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 rapid development of novel experimental techniques has led to the generation of an abundance of biological data, which holds great potential for elucidating many scientific problems. The analysis of such complex heterogeneous information, which we often have to deal with, requires appropriate state-of-the-art analytical methods. Here we demonstrate how an unconventional approach and intelligent data processing can lead to meaningful results.</p><p> This work includes three major parts. In the first part we describe a correction methodology for genome-wide association studies (GWAS). We demonstrate the existing bias for the selection of larger genes for downstream analyses in GWA studies and propose a method to adjust for this bias. Thus, we effectively show the need for data preprocessing in order to obtain a biologically relevant result. In the second part, building on the results obtained in the first part, we attempt to elucidate the underlying mechanisms of aging and longevity by conducting a longevity GWAS. Here we took an unconventional approach to the GWAS analysis by applying the idea of genetic buffering. Doing this allowed us to identify pairs of genetic markers that play a role in longevity. Furthermore, we were able to confirm some of them by means of a downstream network analysis. In the third and final part, we discuss the characteristics of chronic lymphocytic leukemia (CLL) B-cells and perform clustering analysis based on immunoglobulin (Ig) mutation patterns. By comparing the sequences of Ig of CLL patients and healthy donors, we show that different Ig heavy chain (IGHV) regions in CLL exhibit similarities with different B-cell subtypes of healthy donors, which raised a question about the single origin of CLL cases.</p>

<p> Arboviruses are a grade of viruses carried by arthropods, which have been in the headlines due to recent epidemics. Members of this grade are the families <i>Flaviviridae</i> which includes Zika (ZIKV), Dengue (DENV), Yellow Fever (YFV), among other viruses and <i>Togaviridae</i>, which includes Chikungunya (CHIKV).</p><p> Research on some arboviruses has been strong over the past couple of decades. Other arboviruses have not garnered much attention until lately. For example, ZIKV has been understudied until 2015. Since the 1950s ZIKV was considered to cause only a benign infection in humans. ZIKV became well studied only after the recent outbreaks of the virus in the Pacific, Americas, and South-East Asia, was found to be related to severe neuropathology, which includes the development of neurological defects such as microcephaly on the fetus and Guillain Barr&eacute; Syndrome in adults. CHIKV is another arbovirus that although been circulating for a long time in Africa and Asia, has been recently introduced into the Americas in 2013, causing recurring outbreaks in South and Central American na&iuml;ve populations.</p><p> YFV, which been known to be endemic and thought to be controlled in South America, has re-emerged in Brazil beginning in December 2016. This outbreak, although restricted to transmission by the sylvatic mosquito <i>Haemagogus leococelaenus</i>, raised questions among researchers regarding the potential for spread to the United States due to the presence of the urban vectors <i> Aedes aegypti</i> and <i>Aedes albopictus</i>} and a na&iuml;ve, largely unvaccinated population. Another question that still remains is whether YFV will ever reach the Asian continent?</p><p> Today, the time it takes for awareness of the health organizations, to convince the funding agencies, and to work on vaccine development is much more than the time needed for the disease to change from a local outbreak to a global epidemic. The overall objective of this work is to provide the grounds for a viral surveillance system based on evolution, utilizing the current ZIKV and CHIKV outbreaks and other arboviruses as case studies.</p><p> Utilizing phylogenetic and molecular sequence alignment tools I developed a pipeline to evaluate the genomic changes of viruses on CHIKV and ZIKV. I also created a pipeline to generate pathogen transmission networks and compare different disease networks utilizing different network centrality metrics. CHIKV, DENV, YFV and ZIKV were utilized as case studies. The strategies utilized in this work will enable better abatement and management strategies of viral outbreaks.</p><p> My findings indicate that changes in the coding sequence does not seem to be the main reason why ZIKV has changed its behavior in terms of pathogenicity. In CHIKV there is an insertion on the UTR region of a group of sequences and change of virulence has been associated with UTR sizes in different CHIKV strains. Upon analyzing viral 3' and 5' UTRs, a trinuncleotide motif, known as Musashi Binding Element was identified in both CHIKV and ZIKV, its presence and availability on ZIKV may explain a preference to human cells, in CHIKV the motif is present but not available. Although both CHIKV and ZIKV coexist and have spread in the same regions in a short period of time, their spread seems to be from independent events. When looking at transmission networks, there is a high correlation between the different centrality metrics utilized to measure all four DENV serotypes transmission networks, CHIKV, YFV and ZIKV have lower correlation, thus, distinct patterns.