Cellular development and function necessitate precise patterns of gene expression. Control of gene expression is in part orchestrated by a class of remote regulatory elements, termed enhancers, which are brought into contact with promoters via DNA looping. Enhancers typically contain clusters of transcription factor binding sites, and TF recruitment to them is thought to play a key role in transcriptional control. In this thesis I have addressed two issues regarding gene regulation by enhancers. First, with recent genome-wide enhancer mapping, it is becoming increasingly apparent that genes are commonly regulated by multiple enhancers in the same cell type. How a gene’s regulatory information is encoded across multiple enhancers, however, is still not fully understood. Second, numerous recent studies have found that enhancers are enriched for expression-modulating and disease-associated genetic variants. However, understanding and predicting the effects of enhancer variants remains a major challenge. I focussed on a human lymphoblastoid cell line (LCL), GM12878, for which ChIP-Seq data are available for 52 different TFs from the ENCODE project. Significantly, Promoter Capture Hi-C data for the same LCL are available, making it possible to link enhancers to target genes globally. In the first part of the thesis, I investigated how gene regulatory information is encoded across enhancers. Specifically, I asked whether a gene tends to use multiple enhancers to bring the same or distinct regulatory information. I found that there was a general trend towards a “shadow” enhancer architecture, whereby similar combinations of TFs were recruited to multiple enhancers. However, numerous examples of “integrating” enhancers were observed, where the same gene showed large variation in TF binding across enhancers. Distinct groups of TFs were associated with these contrasting models of TF enhancer binding. To investigate the functional effects of variation at enhancers, I additionally took advantage of a panel of LCLs derived from 359 individuals, which have been genotyped by the 1000 Genomes Project, and for which RNA-Seq data are publically available. I used TF binding models to computationally predict variants impacting TF binding, and tested the association of these variants with the expression of the target genes they contact based on Promoter Capture Hi-C. Compared to the standard eQTL calling approach, this offers increased sensitivity as only variants physically contacting the promoter and predicted to impact TF binding are tested. Using this approach, I discovered a set of predicted TF-binding affinity variants at distal regions that associate with gene expression. Interestingly, a large proportion of these binding variants fall at the promoters of other genes. This finding suggests that some promoters may be able to act in an enhancer-like manner via long-range interactions, consistent with very recent findings from alternative approaches.

The role of eosinophilia in allergic disorders indicates hIL-5 as a target of therapy. The conservation of hIL-5 proximal elements suggests they are important in controlling expression. Corticosteroids are important in the treatment of allergy, and are powerful inhibitors of IL-5 expression. Antisense oligonucleotides are new compounds that can specifically inhibit IL-5 production. This study aimed at understanding the role of conserved lymphokine element 0 (CLEO) in induction and inhibition of IL-5.The conserved proximal CLEO/TATA elements driving a luciferase reporter gene gave higher expression than a 500bp promoter in PER1 17 T-cell line. Two and three copies of IL-5 CLEO upstream of the silent IL-4 minimal promoter gave 150-200 fold increases in expression in forward orientation, but little activity in reverse orientation. Consequently, while CLEO is a powerful activator, it is not a classical enhancer. Antisense technology has also shown the dependence of IL-5 gene transcription on the de novo synthesis of the transcription factor Fra2.Inhibition of IL-5 reporter constructs by dexamethasone when induced by PMNcAMP, but not PMNCaI, provided a tool for understanding the mechanism. Deletion analysis identified CLEO as the key element of dexamethasone inhibition. Non-inhibition of IL-5 reporter constructs by dexamethasone in a Jurkat cell line, however, showed a possible intermediary factor involved in the inhibition mechanism.

Protein Kinase C (PKC) represents an important regulatory element in the signal transduction pathways of mammalian cells. Research interest has increased enormously since the discovery that PKC plays critical roles in cell differentiation, tumor promotion, oncogenesis and cell regulatory processes. The primary driving force of this project was the study and development of enantioselective PKC inhibitors. To accomplish this objective the four stereoisomers, (2S/4S)-, (2RI4S)-, (2R14R)-, and (2S/4R)-6-N,N-dimethyl-2-methyl-2-oxo-l,3-dioxa- 4-pentadecyl-6-aza-2-phosphacyclooctane bromides (la-d) were synthesized and evaluated. Long-alkyl chain optically pure epoxides, the key intermediates for the synthesis, were prepared from relatively inexpensive glyceraldehyde surrogates. Several other intermediates exhibited other biological responses including spermicidal, anti-HIV, mycobactericidal, and anti-cancer activities. / Ph. D.

