<p> Tumorigenesis of relatively late onset occurring in patients with no family history of cancer syndromes is assumed to be driven by somatic mutations. The advent of high-throughput sequencing allows unbiased probing for genomic aberrations on an unprecedented scale. Somatic mutations, insertions and deletions, and copy number variations are able to be identified by parallel sequencing of tumor DNA and normal DNA from an individual patient. Somatic aberrations identified are classified as either passenger mutations that do not contribute to tumorigenesis or pathogenic driver mutations. Driver mutations are able to be identified due to their recurrence across multiple affected patients at a frequency greater than would be expected by chance.</p><p> Tumors occurring in the same tissue and from the same cell type often display diverse phenotypes with distinct mutational signatures. Therefore I applied high-throughput sequencing to probe for somatic mutations in two very specific endocrine tumor types - parathyroid-producing adenomas and insulin-producing adenomas (insulinomas). Prior to this study, neither tumor type had been probed for somatic mutations in a large-scale, unbiased manner. Though a limited number of mutated genes had been identified to play a role in familial and sporadic tumorigenesis in these tumor types, the majority of pathogenesis remained unexplained.</p><p> In order to maximize detection of variation in coding regions of the genome, an exome capture array was applied to the DNA prior to sequencing. In both tumor types, exome sequencing was applied to a small number of tumor-normal tissue pairs. Additional targeted sequencing of candidate driver mutations was then performed using Sanger sequencing on larger validation cohorts of tumors.</p><p> Exome sequencing revealed few somatic, protein-altering mutations in each tumor type (average &lt;4 per tumor), therefore any recurrent variation was highly probable to be tumorigenic. Exome sequencing of the parathyroid adenomas revealed that four of eight tumors harbored a frameshift deletion or nonsense mutation in <i>MEN1</i>, which was always accompanied by loss of heterozygosity (LOH) of the remaining wild-type allele. No other mutated genes were shared among the eight tumors. One tumor harbored a Y641N missense mutation of the histone methyltransferase <i>EZH2</i> gene, previously linked to myeloid and lymphoid malignancy formation. Targeted sequencing in an additional 185 parathyroid adenomas revealed somatic <i>MEN1</i> mutations in a large number of tumors (35%). Furthermore, this targeted sequencing identified an additional parathyroid adenoma that contained the identical, somatic <i>EZH2</i> mutation that was found by exome sequencing. This confirms the frequent role of LOH of chromosome 11 and <i>MEN1</i> gene alterations in sporadic parathyroid adenomas and implicates a previously unassociated methyltransferase gene, <i>EZH2</i>, in endocrine tumorigenesis. </p><p> Exome sequencing identified an identical somatic, heterozygous mutation in Yin Yang 1 transcription factor (<i>YY1</i>) in two of seven insulinomas. Targeted sequencing of an additional 36 insulinomas revealed twelve more insulinomas that harbored this identical T372R missense mutation in <i>YY1.</i> This mutation occurs at a highly-conserved residue in a highly-conserved zinc finger DNA-binding domain. ChIP-Seq demonstrated that this mutation changes the DNA motif bound by YY1. This altered binding likely drives pathogenesis due to aberrant regulation of genes not regulated by YY1^WT. With the goal of identifying differentially-expressed genes in <i>YY1^T372R</i> tumors, I performed gene expression analysis on eleven tumors; six that were <i>YY1^WT</i> and five that were <i>YY1^T372R.</i> This demonstrated that YY1^T372R imparts a unique expression signature. Interestingly, several differentially-expressed genes were involved in key pathways regulating insulin secretion, including <i>ADCY1</i> (an adenylyl cyclase) and <i>CACNA2D2</i> (a Ca²⁺ channel pore-forming subunit), both of which were upregulated in <i>YY1^T372R</i>-tumors. Importantly, <i>in vitro</i> studies using the INS-1 rat insulinoma cell line demonstrated that upregulation of each of gene is sufficient to markedly increase insulin secretion. Furthermore, both genes harbored specific YY1^T372R binding sites that may account for their significantly altered expression.</p><p> Both studies identify novel driver mutations that shed light on the mechanisms of endocrine tumorigenesis. Furthermore, my findings reinforce the notion that common somatic mutations within the exome account for the majority of instances of sporadic tumorigenesis.</p>

