Cell proliferation is a fundamental biological process, and the ability of human cells to transition from a quiescent to proliferative state is essential for tissue homeostasis. Most cells in eukaryotic organisms are in a quiescent state, but on appropriate physiological or pathological stimuli, many types of somatic cells may exit quiescence, re-enter the cell cycle and begin to proliferate. The ability of cells to remain viable while quiescent, exit quiescence and re-enter into the cell cycle is the basis for varied physiological processes such as wound healing, lymphocyte activation and hepatocyte regeneration, but is also a hallmark of cancer. The transition of mammalian cells from quiescence to proliferation is accompanied by the differential expression of several microRNAs (miRNAs) and transcription factors. Our understanding of miRNA biology has significantly improved, but the miRNA regulatory networks that govern cell proliferation are still largely unknown. We characterized a miR-22 Myc network that mediates proliferation through regulation of the interferon response and multiple cell cycle arrest genes. We identified several cell cycle arrest genes that regulate the effects of the tumor suppressor p53 as direct targets of miR-22, and discovered that miR-22 suppresses interferon gene expression. We go on to show that miR-22 is activated by the transcription factor Myc as quiescent cells enter proliferation, and that miR-22 represses the Myc transcriptional repressor MXD4, mediating a feed forward loop to elevate Myc expression levels. To more effectively determine miRNA targets, we utilized a combination of RNA-induced silencing complex immunoprecipitations and gene expression profiling. Using this approach for miR-191, we constructed an extensive transcriptome wide miR-191 target set. We show that miR-191 regulates proliferation, and targets multiple proto-oncogenes, including CDK9, NOTCH2, and RPS6KA3. Recent advances in determining miRNA targetomes have revealed widespread non-canonical miRNA-target pairing. We experimentally identified the transcriptome wide targets of miR-503, miR-103, and miR-494, and observed evidence of non-canonical target pairing for these miRNAs. We went on to confirm that miR-503 requires pairing outside of the canonical 5' seed region to directly target the oncogene DDHD2. Further bioinformatics analysis implicated miR-503 and DDHD2 in breast cancer tumorigenesis. / text

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Genes, the fundamental building blocks of life, act together (often through their derived proteins) in modules such as protein complexes and molecular pathways to achieve a cellular function such as DNA repair and cellular transport. A current emphasis in genomics research is to identify gene modules from gene profiles, which are measurements (such as a mutant phenotype or an expression level), associated with the individual genes under conditions of interest; genes in modules often have similar gene profiles. Clustering groups of genes with similar profiles can hence deliver candidate gene modules. Pairwise similarity measures derived from these profiles are used as input to the popular hierarchical agglomerative clustering algorithms; however, these algorithms offer little guidance on how to choose candidate modules and how to improve a clustering as new data becomes available. As an alternative, there are methods based on thresholding the similarity values to obtain a graph; such a graph can be analyzed through (probabilistic) methods developed in the social sciences. However, thresholding the data discards valuable information and choosing the threshold is difficult. Extending binary relational analysis, we exploit ranked relational data as the basis for two distinct approaches for identifying modules from genomic data, both based on the theory of random graph processes. We propose probabilistic models for ranked relational data that allow candidate modules to be accompanied by objective confidence scores and that permit an elegant integration of external information on gene-gene relationships. We first followed theoretical work by Ling to objectively select exceptionally isolated groups as candidate gene modules. Secondly, inspired by stochastic block models used in the social sciences, we construct a novel model for ranked relational data, where all genes have hidden module parameters which govern the strength of all gene-gene relationships. Adapting a classical likelihood often used for the analysis of horse races, clustering is performed by estimating the module parameters using standard Bayesian methods. The method allows the incorporation of prior information on gene-gene relationships; the utility of using prior information in the form of protein-protein interaction data in clustering of yeast mutant phenotype profiles is demonstrated.

