Tijssen, MR, Cvejic, A, Joshi, A, Hannah, RL, Ferreira, R, Forrai, A et al.. Genome-wide analysis of simultaneous GATA1/2, RUNX1, FLI1, and SCL binding in megakaryocytes identifies hematopoietic regulators. Dev Cell 20: 597-609

Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK.
Developmental Cell (Impact Factor: 9.71). 05/2011; 20(5):597-609. DOI: 10.1016/j.devcel.2011.04.008
Source: PubMed


Hematopoietic differentiation critically depends on combinations of transcriptional regulators controlling the development of individual lineages. Here, we report the genome-wide binding sites for the five key hematopoietic transcription factors--GATA1, GATA2, RUNX1, FLI1, and TAL1/SCL--in primary human megakaryocytes. Statistical analysis of the 17,263 regions bound by at least one factor demonstrated that simultaneous binding by all five factors was the most enriched pattern and often occurred near known hematopoietic regulators. Eight genes not previously appreciated to function in hematopoiesis that were bound by all five factors were shown to be essential for thrombocyte and/or erythroid development in zebrafish. Moreover, one of these genes encoding the PDZK1IP1 protein shared transcriptional enhancer elements with the blood stem cell regulator TAL1/SCL. Multifactor ChIP-Seq analysis in primary human cells coupled with a high-throughput in vivo perturbation screen therefore offers a powerful strategy to identify essential regulators of complex mammalian differentiation processes.

