Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2006. / "Title from dissertation home page (viewed June 28, 2007)." Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3242. Adviser: Kay H. Connelly.

<p> Understanding how protein structures evolve is essential for deciphering relationships between homologous proteins, which can inform structure classification and function annotation and aid in protein modeling and design methods. The observation that structure is more conserved than sequence, over the course of evolution, implies a model of evolution where sequences diverge within a discrete set of well-defined folds, which suggests that homology does not exist across fold definitions. However, as more structures have been experimentally solved and the coverage of the universe of folds has increased, the original view of a discrete fold space has been revised to include a more nuanced view of a continuous space defined by regions in which structural similarities can connect globally disparate topologies.</p><p> Structural, functional and evolutionary relationships are known to, in some cases, span fold definitions. A hallmark of relationships connecting disparate topologies is the conservation of local structure motifs within globally different folds. In order to systematically identify and analyze these relationships, a new approach to structure comparisons and structure classification is required. The goal of this work is to systematically identify evolutionary relationships between folds and to generate a classification of the fold universe that can accurately represent even the relationships that exist across disparate topologies. </p><p> An exhaustive library of supersecondary-structure motifs (Smotif), defined as two secondary structures connected by a loop, is established and characterized. A novel Smotif-based, superposition-independent structure comparison method (SmotifCOMP) is developed that quantitatively measures the Smotif-based similarity of compared structures in order to identify evolutionary relationships. SmotifCOMP is able to provide a quantitative and robust measure of similarity between disparate topologies since it does not rely on a global superposition. The comparison method is used to perform a systematic analysis of the SCOP Superfamilies and generate a non-hierarchical, network-based representation of the fold universe.</p><p> This thesis describes the development of a novel method of comparing structures and an improved representation of the relationships within the fold space. This work provides insight into the existence of evolutionary relationships between folds and strengthens the view of a connected and continuous fold universe.</p>

<p> The <i>BRCA1</i> and <i>BRCA2</i> (<i>BRCA</i>) genes are two tumor suppressors that when mutated, predispose patients to breast and ovarian cancer. The <i>BRCA</i> genes encode proteins that mediate the repair of DNA double strand breaks. Functional loss of the <i> BRCA</i> genes is detrimental to the integrity of the genome because without access to functional <i>BRCA</i> protein, inefficient and error-prone repair pathways are used instead. These pathways, such as Non-homologous end joining, do not accurately repair the DNA, which can introduce mutations and genomic rearrangements. Ultimately the genome is not repaired faithfully and the predisposition to cancer greatly increases. In addition to their contribution to DNA repair, the <i>BRCA</i> genes have been shown to have transcriptional activity, and this functional role can also be a driving factor behind the tumor suppressor activity.</p><p> Robustness is the ability of a complex system to sustain viability despite perturbations to it. In the context of a complex disease such as cancer, robustness gives cancers the ability to sustain uncontrollable growth and invasiveness despite treatments such as chemotherapy that attempt to eliminate the tumor. A complex system is robust however can be fragile to perturbations that the system not optimized against. In cancers, these fragilities have the potential to be cancer specific targets that can eradicate the disease specifically. </p><p> Patients with mutations in <i>BRCA</i> tend to have breast and ovarian cancers that are difficult to treat; chemotherapy is the only option and no targeted therapies are available. Targeting the synthetic lethal interaction (SLI), a mechanism of robustness, between <i>BRCA</i> and <i>PARP1</i> genes was clinically effective in treating BRCA-mutated breast and ovarian cancers. This suggests that understanding robustness in cancers can reveal potential cancer specific therapies.</p><p> In this thesis, a computational approach was developed to identify candidate mechanisms of robustness in <i>BRCA</i>-mutated breast and ovarian cancers using the publicly accessible patient gene expression and mutation data from the Cancer Genome Atlas (TCGA). Results showed that in ovarian cancer patients with a <i>BRCA2</i> mutation, the expression of genes that function in the DNA damage response were kept at stable expression state compared to those patients without a mutation. The stable expression of genes in the DNA damage response may highlight a SLI gene network that is precisely controlled. This result is significant as disrupting this precision can potentially lead to cancer specific death. In breast cancers, genes that were differentially expressed in patients with <i>BRCA</i> mutations were identified. A Bayesian network was performed to infer candidate interactions between <i> BRCA1</i> and <i>BRCA2</i> and the differentially expressed <i> FLT3, HOXA11, HPGD, MLF1, NGFR, PLAT,</i> and <i>ZBTB16</i> genes. These genes function in processes important to cancer progression such as apoptosis and cell migration. The connection between these genes with BRCA may highlight how the BRCA genes influence cancer progression.</p><p> Taken together, the findings of this thesis enhance our understanding of the <i>BRCA</i> genes and their role in DNA damage response and transcriptional regulation in human breast and ovarian cancers. These results have been attained from systems-level models to identify candidate mechanisms underlying robustness of cancers. The work presented predicts interesting candidate genes that may have potential as drug targets or biomarkers in <i> BRCA</i>-mutated breast and ovarian cancers.</p><p>

