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    Currently a stay-at-home mom, working on a book about hypothalamic amenorrhea, tentatively titled, "No Period. Now What? -- A Guide to Recovering Your Health and Fertility"
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    Genome-wide location analysis was used to determine how the yeast cell cycle gene expression program is regulated by each of the nine known cell cycle transcriptional activators. We found that cell cycle transcriptional activators that function during one stage of the cell cycle regulate transcriptional activators that function during the next stage. This serial regulation of transcriptional activators forms a connected regulatory network that is itself a cycle. Our results also reveal how the nine transcriptional regulators coordinately regulate global gene expression and diverse stage-specific functions to produce a continuous cycle of cellular events. This information forms the foundation for a complete map of the transcriptional regulatory network that controls the cell cycle.
    Gene arrays demonstrate a promising ability to characterize expression levels across the entire genome, but they su#er from significant levels of measurement noise. We present a statistical technique to estimate transcript levels or transcript level ratios from one or more gene array experiments, incorporating a model of measurement noise and prior information about biological expression levels. The Bayesian Estimation of Array Measurements (BEAM) technique provides a principled method to handle issues currently addressed through rough and varying heuristics, such as identification of changes in expression level, combination of repeated measurements, and rectification of negative measurements. More importantly, the BEAM technique produces associated measures of estimation uncertainty (e.g., p-values) which serve to determine the statistical significance of reported results. While our method applies to any gene array technology, we illustrate it using a detailed noise model that we develop for A#ymetrix yeast chips. The BEAM technique allows for the design of experiments that maximize the useful information derived from a minimum number of chips. Further, it can be used to extract additional and more statistically rigorous conclusions from existing data.
    We present a new statistically optimal approach to estimate transcript levels and ratios from one or more gene array experiments. The Bayesian Estimation of Array Measurements (BEAM) technique uses a model of measurement noise and prior information to estimate biological expression levels. It provides a principled method to deal with negative expression level measurements, combine multiple measurements, and identify changes in expression level. BEAM is more flexible than existing techniques, because it does not assume a specific functional form for noise and prior models. Rather, it uses a more accurate noise model developed from experimental data, a process we illustrate here using Affymetrix yeast chips.
    Gene arrays demonstrate a promising ability to characterize expression levels across the entire genome but suffer from significant levels of measurement noise. We present a rigorous new approach to estimate transcript levels and ratios from one or more gene array experiments, given a model of measurement noise and available prior information. The Bayesian estimation of array measurements (BEAM) technique provides a principled method to identify changes in expression level, combine repeated measurements, or deal with negative expression level measurements. BEAM is more flexible than existing techniques, because it does not assume a specific functional form for noise and prior models. Instead, it relies on computational techniques that apply to a broad range of models. We use Affymetrix yeast chip data to illustrate the process of developing accurate noise and prior models from existing experimental data. The resulting noise model includes novel features such as heavy-tailed additive noise and a gene-specific bias term. We also verify that the resulting noise and prior models fit data from an Affymetrix human chip set.
    We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiaeassociate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
    The transcriptional regulatory networks that specify and maintain human tissue diversity are largely uncharted. To gain insight into this circuitry, we used chromatin immunoprecipitation combined with promoter microarrays to identify systematically the genes occupied by the transcriptional regulators HNF1alpha, HNF4alpha, and HNF6, together with RNA polymerase II, in human liver and pancreatic islets. We identified tissue-specific regulatory circuits formed by HNF1alpha, HNF4alpha, and HNF6 with other transcription factors, revealing how these factors function as master regulators of hepatocyte and islet transcription. Our results suggest how misregulation of HNF4alpha can contribute to type 2 diabetes.