</p><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> Communication is one of the key causes of healthcare-related harm. An estimated 210,000 to 400,000 deaths each year in the United States are attributed to healthcare-related harm. Interprofessional communication and collaboration have been identified as critical to providing safe care. Documentation is intended to support interprofessional communication and collaboration. However, research has demonstrated that documentation in the electronic health record (EHR) is not regularly used to support interprofessional communication. Previous research has examined the use of the patient record for information sharing and has identified several barriers that inhibit its use for communication; yet, little is known regarding how the record <i>ought to be used</i> for interprofessional communication. </p><p> Healthcare-associated infections (HAI), including central line associated-blood stream infections (CLABSI), is one category of healthcare-associated harm. The purpose of this study was to describe, using the Delphi technique, what an expert panel of nurses and physicians believe regarding how the EHR ought to be used to optimize interprofessional communication in central venous catheter (CVC) management and prevention of CLABSI. The study was guided by the frameworks of Distributed Cognition and Coiera&rsquo;s Communication Space. </p><p> The expert panel consisted of six nurses and four physicians from a large academic healthcare system who had experience caring for patients with CVCs and using the EHR for retrieving, documenting, and communicating information. The panel members held such positions as staff nurse, nurse leader, resident, attending, and physician leader/medical director. Four Delphi rounds, which included an initial individual interview followed by three survey rounds, were completed to achieve stability in panel member responses. </p><p> The panel identified 12 information types necessary for decisions regarding whether to keep or discontinue a CVC, the best channels for communicating each of the information types, and factors that promote or inhibit the use of the EHR for interprofessional communication. </p><p> The findings have implications for the creation of interprofessional practice guidelines, interprofessional education, and the development of EHRs that better support interprofessional communication and team-based care. Understanding how to optimize the EHR in order to leverage the knowledge captured in clinicians&rsquo; documentation has the potential to improve patient care and reduce harm.</p><p>

<p> With the push for national EHR adoption and the subsequent increase in meaningful use of HIT applications, the healthcare industry has sought to realize reduced cost, increased safety, and improved patient outcomes. In an effort to evaluate the goal of improved patient outcomes, this study examines the effect of HIT adoption stage on the length of stay (LOS) for children admitted with an asthma diagnosis. Asthma is a chronic disease affecting millions of children each year, and has significant health, monetary, and emotional costs. As asthma is in the top three of most common conditions requiring hospital admissions for children and that nearly 50% of inpatient pediatric patients are covered by Medicaid, improving quality outcomes for this condition has large implications across the healthcare delivery system. </p><p> Using comparisons from the KID 2009 and 2012 datasets, the differences between mean LOS for pediatric asthma patients between stages of adoption of Health IT as measured by the EMRAM scale are statistically significant at the p &lt; .05 level, demonstrating that increased use of Health IT has lowered the mean length of stay for this population. Thus, the utilization of a medical best practice, here the adoption of Health IT, resulted in shorter hospital stays and thus cost savings, in this defined pediatric patient population. While further studies examining Health IT implementation in other patient populations are necessary, these results demonstrate that the implementation of Health IT can lead to both better standards of care and lower healthcare costs, which should be of significant interest to those charting the future course of healthcare and healthcare reimbursement in this country.</p><p>

