AGROBACTERIUM VIRB10 CONTRIBUTIONS TO TYPE IV SUBSTRATE SECRETION, T-PILUS BIOGENESIS, AND OUTER MEMBRANE PORE FORMATION

The VirB/D4 type IV secretion system (T4SS) of Agrobacterium tumefaciens functions to transfer substrates to infected plant cells through assembly of a translocation channel and a surface structure termed a T-pilus. This thesis is focused on identifying contributions of VirB10 to substrate transfer and T-pilus formation through a mutational analysis. VirB10 is a bitopic protein with several domains, including a: (i) cytoplasmic N-terminus, (ii) single transmembrane (TM) α-helix, (iii) proline-rich region (PRR), and (iv) large C-terminal modified β-barrel. I introduced cysteine insertion and substitution mutations throughout the length of VirB10 in order to: (i) test a predicted transmembrane topology, (ii) identify residues/domains contributing to VirB10 stability, oligomerization, and function, and (iii) monitor structural changes accompanying energy activation or substrate translocation. These studies were aided by recent structural resolution of a periplasmic domain of a VirB10 homolog and a ‘core’ complex composed of homologs of VirB10 and two outer membrane associated subunits, VirB7 and VirB9. By use of the substituted cysteine accessibility method (SCAM), I confirmed the bitopic topology of VirB10. Through phenotypic studies of Ala-Cys insertion mutations, I identified “uncoupling” mutations in the TM and β-barrel domains that blocked T-pilus assembly but permitted substrate transfer. I showed that cysteine replacements in the C-terminal periplasmic domain yielded a variety of phenotypes in relation to protein accumulation, oligomerization, substrate transfer, and T-pilus formation. By SCAM, I also gained further evidence that VirB10 adopts different structural states during machine biogenesis. Finally, I showed that VirB10 supports substrate transfer even when its TM domain is extensively mutagenized or substituted with heterologous TM domains. By contrast, specific residues most probably involved in oligomerization of the TM domain are required for biogenesis of the T-pilus.

Molecular mechanisms of prostacyclin receptor signaling: The intracellular domains in G protein coupling

To better understand the mechanisms of how the human prostacyclin receptor (1P) mediates vasodilation and platelet anti-aggregation through Gs protein coupling, a strategy integrating multiple approaches including high resolution NMR experiments, synthetic peptide, fluorescence spectroscopy, molecular modeling, and recombinant protein was developed and used to characterize the structure/function relationship of important segments and residues of the IP receptor and the α-subunit of the Gs protein (Gαs). The first (iLP1) and third (iLP3) intracellular loops of the IP receptor, as well as the Gαs C-terminal domain, relevant to the Gs-mediated IP receptor signaling, were first identified by observation of the effects of the mini gene-expressed corresponding protein segments in HEK293 cells which co-expressed the receptor and Gαs. Evidence of the IP iLP1 domain interacted with the Gαs C-terminal domain was observed by fluorescence and NMR spectroscopic studies using a constrained synthetic peptide, which mimicked the IP iLP1 domain, and the synthetic peptide, which mimicked Gαs C-terminal domain. The solution structural models and the peptide-peptide interaction of the two synthetic protein segments were determined by high resolution NMR spectroscopy. The important residues in the corresponding domains of the IP receptor and the Gαs predicted by NMR chemical shift mapping were used to guide the identification of their protein-protein interaction in cells. A profile of the residues Arg42 - Ala48 of the IP iLP1 domain and the three residues Glu392 ∼ Leu394 of the Gαs C-terminal domain involved in the IP/Gs protein coupling were confirmed by recombinant proteins. The data revealed an intriguing speculation on the mechanisms of how the signal of the ligand-activated IP receptor is transmitted to the Gs protein in regulating vascular functions and homeostasis, and also provided substantial insights into other prostanoid receptor signaling. ^

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Structural plasticity can produce metaplasticity.

BACKGROUND: Synaptic plasticity underlies many aspect of learning memory and development. The properties of synaptic plasticity can change as a function of previous plasticity and previous activation of synapses, a phenomenon called metaplasticity. Synaptic plasticity not only changes the functional connectivity between neurons but in some cases produces a structural change in synaptic spines; a change thought to form a basis for this observed plasticity. Here we examine to what extent structural plasticity of spines can be a cause for metaplasticity. This study is motivated by the observation that structural changes in spines are likely to affect the calcium dynamics in spines. Since calcium dynamics determine the sign and magnitude of synaptic plasticity, it is likely that structural plasticity will alter the properties of synaptic plasticity. METHODOLOGY/PRINCIPAL FINDINGS: In this study we address the question how spine geometry and alterations of N-methyl-D-aspartic acid (NMDA) receptors conductance may affect plasticity. Based on a simplified model of the spine in combination with a calcium-dependent plasticity rule, we demonstrated that after the induction phase of plasticity a shift of the long term potentiation (LTP) or long term depression (LTD) threshold takes place. This induces a refractory period for further LTP induction and promotes depotentiation as observed experimentally. That resembles the BCM metaplasticity rule but specific for the individual synapse. In the second phase, alteration of the NMDA response may bring the synapse to a state such that further synaptic weight alterations are feasible. We show that if the enhancement of the NMDA response is proportional to the area of the post synaptic density (PSD) the plasticity curves most likely return to the initial state. CONCLUSIONS/SIGNIFICANCE: Using simulations of calcium dynamics in synaptic spines, coupled with a biophysically motivated calcium-dependent plasticity rule, we find under what conditions structural plasticity can form the basis of synapse specific metaplasticity.

