Development and Characterization of an in vitro Four-species Anaerobic Dental Biofilm Model

Dental caries is the most common chronic disease worldwide. It is characterized by the demineralization of tooth enamel caused by acid produced by cariogenic dental bacteria growing on tooth surfaces, termed bacterial biofilms. Cariogenesis is a complex biological process that is influence by multiple factors and is not attributed to a sole causative agent. Instead, caries is associated with multispecies microbial biofilm communities composed of some bacterial species that directly influence the development of a caries lesion and other species that are seemingly benign but must contribute to the community in an uncharacterized way. Clinical analysis of dental caries and its microbial populations is challenging due to many factors including low sensitivity of clinical measurement tools, variability in saliva chemistry, and variation in the microbiota. Our laboratory has developed an in vitro anaerobic biofilm model for dental carries to facilitate both clinical and basic research-based analyses of the multispecies dynamics and individual factors that contribute to cariogenicity. The rational for development of this system was to improve upon the current models that lack key elements. This model places an emphasis on physiological relevance and ease of maintenance and reproducibility. The uniqueness of the model is based on integrating four critical elements: 1) a biofilm community composed of four distinct and representative species typically associated with dental caries, 2) a semi-defined synthetic growth medium designed to mimic saliva, 3) physiologically relevant biofilm growth substrates, and 4) a novel biofilm reactor device designed to facilitate the maintenance and analysis. Specifically, human tooth sections or hydroxyapatite discs embedded into poly(methyl methacrylate) (PMMA) discs are incubated for an initial 24 hr in a static inverted removable substrate (SIRS) biofilm reactor at 37°C under anaerobic conditions in artificial saliva (CAMM) without sucrose in the presence of 1 X 106 cells/ml of each Actinomyces odontolyticus, Fusobacterium nucleatum, Streptococcus mutans, and Veillonella dispar. During days 2 and 3 the samples are maintained continually in CAMM with various exposures to 0.2% sucrose; all of the discs are transferred into fresh medium every 24 hr. To validate that this model is an appropriate in vitro representation of a caries-associated multispecies biofilm, research aims were designed to test the following overarching hypothesis: an in vitro anaerobic biofilm composed of four species (S. mutans, V. dispar, A. odontolyticus, and F. nucleatum) will form a stable biofilm with a community profile that changes in response to environmental conditions and exhibits a cariogenic potential. For these experiments the biofilms as described above were exposed on days 2 and 3 to either CAMM lacking sucrose (no sucrose), CAMM with 0.2% sucrose (constant sucrose), or were transferred twice a day for 1 hr each time into 0.2% sucrose (intermittent sucrose). Four types of analysis were performed: 1) fluorescence microscopy of biofilms stained with Syto 9 and hexidium idodine to determine the biofilm architecture, 2) quantitative PCR (qPCR) to determine the cell number of each species per cm2, 3) vertical scanning interferometry (VSI) to determine the cariogenic potential of the biofilms, and 4) tomographic pH imaging using radiometric fluorescence microscopy after exposure to pH sensitive nanoparticles to measure the micro-environmental pH. The qualitative and quantitative results reveal the expected dynamics of the community profile when exposed to different sucrose conditions and the cariogenic potential of this in vitro four-species anaerobic biofilm model, thus confirming its usefulness for future analysis of primary and secondary dental caries.

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

The number and timing of time -dependent covariate measurements for the Cox regression model

The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^

Neocortical hyperexcitability defect in a mutant mouse model of spike-wave epilepsy, {\it stargazer}

