Thesis (Ph.D.) - University of Queensland, 2004. / Includes bibliography.

Thesis (Ph.D.)--University of California, San Francisco, 2009. / Source: Dissertation Abstracts International, Volume: 70-10, Section: B, page: 5922. Adviser: Brian K. Shoichet.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3639. Adviser: Huimin Zhao. Includes bibliographical references. Available on microfilm from Pro Quest Information and Learning.

A hallmark of the joint disease osteoarthritis (OA) is the degradation of the articular cartilage in the affected joint, debilitating pain and decreased mobility. At present there are no disease modifying drugs for treatment of osteoarthritis. This represents a significant, unmet medical need as there is a large and increasing prevalence of OA. Using a systems biology approach, we aimed to better understand the pathogenic mechanisms of OA and ultimately aid development of therapeutics. This thesis focuses on the analysis of gene expression data from human OA cartilage obtained at total knee replacement (TKR). This transcriptomics approach gives a genome-wide overview of changes, but can be challenging to interpret. Network-based algorithms provide a framework for the fusion of knowledge so allowing effective interpretation. The PhenomeExpress algorithm was developed as part of this thesis to aid the interpretation of gene expression data. PhenomeExpress uses known disease gene associations to identify relevant dysregulated pathways in the data. PhenomeExpress was further developed into an 'app' for Cytoscape, the widely used network analysis and visualisation platform. To investigate the processes that occur during the degradation of cartilage we examined the gene expression of damaged and intact OA cartilage using RNA-Seq and identified key altered pathways with PhenomeExpress. A regulatory network driven by four transcription factors accounts for a significant proportion of the observed differential expression of damage-associated genes in the PhenomeExpress identified pathways. We further explored the role of the cytokines IL-1 and TNF that have been reported to β drive the progression of OA. Comparison of the expression response of in vitro cytokine-treated explants with the in vivo damage response revealed major differences, providing little evidence for any significant role of IL-1 and TNF as drivers of OA β damage in vivo. Finally, we examined the heterogeneity of OA through analysis of cartilage expression profiles at TKR. Through a network-based clustering method, we found two subgroups of patients on the basis of their gene expression profiles. These subgroups were found to have distinct OA expression perturbations and we identified TGF and S100A8/9 β signalling as potentially explaining the observed differential expression. We developeda RT-qPCR based classifier that allowed classification of new samples into these subgroups so allowing future assessment of the clinical significance of these subgroups. The work presented in this thesis includes a novel, widely-accessible tool for the analysis of disease gene expression data, which we used to give new insights into the pathogenesis of osteoarthritis. We have produced a rich dataset for future research and our analysis of this data has increased our understanding of cartilage damage processes and the heterogeneity of OA.

DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Novel methodologies are required to accommodate this growing volume of DNA methylation data. In this dissertation, I propose two novel methods to analyze DNA methylation data: (1) a new statistic based on spatial location information of DNA methylation sites to detect differentially methylated regions in the genome in case and control studies; and (2) a principal component approach for the detection of unknown substructure in DNA methylation data. For each method, I review existing ones and demonstrate the efficacy of my proposed method using simulation and data application. Medical research is increasingly focused on personalizing the care of patients. A better understanding of the interaction between treatment and patient specific prognostic factors will enable practitioners to expand the availability of tailored therapies improving patient outcomes. The Subpopulation Treatment Effect Pattern Plot (STEPP) approach was developed to allow researchers to investigate the heterogeneity of treatment effects on survival outcomes across increasing values of a continuously measured covariate, such as biomarker measurement. I extend the STEPP approach to continuous, binary and count outcomes which can be easily modeled with generalized linear models (GLM). The statistical significance of any observed heterogeneity of treatment effect is assessed using permutation tests. The method is implemented in the R software package (stepp) and is available in R version 3.1.1. The efficacy of my STEPP extension is demonstrated by using simulation and data application.