<p> Cancer is a leading cause of morbidity and mortality, and one in three individuals in the U.S. will be diagnosed with cancer in their lifetime. At the molecular level, cancer is driven by the activity of oncogenes and the loss of activity of tumor suppressors. The availability of genomic data from large sets of tumor tissue have facilitated the identification of subgroups of patients whose tumors share molecular patterns of expression. These molecular signatures, in turn, can help identify clinically-useful patient subgroups and inform potential therapeutic strategies against cancer.</p><p> In chapter 1, I review the current theories behind carcinogenesis, the molecular factors that regulate gene expression, and statistical methods for analyzing genomic data. In chapter 2, I describe an approach, termed oncomix, developed to identify oncogene candidates from expression data obtained from tumor and adjacent normal tissue. I apply oncomix to breast cancer expression data and identify an oncogene candidate, <i>CBX2</i>, whose expression is gained in a subset of breast tumors. <i>CBX2</i> is expressed at low levels in most normal adult tissue, and the CBX2 protein contains a drug-targetable chromodomain, both of which are desirable properties in a potential therapeutic target. We then provide the first experimental evidence that <i>CBX2</i> regulates the growth of breast cancer cells. In chapter 3, I develop a method for identifying nuclear hormone receptors whose expression is lost in endometrial cancers relative to normal tissue. I report, for the first time, that the loss of expression of Thyroid Hormone Receptor Beta (<i>THRB</i>) is associated with better 5-year survival in endometrial cancer. The loss of <i>THRB</i> expression is independent of the loss of estrogen and progesterone receptor expression, two genes whose loss of expression is known to be associated with poor survival. <i> THRB</i> expression could be considered as a biomarker to risk-stratify endometrial cancer patients. In Chapter 4, I develop a user-friendly application for visualizing chromosomal copy number state obtained from three types of copy number input in single cells &ndash; fluorescence in situ hybridization (FISH), spectral karyotyping (SKY), and whole genome sequencing (WGS). This web application, termed aneuvis, automatically creates novel visualizations and summary statistics from a set of user-uploaded files that contain chromosomal copy number information.</p><p> In this thesis, I develop new computational approaches for identifying candidate molecular regulators of cancer. I also develop a new user-friendly tool to enable biological researchers to identify aneuploidy and chromosomal instability within populations of single cells. Applying these tools to breast and endometrial cancer genomic datasets has highlighted novel aspects of breast and endometrial cancer biology and may inform novel therapeutic strategies based on molecular patterns of genomic heterogeneity. The freely available software developed as part of these projects has the potential to enable other researchers to advance our understanding of cancer genomics and to inform novel therapeutic strategies against cancer.</p><p>

