AN INVESTIGATION OF VOLATILE ORGANIC AIR CONTAMINANTS IN THE INDOOR MANUFACTURED HOUSING ENVIRONMENT (TEXAS)

Manufactured housing has been found to have substantial levels of formaldehyde in the indoor air. Because mobile homes are more affordable than conventional housing, there has been a large increase in their use in the U.S. This increase in mobile home use has been substantial in the sunbelt regions such as Texas, where high temperatures and humidities may enhance out-gassing of formaldehyde and other volatile organic compounds from construction and furnishing materials and increase any potential health hazards.^ The influences of environmental, architectural and temporal factors on the presence of indoor formaldehyde and other organic compounds were investigated in conjunction with the Texas Indoor Air Quality Study of manufactured housing. A matched pair of mobile homes, one with electric heating and cooking utilities and the other with propane gas utilities, were used for a series of controlled experiments over a fourteen month period from October, 1982 through November, 1983.^ Over this fourteen month period formaldehyde levels decreased approximately 33%. Daily fluctuations of 20% to 40% were observed even with a constant indoor temperature. An increase in indoor temperature of 8(DEGREES)C doubled the measured formaldehyde concentration. Opening windows resulted in decreases of indoor formaldehyde levels of up to 50%. Studies of the impact of propane as a cooking source showed no increase in formaldehyde levels with stove use.^ The presence and concentration of selected volatile organic compounds is influenced greatest by occupancy. Occupants continually open and close windows and doors, vary the operation and settings (temperature) of air control systems, and vary in their selection of furnishings and use of consumer products, which may act as sources of indoor air contaminants. ^

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.

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.

Delta like ligand 4 is a critical regulator of bone marrow cell differentiation into pericytes/vascular smooth muscle cells and is essential for the vasculogenesis that supports the growth of Ewing’s sarcoma

We have previously shown that vasculogenesis, the process by which bone marrow-derived cells are recruited to the tumor and organized to form a blood vessel network de novo, is essential for the growth of Ewing’s sarcoma. We further demonstrated that these bone marrow cells differentiate into pericytes/vascular smooth muscle cells(vSMC) and contribute to the formation of the functional vascular network. The molecular mechanisms that control bone marrow cell differentiation into pericytes/vSMC in Ewing’s sarcoma are poorly understood. Here, we demonstrate that the Notch ligand Delta like ligand 4 (DLL4) plays a critical role in this process. DLL4 is essential for the formation of mature blood vessels during development and in several tumor models. Inhibition of DLL4 causes increased vascular sprouting, decreased pericyte coverage, and decreased vessel functionality. We demonstrate for the first time that DLL4 is expressed by bone marrow-derived pericytes/vascular smooth muscle cells in two Ewing’s sarcoma xenograft models and by perivascular cells in 12 out of 14 patient samples. Using dominant negative mastermind to inhibit Notch, we demonstrate that Notch signaling is essential for bone marrow cell participation in vasculogenesis. Further, inhibition of DLL4 using either shRNA or the monoclonal DLL4 neutralizing antibody YW152F led to dramatic changes in blood vessel morphology and function. Vessels in tumors where DLL4 was inhibited were smaller, lacked lumens, had significantly reduced numbers of bone marrow-derived pericyte/vascular smooth muscle cells, and were less functional. Importantly, growth of TC71 and A4573 tumors was significantly inhibited by treatment with YW152F. Additionally, we provide in vitro evidence that DLL4-Notch signaling is involved in bone marrow-derived pericyte/vascular smooth muscle cell formation outside of the Ewing’s sarcoma environment. Pericyte/vascular smooth muscle cell marker expression by whole bone marrow cells cultured with mouse embryonic stromal cells was reduced when DLL4 was inhibited by YW152F. For the first time, our findings demonstrate a role for DLL4 in bone marrow-derived pericyte/vascular smooth muscle differentiation as well as a critical role for DLL4 in Ewing’s sarcoma tumor growth.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

THE ROLE OF MUCOSAL EPITHELIAL CELLS IN HIV-1 INFECTION

The predominant route of human immunodeficiency virus type 1 (HIV-1) transmission is infection across the vaginal mucosa. Epithelial cells, which form the primary barrier of protection against pathogens, are the first cell type at these mucosal tissues to encounter the virus but their role in HIV infection has not been clearly elucidated. Although mucosal epithelial cells express only low levels of the receptors required for successful HIV infection, productive infection does occur at these sites. The present work provides evidence to show that HIV exposure, without the need for productive infection, induces human cervical epithelial cells to produce Thymic Stromal Lymphopoietin (TSLP), an IL7-like cytokine, which potently activated human myeloid dendritic cells (mDC) to cause the homeostatic proliferation of autologous CD4+ T cells that serve as targets for HIV infection. Rhesus macaques inoculated with simian immunodeficiency virus (SIV) or with the simian-human immunodeficiency virus (SHIV) by the vaginal, oral or rectal route exhibited dramatic increases in: TSLP expression, DC and CD4+ T cell numbers, and viral replication, in the vaginal, oral, and rectal tissues, respectively within the first 2 weeks after virus exposure. Evidence obtained showed that HIV-mediated TSLP production by cervical cells is dependent upon the expression of the cell surface salivary agglutinin (SAG) protein gp340. Epithelial cells expressing gp340 exhibited HIV endocytosis and TSLP expression and genetic knockdown of gp340 or use of a gp340-blocking antibody inhibited TSLP expression by HIV. On the other hand, gp340-null epithelial cells failed to endocytose HIV and produce TSLP, but transfection of gp340 resulted in HIV-induced TSLP expression. Finally, HIV-induced TSLP expression was found to be mediated by TLR7/8 signaling and NF-kB activity because silencing these pathways or use of specific inhibitors abrogated TSLP expression in gp340-postive but not in gp340-null epithelial cells. Overall these studies identify TSLP as a key player in the acute phase of HIV-1 infection in permitting HIV to successfully maneuver the hostile vaginal mucosal microenvironment by creating a conducive environment for sustaining the small amount of virus that initially crosses the mucosal barrier allowing it to successfully cause infection and spread to distal compartments of the body

