A NEW TUMOR SUPPRESSOR GENE CANDIDATE REGULATED BY THE NON-CODING RNA PCA3 IN HUMAN PROSTATE CANCER

Prostate cancer is the second leading cause of cancer-related death and the most common non-skin cancer in men in the USA. Considerable advancements in the practice of medicine have allowed a significant improvement in the diagnosis and treatment of this disease and, in recent years, both incidence and mortality rates have been slightly declining. However, it is still estimated that 1 man in 6 will be diagnosed with prostate cancer during his lifetime, and 1 man in 35 will die of the disease. In order to identify novel strategies and effective therapeutic approaches in the fight against prostate cancer, it is imperative to improve our understanding of its complex biology since many aspects of prostate cancer initiation and progression still remain elusive. The study of tumor biomarkers, due to their specific altered expression in tumor versus normal tissue, is a valid tool for elucidating key aspects of cancer biology, and may provide important insights into the molecular mechanisms underlining the tumorigenesis process of prostate cancer. PCA3, is considered the most specific prostate cancer biomarker, however its biological role, until now, remained unknown. PCA3 is a long non-coding RNA (ncRNA) expressed from chromosome 9q21 and its study led us to the discovery of a novel human gene, PC-TSGC, transcribed from the opposite strand and in an antisense orientation to PCA3. With the work presented in this thesis, we demonstrate that PCA3 exerts a negative regulatory role over PC-TSGC, and we propose PC-TSGC to be a new tumor suppressor gene that contrasts the transformation of prostate cells by inhibiting Rho-GTPases signaling pathways. Our findings provide a biological role for PCA3 in prostate cancer and suggest a new mechanism of tumor suppressor gene inactivation mediated by non-coding RNA. Also, the characterization of PCA3 and PC-TSGC led us to propose a new molecular pathway involving both genes in the transformation process of the prostate, thus providing a new piece of the jigsaw puzzle representing the complex biology of prostate cancer.

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

LINKAGE RELATIONSHIPS AMONG PROTEIN-CODING LOCI IN FISHES OF THE GENUS XIPHOPHORUS

Inbred strains of three species of fishes of the genus Xiphophorus (platyfish and swordtails) were crossed to produce intra- and interspecific F(,1) hybrids, which were then backcrossed to one or both parental stocks. Backcross hybrids were used for the analysis of segregation and linkage of 33 protein-coding loci (whose products were visualized by starch gel electrophoresis) and a sex-linked pigment pattern gene. Segregation was Mendelian for all loci with the exception of one instance of segregation distortion. Six linkage groups of enzyme-coding loci were established: LG I, ADA --6%-- G(,6)PD --24%-- 6PGD; LG II, Est-2 --27%-- Est-3 --0%-- Est-5 --23%-- LDH-1 --16%-- MPI; LG III, AcPh --38%-- G(,3)PD-1 (GUK-2 --14%-- G(,3)PD-1 is also in LG III, but the position of GUK-2 with respect to AcPh has not yet been determined); LG IV, GPI-1 --41%-- IDH-1; LG V, Est-1 --38%-- MDH-2; and LG VI, P1P --7%-- UMPK-1 (P1P is a plasma protein, very probably transferrin).^ Sex-specific recombination appeared absent in LG II and LG IV locus pairs; significantly higher male recombination was demonstrated in LG I but significantly higher female recombination was detected in LG V. Only one significant population-specific difference in recombination was detected, in the G(,6)PD - 6PGD region of LG I; the notable absence of such effects implies close correspondence of the genomes of the species used in the study. Two cases of possible evolutionary conservation of linkage groups in fishes and mammals were described, involving the G(,6)PD - 6PGD linkage in LG I and the cluster of esterase loci in LG II. One clear case of divergence was observed, that of the linkage of ADA in LG I. It was estimated that a minimum of (TURN)50% of the Xiphophorus genome was marked by the loci studied. Therefore, the prior probability that a new locus will assort independently from the markers already established is estimated to be less than 0.5. A maximum of 21 of the 24 pairs of chromosomes could be marked with at least one locus.^ Only the two LG V loci showed a significant association with a postulated gene controlling the severity of a genetically controlled melanoma caused by abnormal proliferation of macromelanophore pigment pattern cells. The independence of melanotic severity from all other informative markers implies that one or at most a few major genes are involved in control of melanotic severity in this system. ^

