performing kinetic proteomics. I will then demonstrate that I have published an open source, user-friendly program for other scientists to use to perform kinetic proteomics data analysis, as well as publishing a novel discovery of key ribosomal subunits being replaced within the lifetime of the ribosome, which was discovered through use of kinetic proteomics. Finally, I will discuss work that is ongoing to improve my software tool for use in human subjects, and work being done to combine kinetic proteomics with other global analysis methods to make novel biological discoveries. Proteins are constantly synthesized and destroyed to ensure sufficient functioning proteins to meet cellular needs, a process called protein turnover. Synthesis and degradation are carefully balanced over time to ensure that average protein concentrations do not change drastically. The status quo of the cell, or protein homeostasis, is required for the health of the organism. If protein homeostasis breaks down, serious diseases, such as Alzheimers, can result when proteins aggregate instead of being degraded properly. Because protein turnover is the means to maintain protein homeostasis while keeping sufficient functioning proteins, measuring protein turnover is critical to understanding biological processes and disease states. Measuring protein turnover rates on a broad scale is possible using a method called kinetic proteomics, and the improvement of kinetic proteomics is where I have focused the work for this dissertation.

Throughout society’s continual war against cancer, we have attempted pharmacological intervention only to find that tumors develop modes of resistance. It is well known that genetics play an integral role in cancer. Technological advances have greatly improved our ability to study cancer biochemistry beyond the genome by measuring changes in the expression and activity of RNA, proteins, and lipids in experimental models and human patients. As our techniques and technology to perform cancer research progresses, it is becoming more evident that cancer cells develop stress tolerance mechanisms at multiple levels within the central dogma, including altering mRNA expression, enzyme concentrations, and functional activity of cellular proteins and lipids. In the first chapter, I review previous discoveries demonstrating the importance of metabolic reprogramming in cancer cells and how shifts in metabolic pathways contribute to cancer progression and therapeutic challenges. I discuss how mass spectrometry is a multifunctional research tool that can be used to identify global shifts in gene expression, identify oncogenic roles of specific metabolites and corresponding metabolic pathways, conduct enzyme activity assays, and understand the effects of drugs on cell signaling and metabolic flux through specific pathways. While metabolic reprogramming is a complex and multifaceted concept, the following chapters focus on two specific stress tolerance pathways of lipid and protein metabolism we have shown to significantly promote cancer cell evolution, proliferation, and drug resistance in models of human pancreatic and colon cancer. I describe novel mass spectrometry-based lipidomics and proteomics methods we developed to measure and determine the biological impact of these pathways in each model. I discuss the contributions we have made toward increasing general knowledge of metabolic reprogramming networks in cancer and how they may be targeted in more specific and effective manners to sensitize cancers to therapeutic drugs. Specifically, the second chapter entails our study of a pro-survival lipid metabolic pathway driven by the sphingolipid modifying enzyme sphingosine kinase in a panel of differentially reprogrammed pancreatic cancer subclones. The third chapter describes our novel kinetic proteomics approach to identify how the cellular degradation system autophagy is used to selectively remodel the proteome of colon tumor cells in a xenograft mouse model of colon cancer. Lastly, I discuss how these and other projects completed during my graduate work lay a foundation for ongoing research to further our fundamental understanding of cancer metabolism and treatment development.

One of the greatest risk factors for disease is advanced age. As the human lifespan has increased, so too have the burdens of caring for an increasingly older population suffering from rising rates of cardiovascular disease, kidney disease, diabetes, and dementia. The need for improving medical technology and developing new therapies for age-related diseases is manifest. Yet our understanding of the processes of aging and how to attenuate the effects of aging remains incomplete. Various studies have established calorie restriction as a robust method for extending lifespan in laboratory organisms; however the mechanism is a topic of much debate. Advancing our understanding of calorie restriction holds promise for illuminating biochemical processes involved in the aging process. One of the best explanations for the lifespan extension benefits of calorie restriction is that it improves cellular protein homeostasis (proteostasis), but because proteostasis is dynamic, it can be difficult to measure. We developed a novel combined omics methodology integrating kinetic proteomics, and applied it to a mouse model placed on calorie restriction. Our unbiased approach integrating just three measurements (kinetic proteomics, quantitative proteomics, and transcriptomics) enabled us to characterize the synthesis and degradation of thousands of proteins, and determine that calorie restriction largely alters proteostasis by slowing global protein synthesis post-transcriptionally. Validating our omics approach, we were able to replicate many previous results found in the literature, demonstrating the differential regulation of various protein ontologies in response to the nutrient stress of calorie restriction. Moreover, we were able to detect differential degradation of the large and small ribosomal subunits under calorie restriction, and proposed a model in which the rate of protein synthesis could be attenuated by the depletion of the large ribosomal subunit relative to the small subunit. The flexibility of our dynamic combined omics approach was demonstrated by the expansion of measurements to include nucleic acids and lipids. Flux measurements of DNA, ribosomal RNA, and lipids yielded cellular division rates, ribosome turnover, and lipid metabolism insights, respectively. We also adapted this approach to two-dimensional tissue imaging by DESI-MS in a proof-of-concept study to demonstrate its utility for studying regional differences in metabolism. The future integration of metabolomics and lipidomics into our combined omics approach would be facile, and add unprecedented depth to systems-wide studies involving cellular metabolism. Applied to the regulation of cellular homeostasis in humans, this has the potential to open new avenues for elucidating the etiology of aging, understanding the pathology of age-related diseases, and identifying novel targets for therapeutics.