Pulmonary hypertension (PH) is a deadly vascular disease characterized by multiple disparate molecular pathways controlling vasoconstriction and hyperproliferation throughout the pulmonary vasculature. Importantly, the selection and proliferation of hardier pulmonary endothelial cells in PH may describe the origins of the anti-apoptotic and hyperproliferative phenotypes characterizing severe PH pathogenesis. However, the importance of cellular survival in mediating intercellular transfer of regulatory factors during PH progression has yet to be clearly defined. MicroRNAs (miRNAs) may coordinately regulate PH progression, but their integrative functions have been difficult to describe with conventional methods. Recently, using a network-based bioinformatics approach, we identified the miR-130/301 family as a master regulator governing pulmonary vascular proliferation as well as vasoconstriction and vessel stiffening in PH through repression of its direct target gene PPARγ. In this study, we additionally identify miR-130/301 as a regulator of cellular survival and show that the dysregulation of miR-130/301 in PH increases apoptotic activity in pulmonary arterial endothelial cells. Furthermore, we identify NCOA3 and PTEN as direct targets of miR-130/301 through which this miRNA family may control apoptotic signaling in the pulmonary vasculature. Our observations provide critical insight into the systems-level regulation of both cellular survival and proliferation by the miR-130/301 family in PH pathogenesis. Specifically, this model establishes a mechanistic framework describing the importance of miR-130/301 in the initiating apoptotic events of PH development. Moreover, our study suggests broad propositions for miRNA-based therapeutics for treating PH and further endorses the application of in silico network theory to decipher the combinatorial molecular origins of complex diseases such as PH. / Biomedical Engineering

Eukaryotic gene regulation is a complex process requiring the action of many multicomponent complexes in the cell. Specific inhibitors of chromatin-associated factors allow the functional study of protein domains without genetic removal of the entire protein. Here, two small molecule probes were used to study the role of DOT1L and BET proteins in cancer biology. DOT1L is a histone methyltransferase with activity correlating with positive regulation of transcription. In MLL-rearranged leukemia, DOT1L is recruited aberrantly to early developmental transcription factors, leading to their inappropriate expression and leukemia maintenance. The development of an assay platform for DOT1L allowed the investigation of many small molecule DOT1L inhibitors, leading to compounds with improved potency and pharmacokinetics. Studying the action of BET bromodomain inhibitors led to the identification of super enhancers, large tissue-specific regulatory elements driving the expression of genes critical for the function of the cell. Super enhancers are often found in oncogenic translocation events, especially in B cell malignancies. This study identified a subset of super enhancers that promote off-target DNA damage from the B cell antibody diversity enzyme AID, leading to double strand break events and translocations. Super enhancers also regulate the expression of master transcription factors (TFs) in a given cell type. Using the topology of the super enhancer, the sites of master TF binding can be predicted, allowing the construction of network models for transcriptional regulation. These models were built in a large number of healthy and diseased cell types, including the pediatric malignancy medulloblastoma. In medulloblastoma, a network motif was identified that matches an expression pattern seen in a transient cell population in the developing cerebellum, providing evidence for the previously unknown cell of origin for Group 4 medulloblastoma. / Chemical Biology

The characterization of inactivating mutations affecting SNF5 in pediatric rhabdoid tumors constituted the first clear connection between an epigenetic regulator and tumor formation. SNF5 is a core subunit of the evolutionarily conserved SWI/SNF chromatin remodeling complex, a complex that has recently emerged as being frequently mutated across a wide spectrum of cancers. However, the exact mechanism by which SNF5 loss causes rhabdoid tumor, and SWI/SNF mutations are involved cancers generally, remains undetermined. Since SWI/SNF is a chromatin remodeling complex, it is unclear whether the perturbation of chromatin structure and epigenetic dysregulation caused by SNF5 loss alone may drive cancer formation or if SNF5 mutations are dependent on additional cooperative somatic mutations. In order to determine what, if any, additional pathways cooperate with SNF5 loss, we have sequenced the exomes of 35 primary rhabdoid tumors. Despite the lethal nature of these cancers, we identified remarkably few coding mutations, with SNF5 loss being the only significantly recurring event. The mutation rate of rhabdoid tumors is among the lowest of all cancer genomes sequenced to date. Our results demonstrate that loss of SNF5 alone appears to a genetic event sufficient to drive rhabdoid tumor and that genomic instability and high mutation rates are not required for oncogenesis. Because SNF5 loss is the only known genetic driver of these cancers, RT represents a good model system in which to study the effects of SWI/SNF subunit mutations on driving oncogenesis through disruption of transcriptional regulation. As one means of evaluating such disruptions, we performed chromatin immunoprecipitation-sequencing of histone modifications in primary RT tissues and RT cell lines. Despite being genetically indistinguishable, RT group according to tissue of origin when characterized by H3K27ac at active enhancers. Additionally, we found that SNF5 loss impairs SWI/SNF binding to enhancers, but its loss has minimal effect on the targeting of SWI/SNF to super-enhancers. Our data suggest a model whereby SNF5 loss blocks acquisition of enhancers required for differentiation but leaves intact super-enhancer structures that underlie the proliferative fate of the progenitor cells of origin. These studies collectively characterize an epigenetic mechanism underlying tumor formation upon SNF5 loss. / Medical Sciences

