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Gene regulatory networks in living cells are controlled by the interaction of multiple cell type-specific transcription regulators with DNA binding sites in target genes. Interferon regulatory factor 8 (IRF8), also known as interferon consensus sequence binding protein (ICSBP), is a transcription factor expressed predominantly in myeloid and lymphoid cell lineages. To find the functional direct target genes of IRF8, the gene expression profiles of siRNA knockdown samples and genome-wide binding locations by ChIP-chip were analyzed in THP-1 myelomonocytic leukemia cells. Consequently, 84 genes were identified as functional direct targets. The ETS family transcription factor PU.1, also known as SPI1, binds to IRF8 and regulates basal transcription in macrophages. Using the same approach, we identified 53 direct target genes of PU.1; these overlapped with 19 IRF8 targets. These 19 genes included key molecules of IFN signaling such as OAS1 and IRF9, but excluded other IFN-related genes amongst the IRF8 functional direct target genes. We suggest that IRF8 and PU.1 can have both combined, and independent actions on different promoters in myeloid cells.
Pubmed ID: 20573402 RIS Download
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The FANTOM consortium is an international collaborative research project initiated and organized by the RIKEN Omics Science Center. In earlier FANTOM efforts we cloned and annotated 103,000 full-length cDNAs from mouse and distributed them to researchers throughout the world. FANTOM1-3 focused on identifying the transcribed components of mammalian cells. This work improved estimates of the total number of genes and their alternative transcript isoforms in both human and mouse, expanded gene families, and revealed that a large fraction of the transcriptome is non-coding. In addition, with the development of Cap Analysis of Gene Expression (CAGE) FANTOM3 could map a large fraction of transcription start sites and revise our models of promoter structure. This updated web resource provides the previous FANTOM results mapped to current genome builds and presents the results of FANTOM4. In FANTOM4 the focus has changed to understanding how these components work together in the context of a biological network. Using deepCAGE (deep sequencing with CAGE) we monitored the dynamics of transcription start site (TSS) usage during a time course of monocytic differentiation in the acute myeloid leukemia cell line THP-1. This allowed us to identify active promoters, monitor their relative expression and define relevant regions for carrying out transcription factor binding site predictions. Computational methods were then used to build a network model of gene expression in this leukemia and the transcription factors key to its regulation. This work gives the first picture of the wiring between genes involved in acute myeloid leukemia and provides a strategy for identifying key factors that determine cell fates. In addition to the network, FANTOM4 data was used in two additional analyses. The first identified a novel class of short RNAs associated with transcription start sites and the second focused on the role of repetitive element expression in the transcriptome. TOOLS *Genome Browser: graphical display of genomic features, such as promoters, exon structures, H3K9 acetylation, transcription factors positioning on the genome, coupled with gene and promoter activities. *EdgeExpressDB: regulatory interactions, such as transcriptional regulation, post-transcriptional silencing with miRNA, and PPI, coupled with gene and promoter activities. *SwissRegulon: FANTOM4 TF regulation is predicted using Motif Activity Response Analysis (MARA) developed by Erik van Nimwegen at Biozentrum. Follow the link to carry out MARA on your own dataset. *Custom Tracks on the UCSC Genome Browser: FANTOM4 tracks on the UCSC Genome Browser Database. *The RIKEN integrated database of mammals: Integration of FANTOM4 data with other mammalian resources, in particular, produced by RIKEN.
View all literature mentionsGOstat is a tool that allows you to find statistically overrepresented Gene Ontologies within a group of genes. The Gene-Ontology database (GO: http://www.geneontology.org) provides a useful tool to annotate and analyze the function of large numbers of genes. Modern experimental techniques, as e.g. DNA microarrays, often result in long lists of genes. To learn about the biology in this kind of data it is desirable to find functional annotation or Gene-Ontology groups which are highly represented in the data. This program (GOstat) should help in the analysis of such lists and will provide statistics about the GO terms contained in the data and sort the GO annotations giving the most representative GO terms first. Run GOstat: * Go to search form - Computes GO statistics of a list of genes selected from a microarray. * GOstat Display - You can store results from a previously run and view them here, either by uploading them as a file or putting them on a selected URL. * Upload Custom GO Annotations - This allows you to upload your own GO annotation database and use it with GOstat. Variants of GOstat: * Rank GOstat - Takes input from all genes on microarray instead of using a fixed cutoff and uses ranks using a Wilcoxon test or either ranks or pvalues to score GOs using Kolmogorov-Smirnov statistics. * Gene Abundance GOstats - Takes input from all genes on microarray and sums up the gene abundances for each GO to compute statistics. * Two list GOstat - Compares GO statistics in two independent lists of genes, not necessarily one of them being the complete list the other list is sampled from. Platform: Online tool
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