Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Title from first page of PDF file (viewed June 5, 2008). Includes bibliographical references.

Thesis (Ph. D.)--University of Texas Southwestern Medical Center at Dallas, 2008. / Vita. Includes bibliographical references (p. 163-183).

Thesis (Ph.D. in Physiology & Biophysics) -- University of Colorado Denver, 2007. / Typescript. Includes bibliographical references (leaves 125-138). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;

Dissertation (Ph.D.)--University of Toledo, 2010. / "Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Biomedical Sciences." Title from title page of PDF document. "A Dissertation entitled"--at head of title. Bibliography: p. 63-71, 126-140.

abstract: Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive. / Dissertation/Thesis / Ph.D. Computer Science 2011

La formation des fleurs comprend trois étapes successives. Tout d'abord, un méristème, contenant les cellules souches, se forme sur les flancs du méristème d'inflorescence. Puis, le méristème adopte une identité florale. Enfin, la morphogenèse florale permet le développement des différents organes floraux répartis en quatre verticilles. Ces étapes font intervenir des réseaux génétiques distincts. Le facteur de transcription LEAFY (LFY) est un régulateur majeur du développement floral chez les plantes à fleurs. Le but de ma thèse était de comprendre les fonctions précises de LFY au cours du développement floral, en particulier dans les étapes précoces du développement. Les études moléculaires de LFY chez la plante modèle A. thaliana ont permis de montrer que cette protéine a la capacité de se multimériser lors de sa liaison à l'ADN. En étudiant l'importance fonctionnelle de la dimérisation de LFY, j'ai pu mettre en évidence l'importante de cette propriété pour la régulation de ses gènes cibles, responsables de l'identité florale. De plus, en couplant des études génétiques, les études transcriptomiques et les données de liaisons à l'ADN à l'échelle génomique, j'ai mis en évidence un nouveau réseau de gènes régulé par LFY et impliqué dans le développement du méristème, avant sa détermination en fleur. Ces données ouvrent la perspective que cette nouvelle fonction de LFY est une fonction indépendante de sa fonction florale et déjà présente chez la plupart des plantes terrestres.LFY est hautement conservé chez toutes les plantes terrestres, mais ne fait pas partie d'une famille multigénique contrairement à la plupart des facteurs de transcription qui ont formé des familles multigéniques par duplication au cours de l'évolution. J'ai étudié l'évolution des propriétés de LFY, notamment sa capacité de se dimériser. Pour cela, nous nous sommes intéressés aux homologues de LFY et nous avons découvert que LFY était déjà présent chez les algues vertes multicellulaires. En étudiant l'interface de dimérisation chez les différents homologues de LFY, nous avons mis en évidence que l'acquisition de cette propriété a joué un rôle crucial dans l'évolution de la protéine.Enfin, je me suis intéressé au contrôle post-traductionnel de l'activité de la protéine LFY. Les résultats préliminaires sont présentés et permettent de penser que ce mode de régulation est important pour les fonctions de ce facteur de transcription unique. / Flower formation comprises three successive steps. First, a new meristem, containing stem cells, is formed on the flanc of the inflorescence meristem. Then, this meristem adopts a floral identity. Finally, floral morphogenesis occurs that allow the development of floral organs arranged into four distinct whorls. The LEAFY (LFY) transcription factor is a major regulator of floral development in flowering plants. The aim of my thesis was to precisely understand the roles of LFY during floral development, especially during early stages. Previous studies in the model plant A. thaliana demonstrate that LFY can multimerize upon binding to DNA. By studying the functional importance of the dimerization property of LFY, we were able to show that this property is important for the regulation of its target genes, including those responsible for floral identity. In addition, by combining genetic studies, transcriptomic datas as well as whole-genome LFY binding sites, we have shown that LFY controls a new network of genes which are directly involved in meristem formation, before its determination into flower. These data raise the prospect that this new function of LFY is in fact a non-floral function already present in most land plants.LFY is highly conserved in all land plants, but is not part of a multigene family in contrast to most transcription factors. I studied the evolution of LFY properties, including its ability to dimerize on specific DNA sequences. For this purpose, we looked for the ancestor form of LFY and found out that LFY was already present in multicellular green algae. By studying the dimerization interface in different counterparts of LFY, we demonstrate that the acquisition of this dimerization property has played a crucial role during the evolution of the protein.Finally, I studied the post-translational control of LFY activity which remains largely unknown. Preliminary results are presented and suggest that this mode of regulation is important for many functions of this orphan transcription factor.