Deletion mutant collections allow for the systematic study of gene function by linking a genotype to a phenotype. Furthermore, these collections permit the parallel and quantitative study of phenotypes, which is the foundation of functional genomics. I begin by summarizing the methods used and data derived from the field of functional genomics using the Baker’s yeast Saccharomyces cerevisiae, and provide important background information on the origins of the filamentous growth-competent S.cerevisiae strain Σ1278b, and the developmental process of fungal dimorphism. I describe my efforts in creating a complete deletion mutant collection in the filamentous growth-competent S.cerevisiae strain Σ1278b, and the subsequent phenotypic analysis of that deletion mutant collection. By quantitatively measuring mutant phenotypes of cells undergoing haploid invasive growth, biofilm mat formation and diploid pseudohyphal growth, I studied the clinically relevant developmental process of fungal dimorphism. I present the first genome-wide and quantitative phenotypic analysis of fungal dimorphism and identify a novel transcription factor encoded by the open reading frame YDL233W, which I named FMR1for Filamentation Master Regulator 1. By performing genetic, cell biological, biochemical, and expression analysis, I demonstrate that Ydl233w acts by forming a protein complex with the DNA-binding transcription factors Flo8 and Mss11 and this complex binds to a specific element within the promoter of the surface adherence mediating gene FLO11. I directly compare the essential gene sets between the Σ1278b deletion collection and the reference deletion collection made in the S288c genetic background completed by the Yeast Deletion Consortium in 2002. I find that most essential genes are shared between these two strains but a number of genes are essential for viability in only one genetic background, a phenomenon termed conditional essentiality. I describe the genetic basis of conditional essentiality as a consequence of the complex inheritance of background-specific alleles. Lastly, I summarize the scientific advancements of my research using the Σ1278b deletion collection, and highlight some potential applications for both the data derived from my research and the deletion mutant collection itself. The Σ1278b deletion collection provides a valuable resource for yeast geneticists, evolutionary biologists, researchers of fungal disease, and researchers interested in modeling the genetics that underlie complex traits and diseases.

I examined the genetic architecture and evolutionary significance of the considerable morphological and life history variation in Arctic charr (Salvelinus alpinus) from natural and cultured populations in Iceland. I found that the sex determining locus in Icelandic Arctic charr is located on a different linkage group relative to the majority of the Atlantic lineage Arctic charr, including populations from the Fraser River in Labrador Canada and Swedish and Norwegian strains. In addition, there may be a possible conservation of a sex linkage arrangement in Icelandic Arctic charr and Atlantic salmon. These observations suggest that the differentiation of the sex determination chromosome in salmonids is still in the early stages. I then tested hypotheses regarding the genetic architecture of wild and cultured populations of Icelandic Arctic charr identifying the number and effect size of quantitative trait loci (QTL) and their conservation within and across salmonid species. QTL with genome-wide significance for body size, condition factor and age of maturation in cultured fish from the Icelandic breeding program were detected on multiple linkage groups. Comparisons with a North American cultured strain of Arctic charr and North American populations of Atlantic salmon and rainbow trout revealed some conservation in QTL. Additionally, I compared the genetic architecture of fork lengths in juvenile wild and cultured populations and found moderate conservation of genomic regions among Icelandic populations. I compared linear measurements taken on the cleared and stained heads of benthic and limnetic morphs from two lake populations expressing varying degrees of divergence. I found high genetic variation for craniofacial morphology in all morphs with no significant difference of plasticity levels between them. However, stronger family effects and weaker morph effects in the less derived lake population suggest higher genetic variation for craniofacial traits compared to the more derived system. Overall QTL (suggestive and significant) number and effect size was similar but the morphs hypothesized to be more derived within each lake had about half the number of significant QTL (p<0.01) compared to their sympatric counterpart, suggesting fixation of alleles through canalization.

<p>The origin of man has motivated researchers to investigate differences between humans and our non-human relatives. The striking phenotypic differences that distinguish humans from chimpanzees are likely controlled by a relatively modest number of genetic changes present between these species. As energy acquisition and processing effect multiple organ systems, the dramatic changes in the human diet are thought to underpin many of these unique phenotypes. The evolution of the human diet is marked by omnivory with increased consumption of animal products, cereal grain and vegetable oil associated with the Paleolithic era, domestication of plants and the industrial revolution respectively. Nutrition is essential for life and is unique as it both shapes, and is shaped by the genome. Given this complex interaction, teasing out actors and responders in the genome-diet relationship is a challenge. I took several expression approaches by interrogating regulatory regions, candidate networks and genomes in tissues of dietary relevance. These experiments uncovered combinations of physiological and morphological changes between humans and non-human primates. Taken together, the combined power of <italic>in vitro</italic> and <italic>in vivo</italic> approaches elucidates several genetic mechanisms important in uniquely human bioenergetic processes.</p> / Dissertation