FUNCTIONAL CHARACTERIZATION OF THE LINK BETWEEN CARBOHYDRATE METABOLISM AND THE PATHOGENESIS OF THE INVASIVE M1T1 GROUP A STREPTOCOCCUS

The Group A Streptococcus (GAS), or Streptococcus pyogenes, is a strict human pathogen that colonizes a variety of sites within the host. Infections can vary from minor and easily treatable, to life-threatening, invasive forms of disease. In order to adapt to niches, GAS utilizes environmental cues, such as carbohydrates, to coordinate the expression of virulence factors. Research efforts to date have focused on identifying how either components of the phosphoenolpyruvate-phosphotransferase system (PTS) or global transcriptional networks affect the regulation of virulence factors, but not the synergistic relationship between the two. The present study investigates the role of a putative PTS-fructose operon encoded by fruRBA and its role in virulence in the M1T1 strain 5448. Growth in fructose resulted in induction of fruRBA. RT-PCR showed that fruRBA formed an operon, which was repressed by FruR in the absence of fructose. Growth and carbon utilization profiles revealed that although the entire fruRBA operon was required for growth in fructose, FruA was the main fructose transporter. The ability of both ΔfruR and ΔfruB mutants to survive in whole human blood or neutrophils was impaired. However, the phenotypes were not reproduced in murine whole blood or in a mouse intraperitoneal infection, indicating a human-specific mechanism. While it is known that the PTS can affect activity of the Mga virulence regulator, further characterization of the mechanism by which sugars and its protein domains affect activity have not been studied. Transcriptional studies revealed that the core Mga regulon is activated more in a glucose-rich than a glucose-poor environment. This activation correlates with the differential phosphorylation of Mga at its PTS regulatory domains (PRDs). Using a 5448 mga mutant, transcriptome studies in THY or C media established that the Mga regulon reflects the media used. Interestingly, Mga regulates phage-encoded DNases in a low glucose environment. We also show that Mga activity is dependent on C-terminal amino acid interactions that aid in the formation of homodimers. Overall, the studies presented sought to define how external environmental cues, specifically carbohydrates, control complex regulatory networks used by GAS, contribute to pathogenesis, and aid in adaptation to various nutrient conditions encountered.

Tamoxifen metabolism in breast cancer treatment: Taking the focus off the CYP2D6 gene

Tamoxifen (TAM) has been widely used to treat estrogen receptor (ER)-positive breast cancer, and has led to reduction of 50% in the annual recurrence rate and 30% in breast cancer mortality after 5 years of treatment.1 The prodrug TAM is a selective ER modulator that antagonizes ERs in cancer cells. However, compared with its two active metabolites 4-hydroxy-TAM (4-OH-TAM) and endoxifen, it is a relatively weak antiestrogen.

Latent variable models in statistical genetics

Understanding the complexities that are involved in the genetics of multifactorial diseases is still a monumental task. In addition to environmental factors that can influence the risk of disease, there is also a number of other complicating factors. Genetic variants associated with age of disease onset may be different from those variants associated with overall risk of disease, and variants may be located in positions that are not consistent with the traditional protein coding genetic paradigm. Latent Variable Models are well suited for the analysis of genetic data. A latent variable is one that we do not directly observe, but which is believed to exist or is included for computational or analytic convenience in a model. This thesis presents a mixture of methodological developments utilising latent variables, and results from case studies in genetic epidemiology and comparative genomics. Epidemiological studies have identified a number of environmental risk factors for appendicitis, but the disease aetiology of this oft thought useless vestige remains largely a mystery. The effects of smoking on other gastrointestinal disorders are well documented, and in light of this, the thesis investigates the association between smoking and appendicitis through the use of latent variables. By utilising data from a large Australian twin study questionnaire as both cohort and case-control, evidence is found for the association between tobacco smoking and appendicitis. Twin and family studies have also found evidence for the role of heredity in the risk of appendicitis. Results from previous studies are extended here to estimate the heritability of age-at-onset and account for the eect of smoking. This thesis presents a novel approach for performing a genome-wide variance components linkage analysis on transformed residuals from a Cox regression. This method finds evidence for a dierent subset of genes responsible for variation in age at onset than those associated with overall risk of appendicitis. Motivated by increasing evidence of functional activity in regions of the genome once thought of as evolutionary graveyards, this thesis develops a generalisation to the Bayesian multiple changepoint model on aligned DNA sequences for more than two species. This sensitive technique is applied to evaluating the distributions of evolutionary rates, with the finding that they are much more complex than previously apparent. We show strong evidence for at least 9 well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least 7 classes in an alignment of four mammals, including human. A pattern of enrichment and depletion of genic regions in the profiled segments suggests they are functionally significant, and most likely consist of various functional classes. Furthermore, a method of incorporating alignment characteristics representative of function such as GC content and type of mutation into the segmentation model is developed within this thesis. Evidence of fine-structured segmental variation is presented.