<p>Nuclear functions in eukaryotic cells are regulated by the NuA4 histone acetyltransferase complex. This is a highly-conserved protein complex that regulates multiple vital nuclear functions like the cell cycle, DNA repair and transcription. Gene expression is regulated by this complex though epigenetics by adding acetyl groups to lysine residues on histone H4. This affects the expression of genes in the regions of the chromosome where the addition occurred. The planarian flatworm Schmidtea mediterranea is thought to be, effectively, immortal due to its amazing ability to regenerate and maintain pluripotent stem cells throughout its life time. Better understanding of the genes that control differentiation and pluripotency is needed. Humans have gene homologs to the planarian counterparts; therefore, it could be possible to gain knowledge about our own stem cells from these worms. I have identified planarian homologs of 15 proteins in the human NuA4 complex (Ruvbl2, Morf4l2, Mrg15, Epc1, Tip60, Trrap, Gas41, Ruvbl1, Brd8, Yl-1, Baf53a, Dmap1, Ing3, hEaf6-1 and hEaf6-2) and silenced them by RNA interference (RNAi) to examine the role of the complex in stem cell maintenance and regeneration. The RNAi method involves feeding the worms double-stranded RNA with a sequence matching the gene of interest to target the destruction of the mRNA expressed from that gene, thus knocking down its expression. I will observe two groups of RNAi worms; a regenerating group and a homeostasis group. The regenerating will be cut following the knockdown to observe how well they restore their lost tissue. The homeostasis group will be fixed and stained to mark mitotic cells and find out if the stem cells are dividing normally. I will also use in-situ staining to determine where each of these genes are being expressed. I hypothesize that knockdown of these important regulatory complex genes will result in reduced regenerative ability and that the worms? stem cell population will not be properly maintained.

Modern next-generation sequencing and microarray-based assays have empowered the computational biologist to measure various aspects of biological activity. This has led to the growth of genomics, transcriptomics and proteomics as fields of study of the complete set of DNA, RNA and proteins in living cells respectively. One major challenge in the analysis of this data, however, has been the widespread lack of sufficiently large sample sizes due to the high cost of new emerging technologies, making statistical inference difficult. In addition, due to the hierarchical nature of the various types of data, it is important to correctly integrate them to make meaningful biological discoveries and better informed decisions for the successful treatment of disease. In this dissertation I propose: (1) a novel method for more powerful statistical testing of differential digital gene expression between two conditions, (2) a framework for the integration of multi-level biologic data, demonstrated with the compositional analysis of gene expression and its link to promoter structure, and (3) an extension to a more complex generalized linear modeling framework, demonstrated with the compositional analysis of gene expression and its link to pathway structure adjusted for confounding covariates.

One of the central challenges in systems biology research is disentangling relevant and irrelevant sources of variation. While the relevant quantities are always context dependent, an important distinction can be drawn between variability due to biological processes and variability due measurement error. Biological variability includes variability between mRNA or protein abundances within a well defined condition, variability of these abundances across conditions (physiological variability), and between species or between subject variability. Technical variability includes measurement error, technological bias, and variability due to missing data. In this dissertation, we explore statistical challenges associated with disentangling sources of variability, both biological and technical, in the analysis of high-throughput biological data. In the first chapter, we present a careful meta-analysis of 27 yeast data sets supported by a multilevel model to separate biological variability from structured technical variability. In the second chapter, we suggest a simple and general approach for deconvolving the contributions of orthogonal sources of biological variability, both between and within molecules, across multiple physiological conditions. The results discussed in these two chapters elucidate the relative importance of transcriptional and post-transcriptional regulation of protein levels. Finally, in the third chapter we introduce a novel approach for modeling non-ignorable missing data. We illustrate the utility of this methodology on missing data in mRNA and protein measurements. / Statistics

Translation is a key process during gene expression: to produce proteins, ribosomes translate the coding sequences of mRNAs. However, vertebrate genomes contain more translation potential than these annotated coding sequences: translation has been detected in many non-coding RNAs and in the non-coding regions of mRNAs. To understand the role of such translation in vertebrates, I investigated: 1) the distribution of translation in vertebrate long non-coding RNAs, and 2) the effects of translation in the 5’ leaders of vertebrate mRNAs. To quantify and localize translation in a genome-wide manner, we produced and analyzed ribosome profiling data in zebrafish, and analyzed ribosome profiling data produced by others. The nucleotide resolution afforded by ribosome profiling allows localization of translation to individual ORFs within a transcript, while its quantitative nature enables measurement of how much translation occurs within individual ORFs. We combined ribosome profiling with a machine-learning approach to classify lncRNAs during zebrafish development and in mouse ES cells. We found that dozens of proposed lncRNAs are protein-coding contaminants and that many lncRNAs have ribosome profiles that resemble that of the 5’ leaders of coding mRNAs. These results clarify the annotation of lncRNAs and suggest a potential role for translation in lncRNA regulation. Because much of the translation in non-coding regions of mRNAs occurs within uORFs, we further examined the effects of their translation on the cognate gene expression. While much is known about the repression of individual genes by their uORFs, how uORF repressiveness varies within a genome and what underlies this variation had not been characterized. To address these questions, we analyzed transcript sequences and ribosome profiling data from human, mouse and zebrafish. Linear modeling revealed that sequence features at both uORFs and coding sequences contribute similarly and substantially toward modulating uORF repressiveness and coding sequence translational efficiency. Strikingly, uORF sequence features are conserved in mammals, and mediate the conservation of uORF repressiveness in vertebrates. uORFs are depleted near coding sequences and have initiation contexts that diminish their translation. These observations suggest that the prevalence of vertebrate uORFs may be explained by their functional conservation as weak repressors of coding sequence translation. / Biology, Molecular and Cellular

