Effects of salinity, temperature and cadmium stress on cadmium -binding protein in the grass shrimp, {\it Palaemonetes pugio}

The combined effects of salinity, temperature and cadmium stress on survival and adaptation through cadmium-binding protein (CdBP) accumulation were studied in the grass shrimp, Palaemonetes pugio. In 96-hour bioassays, shrimp were exposed to zero or one of three levels of cadmium, under one of six different salinity (15, 25, or 35$\perthous$) and temperature (20 or 30$\sp\circ$C) regimes. CdBP concentrations were quantified in survivors from the 24 exposure groups. Salinity and temperature did not affect survivorship unless the shrimp were also exposed to cadmium. Grass shrimp were most sensitive to cadmium at low salinity-high temperature, and least sensitive at high salinity-low temperature. The incidence of cadmium-associated black lesions in gill tissue was influenced by salinity and temperature stress. P. pugio produced a 10,000 dalton metallothionein-like CdBP when exposed to at least 0.1 mg Cd$\sp{2+}$/L for 96 hours. Accumulation of CdBP was increased with increases in the exposure cadmium level, increases in temperature and decreases in salinity, independently and in conjunction with one another. Maximum CdBP concentrations occurred in grass shrimp that survived the salinity-temperature-cadmium conditions creating maximum stress as measured by highest mortality, not necessarily in shrimp exposed to the highest cadmium levels. The potential utility of this method as a monitor of physiological stress in estuarine biota inhabiting metal-polluted environments is discussed. ^

Expression and function of $\beta$-tubulin isotypes in Chinese hamster ovary (CHO) cells

The availability of isotype specific antisera for $\beta$-tubulin, coupled with genetic and biochemical analysis, has allowed the determination of $\beta$-tubulin isotype expression and distribution in Chinese hamster ovary (CHO) cells. Using genetic manipulations involving selection for colcemid resistance followed by reversion and reselection for drug resistance, we have succeeded in isolating cell lines that exhibit three major and one minor $\beta$-tubulin spots by two-dimensional gel electrophoresis. In concert with isotype specific antibodies, analysis of these mutants demonstrates that CHO cells express two copies of isotype I, at least one copy of isotype IV, and very small amounts of isotype V. Their stoichiometry is approximately 1:1:0.7:0.2. All three isotypes assemble into both cytoplasmic and spindle microtubules, and are similar in their responses to cold, colcemid, and calcium induced depolymerization. They have comparable turnover rates and are equally sensitive to depression of synthesis upon colchicine treatment. These results suggest that $\beta$-tubulin isotypes are used interchangeably to assemble microtubule structures in CHO cells. However, of 18 colcemid resistant mutants with a demonstrable alteration in $\beta$-tubulin, all were found to have the alteration in isotype I, thus leaving open the possibility that subtle differences in isotype properties may exist. Under various conditions of the cell growth, the relative proportion of each expressed isotype does not significantly seem to change except in the early G1 phase of the cell cycle. At this time the synthesis of isotype V increases more than two fold relative to isotype I and IV, while at the same time, total $\beta$-tubulin synthesis is decreased about 60-70%. ^

Estimating the mean and standard deviation of concentration distributions using censored samples

Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^

Ozone and fine particulate matter association with CVD and COPD emergency room visits in Harris County, Texas: Spatiotemporal analysis

