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      The genomic response to 20-hydroxyecdysone at the onset of Drosophila metamorphosis

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      1 , 1 , 1 ,
      Genome Biology
      BioMed Central

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          Abstract

          The genome-wide transcriptional response to 20-hydroxyecdisone at the onset of Drosophila metamorphosis, as well as its dependency on one of the ecdysone receptors is described.

          Abstract

          Background

          The steroid hormone 20-hydroxyecdysone (20E) triggers the major developmental transitions in Drosophila, including molting and metamorphosis, and provides a model system for defining the developmental and molecular mechanisms of steroid signaling. 20E acts via a heterodimer of two nuclear receptors, the ecdysone receptor (EcR) and Ultraspiracle, to directly regulate target gene transcription.

          Results

          Here we identify the genomic transcriptional response to 20E as well as those genes that are dependent on EcR for their proper regulation. We show that genes regulated by 20E, and dependent on EcR, account for many transcripts that are significantly up- or downregulated at puparium formation. We provide evidence that 20E and EcR participate in the regulation of genes involved in metabolism, stress, and immunity at the onset of metamorphosis. We also present an initial characterization of a 20E primary-response regulatory gene identified in this study, brain tumor ( brat), showing that brat mutations lead to defects during metamorphosis and changes in the expression of key 20E-regulated genes.

          Conclusion

          This study provides a genome-wide basis for understanding how 20E and its receptor control metamorphosis, as well as a foundation for functional genomic analysis of key regulatory genes in the 20E signaling pathway during insect development.

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          Most cited references53

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

            Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
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              Gene expression during the life cycle of Drosophila melanogaster.

              Molecular genetic studies of Drosophila melanogaster have led to profound advances in understanding the regulation of development. Here we report gene expression patterns for nearly one-third of all Drosophila genes during a complete time course of development. Mutations that eliminate eye or germline tissue were used to further analyze tissue-specific gene expression programs. These studies define major characteristics of the transcriptional programs that underlie the life cycle, compare development in males and females, and show that large-scale gene expression data collected from whole animals can be used to identify genes expressed in particular tissues and organs or genes involved in specific biological and biochemical processes.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                2005
                21 November 2005
                : 6
                : 12
                : R99
                Affiliations
                [1 ]Department of Human Genetics, Howard Hughes Medical Institute, University of Utah School of Medicine, Salt Lake City, UT 84112-5331, USA
                Article
                gb-2005-6-12-r99
                10.1186/gb-2005-6-12-r99
                1414087
                16356271
                a452c1a3-2b00-4555-b732-884151f87275
                Copyright © 2005 Beckstead et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 June 2005
                : 5 August 2005
                : 20 October 2005
                Categories
                Research

                Genetics
                Genetics

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