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 regulation of gene expression is the driver of cellular differentiation in multicellular organisms; the result is a diverse range of cell types each with their own unique profile of expression. Within these cell types the transcriptional product of a gene is up or down regulated in response to intrinsic and extrinsic stimuli according to its own regulatory programme encoded within the cell. The complexity of this regulatory programme depends on the requirements of the gene to change expression states in different cell lineages or temporally in response to a range of conditions. In the case of many housekeeping genes integral to the survival of the cell, this programme is simple - switch on the gene and leave it on, whereas often the required level and precision of regulatory control is much more involved and lends to subtle changes in expression. This raises many questions of precisely where and how that regulatory information is encoded and whether different biological systems encode it in the same way. This project attempts to answer these questions through the development of novel approaches in quantifying the output of this regulatory programme according to the state changes as observed from the expression profile of a given gene. Measures of complexity in gene expression are calculated over a wide range of cell types and conditions collected using CAGE, which provides a quantitative estimate of gene expression that precisely defines the promoter utilised to initiate that expression. As expected, housekeeping genes were found to be amongst the least complex, as a result of their uniform expression profiles, as well as those genes highly restricted in their expression. The genes most complex in their expression output were those associated with the presence of H3K27me3 repressive marks; genes poised for activation in a specific set of cell types, as well as those enriched in DNAse I hypersensitive sites in their upstream region but not necessarily conserved in that region. Evidence also suggests that different promoters associated with a gene contribute in different ways to its resultant regulatory complexity, suggesting that certain promoters may be more crucial in driving the regulation of some genes. This allows for the targeting of such promoters in the analysis of certain diseases implicated by changes in regulatory regions. Indeed, genes known to be associated with diseases such as leukaemia and Alzheimer’s are found to be highly complex in their expression.

The neural crest is a transient, multipotent, cell population that gives rise to several important tissues during embryonic development, including the craniofacial skeleton, peripheral nervous system, and melanocytes. The neural crest arises from the ectoderm, along with the skin and central nervous system. This process of specification is dependent on a gene regulatory network (GRN) which is made up of transcription factors that regulate each other. While we know many of the members of this GRN, the direct connections among the members are largely unsolved. Breakdown of this GRN can lead to birth defects, such as cleft lip and palate, and cancer of neural crest derivatives, such as melanoma, thus understanding the intricate details of this network is important. The transcription factor Tfap2a is an important member of the GRN, as loss of tfap2a and its paralog tfap2c leads to loss of pre-migratory neural crest and all neural crest derivatives. Despite its importance in this network little is known about how its expression is regulated. We hypothesized that, due to its importance in this network, it will have multiple enhancers that drive its expression in the neural crest. We have identified two neural crest enhancers of tfap2a. We found that one of these enhancers is responsive to WNT signals and is maintained by forming a positive feedback loop with Sox10. Our results suggest that this enhancer is important for both induction and maintenance of tfap2a expression in the neural crest. Tfap2 paralogs are important at several different stages throughout neural crest lineage specification. However, the only direct target of Tfap2a that has been identified is sox10. Thus, we wanted to determine the direct targets of Tfap2 in this network. Through the integration of several data sets, including ATAC-seq and expression profiling of tfap2a/c double mutants, we have identified several direct targets including sox9b and alx1. Melanoma is cancer of the melanocytes, a neural crest derivative. Recent studies have shown that melanoma and the neural crest share genetic similarities. TFAP2A expression is decreased in metastatic melanoma compared to primary tumors, thus we wanted to investigate the mechanism of TFAP2A in metastatic melanoma. We found that the promoter of TFAP2A is hypermethylated in some metastatic melanoma tumors. This was confirmed by samples in the TCGA database. Hypermethylation of the promoter contributes to the downregulation of TFAP2A in metastatic melanoma. In conclusion, we have further illuminated the connections among transcription factors in the GRN important for neural crest lineage specification. Further, we have identified a new mechanism regulating TFAP2A expression in metastatic melanoma. Together, these studies reveal regulatory mechanisms of TFAP2A gene expression.

