Thioredoxin and Jab1 control estrogen- and antiestrogen-mediated progression of the cell cycle through p27 interactions

A major problem with breast cancer treatment is the prevalence of antiestrogen resistance, be it de novo or acquired after continued use. Many of the underlying mechanisms of antiestrogen resistance are not clear, although estrogen receptor-mediated actions have been identified as a pathway that is blocked by antiestrogens. Selective estrogen receptor modulators (SERMs), such as tamoxifen, are capable of producing reactive oxygen species (ROS) through metabolic activation, and these ROS, at high levels, can induce irreversible growth arrest that is similar to the growth arrest incurred by SERMs. This suggests that SERM-mediated growth arrest may also be through ROS accumulation. Breast cancer receiving long-term antiestrogen treatment appears to adapt to this increased, persistent level of ROS. This, in turn, leads to the disruption of reversible redox signaling that involves redox-sensitive phosphatases and protein kinases and transcription factors. This has downstream consequences for apoptosis, cell cycle progression, and cell metabolism. For this dissertation, we explored if altering the ROS formed by tamoxifen also alters sensitivity of the drug in resistant cells. We explored an association with a thioredoxin/Jab1/p27 pathway, and a possible role of dysregulation of thioredoxin-mediated redox regulation contributing to the development of antiestrogen resistance in breast cancer. We used standard laboratory techniques to perform proteomic assays that showed cell proliferation, protein concentrations, redox states, and protein-protein interactions. We found that increasing thioredoxin reductase levels, and thus increasing the amount of reduced thioredoxin, increased tamoxifen sensitivity in previously resistant cells, as well as altered estrogen and tamoxifen-induced ROS. We also found that decreasing levels of Jab1 protein also increased tamoxifen sensitivity, and that the downstream effects showed a decrease p27 phosphorylation in both cases. We conclude that the chronic use of tamoxifen can lead to an increase in ROS that alters cell signaling and causing cell growth in the presence of tamoxifen, and that this resistant cell growth can be reversed with an alteration to the thioredoxin/Jab1 pathway.

Multimode Analysis of Nanoscale Biomolecular Interactions

Biomolecular interactions, including protein-protein, protein-DNA, and protein-ligand interactions, are of special importance in all biological systems. These interactions may occer during the loading of biomolecules to interfaces, the translocation of biomolecules through transmembrane protein pores, and the movement of biomolecules in a crowded intracellular environment. The molecular interaction of a protein with its binding partners is crucial in fundamental biological processes such as electron transfer, intracellular signal transmission and regulation, neuroprotective mechanisms, and regulation of DNA topology. In this dissertation, a customized surface plasmon resonance (SPR) has been optimized and new theoretical and label free experimental methods with related analytical calculations have been developed for the analysis of biomolecular interactions. Human neuroglobin (hNgb) and cytochrome c from equine heart (Cyt c) proteins have been used to optimize the customized SPR instrument. The obtained Kd value (~13 µM), from SPR results, for Cyt c-hNgb molecular interactions is in general agreement with a previously published result. The SPR results also confirmed no significant impact of the internal disulfide bridge between Cys 46 and Cys 55 on hNgb binding to Cyt c. Using SPR, E. coli topoisomerase I enzyme turnover during plasmid DNA relaxation was found to be enhanced in the presence of Mg2+. In addition, a new theoretical approach of analyzing biphasic SPR data has been introduced based on analytical solutions of the biphasic rate equations. In order to develop a new label free method to quantitatively study protein-protein interactions, quartz nanopipettes were chemically modified. The derived Kd (~20 µM) value for the Cyt c-hNgb complex formations matched very well with SPR measurements (Kd ~16 µM). The finite element numerical simulation results were similar to the nanopipette experimental results. These results demonstrate that nanopipettes can potentially be used as a new class of a label-free analytical method to quantitatively characterize protein-protein interactions in attoliter sensing volumes, based on a charge sensing mechanism. Moreover, the molecule-based selective nature of hydrophobic and nanometer sized carbon nanotube (CNT) pores was observed. This result might be helpful to understand the selective nature of cellular transport through transmembrane protein pores.

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.

Specific binding of the mammalian high mobility group protein AT-hook 2 to the minor groove of AT -rich DNAs: Thermodynamic and specificity studies

The mammalian high mobility group protein AT-hook 2 (HMGA2) is a small transcriptional factor involved in cell development and oncogenesis. It contains three "AT-hook" DNA binding domains, which specifically recognize the minor groove of AT-rich DNA sequences. It also has an acidic C-terminal motif. Previous studies showed that HMGA2 mediates all its biological effects through interactions with AT-rich DNA sequences in the promoter regions. In this dissertation, I used a variety of biochemical and biophysical methods to examine the physical properties of HMGA2 and to further investigate HMGA2's interactions with AT-rich DNA sequences. The following are three avenues perused in this study: (1) due to the asymmetrical charge distribution of HMGA2, I have developed a rapid procedure to purify HMGA2 in the milligram range. Preparation of large amounts of HMGA2 makes biophysical studies possible; (2) Since HMGA2 binds to different AT-rich sequences in the promoter regions, I used a combination of isothermal titration calorimetry (ITC) and DNA UV melting experiment to characterize interactions of HMGA2 with poly(dA-dT) 2 and poly(dA)poly(dT). My results demonstrated that (i) each HMGA2 molecule binds to 15 AT bp; (ii) HMGA2 binds to both AT DNAs with very high affinity. However, the binding reaction of HMGA2 to poly(dA-dT) 2 is enthalpy-driven and the binding reaction of HMGA2 with poly(dA)poly(dT) is entropy-driven; (iii) the binding reactions are strongly depended on salt concentrations; (3) Previous studies showed that HMGA2 may have sequence specificity. In this study, I used a PCR-based SELEX procedure to examine the DNA binding specificity of HMGA2. Two consensus sequences for HMGA2 have been identified: 5'-ATATTCGCGAWWATT-3' and 5'-ATATTGCGCAWWATT-3', where W represents A or T. These consensus sequences have a unique feature: the first five base pairs are AT-rich, the middle four to five base pairs are GC-rich, and the last five to six base pairs are AT-rich. All three segments are critical for high affinity binding. Replacing either one of the AT-rich sequences to a non-AT-rich sequence causes at least 100-fold decrease in the binding affinity. Intriguingly, if the GC-segment is substituted by an AT-rich segment, the binding affinity of HMGA2 is reduced approximately 5-fold. Identification of the consensus sequences for HMGA2 represents an important step towards finding its binding sites within the genome.