DOUBLE-STRAND BREAK REPAIR PATHWAYS IN DNA STRUCTURE-INDUCED GENETIC INSTABILITY

Genetic instability in mammalian cells can occur by many different mechanisms. In the absence of exogenous sources of DNA damage, the DNA structure itself has been implicated in genetic instability. When the canonical B-DNA helix is naturally altered to form a non-canonical DNA structure such as a Z-DNA or H-DNA, this can lead to genetic instability in the form of DNA double-strand breaks (DSBs) (1, 2). Our laboratory found that the stability of these non-B DNA structures was different in mammals versus Escherichia coli (E.coli) bacteria (1, 2). One explanation for the difference between these species may be a result of how DSBs are repaired within each species. Non-homologous end-joining (NHEJ) is primed to repair DSBs in mammalian cells, while bacteria that lack NHEJ (such as E.coli), utilize homologous recombination (HR) to repair DSBs. To investigate the role of the error-prone NHEJ repair pathway in DNA structure-induced genetic instability, E.coli cells were modified to express genes to allow for a functional NHEJ system under different HR backgrounds. The Mycobacterium tuberculosis NHEJ sufficient system is composed of Ku and Ligase D (LigD) (3). These inducible NHEJ components were expressed individually and together in E.coli cells, with or without functional HR (RecA/RecB), and the Z-DNA and H-DNA-induced mutations were characterized. The Z-DNA structure gave rise to higher mutation frequencies compared to the controls, regardless of the DSB repair pathway(s) available; however, the type of mutants produced after repair was greatly dictated on the available DSB repair system, indicated by the shift from 2% large-scale deletions in the total mutant population to 24% large-scale deletions when NHEJ was present (4). This suggests that NHEJ has a role in the large deletions induced by Z-DNA-forming sequences. H-DNA structure, however, did not exhibit an increase in mutagenesis in the newly engineered E.coli environment, suggesting the involvement of other factors in regulating H-DNA formation/stability in bacterial cells. Accurate repair by established DNA DSB repair pathways is essential to maintain the stability of eukaryotic and prokaryotic genomes and our results suggest that an error-prone NHEJ pathway was involved in non-B DNA structure-induced mutagenesis in both prokaryotes and eukaryotes.

The Immune Inhibitor A1 Protease of Bacillus anthracis

Bacillus anthracis, an organism ubiquitous in the soil and the causative agent of anthrax, utilizes multiple mechanisms to regulate secreted factors; one example is the activity of secreted proteases. One of the most abundant proteins in the culture supernates of B. anthracis is the Immune Inhibitor A1 (InhA1) protease. Here, I demonstrate that InhA1 modulates the abundance of approximately half of the proteins secreted into the culture supernates, including substrates that are known to contribute to the ability of the organism to cause virulence. For example, InhA1 cleaves the anthrax toxin proteins, PA, LF, and EF. InhA1 also targets a number of additional proteases, including Npr599, contributing to a complex proteolytic regulatory cascade with far-reaching affects on the secretome. Using an intra-tracheal mouse model of infection, I found that an inhA-null strain is attenuated in relation to the parent strain. The data indicate that reduced virulence of the inhA mutant strain may be the result of toxin protein deregulation, decreased association with macrophages, and/or the inability to degrade host antimicrobial peptides. Given the significant modulation of the secretome by InhA1, it is likely that expression of the protease is tightly regulated. To test this I examined inhA1 transcript and protein levels in the parent and various isogenic mutant strains and found that InhA1 expression is regulated by several mechanisms. First, the steady state levels of inhA1 transcript are controlled by the regulatory protein SinR, which inhibits inhA1 expression. Second, InhA1 abundance is inversely proportional to the SinR-regulated protease camelysin, indicating the post-transcriptional regulation of InhA1 by camelysin. Third, InhA1 activity is dependent on a conserved zinc binding motif, suggesting that zinc availability regulates InhA1 activity. The convergence of these regulatory mechanisms signifies the importance of tight regulation of InhA1 activity, activity that substantially affects how B. anthracis interacts with its environment.