Global Analysis of Protein Folding Thermodynamics for Disease State Characterization and Biomarker Discovery

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Adhikari, Jagat


Fitzgerald, Michael C.

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Protein biomarkers can facilitate the diagnosis of many diseases such as cancer and they can be important for the development of effective therapeutic interventions. Current large-scale biomarker discovery and disease state characterization studies have largely focused on the global analysis of gene and protein expression levels, which are not directly tied to function. Moreover, functionally significant proteins with similar expression levels go undetected in the current paradigm of using gene and protein expression level analyses for protein biomarker discovery. Protein-ligand interactions play an important role in biological processes. A number of diseases such as cancer are reported to have altered protein interaction networks. Current understanding of biophysical properties and consequences of altered protein interaction network in disease state is limited due to the lack of reproducible and high-throughput methods to make such measurements. Thermodynamic stability measurements can report on a wide range of biologically significant phenomena (e.g., point mutations, post-translational modifications, and new or altered binding interactions with cellular ligands) associated with proteins in different disease states. Investigated here is the use of thermodynamic stability measurements to probe the altered interaction networks and functions of proteins in disease states. This thesis outlines the development and application of mass spectrometry based methods for making proteome-wide thermodynamic measurements of protein stability in multifactorial complex diseases such as cancer. Initial work involved the development of SILAC-SPROX and SILAC-PP approaches for thermodynamic stability measurements in proof-of-concept studies with two test ligands, CsA and a non-hydrolyzable adenosine triphosphate (ATP) analogue, adenylyl imidodiphosphate (AMP-PNP). In these proof-of-principle studies, known direct binding target of CsA, cyclophilin A, was successfully identified and quantified. Similarly a number of known and previously unknown ATP binding proteins were also detected and quantified using these SILAC-based energetics approaches.

Subsequent studies in this thesis involved thermodynamic stability measurements of proteins in the breast cancer cell line models to differentiate disease states. Using the SILAC-SPROX, ~800 proteins were assayed for changes in their protein folding behavior in three different cell line models of breast cancer including the MCF-10A, MCF-7, and MDA-MB-231 cell lines. Approximately, 10-12% of the assayed proteins in the comparative analyses performed here exhibited differential stability in cell lysates prepared from the different cell lines. Thermodynamic profiling differences of 28 proteins identified with SILAC-SPROX strategy in MCF-10A versus MCF-7 cell line comparison were also confirmed with SILAC-PP technique. The thermodynamic analyses performed here enabled the non-tumorigenic MCF-10A breast cell line to be differentiated from the MCF-7 and MDA-MB-231 breast cancer cell lines. Differentiation of the less invasive MCF-7 breast cancer cell line from the more highly invasive MDA-MB-231 breast cancer cell line was also possible using thermodynamic stability measurements. The differentially stabilized protein hits in these studies encompassed those with a wide range of functions and protein expression levels, and they included a significant fraction (~45%) with similar expression levels in the cell line comparisons. These proteins created novel molecular signatures to differentiate the cancer cell lines studied here. Our results suggest that protein folding and stability measurements complement the current paradigm of expression level analyses for biomarker discovery and help elucidate the molecular basis of disease.






Adhikari, Jagat (2015). Global Analysis of Protein Folding Thermodynamics for Disease State Characterization and Biomarker Discovery. Dissertation, Duke University. Retrieved from


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