Single-locus mutations in mice can express epileptic phenotypes and provide critical insights into the naturally occurring defects that alter excitability and mediate synchronization in the central nervous system (CNS). One such recessive mutation (on chromosome (Chr) 15), stargazer(stg/stg) expresses frequent bilateral 6-7 cycles per second (c/sec) spike-wave seizures associated with behavioral arrest, and provides a valuable opportunity to examine the inherited lesion associated with spike-wave synchronization.^ The existence of distinct and heterogeneous defects mediating spike-wave discharge (SWD) generation has been demonstrated by the presence of multiple genetic loci expressing generalized spike-wave activity and the differential effects of pharmacological agents on SWDs in different spike-wave epilepsy models. Attempts at understanding the different basic mechanisms underlying spike-wave synchronization have focused on $\gamma$-aminobutyric acid (GABA) receptor-, low threshold T-type Ca$\sp{2+}$ channel-, and N-methyl-D-aspartate receptor (NMDA-R)-mediated transmission. It is believed that defects in these modes of transmission can mediate the conversion of normal oscillations in a trisynaptic circuit, which includes the neocortex, reticular nucleus and thalamus, into spike-wave activity. However, the underlying lesions involved in spike-wave synchronization have not been clearly identified.^ The purpose of this research project was to locate and characterize a distinct neuronal hyperexcitability defect favoring spike-wave synchronization in the stargazer brain. One experimental approach for anatomically locating areas of synchronization and hyperexcitability involved an attempt to map patterns of hypersynchronous activity with antibodies to activity-induced proteins.^ A second approach to characterizing the neuronal defect involved examining the neuronal responses in the mutant following application of pharmacological agents with well known sites of action.^ In order to test the hypothesis that an NMDA receptor mediated hyperexcitability defect exists in stargazer neocortex, extracellular field recordings were used to examine the effects of CPP and MK-801 on coronal neocortical brain slices of stargazer and wild type perfused with 0 Mg$\sp{2+}$ artificial cerebral spinal fluid (aCSF).^ To study how NMDA receptor antagonists might promote increased excitability in stargazer neocortex, two basic hypotheses were tested: (1) NMDA receptor antagonists directly activate deep layer principal pyramidal cells in the neocortex of stargazer, presumably by opening NMDA receptor channels altered by the stg mutation; and (2) NMDA receptor antagonists disinhibit the neocortical network by blocking recurrent excitatory synaptic inputs onto inhibitory interneurons in the deep layers of stargazer neocortex.^ In order to test whether CPP might disinhibit the 0 Mg$\sp{2+}$ bursting network in the mutant by acting on inhibitory interneurons, the inhibitory inputs were pharmacologically removed by application of GABA receptor antagonists to the cortical network, and the effects of CPP under 0 Mg$\sp{2+}$aCSF perfusion in layer V of stg/stg were then compared with those found in +/+ neocortex using in vitro extracellular field recordings. (Abstract shortened by UMI.) ^

Behavioral decision making and the levels of change: An application of the transtheoretical model to prenatal smoking in low -income women

Despite continued research and public health efforts to reduce smoking during pregnancy, prenatal cessation rates in the United States have decreased and the incidence of low birth weight has increased from 1985 to 1991. Lower socioeconomic status women who are at increased risk for poor pregnancy outcomes may be resistant to current intervention efforts during pregnancy. The purpose of this dissertation was to investigate the determinants of continued smoking and quitting among low-income pregnant women.^ Using data from cross-sectional surveys of 323 low-income pregnant smokers, the first study developed and tested measures of the pros and cons of smoking during pregnancy. The original decisional balance measure for smoking was compared with a new measure that added items thought to be more salient to the target population. Confirmatory factor analysis using structural equation modeling showed neither the original nor new measure fit the data adequately. Using behavioral science theory, content from interviews with the population, and statistical evidence, two 7-item scales representing the pros and cons were developed from a portion (n = 215) of the sample and successfully cross-validated on the remainder of the sample (n = 108). Logistic regression found only pros were significantly associated with continued smoking. In a discriminant function analysis, stage of change was significantly associated with pros and cons of smoking.^ The second study examined the structural relationships between psychosocial constructs representing some of the levels of and the pros and cons of smoking. The cross-sectional design mandates that statements made regarding prediction do not prove causation or directionality from the data or methods analysis. Structural equation modeling found the following: more stressors and family criticism were significantly more predictive of negative affect than social support; a bi-directional relationship was found between negative affect and current nicotine addiction; and negative affect, addiction, stressors, and family criticism were significant predictors of pros of smoking.^ The findings imply reversing the trend of decreasing smoking cessation during pregnancy may require supplementing current interventions for this population of pregnant smokers with programs addressing nicotine addiction, negative affect, and other psychosocial factors such as family functioning and stressors. ^