Cultural models of healing and health: An ethnography of professional nurses and healers

Cultural models of the domains healing and health are important in how people understand health and their behavior regarding it. The biomedicine model has been predominant in Western society. Recent popularity of holistic health and alternative healing modalities contrasts with the biomedical model and the assumptions upon which that model has been practiced. The holistic health movement characterizes an effort by health care providers and others such as nurses to expand the biomedical model and has often incorporated alternative modalities. This research described and compared the cultural models of healing of professional nurses and alternative healers. A group of nursing faculty who promote a holistic model were compared to a group of healers using healing touch. Ethnographic methods of participant observation, free listing and pile sort were used. Theoretical sampling in the free listings reached saturation at 18 in the group of nurses and 21 in the group of healers. Categories consistent for both groups emerged from the data. These were: physical, mental, attitude, relationships, spiritual, self management, and health seeking including biomedical and alternative resources. The healers had little differentiation between the concepts health and healing. The nurses, however, had more elements in self management for health and in health seeking for healing. This reflects the nurse's role in facilitating the shift in locus of responsibility between health and healing. The healers provided more specific information regarding alternative resources. The healer's conceptualization of health was embedded in a spiritual belief system and contrasted dramatically with that of biomedicine. The healer's models also contrasted with holistic health in the areas of holism, locus of responsibility, and dealing with uncertainty. The similarity between the groups and their dissimilarity to biomedicine suggest a larger cultural shift in beliefs regarding health care. ^

Structural equation modeling of the medical outcome study: Short Form 36 (SF-36)

The factorial validity of the SF-36 was evaluated using confirmatory factor analysis (CFA) methods, structural equation modeling (SEM), and multigroup structural equation modeling (MSEM). First, the measurement and structural model of the hypothesized SF-36 was explicated. Second, the model was tested for the validity of a second-order factorial structure, upon evidence of model misfit, determined the best-fitting model, and tested the validity of the best-fitting model on a second random sample from the same population. Third, the best-fitting model was tested for invariance of the factorial structure across race, age, and educational subgroups using MSEM.^ The findings support the second-order factorial structure of the SF-36 as proposed by Ware and Sherbourne (1992). However, the results suggest that: (a) Mental Health and Physical Health covary; (b) general mental health cross-loads onto Physical Health; (c) general health perception loads onto Mental Health instead of Physical Health; (d) many of the error terms are correlated; and (e) the physical function scale is not reliable across these two samples. This hierarchical factor pattern was replicated across both samples of health care workers, suggesting that the post hoc model fitting was not data specific. Subgroup analysis suggests that the physical function scale is not reliable across the "age" or "education" subgroups and that the general mental health scale path from Mental Health is not reliable across the "white/nonwhite" or "education" subgroups.^ The importance of this study is in the use of SEM and MSEM in evaluating sample data from the use of the SF-36. These methods are uniquely suited to the analysis of latent variable structures and are widely used in other fields. The use of latent variable models for self reported outcome measures has become widespread, and should now be applied to medical outcomes research. Invariance testing is superior to mean scores or summary scores when evaluating differences between groups. From a practical, as well as, psychometric perspective, it seems imperative that construct validity research related to the SF-36 establish whether this same hierarchical structure and invariance holds for other populations.^ This project is presented as three articles to be submitted for publication. ^

Interrelationships between the structural and functional aspects of social relationships, comorbidity and health related quality of life in breast cancer survivors