The mapping of biomolecular interactions reveals that the function of most biological components depends on a web of interrelations with other cellular components, stressing the need for a systems-level view of biological functions. In this work, I explore ways in which the integration of network and genomic information from different organizational levels can lead to a better understanding of cellular systems and components. First, studying yeast, I show that the evolutionary properties of target genes constitute the dominant determinant of transcription factor (TF) evolutionary rate and that this evolutionary modularity is limited to activating regulatory relationships. I also show that targets of fast-evolving TFs show greater evolutionary expression changes and are enriched for niche-specific functions and other TFs. This work highlights the importance of trans-regulatory network evolution in species-specific gene expression and network adaptation. Next, I show that genes either lost or gained across fungal evolution are enriched in TFs and have very different network and genomic properties than universally conserved genes, including, in sharp contrast to other networks, a greater number of transcriptional regulators. Placing genes in the context of their evolutionary life-cycle reveals principles of network integration of gained genes and evidence for the progressive network and functional marginalization of genes as an evolutionary process preceding gene loss. In the final chapter, I study how alternative splicing (AS)-driven expansion of human proteome diversity leads to system-level complexity through the AS-mediated rewiring of the protein-protein interaction network. By overlaying different network and genomic datasets onto the first large-scale isoform-resolution interactome, I found that differentiating between splice variants is essential to capturing the full extent of the network's functional modularity. I also discovered that AS-mediated rewiring preferentially affects tissue-specific genes and that topologically different patterns of rewiring have distinct functional consequences. Furthermore, I found that most rewiring can be traced to the AS of evolutionarily conserved sequence modules, which promote or block interactions and tend to overlap linear motifs and disrupt known domain-domain interactions. Together, this work demonstrates that a network-level perspective and genomic data integration are essential to understanding the evolution and functional diversity of proteomes.

Synthetic biology in mammalian cells holds great promise for reverse engineering biological processes and rewiring cellular behaviors for therapeutic purpose. An essential aspect in our ability to reprogram the cellular code is the availability of highly orthogonal, inducible transcriptional regulators. CRISPR-based strategies employing effector-domain tethering to the single guide RNA (sgRNA)-dCas9 complex have greatly advanced this field by allowing for precise activation or repression of any gene via simple sgRNA reprograming. However, the implementation of inducible CRISPR-based transcriptional regulators (CRISPR-TRs) has so far been restricted to dCas9 protein engineering and conditional effector tethering. Although elegant, these approaches are limited by dCas9 promiscuous loading of sgRNAs, which hinders their use for the creation of independent multi-gene transcriptional programs. To address this limitation, I have developed a modular framework for the rational design of inducible CRISPR-TR, based on simple and reversible modifications of the sgRNA sequence. At the core of this conceptual framework lies the ability to inactivate native sgRNAs by appending on their 5'-end a short RNA segment, which folds to form a spacer-blocking hairpin (SBH). Base-pairing between the extension and the sgRNA spacer prevents docking of the CRISPR-TR on-target, fully abrogating its activity. Subsequently, I have created inducible SBH variants (iSBH) by replacing the hairpin loop with conditional RNA cleaving units. Using a variety of sensing-loops, I was able to engineer a panel of switchable iSBH-sgRNAs, designed to activate specifically in the presence of protein, oligonucleotide, and small molecule inducers. Leveraging the versatility of this method, I demonstrate that iSBH-sgRNAs expression can be multiplexed to assemble synthetic gene circuits implementing parallel and orthogonal regulation of multiple endogenous gene targets. Finally, I have distilled the design principles derived throughout this project to develop a web tool that automates the creation of iSBH- sgRNAs. Already a valuable addition to the synthetic biology toolkit, iSBH-based inducibility should in theory also be applicate to all CRISPR-Cas9 derivatives (genome editing, epigenetic alteration, DNA labelling, etc.) as well as other newly characterized RNA-guide nucleases from the CRISPR family.

