Studies to localize and characterize the isoform specific functional differences between cardiac and skeletal troponin C

Thin filament regulation of muscle contraction is a calcium dependent process mediated by the Tn complex. Calcium is released into the sarcomere and is bound by TnC. The subsequent conformation change in TnC is thought to begin a cascade of events that result in the activation of the actin-myosin ATPase. While the general events of this cascade are known, the molecular mechanisms of this signal transduction event are not. Recombinant DNA techniques, physiological and biochemical studies have been used to localize and characterize the structural domains of TnC that play a role in the calcium dependent signal transduction event that serves to trigger muscle contraction. The strategy exploited the observed functional differences between the isoforms of TnC to map regions of functional significance to the proteins. Chimeric cardiac-skeletal TnC proteins were generated to localize the domains of TnC that are required for maximal function in the myofibrilar ATPase assay. Characterization of these regions has yielded information concerning the molecular mechanism of muscle contraction. ^

Determining the roles of dendritic cells and ICAM-1 in the transpresentation of IL-15 to CD8 T cells

The maintenance and generation of memory CD8 T cells is dependent on the cytokine IL-15. IL-15 is delivered by a novel mechanism termed transpresentation: IL-15 is presented by a cell expressing IL-15Ralpha to the CD8 T cell which responds via IL-2Rbeta/gammac. The identity of what cells transpresent IL-15 to support the survival and homeostatic proliferation of memory CD8 T cells is unknown. Using a transgenic mouse model that limits IL-15 transpresentation to DCs, I have demonstrated that DCs transpresent IL-15 to CD8 T cells. DCs transpresent IL-15 to CD8 T cells during the contraction of an immune response and also drive homeostatic proliferation of memory CD8 T cells. Additionally, I identified a role for ICAM-1 in promoting homeostatic proliferation. Wt memory CD8 T cells displayed impaired homeostatic proliferation in ICAM-1-/- hosts but not in models of acute IL-15-driven proliferation. In this way, the role of ICAM-1 in IL-15 transpresentation resembles the role for ICAM-1 in antigenpresentation: where antigen or IL-15 is limited, adhesion molecules are important for generating maximal responses. In vitro cultures between CD8 T cells and bone marrowdifferentiated DCs (BMDC) activated with a TLR agonist established a model of proliferation and signaling in CD8 T cells that was dependent on IL-15 transpresentation and required ICAM-1 expression by BMDCs. Regarding the expression of IL-15, I demonstrated that in normal mice it is undetectable without stimulation but is elevated in lymphopenic mice, suggesting a role for T cells in regulating IL-15 expression. Overall, these studies have identified many novel aspects of the interaction between DCs and CD8 T cells that were previously unknown. The study of adhesion molecules in IL-15 transpresentation describes a novel role for these well-known adhesion molecules and it will be interesting for future studies to further characterize this relationship for other IL-15-dependent cell types.

Determining the roles of dendritic cells and ICAM-1 in the transpresentation of IL-15 to CD8 T cells

The maintenance and generation of memory CD8 T cells is dependent on the cytokine IL-15. IL-15 is delivered by a novel mechanism termed transpresentation: IL-15 is presented by a cell expressing IL-15Ralpha to the CD8 T cell which responds via IL-2Rbeta/gammac. The identity of what cells transpresent IL-15 to support the survival and homeostatic proliferation of memory CD8 T cells is unknown. Using a transgenic mouse model that limits IL-15 transpresentation to DCs, I have demonstrated that DCs transpresent IL-15 to CD8 T cells. DCs transpresent IL-15 to CD8 T cells during the contraction of an immune response and also drive homeostatic proliferation of memory CD8 T cells. Additionally, I identified a role for ICAM-1 in promoting homeostatic proliferation. Wt memory CD8 T cells displayed impaired homeostatic proliferation in ICAM-1-/- hosts but not in models of acute IL-15-driven proliferation. In this way, the role of ICAM-1 in IL-15 transpresentation resembles the role for ICAM-1 in antigenpresentation: where antigen or IL-15 is limited, adhesion molecules are important for generating maximal responses. In vitro cultures between CD8 T cells and bone marrowdifferentiated DCs (BMDC) activated with a TLR agonist established a model of proliferation and signaling in CD8 T cells that was dependent on IL-15 transpresentation and required ICAM-1 expression by BMDCs. Regarding the expression of IL-15, I demonstrated that in normal mice it is undetectable without stimulation but is elevated in lymphopenic mice, suggesting a role for T cells in regulating IL-15 expression. Overall, these studies have identified many novel aspects of the interaction between DCs and CD8 T cells that were previously unknown. The study of adhesion molecules in IL-15 transpresentation describes a novel role for these well-known adhesion molecules and it will be interesting for future studies to further characterize this relationship for other IL-15-dependent cell types.

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.