It is widely acknowledged in theoretical and empirical literature that social relationships, comprising of structural measures (social networks) and functional measures (perceived social support) have an undeniable effect on health outcomes. However, the actual mechanism of this effect has yet to be clearly understood or explicated. In addition, comorbidity is found to adversely affect social relationships and health related quality of life (a valued outcome measure in cancer patients and survivors). ^ This cross sectional study uses selected baseline data (N=3088) from the Women's Healthy Eating and Living (WHEL) study. Lisrel 8.72 was used for the latent variable structural equation modeling. Due to the ordinal nature of the data, Weighted Least Squares (WLS) method of estimation using Asymptotic Distribution Free covariance matrices was chosen for this analysis. The primary exogenous predictor variables are Social Networks and Comorbidity; Perceived Social Support is the endogenous predictor variable. Three dimensions of HRQoL, physical, mental and satisfaction with current quality of life were the outcome variables. ^ This study hypothesizes and tests the mechanism and pathways between comorbidity, social relationships and HRQoL using latent variable structural equation modeling. After testing the measurement models of social networks and perceived social support, a structural model hypothesizing associations between the latent exogenous and endogenous variables was tested. The results of the study after listwise deletion (N=2131) mostly confirmed the hypothesized relationships (TLI, CFI >0.95, RMSEA = 0.05, p=0.15). Comorbidity was adversely associated with all three HRQoL outcomes. Strong ties were negatively associated with perceived social support; social network had a strong positive association with perceived social support, which served as a mediator between social networks and HRQoL. Mental health quality of life was the most adversely affected by the predictor variables. ^ This study is a preliminary look at the integration of structural and functional measures of social relationships, comorbidity and three HRQoL indicators using LVSEM. Developing stronger social networks and forming supportive relationships is beneficial for health outcomes such as HRQoL of cancer survivors. Thus, the medical community treating cancer survivors as well as the survivor's social networks need to be informed and cognizant of these possible relationships. ^

The application of latent variable models to the assessment of determinants of HIV risk behavior

Studies on the relationship between psychosocial determinants and HIV risk behaviors have produced little evidence to support hypotheses based on theoretical relationships. One limitation inherent in many articles in the literature is the method of measurement of the determinants and the analytic approach selected. ^ To reduce the misclassification associated with unit scaling of measures specific to internalized homonegativity, I evaluated the psychometric properties of the Reactions to Homosexuality scale in a confirmatory factor analytic framework. In addition, I assessed the measurement invariance of the scale across racial/ethnic classifications in a sample of men who have sex with men. The resulting measure contained eight items loading on three first-order factors. Invariance assessment identified metric and partial strong invariance between racial/ethnic groups in the sample. ^ Application of the updated measure to a structural model allowed for the exploration of direct and indirect effects of internalized homonegativity on unprotected anal intercourse. Pathways identified in the model show that drug and alcohol use at last sexual encounter, the number of sexual partners in the previous three months and sexual compulsivity all contribute directly to risk behavior. Internalized homonegativity reduced the likelihood of exposure to drugs, alcohol or higher numbers of partners. For men who developed compulsive sexual behavior as a coping strategy for internalized homonegativity, there was an increase in the prevalence odds of risk behavior. ^ In the final stage of the analysis, I conducted a latent profile analysis of the items in the updated Reactions to Homosexuality scale. This analysis identified five distinct profiles, which suggested that the construct was not homogeneous in samples of men who have sex with men. Lack of prior consideration of these distinct manifestations of internalized homonegativity may have contributed to the analytic difficulty in identifying a relationship between the trait and high-risk sexual practices. ^

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.

The association between parity and selected structural birth defects

BACKGROUND: Parity is a risk factor in neonatal morbidity and mortality. This dissertation examined the association between first births and selected birth defects. The first aim was to assess the risk of 66 birth defects among first births and third or greater births. The second aim was to determine if maternal race, maternal age, infant sex or infant birth weight modify the association between first births and selected birth defects. METHODS: The Texas Birth Defects Registry provided data for 1999-2009. For the first aim, odds ratios were calculated for each birth defect. For the second aim, analysis was restricted to the ten birth defects significantly associated with first births. Stratified analyses were conducted and interaction terms were added to logistic regression models to assess whether differences in the odds ratios for the effect of first birth were statistically significant across strata. RESULTS: Findings for the first aim showed that first births had significantly increased odds of having an infant with 24 of the 66 birth defects. Third or greater births had significantly increased odds of having four of the 66 birth defects. For the second aim, a number of significant effect modifiers were observed. For patent ductus arteriosis, obstructive urinary defects and gastroschisis, the effect of first births was significantly modified by black or U.S.-born Hispanic mothers. The effect of first birth was also significantly modified among mothers ≥30 years for mitral valve insufficiency, atrial septal defect and congenital hip dislocation. The effect of first births was significantly modified among infants with low birth weight for hypospadias, congenital hip dislocation and gastroschisis. CONCLUSIONS: First births were associated with an elevated risk of 24 categories of birth defects. For some of the birth defects studied, the effect of first birth is modified by maternal age, maternal race and low birth weight. Knowledge of the increased risk for birth defects among women having their first birth allows physicians and midwives to provide better patient care and spur further research into the etiology of associated birth defects. This knowledge may bring about interventions prior to conception in populations most likely to conceive.^