Transcription factor (TF) binding has been studied extensively in relation to binding site affinity and chromosome modifications; however, the relationship between genome spatial organisation and transcription factor binding is not well studied. Using the recently available high resolution Hi-C contact map of human GM12878 lymphoblastoid cells, we investigated computationally the genome-wide spatial co-localization of transcription factor binding sites, for both within the same type and between different types. First, we observed a strong positive correlation between site occupancy and homotypic TF co-localization based on Hi-C contacts, consistent with our predictions from biophysical simulations of TF target search. This trend is more prominent in binding sites with weak binding sequences and within enhancers, suggesting genome spatial organisation plays an essential role in determining binding site occupancy, especially for weak regulatory elements. Furthermore, when investigating spatial co-localization between different TFs, we discovered two distinct co-localization networks of TFs in lymphoblastoid cells, one of which is enriched in lymphocyte specific pathways and distal enhancer binding. These two TF networks have strong biases for either the A1 or A2 chromosome subcompartment, but nonetheless are still preserved within each, indicating a potential causal link between cell-type-specific transcription factor binding and chromosome subcompartment segregation. We called 40 pairs of significantly co-localized TFs according to the genome wide Hi-C contact map, which are enriched in previously reported, physical interactions, thus linking TF spatial network to co-functioning. In addition to the above main project, I also worked on a side project to find compute-efficient ways in scaling binding site strength across different TFs based on Position-Weight-Matrices (PWM). While common bioinformatics tools produce scores that can reflect the binding strength between a specific TF and the DNA, these scores are not directly comparable between different TFs. We provided two approaches in estimating a scaling parameter $\lambda$ to the PWM score for different TFs. The first approach uses a PWM and background genomic sequence as input to estimate $\lambda$ for a specific TF, which we applied to show that $\lambda$ distributions for different TF families correspond with their DNA binding properties. Our second method can reliably convert $\lambda$ between different PWMs of the same TF, which allows us to directly compare PWMs that were generated by different approaches.

Cervical malignancy is the fourth most common cause of cancer-related mortality in women worldwide; infection with high-risk human papillomavirus (HRHPV) is responsible for over 500,000 cases of cervical carcinoma each year, approximately 90% of which are squamous cell carcinomas (SCCs). Over half of all HPV-positive cervical SCCs are caused by the deregulated expression of HPV16 oncogenes E6 and E7 in proliferating basal cells of the cervical squamous epithelium. The major risk factor associated with cervical neoplastic progression is integration of HRHPV into the host genome, which is detected in $~$85% of HPV16-positive cervical carcinomas. The work presented in this doctoral thesis sought to provide insights into our understanding of the process of HPV16 integration as well as to elucidate mechanisms that deregulate both virus and host gene expression following integration. The W12 cell model system used in this project is a polyclonal cervical keratinocyte line generated by explant culture of a low-grade cervical squamous intraepithelial lesion (LSIL) that arose following natural infection with HPV16. The W12 clones were isolated in the absence of selective pressure, and as such represent the range of integration events that occur in a pre-malignant lesion at the early stages of carcinogenesis, prior to integrant selection. Despite identical genetic backgrounds, expression levels of oncogenes E6 and E7 varied up to 16-fold between the W12 clones. Expression of HPV oncogenes is ultimately determined by transcription factor binding to the non-coding long control region (LCR) of the viral genome. The initial result of this study found that genomic mutations affecting transcription factor binding at the LCR of the W12 clones was not a cause of differential viral expression, concluding that epigenetic control may be at play. Higher levels of virus expression per template were associated with increased levels of histone post-translational modification (PTM) hallmarks of transcriptionally active chromatin and reduced levels of repressive hallmarks. There was greater abundance of the active/elongating form of the RNA polymerase-II enzyme (RNAPII-Ser2P), together with CDK9, the component of positive transcription elongation factor-b (P-TEFb) responsible for the Ser2 phosphorylation. The changes observed were functionally significant, as cells with higher HPV16 expression per template showed greater sensitivity to depletion and/or inhibition of histone acetyl transferases and CDK9, as well as reduced sensitivity to histone deacetylase inhibition. Employing next generation sequencing data available for five representative W12 clones, the sites of HPV16 host integration were identified. The three-dimensional (3D) structure of the nucleus and physical interactions between stretches of the genome over long distances (i.e. enhancer and promoters) are known to exert an additional level of gene regulation. Identification of 3D virus-host interactions in the W12 clones employing the newly developed and unique 'Sequence Capture of Regions Interacting with Bait Loci Hi-C' (SCRiBL-Hi-C) protocol showed that both short- ($~$50 kb), and long-range ($~$1 Mb) interactions occur during the early stages of carcinogenesis. Together, the data in this thesis indicate that transcription and subsequent expression of the HPV16 genome is controlled by multiple layers of epigenetic regulation.