A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.

Short-Term Plasticity in a Computational Model of the Tail-Withdrawal Circuit in Aplysia.

The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit responded preferentially to long-duration, low-frequency inputs.

THE INTERACTION BETWEEN INTERNAL AND EXTERNAL INFORMATION ON RELATIONAL DATA SEARCH

People often use tools to search for information. In order to improve the quality of an information search, it is important to understand how internal information, which is stored in user’s mind, and external information, represented by the interface of tools interact with each other. How information is distributed between internal and external representations significantly affects information search performance. However, few studies have examined the relationship between types of interface and types of search task in the context of information search. For a distributed information search task, how data are distributed, represented, and formatted significantly affects the user search performance in terms of response time and accuracy. Guided by UFuRT (User, Function, Representation, Task), a human-centered process, I propose a search model, task taxonomy. The model defines its relationship with other existing information models. The taxonomy clarifies the legitimate operations for each type of search task of relation data. Based on the model and taxonomy, I have also developed prototypes of interface for the search tasks of relational data. These prototypes were used for experiments. The experiments described in this study are of a within-subject design with a sample of 24 participants recruited from the graduate schools located in the Texas Medical Center. Participants performed one-dimensional nominal search tasks over nominal, ordinal, and ratio displays, and searched one-dimensional nominal, ordinal, interval, and ratio tasks over table and graph displays. Participants also performed the same task and display combination for twodimensional searches. Distributed cognition theory has been adopted as a theoretical framework for analyzing and predicting the search performance of relational data. It has been shown that the representation dimensions and data scales, as well as the search task types, are main factors in determining search efficiency and effectiveness. In particular, the more external representations used, the better search task performance, and the results suggest the ideal search performance occurs when the question type and corresponding data scale representation match. The implications of the study lie in contributing to the effective design of search interface for relational data, especially laboratory results, which are often used in healthcare activities.

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

ROLE OF GSH METABOLISM IN MEDIATING STROMAL-LEUKEMIA INTERACTION AND PROMOTING CELL SURVIVAL AND DRUG RESISTANCE IN CHRONIC LYMPHOCYTIC LEUKEMIA

Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in the western countries. The interaction between CLL cells and the bone marrow stromal environment is thought to play a major role in promoting the leukemia cell survival and drug resistance. My dissertation works proved a novel biochemical mechanism by which the bone marrow stromal cells exert a profound influence on the redox status of primary CLL cells and enhance their ability to sustain oxidative stress and drug treatment. Fresh leukemia cells isolated from the peripheral blood of CLL patients exhibited two major redox alterations when they were cultured alone: a significant decrease in cellular glutathione (GSH) and an increase in basal ROS levels. However, when cultured in the presence of bone marrow stromal cells, CLL cells restored their redox balance with an increased synthesis of GSH, a decrease in spontaneous apoptosis, and an improved cell survival. Further study showed that CLL cells were under intrinsic ROS stress and highly dependent on GSH for survival, and that the bone marrow stromal cells promoted GSH synthesis in CLL cells through a novel biochemical mechanism. Cysteine is a limiting substrate for GSH synthesis and is chemically unstable. Cells normally obtain cysteine by uptaking the more stable and abundant precursor cystine from the tissue environment and convert it to cysteine intracellularly. I showed that CLL cells had limited ability to take up extracellular cystine for GSH synthesis due to their low expression of the transporter Xc-, but had normal ability to uptake cysteine. In the co-culture system, the bone marrow stromal cells effectively took up cystine and reduced it to cysteine for secretion into the tissue microenvironment to be taken up by CLL cells for GSH synthesis. The elevated GSH in CLL cells in the presence of bone marrow stromal cells significantly protected the leukemia cells from stress-induced apoptosis, and rendered them resistant to standard therapeutic agents such as fludarabine and oxaliplatin. Importantly, disabling of this protective mechanism by depletion of cellular GSH using a pharmacological approach potently sensitized CLL cells to drug treatment, and effectively enhanced the cytotoxic action of fludarabine and oxaliplatin against CLL in the presence of stromal cells. This study reveals a key biochemical mechanism of leukemia-stromal cells interaction, and identifies a new therapeutic strategy to overcome drug resistance in vivo.

Short-Term Plasticity in a Computational Model of the Tail-Withdrawal Circuit in Aplysia.

The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit responded preferentially to long-duration, low-frequency inputs.