The two -state model of receptor activation: The agonist and the efficacy

The Two State model describes how drugs activate receptors by inducing or supporting a conformational change in the receptor from “off” to “on”. The beta 2 adrenergic receptor system is the model system which was used to formalize the concept of two states, and the mechanism of hormone agonist stimulation of this receptor is similar to ligand activation of other seven transmembrane receptors. Hormone binding to beta 2 adrenergic receptors stimulates the intracellular production of cyclic adenosine monophosphate (cAMP), which is mediated through the stimulatory guanyl nucleotide binding protein (Gs) interacting with the membrane bound enzyme adenylylcyclase (AC). ^ The effects of cAMP include protein phosphorylation, metabolic regulation and transcriptional regulation. The beta 2 adrenergic receptor system is the most well known of its family of G protein coupled receptors. Ligands have been scrutinized extensively in search of more effective therapeutic agents at this receptor as well as for insight into the biochemical mechanism of receptor activation. Hormone binding to receptor is thought to induce a conformational change in the receptor that increases its affinity for inactive Gs, catalyzes the release of GDP and subsequent binding of GTP and activation of Gs. ^ However, some beta 2 ligands are more efficient at this transformation than others, and the underlying mechanism for this drug specificity is not fully understood. The central problem in pharmacology is the characterization of drugs in their effect on physiological systems, and consequently, the search for a rational scale of drug effectiveness has been the effort of many investigators, which continues to the present time as models are proposed, tested and modified. ^ The major results of this thesis show that for many b2 -adrenergic ligands, the Two State model is quite adequate to explain their activity, but dobutamine (+/−3,4-dihydroxy-N-[3-(4-hydroxyphenyl)-1-methylpropyl]- b -phenethylamine) fails to conform to the predictions of the Two State model. It is a weak partial agonist, but it forms a large amount of high affinity complexes, and these complexes are formed at low concentrations much better than at higher concentrations. Finally, dobutamine causes the beta 2 adrenergic receptor to form high affinity complexes at a much faster rate than can be accounted for by its low efficiency activating AC. Because the Two State model fails to predict the activity of dobutamine in three different ways, it has been disproven in its strictest form. ^

Gain-of-function of the p53R172H mutation in a mouse model of Li -Fraumeni syndrome

Missense mutations in the p53 tumor-suppressor gene are the most common alterations of p53 in somatic tumors and in patients with Li-Fraumeni syndrome. p53 missense mutations occur in the DNA binding region and disrupt the ability of p53 to activate transcription. In vitro studies have shown that some p53 missense mutants have a gain-of-function or dominant-negative activity. ^ The p53 175 Arg-to-His (p53 R175H) mutation in humans has been shown to have dominant-negative and gain-of-function properties in vitro. This mutation is observed in the germline of individuals with Li-Fraumeni syndrome. To accurately model Li-Fraumeni syndrome and to examine the mechanistic nature of a gain-of-function missense mutation on in vivo tumorigenesis, we generated and characterized a mouse with the corresponding mutation, p53 R172H. p53R172H homozygous and heterozygous mice developed similar tumor spectra and survival curves as p53 −/− and p53+/− mice, respectively. However, tumors in p53+/R172H mice metastasized to various organs with high frequency, suggesting a gain-of-function phenotype by p53R172H in vivo. Mouse embryonic fibroblasts (MEFs) from p53R172H mice also showed gain-of-function phenotypes in cell proliferation, DNA synthesis, and transformation potential, while cells from p53+/− and p53−/− mice did not. ^ To mechanistically characterize the gain-of-function phenotype of the p53R172H mutant, the role of p53 family members, p63 and p73, was analyzed. Disruption of p63 and p73 by siRNAs in p53 −/− MEFs increased transformation potential and reinitiated DNA synthesis to levels observed in p53R172H/R172H cells. Additionally, p63 and p73 were bound and functionally inactivated by p53R172H in metastatic p53 R172H tumor-derived cell lines, indicating a role for the p53 family members in the gain-of-function phenotype. This study provides in vivo evidence for the gain-of-function effect of p53 missense mutations and more accurately models the Li-Fraumeni syndrome. ^