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

Physician coding for mental illness reimbursement and the need for mental health parity

Many patients with anxiety and depression initially seek treatment from their primary care physicians. Changes in insurance coverage and current mental parity laws, make reimbursement for services a problem. This has led to a coding dilemma for physicians seeking payment for their services. This study seeks to determine first the frequency at which primary care physicians use alternative coding, and secondly, if physicians would change their coding practices, provided reimbursement was assured through changes in mental parity laws. A mail survey was sent to 260 randomly selected primary care physicians, who are family practice, internal medicine, and general practice physicians, and members of the Harris County Medical Society. The survey evaluated the physicians' demographics, the number of patients with psychiatric disorders seen by primary care physicians, the frequency with which physicians used alternative coding, and if mental parity laws changed, the rate at which physicians would use a psychiatric illness diagnosis as the primary diagnostic code. The overall response rate was 23%. Only 47 of the 59 physicians, who responded, qualified for the study and of those 45% used a psychiatric disorder to diagnose patients with a primary psychiatric disorder, 47% used a somatic/symptom disorder, and 8% used a medical diagnosis. From the physicians who would not use a psychiatric diagnosis as a primary ICD-9 code, 88% were afraid of not being reimbursed and 12% were worried about stigma or jeopardizing insurability. If payment were assured using a psychiatric diagnostic code, 81% physicians would use a psychiatric diagnosis as the primary diagnostic code. However, 19% would use an alternative diagnostic code in fear of stigmatizing and/or jeopardizing patients' insurability. Although the sample size of the study design was adequate, our survey did not have an ideal response rate, and no significant correlation was observed. However, it is evident that reimbursement for mental illness continues to be a problem for primary care physicians. The reformation of mental parity laws is necessary to ensure that patients receive mental health services and that primary care physicians are reimbursed. Despite the possibility of improved mental parity legislation, some physicians are still hesitant to assign patients with a mental illness diagnosis, due to the associated stigma, which still plays a role in today's society. ^

Dynamic population coding in primary visual cortex

More than a century ago Ramon y Cajal pioneered the description of neural circuits. Currently, new techniques are being developed to streamline the characterization of entire neural circuits. Even if this 'connectome' approach is successful, it will represent only a static description of neural circuits. Thus, a fundamental question in neuroscience is to understand how information is dynamically represented by neural populations. In this thesis, I studied two main aspects of dynamical population codes. ^ First, I studied how the exposure or adaptation, for a fraction of a second to oriented gratings dynamically changes the population response of primary visual cortex neurons. The effects of adaptation to oriented gratings have been extensively explored in psychophysical and electrophysiological experiments. However, whether rapid adaptation might induce a change in the primary visual cortex's functional connectivity to dynamically impact the population coding accuracy is currently unknown. To address this issue, we performed multi-electrode recordings in primary visual cortex, where adaptation has been previously shown to induce changes in the selectivity and response amplitude of individual neurons. We found that adaptation improves the population coding accuracy. The improvement was more prominent for iso- and orthogonal orientation adaptation, consistent with previously reported psychophysical experiments. We propose that selective decorrelation is a metabolically inexpensive mechanism that the visual system employs to dynamically adapt the neural responses to the statistics of the input stimuli to improve coding efficiency. ^ Second, I investigated how ongoing activity modulates orientation coding in single neurons, neural populations and behavior. Cortical networks are never silent even in the absence of external stimulation. The ongoing activity can account for up to 80% of the metabolic energy consumed by the brain. Thus, a fundamental question is to understand the functional role of ongoing activity and its impact on neural computations. I studied how the orientation coding by individual neurons and cell populations in primary visual cortex depend on the spontaneous activity before stimulus presentation. We hypothesized that since the ongoing activity of nearby neurons is strongly correlated, it would influence the ability of the entire population of orientation-selective cells to process orientation depending on the prestimulus spontaneous state. Our findings demonstrate that ongoing activity dynamically filters incoming stimuli to shape the accuracy of orientation coding by individual neurons and cell populations and this interaction affects behavioral performance. In summary, this thesis is a contribution to the study of how dynamic internal states such as rapid adaptation and ongoing activity modulate the population code accuracy. ^