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.

Recently, it has been shown that genetic variation that perturbs the regulation of gene expression is widespread in eukaryotic genomes. Regulatory variation (RV) is expected to be an important driver of phenotypic differences, evolutionary change, and susceptibility to complex genetic diseases. Because trans-acting regulators of gene expression control mRNA levels of multiple genes simultaneously, we hypothesise that RV that affects these components will have a shared-influence upon the expression levels of multiple genes. Since genes are regulated in trans by combinations of basal and tissue specific factors, we further hypothesise that RV in these components may have different effects in each tissue. We used microarrays to identify 755 genes that were affected by RV in at least one of the brain, kidney and liver of two inbred mouse strains, C57BL/6J and DBA/2J. Just 2% were affected in all three tissues, suggesting that the influence of RV is predominantly tissue specific. To study shared-RV, we measured the expression levels of these 755 genes in the same 3 tissues from a panel of recombinant inbred mice, and identified groups of correlated genes that are putatively under the influence of shared trans-acting RV. Using methods that we developed for studying the effects of RV in multiple tissues, we identified 212 genes that are correlated in all three tissues, which include 10 groups of at least 3 genes. We developed a novel method called coherency analysis to show that RV consistently affected the expression levels of these groups of genes in different genetic backgrounds. Strikingly, the relative up- or down-regulation of genes in each group was markedly different in the three tissues of the same mouse, suggesting that the influence of RV itself is not tissue specific as previously expected, but that RV can influence genes with differing outcomes in each tissue. These observations are compatible with RV affecting combinations of basal and tissue specific regulatory factors. This is the first cross-tissue investigation into the influence of shared-RV in multiple tissues, which has important implications in humans, where access to the phenotypically relevant tissue may be necessarily limited.

Recently, it has been shown that genetic variation that perturbs the regulation of gene expression is widespread in eukaryotic genomes. Regulatory variation (RV) is expected to be an important driver of phenotypic differences, evolutionary change, and susceptibility to complex genetic diseases. Because trans-acting regulators of gene expression control mRNA levels of multiple genes simultaneously, we hypothesise that RV that affects these components will have a shared-influence upon the expression levels of multiple genes. Since genes are regulated in trans by combinations of basal and tissue specific factors, we further hypothesise that RV in these components may have different effects in each tissue. We used microarrays to identify 755 genes that were affected by RV in at least one of the brain, kidney and liver of two inbred mouse strains, C57BL/6J and DBA/2J. Just 2% were affected in all three tissues, suggesting that the influence of RV is predominantly tissue specific. To study shared-RV, we measured the expression levels of these 755 genes in the same 3 tissues from a panel of recombinant inbred mice, and identified groups of correlated genes that are putatively under the influence of shared trans-acting RV. Using methods that we developed for studying the effects of RV in multiple tissues, we identified 212 genes that are correlated in all three tissues, which include 10 groups of at least 3 genes. We developed a novel method called coherency analysis to show that RV consistently affected the expression levels of these groups of genes in different genetic backgrounds. Strikingly, the relative up- or down-regulation of genes in each group was markedly different in the three tissues of the same mouse, suggesting that the influence of RV itself is not tissue specific as previously expected, but that RV can influence genes with differing outcomes in each tissue. These observations are compatible with RV affecting combinations of basal and tissue specific regulatory factors. This is the first cross-tissue investigation into the influence of shared-RV in multiple tissues, which has important implications in humans, where access to the phenotypically relevant tissue may be necessarily limited.