This study represents a secondary analysis of the merging of emergency room visits and daily ozone and PM2.5. Although the adverse health effects of ozone and fine particulate matter have been documented in the literature, evidence regarding the health risks of these two pollutants in Harris County, Texas, is limited. Harris County (Houston) has sufficiently unique characteristics that analysis of these relationships in this setting and with the ozone and industry issues in Houston is informative. The objective of this study was to investigate the association between the joint exposure to ozone and fine particulate matter, and emergency room diagnoses of chronic obstructive pulmonary disease and cardiovascular disease in Harris County, Texas, from 2004 to 2009, with zero and one day lags. ^ The study variables were daily emergency room visits for Harris County, Texas, from 2004 to 2009, temperature, relative humidity, east wind component, north wind component, ozone, and fine particulate matter. Information about each patient's age, race, and gender was also included. The two dichotomous outcomes were emergency room visits diagnoses for chronic obstructive pulmonary disease and cardiovascular disease. Estimates of ozone and PM2.5 were interpolated using kriging, in which estimates of the two pollutants were predicted from monitoring data for every case residence zip code for every day of the six years, over 3 million estimates (one of each pollutant for each case in the database). ^ Logistic regressions were conducted to estimate odds ratios of the two outcomes. Three analyses were conducted: one for all records, another for visits during the four months of April and September of 2005 and 2009, and a third one for visits from zip codes that are close to PM2.5 monitoring stations (east area of Harris County). The last two analyses were designed to investigate special temporal and spatial characteristics of the associations. ^ The dataset included all ER visits surveyed by Safety Net from 2004 to 2009, exceeding 3 million visits for all causes. There were 95,765 COPD and 96,596 CVD cases during this six year period. A 1-μg/m3 increase in PM2.5 on the same day was associated with a 1.0% increase in the odds of chronic obstructive pulmonary disease emergency room diagnoses, a 0.4% increase in the odds of cardiovascular disease emergency room diagnoses, and a 0.2% increase in the odds of cardiovascular disease emergency room diagnoses on the following day. A 1-ppb increase in ozone was associated with a 0.1% increase in the odds of chronic obstructive pulmonary disease emergency room diagnoses on the same day. These four percentages add up to 1.7% of ER visits. That is, over the period of six years, one unit increase for both ozone and PM2.5 (joint increase), resulted in about 55,286 (3,252,102 * 0.017) extra ER visits for CVD or COPD, or 9,214 extra ER visits per year. ^ After adjustment for age, race, gender, day of the week, temperature, relative humidity, east wind component, north wind component, and wind speed, there were statistically significant associations between emergency room chronic obstructive pulmonary disease diagnosis in Harris County, Texas, with joint exposure to ozone and fine particulate matter for the same day; and between emergency room cardiovascular disease diagnosis and exposure to PM2.5 of the same day and the previous day. ^ Despite the small association between the two air pollutants and the health outcomes, this study points to important findings. Namely, the need to identify reasons for the increase of CVD and COPD ER visits over the course of the project, the statistical association between humidity (or whatever other variables for which it may serve as a surrogate) and CVD and COPD cases, and the confirmatory finding that males and blacks have higher odds for the two outcomes, as consistent with other studies. ^ An important finding of this research suggests that the number and distribution of PM2.5 monitors in Harris County - although not evenly spaced geographically—are adequate to detect significant association between exposure and the two outcomes. In addition, this study points to other potential factors that contribute to the rising incidence rates of CVD and COPD ER visits in Harris County such as population increases, patient history, life style, and other pollutants. Finally, results of validation, using a subset of the data demonstrate the robustness of the models.^

Expression and regulation of interleukins in human head and neck squamous cell carcinoma

Patients with head and neck squamous cell carcinoma (HNSCC) demonstrate abnormal cell-mediated immunity which is most pronounced at the primary tumor site. Therefore, we tested whether this aberrant immunity could be due to tumor-derived cytokines. We investigated the presence of cytokine mRNA and protein in 8 HNSCC-derived cell lines; RT-PCR results indicated mRNA's for IL-1$\alpha$ and TGF-$\alpha$ (8/8), TGF-$\beta$ (7/8), IL-1$\beta$ (7/8), IL-4 and IL-6 (4/8). IL-2, IFN-$\gamma,$ and TNF-$\alpha$ mRNA was not detected. Supernatants from 6 of these cell lines were analyzed by ELISA and IL-1$\alpha,$ IL-1$\beta,$ and IL-6 were markedly increased compared to HPV-16 immortalized human oral keratinocytes. IL-1$\alpha$ was found in the highest concentration $>$IL-6 $>$ IL-1$\beta.$^ To approach the mechanisms of cytokine regulation, 4 cell lines were compared for HPV DNA presence, p53 status, and cytokine expression. An association between HPV DNA and cytokine expression was not found. However, cell lines secreting the most IL-6 had mutant p53 and/or HPV 16 E6/E7 expression. Further regulatory investigations revealed that exogenous IL-1$\alpha$ and/or IL-1$\beta$ minimally stimulated the proliferation of 2/3 cell lines, as well as strongly induced IL-6 production in 3/3; this effect was completely abrogated by IL-1Ra. IL-1Ra also inhibited the secretion of IL-1$\alpha$ and IL-1$\beta$ in 2/3 cell lines. These data suggest an IL-1 autocrine loop in certain HNSCC cell lines. Because IL-2 induces IL-1 and is used in therapy of HNSCC, the expression of IL-2 receptor was also investigated; IL-2 $\alpha$ and $\beta$ subunits were detected in 3/3 cell lines and $\gamma$ subunits was detected in one. Exogenous IL-2 inhibited the proliferation, but stimulated the secretion of IL-1$\alpha$ in 2/3, and IL-1$\beta$ and IL-6 in 1/3 cell lines.^ To determine if our cell line findings were applicable to patients, immunohistochemistry was performed on biopsies from 12 invasive tumors. Unexpectedly, universal intracellular production of IL-1$\alpha,$ IL-1$\beta,$ and IL-6 protein was detected. Therefore, the aberrant elaboration of biologically active IL-1 and IL-6 may contribute to altered immune status in HNSCC patients. ^

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.