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Available from: Katrin Ottersbach, Oct 07, 2015
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    • "The final collection of datasets contained 191 GEO series containing a total of 917 ChIP-seq and 292 control libraries. Except for a limited number of cases in which a GEO series was associated with multiple publications, two or three GEO series were associated with the same publication, or a GEO series has not yet been used in a publication, and there is a one-to-one relationship between GEO series and published articles in the literature (Robertson et al. 2007; Chen et al. 2008; Marson et al. 2008; Bilodeau et al. 2009; Cheng et al. 2009; De Santa et al. 2009; Lister et al. 2009; Nishiyama et al. 2009; Visel et al. 2009; Welboren et al. 2009; Wilson et al. 2009; Yu et al. 2009; Yuan et al. 2009; Barish et al. 2010; Blow et al. 2010; Blow et al. 2010; Cao et al. 2010; Chi et al. 2010; Chia et al. 2010; Chicas et al. 2010; Corbo et al. 2010; Cuddapah et al. 2009; Durant et al. 2010; Fortschegger et al. 2010; Gotea et al. 2010; Gu et al. 2010; Han et al. 2010; Heinz et al. 2010; Heng et al. 2010; Ho et al. 2009; Hollenhorst et al. 2009; Hu et al. 2010; Johannes et al. 2010; Jung et al. 2010; Kagey et al. 2010; Kassouf et al. 2010; Kim et al. 2010; Kong et al. 2010; Kouwenhoven et al. 2010; Krebs et al. 2010; Kunarso et al. 2010; Kwon et al. 2009; Law et al. 2010; Lee et al. 2010; Lefterova et al. 2010; Li et al. 2010; Lin et al. 2010; Liu et al. 2010; Ma et al. 2010; MacIsaac et al. 2010; Mahony et al. 2010; Martinez et al. 2010; Palii et al. 2010; Qi et al. 2010; Rada-Iglesias et al. 2010; Rahl et al. 2010; Ramagopalan et al. 2010; Ramos et al. 2010; Schlesinger et al. 2010; Schnetz et al. 2010; Sehat et al. 2010; Steger et al. 2010; Tallack et al. 2010; Tang et al. 2010; Vermeulen et al. 2010; Verzi et al. 2010; Vivar et al. 2010; Wei et al. 2010; Woodfield et al. 2010; Yang et al. 2010; Yao et al. 2010; Yu et al. 2010; An et al. 2011; Ang et al. 2011; Bergsland et al. 2011; Bernt et al. 2011; Botcheva et al. 2011; Brown et al. 2011; Bugge et al. 2011; Ceol et al. 2011; Ceschin et al. 2011; Costessi et al. 2011; Ebert et al. 2011; Fang et al. 2011; Handoko et al. 2011; He et al. 2011; Heikkinen et al. 2011; Holmstrom et al. 2011; Horiuchi et al. 2011; Hu et al. 2011; Joseph et al. 2010; Kim et al. 2011; Klisch et al. 2011; Koeppel et al. 2011; Kong et al. 2011; Little et al. 2011; Liu et al. 2011; Lo et al. 2011; Marban et al. 2011; Mazzoni et al. 2011; McManus et al. 2011; Mendoza-Parra et al. 2011; Meyer et al. 2012; Miyazaki et al. 2011; Mullen et al. 2011; Mullican et al. 2011; Nakayamada et al. 2011; Nitzsche et al. 2011; Norton et al. 2011; Novershtern et al. 2011; Quenneville et al. 2011; Rao et al. 2011; Rey et al. 2011; Sahu et al. 2011; Schmitz et al. 2011; Seitz et al. 2011; Shen et al. 2011; Shukla et al. 2011; Siersbæk et al. 2011; Smeenk et al. 2011; Smith et al. 2011; Soccio et al. 2011; Stadler et al. 2011; Sun et al. 2011; Tan et al. 2011a; Tan et al. 2011b; Teo et al. 2011; Tijssen et al. 2011; Tiwari et al. 2011a; Tiwari et al. 2011b; Trompouki et al. 2011; van Heeringen et al. 2011; Verzi et al. 2011; Wang et al. 2011a; Wang et al. 2011b; Wei et al. 2011; Whyte et al. 2011; Wu et al. 2011a; Wu et al. 2011b; Xu et al. 2011; Yang et al. 2011; Yildirim et al. 2011; Yoon et al. 2011; Zhang et al. 2011; Zhao et al. 2011a; Zhao et al. 2011b; Avvakumov et al. 2012; Barish et al. 2012; Boergesen et al. 2012; Bugge et al. 2012; Canella et al. 2012; Cardamone et al. 2012; Cheng et al. 2012; Chlon et al. 2012; Cho et al. 2012; Doré et al. 2012; Fan et al. 2012; Feng et al. 2011; Fong et al. 2012; Gao et al. 2012; Gowher et al. 2012; Hunkapiller et al. 2012; Hutchins et al. 2012; Li et al. 2012; Lu et al. 2012; Miller et al. 2011; Ntziachristos et al. 2012; Pehkonen et al. 2012; Ptasinska et al. 2012; Remeseiro et al. 2012; Sadasivam et al. 2012; Sakabe et al. 2012; Schödel et al. 2012; Trowbridge et al. 2012; Vilagos et al. 2012; Wu et al. 2012; Xiao et al. 2012; Yu et al. 2012; unpublished at the time of completion of this manuscript are the following GEO accession numbers: GSE33346, GSE33850, GSE36561, GSE30919, GSE33128, GSE35109, GSE25426, GSE31951, GSE26711, GSE23581, GSE26136, GSE26680, GSE15844, GSE21916, GSE22303, and GSE29180; direct links to all GEO series can be found in Table S1). "
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    ABSTRACT: ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique in PubMed as of December 2012. Individually and in aggregate these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia Of DNA Elements (ENCODE) project, developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1st 2012. The majority (55%) of datasets scored as highly successful, but a substantial minority (20%) were of apparently poor quality, and another ~25% were of intermediate quality. We discuss how different uses of ChIP-Seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e. no-immunoprecipitation and mock-immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.
    G3-Genes Genomes Genetics 12/2013; 4(2). DOI:10.1534/g3.113.008680 · 3.20 Impact Factor
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    • "Runx3 occupies thousands of genomic loci in resting and IL-2-activated CD8-TC and NKC, reflecting a common property of many TFs, including the other two RUNX family members Runx1 [15,52] and Runx2 [53]. About 80% of Runx3-bound genes in CD8-TC overlapped those in NKC. "
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    ABSTRACT: The transcription factor Runx3 is highly expressed in CD8(+) T and NK cytotoxic lymphocytes and is required for their effective activation and proliferation but molecular insights into the transcription program regulated by Runx3 in these cells are still missing. Using Runx3-ChIP-seq and transcriptome analysis of wild type vs. Runx3(-/-) primary cells we have now identified Runx3-regulated genes in the two cell types at both resting and IL-2-activated states. Runx3-bound genomic regions in both cell types were distantly located relative to gene transcription start sites and were enriched for RUNX and ETS motifs. Bound genomic regions significantly overlapped T-bet and p300-bound enhancer regions in Runx3-expressing Th1 helper cells. Compared to resting cells, IL-2-activated CD8(+) T and NK cells contain three times more Runx3-regulated genes that are common to both cell types. Functional annotation of shared CD8(+) T and NK Runx3-regulated genes revealed enrichment for immune-associated terms including lymphocyte activation, proliferation, cytotoxicity, migration and cytokine production, highlighting the role of Runx3 in CD8(+) T and NK activated cells.
    PLoS ONE 11/2013; 8(11):e80467. DOI:10.1371/journal.pone.0080467 · 3.23 Impact Factor
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    • "This view also permits easy visualization of comparative binding profiles at these or other regions in primary megakaryocytes (12) and AML cells (20). The gene expression view (Supplementary Figure S2C) to the right shows RUNX1 expression across HSCs, multi-potent progenitors (MPP), common myeloid progenitors (CMP), granulocyte–monocyte progenitors (GMP) or megakaryocyte–erythroid progenitor (MEP) fractions as well as in AML leukaemic stem cells (LSC; Lin-/CD34+/38-/CD90-), AML leukaemic progenitor cells (Lin-/34+/38+) and AML blasts (Lin-/34-) (14), megakaryocytes and AML cells. "
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    ABSTRACT: The BloodChIP database ( supports exploration and visualization of combinatorial transcription factor (TF) binding at a particular locus in human CD34-positive and other normal and leukaemic cells or retrieval of target gene sets for user-defined combinations of TFs across one or more cell types. Increasing numbers of genome-wide TF binding profiles are being added to public repositories, and this trend is likely to continue. For the power of these data sets to be fully harnessed by experimental scientists, there is a need for these data to be placed in context and easily accessible for downstream applications. To this end, we have built a user-friendly database that has at its core the genome-wide binding profiles of seven key haematopoietic TFs in human stem/progenitor cells. These binding profiles are compared with binding profiles in normal differentiated and leukaemic cells. We have integrated these TF binding profiles with chromatin marks and expression data in normal and leukaemic cell fractions. All queries can be exported into external sites to construct TF-gene and protein-protein networks and to evaluate the association of genes with cellular processes and tissue expression.
    Nucleic Acids Research 10/2013; 42(Database issue). DOI:10.1093/nar/gkt1036 · 9.11 Impact Factor
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