Whole-genome duplication (WGD), which leads to polyploidy, has been implicated in speciation and biological novelty. In plants, many species have experienced historical bouts of WGD or exhibit extant ploidy variation, which is likely representative of an early stage in the evolution of new polyploid lineages. To elucidate the evolutionary dynamics of autopolyploids and species with multiple ploidy levels, I develop population genetic theory in Chapter 2 that I use in Chapter 4 to extract information about the evolutionary history of Arabidopsis arenosa, a European wildflower that has diploid and autotetraploid populations. Chapter 3 involves a separate project exploring the ascertainment bias in restriction site associated DNA sequencing (RADseq). In Chapter 2, I develop coalescent models for autotetraploid species with tetrasomic inheritance and show that the ancestral genetic process in a large population without recombination may be approximated using Kingman’s standard coalescent, with a coalescent effective population size 4N. Using this result, I was able to use existing coalescent simulation programs to show in Chapter 4 that, in A. arenosa, a widespread autotetraploid race arose from a single ancestral population. This autopolyploidization event was not accompanied by immediate reproductive isolation between diploids and tetraploids in this species, as I find evidence of extensive interploidy admixture between diploid and tetraploid populations that are geographically close. To draw these conclusions about population history in Chapter 4, I used a reduced representation genome-sequencing approach based on restriction digestion. However, I was bothered by the possibility that sampling chromosomes based on restriction digestion may introduce a bias in allele frequency estimation due to polymorphisms in restriction sites. To explore the effects of this nonrandom sampling and its sensitivity to different evolutionary parameters, we developed a coalescent-simulation framework in Chapter 3 to mimic the biased recovery of chromosomes in RAdseq experiments. We show that loci with missing haplotypes have estimated diversity statistic values that can deviate dramatically from true values and are also enriched for particular genealogical histories. These results urge caution when applying this technique to make population genetic inferences and helped me tailor analyses in Chapter 4 to accommodate for this particular method of DNA sequencing. / Biology, Organismic and Evolutionary

Mitochondrial outer membrane permeabilization is a key step in the apoptotic cell death program, regulating life-death decisions in response to cytotoxic drugs and other forms of cell stress. In this thesis I use mathematical modeling of a reconstituted biochemical system to identify and integrate mechanisms of apoptotic pore formation. A key bottleneck in using mathematical models to characterize mechanisms has been the difficulty of efficiently creating and revising alternative models and evaluating them against data. This problem is addressed through the use of a software framework, PySB, that allows ensembles of models to be transparently described using tools and approaches from computer programming. These alternative hypotheses can then be evaluated against data using methods from Bayesian statistics for discrimination of models with varying numbers of (possibly non-identifiable) parameters. Using this framework, calibration of a set of models to in vitro kinetic measurements of the membrane insertion of Bax identifies a conformational intermediate associated with BH3-only:Bax complex formation and membrane association but not pore formation. Functional measurements of Bax point mutants from tumors show that the pore formation process can be blocked at the transition into or out of this intermediate, preventing mitochondrial permeabilization. In another study, model-based analysis of Bax insertion and permeabilization kinetics across a range of BH3-only, Bax, and liposome concentrations reveals the context-dependence of the mechanisms regulating pore formation. Bax recruitment is shown to depend on liposome concentration kinetically but not stoichiometrically, whereas cBid recruitment is shown to be limited at high cBid:liposome concentrations. I show that Bax distribution among liposomes is dependent on the presence of pre-existing pores, and that pores grow to include large numbers of Bax monomers but have a minimum size of four subunits. More generally, these studies serve as examples of how ensemble modeling can be used to integrate information about complex mechanisms from disparate sets of experimental observations. / Systems Biology