Immunopathology of postprimary tuberculosis: increased T-regulatory cells and DEC-205-positive foamy macrophages in cavitary lesions.

Postprimary tuberculosis occurs in immunocompetent people infected with Mycobacterium tuberculosis. It is restricted to the lung and accounts for 80% of cases and nearly 100% of transmission. Little is known about the immunopathology of postprimary tuberculosis due to limited availability of specimens. Tissues from 30 autopsy cases of pulmonary tuberculosis were located. Sections of characteristic lesions of caseating granulomas, lipid pneumonia, and cavitary stages of postprimary disease were selected for immunohistochemical studies of macrophages, lymphocytes, endothelial cells, and mycobacterial antigens. A higher percentage of cells in lipid pneumonia (36.1%) and cavitary lesions (27.8%) were positive for the dendritic cell marker DEC-205, compared to granulomas (9.0%, P < .05). Cavities contained significantly more T-regulatory cells (14.8%) than found in lipid pneumonia (5.2%) or granulomas (4.8%). Distribution of the immune cell types may contribute to the inability of the immune system to eradicate tuberculosis.

Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^

Seasonal aspects of leukemia

An investigation of (a) month/season-of-birth as a risk factor and (b) month/season-of-treatment initation as a prognostic factor in acute lymphoblastic leukemia (ALL) in children, 0-15 years of age, was conducted. The study population used was that of the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute and included children diagnosed and treated for ALL from 1973-1986. Two separate sets of analyses using different exclusion criteria led to similar results. Specifically, the inability to reject the null hypothesis of no significant difference in the variation of monthly/seasonal incidence rates among children residing within the 10 SEER sites using either cosinor analysis or one-way analysis of variance. No association was established between month/season of treatment initiation and survival in ALL among children using either Kaplan-Meier or cosinor analysis. In separate Kaplan-Meier analyses, age, gender, and treatment type were each found to be significant univariate prognostic factors for survival, however. ^

Prevalence and determinants of hepatitis B co-infection among a United States Veterans Administration cohort with hepatitis C

Background and aim. Hepatitis B virus (HBV) and hepatitis C virus (HCV) co-infection is associated with increased risk of cirrhosis, decompensation, hepatocellular carcinoma, and death. Yet, there is sparse epidemiologic data on co-infection in the United States. Therefore, the aim of this study was to determine the prevalence and determinants of HBV co-infection in a large United States population of HCV patients. ^ Methods. The National Veterans Affairs HCV Clinical Case Registry was used to identify patients tested for HCV during 1997–2005. HCV exposure was defined as two positive HCV tests (antibody, RNA or genotype) or one positive test combined with an ICD-9 code for HCV. HCV infection was defined as only a positive HCV RNA or genotype. HBV exposure was defined as a positive test for hepatitis B core antibodies, hepatitis B surface antigen, HBV DNA, hepatitis Be antigen, or hepatitis Be antibody. HBV infection was defined as only a positive test for hepatitis B surface antigen, HBV DNA, or hepatitis Be antigen within one year before or after the HCV index date. The prevalence of exposure to HBV in patients with HCV exposure and the prevalence of HBV infection in patients with HCV infection were determined. Multivariable logistic regression was used to identify demographic and clinical determinants of co-infection. ^ Results. Among 168,239 patients with HCV exposure, 58,415 patients had HBV exposure for a prevalence of 34.7% (95% CI 34.5–35.0). Among 102,971 patients with HCV infection, 1,431 patients had HBV co-infection for a prevalence of 1.4% (95% CI 1.3–1.5). The independent determinants for an increased risk of HBV co-infection were male sex, positive HIV status, a history of hemophilia, sickle cell anemia or thalassemia, history of blood transfusion, cocaine and other drug use. Age >50 years and Hispanic ethnicity were associated with a decreased risk of HBV co-infection. ^ Conclusions. This is the largest cohort study in the United States on the prevalence of HBV co-infection. Among veterans with HCV, exposure to HBV is common (∼35%), but HBV co-infection is relatively low (1.4%). There is an increased risk of co-infection with younger age, male sex, HIV, and drug use, with decreased risk in Hispanics.^

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.