Logistic regression with incompletely observed binary

Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.^ Two methods were developed to estimate logistic regression coefficients for mixed dichotomous and continuous covariates including partially observed binary covariates. The data were assumed missing at random (MAR). One method (PD) used predictive distribution as weight to calculate the average of the logistic regressions performing on all possible values of missing observations, and the second method (RS) used a variant of resampling technique. Additional seven methods were compared with these two approaches in a simulation study. They are: (1) Analysis based on only the complete cases, (2) Substituting the mean of the observed values for the missing value, (3) An imputation technique based on the proportions of observed data, (4) Regressing the partially observed covariates on the remaining continuous covariates, (5) Regressing the partially observed covariates on the remaining continuous covariates conditional on response variable, (6) Regressing the partially observed covariates on the remaining continuous covariates and response variable, and (7) EM algorithm. Both proposed methods showed smaller standard errors (s.e.) for the coefficient involving the partially observed covariate and for the other coefficients as well. However, both methods, especially PD, are computationally demanding; thus for analysis of large data sets with partially observed covariates, further refinement of these approaches is needed. ^

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance.

Post-transcriptional Regulation of Mammalian Gene Expression in Non-coding Region of Target RNA

Tumor Suppressor Candidate 2 (TUSC2) is a novel tumor suppressor gene located in the human chromosome 3p21.3 region. TUSC2 mRNA transcripts could be detected on Northern blots in both normal lung and some lung cancer cell lines, but no endogenous TUSC2 protein could be detected in a majority of lung cancer cell lines. Mechanisms regulating TUSC2 protein expression and its inactivation in primary lung cancer cells are largely unknown. We investigated the role of the 5’- and 3’-untranslated regions (UTRs) of the TUSC2 gene in the regulation of TUSC2 protein expression. We found that two small upstream open-reading frames (uORFs) in the 5’UTR of TUSC2 could markedly inhibit the translational initiation of TUSC2 protein by interfering with the “scanning” of the ribosome initiation complexes. Site-specific stem-loop array reverse transcription-polymerase chain reaction (SLA-RT-PCR) verified several micoRNAs (miRNAs) targeted at 3’UTR and directed TUSC2 cleavage and degradation. In addition, we used the established let-7-targeted high mobility group A2 (Hmga2) mRNA as a model system to study the mechanism of regulation of target mRNA by miRNAs in mammalian cells under physiological conditions. There have been no evidence of direct link between mRNA downregulation and mRNA cleavages mediated by miRNAs. Here we showed that the endonucleolytic cleavages on mRNAs were initiated by mammalian miRNA in seed pairing style. Let-7 directed cleavage activities among the eight predicted potential target sites have varied efficiency, which are influenced by the positional and the structural contexts in the UTR. The 5’ cleaved RNA fragments were mostly oligouridylated at their 3’-termini and accumulated for delayed 5’–3’ degradation. RNA fragment oligouridylation played important roles in marking RNA fragments for delayed bulk degradation and in converting RNA degradation mode from 3’–5’ to 5’–3’ with cooperative efforts from both endonucleolytic and non-catalytic miRNA-induced silencing complex (miRISC). Our findings point to a mammalian miRNA-mediated mechanism for the regulation of mRNA that miRNA can decrease target mRNA through target mRNA cleavage and uridine addition

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.