A growing body of evidence suggests that genetic variants that alter gene expression are responsible for many phenotypic differences across individuals, particularly for the risk of developing common diseases. However, the molecular mechanisms that underlie the vast majority of associations between genetic variants and their phenotypes remain unknown. An important limiting factor is that genetic variants remain difficult to interpret, particularly in noncoding sequences. Developing truly systematic approaches for characterizing regulatory variants will require: (a) improved annotations for the genomic sequences that control gene expression, (b) a more complete understanding of the molecular mechanisms through which genetic variants, both coding and noncoding, can affect gene expression, and (c) better experimental tools for testing hypotheses about regulatory variants. In this dissertation, I present conceptual and methodological advances that directly contribute to each of these goals. A recurring theme in all of these developments is the statistical modeling of protein-DNA interactions and its integration with other data types. First, I describe enhancer-FACS-Seq, a high-throughput experimental approach for screening candidate enhancer sequences to test for in vivo, tissue-specific activity. Second, I present an integrative computational analysis of the in vivo binding of NF-kappaB, a key regulator of the immune system, yielding new insights into how genetic variants can affect NF-kappaB binding. Next, I describe the first comprehensive survey of coding variation in human transcription factors and what it reveals about additional sources of genetic variation that can affect gene expression. Finally, I present SIFTED, a statistical framework and web tool for the optimal design of TAL effectors, which have been used successfully in genome editing and can thus be used to test hypotheses about regulatory variants. Together, these developments help fulfill key needs in the quest to understand the molecular basis of human phenotypic variation. / Biophysics

Sequencing exomes—the 1% of the genome that codes for proteins—has increased the rate at which the genetic basis of a patient’s disease is determined. Unfortunately, when a patient does not carry a well-established pathogenic variant, it is extremely challenging to establish which of the tens of thousands of variants identified in that individual is contributing to their disease. In these situations, variants must be prioritized to make further investigation more manageable. In this thesis, we have focused on creating statistical frameworks and models to aid in the interpretation of rare variants and towards establishing gene-level metrics for variant prioritization. We developed a sensitive and specific workflow to detect newly arising (de novo) variants from exome sequencing data of parent-child trios, and created a sequence-context based mutational. This mutational model was the basis of a rigorous statistical framework to evaluate the significance of de novo variant burden not only globally, but also per gene. When we applied this framework to de novo variants identified in patients with an autism spectrum disorder, we found a global excess of de novo loss-of-function variants as well as two genes that harbored significantly more de novo loss-of-function variants than expected. We also used the mutational model to predict the expected number of rare (minor allele frequency < 0.1%) variants in exome sequencing datasets of reference individuals. We found a significant depletion of missense and loss-of-function variants in a subset of genes, indicating that these genes are under strong evolutionary constraint. Specifically, we identified 3,230 genes that are intolerant of loss-of-function variation and that set of genes is enriched for established dominant and haploinsufficient disease genes. Similarly, we searched for regions within genes that were intolerant of missense variation. The most missense depleted 15% of the exome contains 83% of reported pathogenic variants found in haploinsufficient disease genes that cause severe disease. Additionally, both gene-level and region-level constraint metrics highlight a set of de novo variants from patients with a neurodevelopmental disorder that are more likely to be pathogenic, supporting the utility of these metrics when interpreting rare variants within the context of disease. / Medical Sciences