    We describe an algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets. A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds. Unlike previous approaches that relied primarily on functional information from expression data, the GRAM algorithm explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions. We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions data from over 500 expression experiments. We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network
    We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiaeassociate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
    Chromatin regulators play fundamental roles in the regulation of gene expression and chromosome maintenance, but the regions of the genome where most of these regulators function has not been established. We explored the genome-wide occupancy of four different chromatin regulators encoded in Saccharomyces cerevisiae. The results reveal that the histone acetyltransferases Gcn5 and Esa1 are both generally recruited to the promoters of active protein-coding genes. In contrast, the histone deacetylases Hst1 and Rpd3 are recruited to specific sets of genes associated with distinct cellular functions. Our results provide new insights into the association of histone acetyltransferases and histone deacetylases with the yeast genome, and together with previous studies, suggest how these chromatin regulators are recruited to specific regions of the genome.
    DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.
    Transforming growth factor (TGF)-beta has a central role in driving many of the pathological processes that characterize pulmonary fibrosis. Inhibition of the integrin alpha(v)beta6, a key activator of TGF-beta in lung, is an attractive therapeutic strategy, as it may be possible to inhibit TGF-beta at sites of alpha(v)beta6 up-regulation without affecting other homeostatic roles of TGF-beta. To analyze the expression of alpha(v)beta6 in human pulmonary fibrosis, and to functionally test the efficacy of therapeutic inhibition of alpha(v)beta6-mediated TGF-beta activation in murine bleomycin-induced pulmonary fibrosis. Lung biopsies from patients with a diagnosis of systemic sclerosis or idiopathic pulmonary fibrosis were stained for alpha(v)beta6 expression. A range of concentrations of a monoclonal antibody that blocks alpha(v)beta6-mediated TGF-beta activation was evaluated in murine bleomycin-induced lung fibrosis. Alpha(v)beta6 is overexpressed in human lung fibrosis within pneumocytes lining the alveolar ducts and alveoli. In the bleomycin model, alpha(v)beta6 antibody was effective in blocking pulmonary fibrosis. At high doses, there was increased expression of markers of inflammation and macrophage activation, consistent with the effects of TGF-beta inhibition in the lung. Low doses of antibody attenuated collagen expression without increasing alveolar inflammatory cell populations or macrophage activation markers. Partial inhibition of TGF-beta using alpha(v)beta6 integrin antibodies is effective in blocking murine pulmonary fibrosis without exacerbating inflammation. In addition, the elevated expression of alpha(v)beta6, an activator of the fibrogenic cytokine, TGF-beta, in human pulmonary fibrosis suggests that alpha(v)beta6 monoclonal antibodies could represent a promising new therapeutic strategy for treating pulmonary fibrosis.
    METHODS Epitope Tagging of Strains Regulators were tagged by inserting multiple copies of the Myc epitope coding sequence into the normal chomosomal loci of these genes. The plasmid pWZV88 (1) was used as a template to generate PCR products containing the Myc epitope coding sequence and a selectable marker (TRP) flanked by homologous regions designed to recombine at the 3' end of the targeted transcriptional regulator. PCR products were transformed into the W303 strain Z1256. Clones were selected for growth on TRP-plates. Insertion of the epitope coding sequence was confirmed by PCR and expression of the epitope-tagged protein was confirmed by Western blotting using an anti-Myc antibody. Genome-wide Location Analysis The genome-wide location analysis method we have developed allows protein-DNA interactions to be monitored across the entire yeast genome by combining a modified Chromatin Immunoprecipitation (ChIP) procedure, which has been previously used to study in vivo protein-DNA interactions at one or a small number of specific DNA sites, with DNA microarray analysis. Briefly, cells containing a copy of an epitope tagged regulator were fixed with formaldehyde (1% final concentration) and then harvested by centrifugation. Cells were lysed with glass beads and the resulting cell lysate was sonicated to shear DNA. DNA fragments representing promoter regions crosslinked to a protein of interest were enriched by immunoprecipitation with an anti-epitope antibody. After reversal of the crosslinking, the enriched DNA was amplified and labeled with a fluorescent dye by ligation-mediated PCR (LM-PCR). A sample of DNA that had not been enriched by immunoprecipitation was subjected to LM-PCR in the presence of a different fluorophore, and both IP-enriched and unenriched pools of labeled-DNA were hybridized to a single DNA microarray containing all yeast intergenic sequences. Slides were then scanned. For each factor, three independently grown cell cultures were processed and scanned to generate binding information.
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