<p> Neural stem cells (NSCs) participate in a delicate balance between maintaining cellular identity through self-renewal and differentiating into myriad neural cell types. Understanding exactly how epigenetic mechanisms regulate this balance and the subsequent differentiation process in adult mammalian brain is an ongoing effort. We conducted a genome wide association study to elucidate the roles of genes in neural progenitors regulated by chromatin modifications. Neural progenitors of baboon SVZ were examined using ChIP-Seq (chromatin immuneprecipitation followed by deep sequencing) to determine genome wide gene targets of three histone modifications: H3K4me3, H3K9me3 and H3K27me3. Our data suggest these chromatin marks are associated with genes responsible for cellular organization and morphology, proliferation and survival, neuron development. Taken together these processes suggest histone modifications, predominantly H3K27me3, are responsible for maintenance of NSC identity. Our findings also highlight the importance of using in vivo models to study the SVZ neurogenic niche and compel examination of the H3K27me3 catalyzing enzyme EZH2. In the future, the role of EZH2 will be determined by EZH2 conditional knockout and overexpression models, using stereotaxic injections of novel Cre protein and lentiviral delivery of EZH2, respectively.</p>

<p> As fluorescent imaging techniques for biological systems have advanced in recent years, scientists have used fluorescent imaging more and more to capture the state of biological systems at different moments in time. For many researchers, analysis of the fluorescent image data has become the limiting factor of this new technique. Although identification of fluorescing neurons in an image is (seemingly) easily done by the human visual system, manual delineation of the exact pixels comprising these fluorescing regions of interest (or fROIs) in digital images does not scale up well, being time-consuming, reiterative, and error-prone. This thesis introduces NEPIC, the Neuron-to- Environment Pixel Intensity Calculator, which seeks to help resolve this issue. NEPIC is a semi-automated tool for finding and tracking the cell body of a single neuron over an entire movie of grayscale calcium image data. NEPIC also provides a highly extensible, open source framework that could easily support finding and tracking other kinds of fROIs. When tested on calcium image movies of the AWC neuron in <i>C. elegans</i> under highly variant conditions, NEPIC correctly identified the neuronal cell body in 95.48% of the movie frames, and successfully tracked this cell body feature across 98.60% of the frame transitions in the movies. Although support for finding and tracking multiple fROIs has yet to be implemented, NEPIC displays promise as a tool for assisting researchers in the bulk analysis of fluorescent imaging data.</p>

<p>Technological advances in the recent decades have enabled cancer researchers to probe the disease at multiple resolutions. This wealth of experimental data combined with computational systems biology methods is now leading to predictive models of cancer progression and response to therapy. We begin by presenting our research group's multi-scale in silico framework for modeling cancer, whose core is a tissue-scale computational model capable of tracking the progression of tumors from a diffusion-limited avascular phase through angiogenesis, and into invasive lesions with realistic, complex morphologies. We adapt this core model to consider the delivery of systemically-administered anticancer agents and their effect on lesions once they reach their intended nuclear target. We calibrate the model parameters using in vitro data from the literature, and demonstrate through simulation that transport limitations affecting drug and oxygen distributions play a significant role in hampering the efficacy of chemotherapy; a result that has since been validated by in vitro experimentation. While this study demonstrates the capability of our adapted core model to predict distributions (e.g., cell density, pressure, oxygen, nutrient, drug) within lesions and consequent tumor morphology, nevertheless, the underlying factors driving tumor-scale behavior occur at finer scales. What is needed in our multi-scale approach is to parallel reality, where molecular signaling models predict cellular behavior, and ultimately drive what is seen at the tumor level. Models of signaling pathways linked to cell models are already beginning to surface in the literature. We next transition our research to the molecular level, where we employ data mining and bioinformatics methods to infer signaling relationships underlying a subset of breast cancer that might benefit from targeted therapy of Androgen Receptor and associated pathways. Defining the architecture of signaling pathways is a critical first step towards development of pathways models underlying tumor models, while also providing valuable insight for drug discovery. Finally, we develop an agent-based, cell-scale model focused on predicting motility in response to chemical signals in the microenvironment, generally accepted to be a necessary feature of cancer invasion and metastasis. This research demonstrates the use of signaling models to predict emergent cell behavior, such as motility. The research studies presented in this dissertation are critical steps towards developing a predictive, in silico computational model for cancer progression and response to therapy. Our Laboratory for Computational & Predictive Oncology, in collaboration with research groups throughout in the United States and Europe are following a computational systems biology paradigm where model development is fueled by biological knowledge, and model predictions are refining experimental focus. The ultimate objective is a virtual cancer simulator capable of accurately simulating cancer progression and response to therapy on a patient-specific basis.