The relationship between substrate metabolism, exercise and appetite control : does glycogen availability influence the motivation to eat, energy intake or food choice?

The way in which metabolic fuels are utilised can alter the expression of behaviour in the interests of regulating energy balance and fuel availability. This is consistent with the notion that the regulation of appetite is a psychobiological process, in which physiological mediators act as drivers of behaviour. The glycogenostatic theory suggests that glycogen availability is central in eliciting negative feedback signals to restore energy homeostasis. Due to its limited storage capacity, carbohydrate availability is tightly regulated and its restoration is a high metabolic priority following depletion. It has been proposed that such depletion may act as a biological cue to stimulate compensatory energy intake in an effort to restore availability. Due to the increased energy demand, aerobic exercise may act as a biological cue to trigger compensatory eating as a result of perturbations to muscle and liver glycogen stores. However, studies manipulating glycogen availability over short-term periods (1-3 days) using exercise, diet or both have often produced equivocal findings. There is limited but growing evidence to suggest that carbohydrate balance is involved in the short-term regulation of food intake, with a negative carbohydrate balance having been shown to predict greater ad libitum feeding. Furthermore, a negative carbohydrate balance has been shown to be predictive of weight gain. However, further research is needed to support these findings as the current research in this area is limited. In addition, the specific neural or hormonal signal through which carbohydrate availability could regulate energy intake is at present unknown. Identification of this signal or pathway is imperative if a casual relationship is to be established. Without this, the possibility remains that the associations found between carbohydrate balance and food intake are incidental.

Modulation of depth-dependent properties in tissue-engineered cartilage with a semi-permeable membrane and perfusion : a continuum model of matrix metabolism and transport

The functional properties of cartilaginous tissues are determined predominantly by the content, distribution, and organization of proteoglycan and collagen in the extracellular matrix. Extracellular matrix accumulates in tissue-engineered cartilage constructs by metabolism and transport of matrix molecules, processes that are modulated by physical and chemical factors. Constructs incubated under free-swelling conditions with freely permeable or highly permeable membranes exhibit symmetric surface regions of soft tissue. The variation in tissue properties with depth from the surfaces suggests the hypothesis that the transport processes mediated by the boundary conditions govern the distribution of proteoglycan in such constructs. A continuum model (DiMicco and Sah in Transport Porus Med 50:57-73, 2003) was extended to test the effects of membrane permeability and perfusion on proteoglycan accumulation in tissue-engineered cartilage. The concentrations of soluble, bound, and degraded proteoglycan were analyzed as functions of time, space, and non-dimensional parameters for several experimental configurations. The results of the model suggest that the boundary condition at the membrane surface and the rate of perfusion, described by non-dimensional parameters, are important determinants of the pattern of proteoglycan accumulation. With perfusion, the proteoglycan profile is skewed, and decreases or increases in magnitude depending on the level of flow-based stimulation. Utilization of a semi-permeable membrane with or without unidirectional flow may lead to tissues with depth-increasing proteoglycan content, resembling native articular cartilage.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.