Gene regulation lies at the heart of most biological processes and transcription factors are the key molecules that control tissues specific gene expression. In higher eukaryotes transcription factors control gene expression by binding regulatory DNA segments called cis-regulatory modules (CRMs). The increasing number of sequenced genomes of multicellular eukaryotes along with high-throughput methods such as whole genome microarray expression data allows for systematic characterization of the CRMs that control gene expression. A first step towards understanding gene regulation is the identification of the regulatory elements present in the genome. We take advantage of the large database of spatio-temporal patterns of gene expression in D. melanogaster embryogenesis to identify sets of developmentally co-expressed genes. We developed a computational method that identifies DNA binding sites for transcription factors from families of co-regulated genes that are expressed during Drosophila embryo development. This method discovers over-represented motifs in a set of co-regulated genes using the exhaustive motif enumeration technique. Clustering the predicted motifs identifies the CRMs, which assist in translating a combinatorial code of TF inputs into a specific gene expression output. The predicted CRMs were verified experimentally by searching the whole genome for the predicted CRMs and establishing expression pattern of the genes that are associated with these CRMs. It is well know that the gene expression is substantially controlled through CRMs and those key regulatory sequences are conserved in related species. The conservation of CRMs can be studied by comparing the related genomes and alignment methods are widely used computational tools for comparing the sequences. However, in distantly related species the CRM sequences are simply not align able. To identify the similar CRMs in distantly related species we developed a non-alignment based method for discovering similar CRMs in related species. This method is based on word frequencies where the given sequences are compared using Poisson based metric. When starting with a set of CRMs involved in Drosophila early embryo development, we show here that our non-alignment method successfully detects similar CRMs in distantly related species ( D. ananassae, D. pseudoobscura, D. willisoni, D. mojavensis, D. virilis, D. grimshawi ). This method proved efficient in discriminating the functional CRMs from the non-functional ones.

Gene regulation lies at the heart of most biological processes and transcription factors are the key molecules that control tissues specific gene expression. In higher eukaryotes transcription factors control gene expression by binding regulatory DNA segments called cis-regulatory modules (CRMs). The increasing number of sequenced genomes of multicellular eukaryotes along with high-throughput methods such as whole genome microarray expression data allows for systematic characterization of the CRMs that control gene expression. A first step towards understanding gene regulation is the identification of the regulatory elements present in the genome. We take advantage of the large database of spatio-temporal patterns of gene expression in D. melanogaster embryogenesis to identify sets of developmentally co-expressed genes. We developed a computational method that identifies DNA binding sites for transcription factors from families of co-regulated genes that are expressed during Drosophila embryo development. This method discovers over-represented motifs in a set of co-regulated genes using the exhaustive motif enumeration technique. Clustering the predicted motifs identifies the CRMs, which assist in translating a combinatorial code of TF inputs into a specific gene expression output. The predicted CRMs were verified experimentally by searching the whole genome for the predicted CRMs and establishing expression pattern of the genes that are associated with these CRMs. It is well know that the gene expression is substantially controlled through CRMs and those key regulatory sequences are conserved in related species. The conservation of CRMs can be studied by comparing the related genomes and alignment methods are widely used computational tools for comparing the sequences. However, in distantly related species the CRM sequences are simply not align able. To identify the similar CRMs in distantly related species we developed a non-alignment based method for discovering similar CRMs in related species. This method is based on word frequencies where the given sequences are compared using Poisson based metric. When starting with a set of CRMs involved in Drosophila early embryo development, we show here that our non-alignment method successfully detects similar CRMs in distantly related species ( D. ananassae, D. pseudoobscura, D. willisoni, D. mojavensis, D. virilis, D. grimshawi ). This method proved efficient in discriminating the functional CRMs from the non-functional ones.

Separate domestication events and strong selective pressures have created diverse phenotypes among existing cattle populations; however, the genetic determinants underlying most phenotypes are currently unknown. Bos taurus taurus (Bos taurus) and Bos taurus indicus (Bos indicus) cattle are subspecies of domesticated cattle that are characterized by unique morphological and metabolic traits. Because of their divergence, they are ideal model systems to understand the genetic basis of phenotypic variation. Here, we developed DNA and structural variant maps of cattle genomes representing the Bos taurus and Bos indicus breeds. Using this data, we identified genes under selection and biological processes enriched with functional coding variants between the two subspecies. Furthermore, we examined genetic variation at functional non-coding regions, which were identified through epigenetic profiling of indicative histone- and DNA-methylation modifications. Copy number variants, which were frequently not imputed by flanking or tagged SNPs, represented the largest source of genetic divergence between the subspecies, with almost half of the variants present at coding regions. We identified a number of divergent genes and biological processes between Bos taurus and Bos indicus cattle; however, the extent of functional coding variation was relatively small compared to that of functional non-coding variation. Collectively, our findings suggest that copy number and functional non-coding variants may play an important role in regulating phenotypic variation among cattle breeds and subspecies.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 117-142).