Dissecting the Interaction between p53 and TRIM24

Dissecting the Interaction of p53 and TRIM24 Aundrietta DeVan Duncan Supervisory Professor, Michelle Barton, Ph.D. p53, the “guardian of the genome”, plays an important role in multiple biological processes including cell cycle, angiogenesis, DNA repair and apoptosis. Because it is mutated in over 50% of cancers, p53 has been widely studied in established cancer cell lines. However, little is known about the function of p53 in a normal cell. We focused on characterizing p53 in normal cells and during differentiation. Our lab recently identified a novel binding partner of p53, Tripartite Motif 24 protein (TRIM24). TRIM24 is a member of the TRIM family of proteins, defined by their conserved RING, B-box, and coiled coil domains. Specifically, TRIM24 is a member of the TIF1 subfamily, which is characterized by PHD and Bromo domains in the C-terminus. Between the Coiled-coil and PHD domain is a linker region, 437 amino acids in length. This linker region houses important functions of TRIM24 including it’s site of interaction with nuclear receptors. TRIM24 is an E3-ubiquitin ligase, recently discovered to negatively regulate p53 by targeting it for degradation. Though it is known that Trim24 and p53 interact, it is not known if the interaction is direct and what effect this interaction has on the function of TRIM24 and p53. My study aims to elucidate the specific interaction domains of p53 and TRIM24. To determine the specific domains of p53 required for interaction with TRIM24, we performed co-immuoprecipitation (Co-IP) with recombinant full-length Flag-tagged TRIM24 protein and various deletion constructs of in vitro translated GST-p53, as well as the reverse. I found that TRIM24 binds both the carboxy terminus and DNA binding domain of p53. Furthermore, my results show that binding is altered when post-translational modifications of p53 are present, suggesting that the interaction between p53 and TRIM24 may be affected by these post-translational modifications. To determine the specific domains of TRIM24 required for p53 interaction, we performed GST pull-downs with in vitro translated, Flag-TRIM24 protein constructs and recombinant GST-p53 protein purified from E. coli. We found that the Linker region is sufficient for interaction of p53 and TRIM24. Taken together, these data indicate that the interaction between p53 and TRIM24 does occur in vitro and that interaction may be influenced by post-translational modifications of the proteins.

Eluding antibiotic resistance: capitalizing on antimicrobial peptides interaction with the lipid bilayer

It is widely accepted that the emergence of drug-resistant pathogens is the result of the overuse and misuse of antibiotics. Infectious Disease Society of America, Center for Disease Control and World Health Organization continue to view, with concern, the lack of antibiotics in development, especially those against Gram-negative bacteria. Antimicrobial peptides (AMPs) have been proposed as an alternative to antibiotics due to their selective activity against microbes and minor ability to induce resistance. For example, the Food and Drug Administration approved Daptomycin (DAP) in 2003 for treatment of severe skin infections caused by susceptible Gram-positive organisms. Currently, there are 12 to 15 examples of modified natural and synthetic AMPs in clinical development. But most of these agents are against Gram-positive bacteria. Therefore, there is unmet medical need for antimicrobials used to treat infections caused by Gram-negative bacteria. In this study, we show that a pro-apoptotic peptide predominantly used in cancer therapy, (KLAKLAK)2, is an effective antimicrobial against Gram-negative laboratory strains and clinical isolates. Despite the therapeutic promise, AMPs development is hindered by their susceptibility to proteolysis. Here, we demonstrate that an all-D enantiomer of (KLAKLAK)2, resistant to proteolysis, retains its activity against Gram-negative pathogens. In addition, we have elucidated the specific site and mechanism of action of D(KLAKLAK)2 through a repertoire of whole-cell and membrane-model assays. Although it is considered that development of resistance does not represent an obstacle for AMPs clinical development, strains with decreased susceptibility to these compounds have been reported. Staphylococci resistance to DAP was observed soon after its approval for use and has been linked to alterations of the cell wall (CW) and cellular membrane (CM) properties. Immediately following staphylococcal resistance, Enterococci resistance to DAP was seen, yet the mechanism of resistance in enterococci remains unknown. Our findings demonstrate that, similar to S. aureus, development of DAP-resistance in a vancomycin-resistant E. faecalis isolate is associated with alterations of the CW and properties of the CM. However, the genes linked to these changes in enterococci appear to be different from those described in S. aureus.