<p> <b>Background</b> Diabetes is a major cause of morbidity and mortality in the United States. In 2012, 29.1 million people were estimated to have the condition, with type 2 diabetes accounting for 95% of all cases [1]. It is currently one of the most costly conditions in the country [2] and forecasts as a heavier burden for the U.S. with the prevalence expected to significantly increase [3]. For those who live with the disease, it is possible to manage diabetes in order to prevent or delay the onset of complications [4]. However the self-management regimen is complex and impacts nearly every important aspect of one's life [5].</p><p> The ubiquitous nature of mobile technologies and powerful capabilities of smartphones and tablets has led to a significant increased interest in the development and use of mobile health. Diabetes management is an application area where mobile devices could enhance the quality of life for people living with chronic illnesses [6]&ndash;[8], and usability is key to the adoption of such technologies [9], [10]. Past work has evaluated the usability of diabetes management apps for Android, iOS and Blackberry smartphones [11]-[14] despite the fact that no established method to evaluate the usability of mobile apps has emerged [15]. To our knowledge, this study is the first to evaluate the usability of diabetes management apps on iPad.</p><p> <b>Methods</b> This study introduces a novel usability survey that is designed for mHealth and specific to the iOS operating system. The survey is built on previous usability findings [11]&ndash;[14], Nielsen heuristics [16] and the Apple iOS Human Interface Guidelines [17]. The new instrument was evaluated with three evaluators assessing ten iPad apps, selected because they were the most popular diabetes management apps on the Apple AppStore. A focus group was subsequently held to gather more insight on the usability of the apps and the survey itself. Statistical analysis using R and grounded theory were used to analyze the quantitative and qualitative results, respectively. </p><p> <b>Results</b> The survey identified OneTouch Reveal by LifeScan Inc. and TactioHealth by Tactic, Health Group as the most usable apps. GlucoMo by Artificial Life, Inc. and Diabetes in Check by Everyday Health, Inc. rated as the least usable apps. Setting up medication and editing blood glucose were the most problematic tasks. Some apps did not support all functions that were under review. Six main themes emerged from the focus group: the presentation of health information, aesthetic and minimalist design, flexibility and efficiency of data input, task feedback, intuitive design and app stability. These themes suggest important constructs of usability for mHealth apps.</p><p> <b>Discussion and Conclusion</b> Mobile health developers and researchers should focus on the tasks, heuristics and underlying issues that were identified as most problematic throughout the study. Additionally, research should further inquire on the potentially critical relation between the information available on app markets and the usability of apps. Several signs point to the potential of the usability survey that was developed but further adjustments and additional test iterations are warranted to validate its use as a reliable usability evaluation method.</p>