Thesis (Ph.D.)--University of South Florida, 2001. / Includes vita. Includes bibliographical references (leaves 80-104). Also available online.

Maintaining cells in an undifferentiated totipotent state is essential for initiating developmental programs that lead to a fully formed organism in each generation and for maintaining immortal germ cells across generations. Caenorhabditis elegans is a powerful genetic model organism to study early germ cell development due to the animal’s transparency and the ability to screen for mutant phenotypes. However, our ability to use standard techniques to study gene expression using fluorescent reporter genes has been limited due to germline-specific silencing mechanisms that repress transgenes. Therefore, we lack even basic knowledge of how expression is regulated in C. elegans germ cells. In this study, we develop methods to overcome these silencing mechanisms by using a class of noncoding DNA, called Periodic An/Tn Clusters (PATCs), to prevent transgene silencing in the germline. We use these improved tools to test the proposed role of putative germline-specific regulatory DNA motifs and the role a periodic TT signal within germline promoters. We fused GFP to the promoter of a germline expressed gene (pcn-1), which is enriched for PATCs and contains a germline-specific motif (TTAAAG). Our results show that despite enrichment and phylogenetic conservation, the TTAAAG motif is not required for germline expression. To test additional motifs and periodic TTs, we have designed a system that will allow us to test synthetic gene fragments for bi-directional germline expression. These tools will allow us to rapidly test motif redundancy, motif spacing, and TT periodicity using gfp and rfp signals in the germline and will enable experiments aimed at understanding the role of germline regulatory elements.

Splicing regulatory elements (SREs) are short, degenerate sequences on pre-mRNA molecules that enhance or inhibit the splicing process via the binding of splicing factors, proteins that regulate the functioning of the spliceosome. Existing methods for identifying SREs in a genome are either experimental or computational. This work tackles the limitations in the current approaches for identifying SREs. It addresses two major computational problems, identifying variable length SREs utilizing a graph-based model with de Bruijn graphs and discovering co-occurring sets of SREs (combinatorial SREs) utilizing graph mining techniques. In addition, I studied and analyzed the effect of alternative splicing on tissue specificity in human. First, I have used a formalism based on de Bruijn graphs that combines genomic structure, word count enrichment analysis, and experimental evidence to identify SREs found in exons. In my approach, SREs are not restricted to a fixed length (i.e., k-mers, for a fixed k). Consequently, the predicted SREs are of different lengths. I identified 2001 putative exonic enhancers and 3080 putative exonic silencers for human genes, with lengths varying from 6 to 15 nucleotides. Many of the predicted SREs overlap with experimentally verified binding sites. My model provides a novel method to predict variable length putative regulatory elements computationally for further experimental investigation. Second, I developed CoSREM (Combinatorial SRE Miner), a graph mining algorithm for discovering combinatorial SREs. The goal is to identify sets of exonic splicing regulatory elements whether they are enhancers or silencers. Experimental evidence is incorporated through my graph-based model to increase the accuracy of the results. The identified SREs do not have a predefined length, and the algorithm is not limited to identifying only SRE pairs as are current approaches. I identified 37 SRE sets that include both enhancer and silencer elements in human genes. These results intersect with previous results, including some that are experimental. I also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. Further, I report a genome-wide analysis to study alternative splicing on multiple human tissues, including brain, heart, liver, and muscle. I developed a pipeline to identify tissue-specific exons and hence tissue-specific SREs. Utilizing the publicly available RNA-Seq data set from the Human BodyMap project, I identified 28,100 tissue-specific exons across the four tissues. I identified 1929 exonic splicing enhancers with 99% overlap with previously published experimental and computational databases. A complicated enhancer regulatory network was revealed, where multiple enhancers were found across multiple tissues while some were found only in specific tissues. Putative combinatorial exonic enhancers and silencers were discovered as well, which may be responsible for exon inclusion or exclusion across tissues. Some of the enhancers are found to be co-occurring with multiple silencers and vice versa, which demonstrates a complicated relationship between tissue-specific enhancers and silencers. / Ph. D.