Regulation of Alix by an autoinhibitory intramolecular interaction

In eukaryotic cells, the ESCRTs (endosomal sorting complexes required for transport) machinery is required for cellular processes such as endosomal sorting, retroviral budding and cytokinesis. The ALG-2 interacting protein Alix is a modular adaptor protein that is critically involved in these ESCRTs-associated cellular processes and consists of an N-terminal Bro1 domain, a middle V domain and C-terminal Pro-rich domain (PRD). In these cellular processes, Alix interacts with the ESCRT-III component CHMP4 at the Bro1 domain, with HIV-1 p6 Gag or EIAV p9Gag at the V domain, and with the ESCRT-I component TSG101 at the Pro-rich domain. Here we demonstrate that the N-terminal Bro1 domain forms an intramolecular interaction with C-terminal PRD within Alix. This Bro1-PRD intramolecular interaction forms a closed conformation of Alix that autoinhibits Alix interaction with all of these partner proteins. Moreover, the binding of Ca2+-activated ALG-2 to the PRD of Alix relieves the autoinhibitory intramolecular interaction, resulting in an open conformation of Alix which is able to interact with all of these partner proteins. The partner proteins bound to Alix in turn maintain Alix in the open conformation after ALG-2 dissociation with Alix. Consistent with the effect of Ca2+-activated ALG-2 on opening/activating Alix in these ESCRTs-associated functions, ALG-2 overexpression accelerates EGF-induced degradation of EGFR in an Alix-dependent manner. These findings discover an intrinsic autoinhibitory mechanism of Alix and a two-step process to activate/open Alix and then keep Alix active/open. This study has solved long-standing issues on the regulations of Alix in ESCRTs-associated functions and the role of ALG-2-Alix interaction, and may serve as the structural basis for further studies about Alix regulations. ^

Design, Calibration, and Evaluation of Depth-of-Interaction-Capable PET Detector Modules

High-resolution, small-bore PET systems suffer from a tradeoff between system sensitivity, and image quality degradation. In these systems long crystals allow mispositioning of the line of response due to parallax error and this mispositioning causes resolution blurring, but long crystals are necessary for high system sensitivity. One means to allow long crystals without introducing parallax errors is to determine the depth of interaction (DOI) of the gamma ray interaction within the detector module. While DOI has been investigated previously, newly available solid state photomultipliers (SSPMs) well-suited to PET applications and allow new modules for investigation. Depth of interaction in full modules is a relatively new field, and so even if high performance DOI capable modules were available, the appropriate means to characterize and calibrate the modules are not. This work presents an investigation of DOI capable arrays and techniques for characterizing and calibrating those modules. The methods introduced here accurately and reliably characterize and calibrate energy, timing, and event interaction positioning. Additionally presented is a characterization of the spatial resolution of DOI capable modules and a measurement of DOI effects for different angles between detector modules. These arrays have been built into a prototype PET system that delivers better than 2.0 mm resolution with a single-sided-stopping-power in excess of 95% for 511 keV g's. The noise properties of SSPMs scale with the active area of the detector face, and so the best signal-to-noise ratio is possible with parallel readout of each SSPM photodetector pixel rather than multiplexing signals together. This work additionally investigates several algorithms for improving timing performance using timing information from multiple SSPM pixels when light is distributed among several photodetectors.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

INTERACTION OF BACILLUS ANTHRACIS EXOSPORIUM PROTEIN BCLA WITH COMPLEMENT FACTOR H AND SPORE PERSISTENCE IN THE LUNG

Anthrax outbreaks in the United States and Europe and its potential use as a bioweapon have made Bacillus anthracis an interest of study. Anthrax infections are caused by the entry of B. anthracis spores into the host via the respiratory system, the gastrointestinal tract, cuts or wounds in the skin, and injection. Among these four forms, inhalational anthrax has the highest lethality rate and persistence of spores in the lungs of animals following pulmonary exposure has been noted for decades. However, details or mechanisms of spore persistence were not known. In this study, we investigated spore persistence in a mouse model. The results suggest that B. anthracis spores have special properties that promote persistence in the lung, and that there may be multiple mechanisms contributing to spore persistence. Moreover, recent discoveries from our laboratory suggest that spores evolved a sophisticated mechanism to interact with the host complement system. The complement system is a crucial part of the host defense mechanism against foreign microorganisms. Knowledge of the specific interactions that occur between the complement system and B. anthracis was limited. Studies performed in our laboratory have suggested that spores of B. anthracis can target specific proteins, such as Factor H (fH) of the complement system. Spores of B. anthracis are enclosed by an exosporium, which consists of a basal layer surrounded by a nap of hair-like filaments. The major structural component of the filaments is called Bacillus collagen-like protein of anthracis (BclA), which comprises a central collagen-like region and a globular C-terminal domain. BclA is the first point of contact with the innate system of an infected host. In this study, we investigated the molecular details of BclA-fH interaction with respect to the specific binding mechanism and the functional significance of this interaction in a murine model of anthrax infection. We hypothesized that the recruitment of fH to the spore surface by BclA limits the extent of complement activation and promotes pathogen survival and persistence in the infected host. Findings from this study are significant to understanding how to treat post-exposure prophylaxis and improve our knowledge of spores with the host immune system.

