CONFORMATIONAL CHANGES IN THE EXTRACELLULAR DOMAIN OF GLUTAMATE RECEPTORS

The family of membrane protein called glutamate receptors play an important role in the central nervous system in mediating signaling between neurons. Glutamate receptors are involved in the elaborate game that nerve cells play with each other in order to control movement, memory, and learning. Neurons achieve this communication by rapidly converting electrical signals into chemical signals and then converting them back into electrical signals. To propagate an electrical impulse, neurons in the brain launch bursts of neurotransmitter molecules like glutamate at the junction between neurons, called the synapse. Glutamate receptors are found lodged in the membranes of the post-synaptic neuron. They receive the burst of neurotransmitters and respond by fielding the neurotransmitters and opening ion channels. Glutamate receptors have been implicated in a number of neuropathologies like ischemia, stroke and amyotrophic lateral sclerosis. Specifically, the NMDA subtype of glutamate receptors has been linked to the onset of Alzheimer’s disease and the subsequent degeneration of neuronal cells. While crystal structures of AMPA and kainate subtypes of glutamate receptors have provided valuable information regarding the assembly and mechanism of activation; little is known about the NMDA receptors. Even the basic question of receptor assembly still remains unanswered. Therefore, to gain a clear understanding of how the receptors are assembled and how agonist binding gets translated to channel opening, I have used a technique called Luminescence Resonance Energy Transfer (LRET). LRET offers the unique advantage of tracking large scale conformational changes associated with receptor activation and desensitization. In this dissertation, LRET, in combination with biochemical and electrophysiological studies, were performed on the NMDA receptors to draw a correlation between structure and function. NMDA receptor subtypes GluN1 and GluN2A were modified such that fluorophores could be introduced at specific sites to determine their pattern of assembly. The results indicated that the GluN1 subunits assembled across each other in a diagonal manner to form a functional receptor. Once the subunit arrangement was established, this was used as a model to further examine the mechanism of activation in this subtype of glutamate receptor. Using LRET, the correlation between cleft closure and activation was tested for both the GluN1 and GluN2A subunit of the NMDA receptor in response to agonists of varying efficacies. These investigations revealed that cleft closure plays a major role in the mechanism of activation in the NMDA receptor, similar to the AMPA and kainate subtypes. Therefore, suggesting that the mechanism of activation is conserved across the different subtypes of glutamate receptors.

Exploration of genetic susceptibility to pancreatic cancer: A GWAS approach

Pancreatic cancer is the 4th most common cause for cancer death in the United States, accompanied by less than 5% five-year survival rate based on current treatments, particularly because it is usually detected at a late stage. Identifying a high-risk population to launch an effective preventive strategy and intervention to control this highly lethal disease is desperately needed. The genetic etiology of pancreatic cancer has not been well profiled. We hypothesized that unidentified genetic variants by previous genome-wide association study (GWAS) for pancreatic cancer, due to stringent statistical threshold or missing interaction analysis, may be unveiled using alternative approaches. To achieve this aim, we explored genetic susceptibility to pancreatic cancer in terms of marginal associations of pathway and genes, as well as their interactions with risk factors. We conducted pathway- and gene-based analysis using GWAS data from 3141 pancreatic cancer patients and 3367 controls with European ancestry. Using the gene set ridge regression in association studies (GRASS) method, we analyzed 197 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Using the logistic kernel machine (LKM) test, we analyzed 17906 genes defined by University of California Santa Cruz (UCSC) database. Using the likelihood ratio test (LRT) in a logistic regression model, we analyzed 177 pathways and 17906 genes for interactions with risk factors in 2028 pancreatic cancer patients and 2109 controls with European ancestry. After adjusting for multiple comparisons, six pathways were marginally associated with risk of pancreatic cancer ( P < 0.00025): Fc epsilon RI signaling, maturity onset diabetes of the young, neuroactive ligand-receptor interaction, long-term depression (Ps < 0.0002), and the olfactory transduction and vascular smooth muscle contraction pathways (P = 0.0002; Nine genes were marginally associated with pancreatic cancer risk (P < 2.62 × 10−5), including five reported genes (ABO, HNF1A, CLPTM1L, SHH and MYC), as well as four novel genes (OR13C4, OR 13C3, KCNA6 and HNF4 G); three pathways significantly interacted with risk factors on modifying the risk of pancreatic cancer (P < 2.82 × 10−4): chemokine signaling pathway with obesity ( P < 1.43 × 10−4), calcium signaling pathway (P < 2.27 × 10−4) and MAPK signaling pathway with diabetes (P < 2.77 × 10−4). However, none of the 17906 genes tested for interactions survived the multiple comparisons corrections. In summary, our current GWAS study unveiled unidentified genetic susceptibility to pancreatic cancer using alternative methods. These novel findings provide new perspectives on genetic susceptibility to and molecular mechanisms of pancreatic cancer, once confirmed, will shed promising light on the prevention and treatment of this disease. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.