Les maladies rénales ont un impact important sur l'économie de tout système de santé dans le monde. En outre, le nombre de patients augmente régulièrement au cours des dernières décennies avec une prévalence de plus de 500 000 nouveaux cas de maladie rénale en phase terminale (ESRD) dans le monde entier chaque année. L'ESRD est l'étape finale de la maladie rénale chronique (CKD) qui a comme principales causes le diabète et l'hypertension, ainsi que la glomérulonéphrite, urolithiasis, la polykystose rénale autosomique dominante (ADPKD) et la progression de la lésion rénale aiguë (LRA), entre autres. Cependant, dans de nombreux cas, les mécanismes de ces maladies affectant le rein et sa fonction sont mal connus ou difficiles à diagnostiquer. Dans le cadre de cette étude, nous avons utilisé des technologies plus récentes, des méthodologies et des approches d'analyse de données pour jeter un peu de lumière dans les pathomécanismes de la CKD et de l'AKI. En outre, l'amélioration potentielle de la valeur diagnostique des tests diagnostiques déjà existants (par exemple ADPKD). Au cours des dernières années, les progrès dans les technologies de séquençage de l'ADN ont révolutionné le domaine de la recherche clinique et du diagnostic. Le séquençage à haut débit tel que le séquençage de prochaine génération (NG) est utilisé en raison de sa haute qualité et de précision lors que l'analyse des échantillons d'ADN. D'autres technologies de séquençage ont également montré leur valeur, comme le séquençage à longue lecture qui est utilisé en raison de ses longues lectures de séquençage et de la précision de résolution de séquençages de faible complexité, telles que les régions répétitives ou des régions de GC-pourcentage élevé. Dans le cadre de cette thèse, nous avons utilisé plusieurs méthodes de pointe de séquençage appliquées à la recherche clinique sur la maladie rénale afin de: 1. Améliorer la valeur diagnostique des tests diagnostiques déjà existants pour l'ADPKD. ADPKD est une maladie héréditaire qui représente de 5% à 10% de l'ESRD. Cependant, le criblage du principal gène ADPKD PKD1 est difficile en raison de sa structure multi-exon, de son hétérogénéité allélique et de son homologie élevée avec six pseudogènes PKD1, ainsi que d'une teneur en GC extrêmement élevée. En utilisant le séquençage direct à longue lecture, nous avons montré que le diagnostic ADPKD sans interférence des séquences homologues PKD1 est possible. 2. Caractériser le profil d'expression de l'IRA et des mécanismes sous-jacents en utilisant le séquençage de l'ARN. Les patients qui subissent une chirurgie majeure peuvent développer une IRA qui a été associée à un risque de mortalité plus élevé et une fonctionnalité rénale réduite, et un risque élevé de progression de la CKD. Certaines preuves indiquent que le système tubulaire est au milieu de cette pathophysiologie et de la récupération ultérieure. Cependant, les facteurs impliqués dans cette reprise sont encore mal compris. / Renal diseases have a high impact on the economy of any health care system worldwide. In addition, patient numbers are steadily increasing over the past decades with a prevalence of over 500.000 new end stage renal disease (ESRD) worldwide cases every year. ESRD is the final stage of chronic kidney disease (CKD) that has as the leading causes diabetes and hypertension, as well as glomerulonephritis, urolithiasis, autosomal dominant polycystic kidney disease (ADPKD), and progression of acute kidney injury (AKI), among others. However, in many cases, the mechanisms of these diseases affecting kidney and its function are poorly understood, or difficult to diagnose. Within this study, we used newer technologies, methodologies, and data analysis approaches to throw some light into the pathomechanisms of CKD and AKI. Moreover, potentially improving the diagnostic value for already existing diagnostic assays (e.g. ADPKD). In the past years, advances in DNA sequencing technologies have revolutionized the field of clinical research and diagnostics. High throughput sequencing such as next-generation sequencing (NGs) is being used because of its high quality and accuracy when analysing DNA samples. Other sequencing technologies have also shown their value such as long-read sequencing which is used because of its longer sequencing reads and accuracy resolving low-complexity sequences, such as repetitive regions or high GC-percent regions. Within the scope of this thesis we used several cutting-edge sequencing approaches applied to renal disease's clinical research to: 1. Improve the diagnostic value of already existing diagnostic assays for ADPKD. ADPKD is an inherited disease that accounts for 5% to 10% of ESRD. However, the screening of the main ADPKD gene PKD1 is challenging because of its multi-exon structure, allelic heterogeneity, and high homology with six PKD1 pseudogenes, as well as extremely high GC content. Using direct long-read sequencing we showed that ADPKD diagnostics without interference of PKD1 homologous sequences is possible. 2. Characterize the expression profile of AKI and the underlying mechanisms using RNA sequencing. Patients undergoing major surgery may develop AKI which has been associated with higher mortality risk and reduced renal functionality, and high risk of progression of CKD. Some evidences pointed out to the tubular system being at the middle of this pathophysiology and further recovery.

abstract: The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here. / Dissertation/Thesis / Ph.D. Biomedical Informatics 2012