Recently, it has been shown that genetic variation that perturbs the regulation of gene expression is widespread in eukaryotic genomes. Regulatory variation (RV) is expected to be an important driver of phenotypic differences, evolutionary change, and susceptibility to complex genetic diseases. Because trans-acting regulators of gene expression control mRNA levels of multiple genes simultaneously, we hypothesise that RV that affects these components will have a shared-influence upon the expression levels of multiple genes. Since genes are regulated in trans by combinations of basal and tissue specific factors, we further hypothesise that RV in these components may have different effects in each tissue. We used microarrays to identify 755 genes that were affected by RV in at least one of the brain, kidney and liver of two inbred mouse strains, C57BL/6J and DBA/2J. Just 2% were affected in all three tissues, suggesting that the influence of RV is predominantly tissue specific. To study shared-RV, we measured the expression levels of these 755 genes in the same 3 tissues from a panel of recombinant inbred mice, and identified groups of correlated genes that are putatively under the influence of shared trans-acting RV. Using methods that we developed for studying the effects of RV in multiple tissues, we identified 212 genes that are correlated in all three tissues, which include 10 groups of at least 3 genes. We developed a novel method called coherency analysis to show that RV consistently affected the expression levels of these groups of genes in different genetic backgrounds. Strikingly, the relative up- or down-regulation of genes in each group was markedly different in the three tissues of the same mouse, suggesting that the influence of RV itself is not tissue specific as previously expected, but that RV can influence genes with differing outcomes in each tissue. These observations are compatible with RV affecting combinations of basal and tissue specific regulatory factors. This is the first cross-tissue investigation into the influence of shared-RV in multiple tissues, which has important implications in humans, where access to the phenotypically relevant tissue may be necessarily limited.

Thesis (Ph.D.)--McMaster University, 2004. / Supervisor: David W. Andrews. Includes bibliographical references (leaves 134-148).

The development of cDNA microarray technology has made it possible to simultaneously monitor the expression status of thousands of genes. A natural use for this vast amount of information would be to try and figure out inter-gene relationships by studying the gene expression patterns across different experimental conditions and to build Gene Regulatory Networks from these data. In this thesis, we study some of the issues involved in Genetic Regulatory Networks. One of them is to discover and elucidate multivariate logical predictive relations among gene expressions and to demonstrate how these logical relations based on coarse quantization closely reflect corresponding relations in the continuous data. The other issue involves construction of synthetic Probabilistic Boolean Networks with particular attractor structures. These synthetic networks help in testing of various algorithms like Bayesian Connectivity based approach for design of Probabilistic Boolean Networks.

Polymorphisms in the interferon regulatory factor 5 (IRF5) gene have been consistently replicated and shown to confer risk for or protection from the development of systemic lupus erythematosus (SLE). IRF5 expression is significantly upregulated in SLE patients and upregulation associates with IRF5-SLE risk haplotypes. IRF5 alternative splicing has also been shown to be elevated in SLE patients. Given that human IRF5 exists as multiple alternatively spliced transcripts with distinct function(s), it is important to determine whether the IRF5 transcript profile expressed in healthy donor immune cells is different from that expressed in SLE patients. Moreover, it is not currently known whether an IRF5-SLE risk haplotype defines the profile of IRF5 transcripts expressed. Using standard molecular cloning techniques, we identified and isolated 14 new differentially spliced IRF5 transcript variants from purified monocytes of healthy donors and SLE patients to generate an IRF5 variant transcriptome. Next-generation sequencing was then used to perform in-depth and quantitative analysis of full-length IRF5 transcript expression in primary immune cells of SLE patients and healthy donors by next-generation sequencing. Evidence for additional alternatively spliced transcripts was obtained from de novo junction discovery. Data from these studies support the overall complexity of IRF5 alternative splicing in SLE. Results from next-generation sequencing correlated with cloning and gave similar abundance rankings in SLE patients thus supporting the use of this new technology for in-depth single gene transcript profiling. Results from this study provide the first proof that 1) SLE patients express an IRF5 transcript signature that is distinct from healthy donors, 2) an IRF5-SLE risk haplotype defines the top four most abundant IRF5 transcripts expressed in SLE patients, and 3) an IRF5 transcript signature enables clustering of SLE patients with the H2 risk haplotype.