INTERACTION BETWEEN BRK AND HER2 IN BREAST CANCER

INTERACTION BETWEEN BRK AND HER2 IN BREAST CANCER Midan Ai, Ph.D. Supervisory Professor: Zhen Fan, M.D. Breast tumor kinase (Brk) is a nonreceptor protein-tyrosine kinase that is highly expressed in approximately two thirds of breast cancers but is not detectable or is expressed at very low levels in normal mammary epithelium. Brk plays important roles in promoting proliferation, survival, invasion, and metastasis of breast cancer cells, but the mechanism(s) of which remain largely unknown. Recent studies showed that Brk is frequently co-overexpressed with human epidermal growth factor receptor-2 (HER2) and is physically associated with HER2 in breast cancer. The mechanism needs to be determined. In my studies, I found that high expression of HER2 is correlated with high expression of Brk in breast cancer cell lines. Silencing HER2 expression via RNA interference in HER2 over-expressed breast cancer cells resulted in Brk protein decrease and overexpression of HER2 in HER2 low-expressed breast cancer cells up-regulated Brk expression. The mechanism study indicated that overexpression of HER2 increased Brk protein stability. Brk was degraded through a Ca2+-dependent protease pathway involving calpain and HER2 stimulated Brk expression via inhibiting calpain activity. Calpastatin is a calpain endogenous inhibitor and the calpain-calpastatin system has been implicated in a number of cell physiological functions. HER2 restrained calpain activation via up-regulating calpastatin expression and HER2 downstream signaling, MAPK pathway, was involved in the regulation. Furthermore, silencing of Brk expression by RNA interference in HER2-overexpressing breast cancer cells decreased HER2-mediated cell proliferation, survival, invasion/metastasis potential and increased cell sensitivity to HER2 kinase inhibitor, lapatinib, treatment, indicating that Brk plays important roles in regulating and mediating the oncogenic functions of HER2. The Stat3 pathway played important roles in Brk mediated cell survival and invasion/metastasis in the context of HER2-overexpressing breast cancer cells. However, transgenic mice with inducible expression of constitutively active Brk (CA) in the mammary epithelium failed to develop malignant change in the mammary glands after Brk induction for 15 months which indicated that expression of Brk protein alone was not sufficiently to induce spontaneous breast tumor. Bitransgenic mice with co-expression of HER2/neu and inducible expression of Brk in the mammary epithelium developed multifocal mammary tumors, but there were no significant difference in the tumor occurring time, tumor size, tumor weight and tumor multiplicity between the mouse group with co-expression of Brk and HER2/neu and the mouse group with HER2/neu expression only.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

THE MOLECULAR INTERACTION BETWEEN TYPE II DIABETES AND ALZHEIMER’S DISEASE THROUGH CROSS-SEEDING OF PROTEIN MISFOLDING

With the population of the world aging, the prominence of diseases such as Type II Diabetes (T2D) and Alzheimer’s disease (AD) are on the rise. In addition, patients with T2D have an increased risk of developing AD compared to age-matched individuals, and the number of AD patients with T2D is higher than among aged-matched non-AD patients. AD is a chronic and progressive dementia characterized by amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs), neuronal loss, brain inflammation, and cognitive impairment. T2D involves the dysfunctional use of pancreatic insulin by the body resulting in insulin resistance, hyperglycemia, hyperinsulinemia, pancreatic beta cell (β-cell) death, and other complications. T2D and AD are considered protein misfolding disorders (PMDs). PMDs are characterized by the presence of misfolded protein aggregates, such as in T2D pancreas (islet amyloid polypeptide - IAPP) and in AD brain (amyloid– Aβ) of affected individuals. The misfolding and accumulation of these proteins follows a seeding-nucleation model where misfolded soluble oligomers act as nuclei to propagate misfolding by recruiting other native proteins. Cross-seeding occurs when oligomers composed by one protein seed the aggregation of a different protein. Our hypothesis is that the pathological interactions between T2D and AD may in part occur through cross-seeding of protein misfolding. To test this hypothesis, we examined how each respective aggregate (Aβ or IAPP) affects the disparate disease pathology through in vitro and in vivo studies. Assaying Aβ aggregates influence on T2D pathology, IAPP+/+/APPSwe+/- double transgenic (DTg) mice exhibited exacerbated T2D-like pathology as seen in elevated hyperglycemia compared to controls; in addition, IAPP levels in the pancreas are highest compared to controls. Moreover, IAPP+/+/APPSwe+/- animals demonstrate abundant plaque formation and greater plaque density in cortical and hippocampal areas in comparison to controls. Indeed, IAPP+/+/APPSwe+/- exhibit a colocalization of both misfolded proteins in cerebral plaques suggesting IAPP may directly interact with Aβ and aggravate AD pathology. In conclusion, these studies suggest that cross-seeding between IAPP and Aβ may occur, and that these protein aggregates exacerbate and accelerate disease pathology, respectively. Further mechanistic studies are necessary to determine how these two proteins interact and aggravate both pancreatic and brain pathologies.