<p> The RNA-binding proteins U2AF and PTB play important roles in gene expression in many eukaryotic species. Although U2AF and PTB have been well-studied, their functional requirements have not been investigated on a genome-wide scale. In this thesis, I analyze RNA expression data to determine the requirement of the general splicing factor U2AF in <i>S. pombe</i> and to identify genes misregulated in Drosophila PTB mutants. I find that many introns are insensitive to U2AF inactivation in a <i>Schizosaccharomyces pombe</i> U2AF59 mutant, <i>prp2.1.</i> Bioinformatics analysis indicates that U2AF-insensitive introns have stronger 5' splice sites and higher A/U composition. The importance of intronic nucleotide composition was further investigated using wild type RNA expression data sets. I show that nucleotide composition is a relatively important factor for regulated intron retention in a variety of species. I also analyzed the RNA-binding protein PTB using RNA Seq data to reveal genes misregulated in PTB mutants in <i>D. melanogaster.</i> I identify misregulation of alternative splicing in PTB mutants and putative PTB binding sites. In the PTB embryonic lethal mutant, which shows dorsoventral patterning defects, I show that dorsal fate genes are significantly up-regulated. I present a model to link PTB to dorsal closure defects. This thesis provides the first genome-wide analysis of U2AF in <i>S. pombe</i> and PTB in <i>Drosophila melanogaster. </i></p>

<p> Proteins are molecular machines of life in the truest sense. Being the expressors of genotype, proteins have been a focus in structural biology. Since the first characterization and structure determination of protein molecule more than half a century ago1, our understanding of protein structure is improving only incrementally. While computational analysis and experimental techniques have helped scientist view the structural features of proteins, our concepts about protein folding remain at the level of simple hydrophobic interactions packing side-chain at the core of the protein. Furthermore, because the rate of genome sequencing is far more rapid than protein structure characterization, much more needs to be achieved in the field of structural biology. As a step in this direction, my dissertation research uses computational analysis and experimental techniques to elucidate the fine structural features of the tertiary packing in proteins. With these set of studies, the knowledge of the field of structural biology extends to the fine details of higher order protein structure.</p>

<p> Trypanosomatids cause human diseases such as leishmaniasis and African trypanosomiasis. Trypanosomatids are protists from the order Trypanosomatida and include species of the genera <i>Trypanosoma</i> and <i> Leishmania</i>, which occupy a similar ecological niche. Both have digenic life-stages, alternating between an insect vector and a range of mammalian hosts. However, the strategies used to subvert the host immune system differ greatly as do the clinical outcome of infections between species. The genomes of both the host and the parasite instruct us about strategies the pathogens use to subvert the human immune system, and adaptations by the human host allowing us to better survive infections. We have applied unsupervised learning algorithms to aid visualization of amino acid sequence similarity and the potential for recombination events within <i>Trypanosoma brucei </i>'s large repertoire of variant surface glycoproteins (VSGs). Methods developed here reveal five groups of VSGs within a single sequenced genome of <i>T. brucei</i>, indicating many likely recombination events occurring between VSGs of the same type, but not between those of different types. These tools and methods can be broadly applied to identify groups of non-coding regulatory sequences within other Trypanosomatid genomes. To aid in the detection, quantification, and species identification of leishmania DNA isolated from environmental or clinical specimens, we developed a set of quantitative-PCR primers and probes targeting a taxonomically and geographically broad spectrum of <i>Leishmania</i> species. This assay has been applied to DNA extracted from both human and canine hosts as well as the sand fly vector, demonstrating its flexibility and utility in a variety of research applications. Within the host genomes, fine mapping SNP analysis was performed to detect polymorphisms in a family study of subjects in a region of Northeast Brazil that is endemic for <i>Leishmania infantum chagasi</i>, the parasite causing visceral leishmaniasis. These studies identified associations between genetic loci and the development of visceral leishmaniasis, with a single polymorphism associated with an asymptomatic outcome after infection. The methods and results presented here have capitalized on the large amount of genomics data becoming available that will improve our understanding of both parasite and host genetics and their role in human disease.</p>

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. / Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7504. Adviser: Nikolaos V. Sahinidis. Includes bibliographical references (leaves 102-110) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of California, San Francisco, 2007. / Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7115. Adviser: Joseph DeRisi.