Literature DB >> 23974199

Genome-wide analysis of transcriptional regulators in human HSPCs reveals a densely interconnected network of coding and noncoding genes.

Dominik Beck1, Julie A I Thoms, Dilmi Perera, Judith Schütte, Ashwin Unnikrishnan, Kathy Knezevic, Sarah J Kinston, Nicola K Wilson, Tracey A O'Brien, Berthold Göttgens, Jason W H Wong, John E Pimanda.   

Abstract

Genome-wide combinatorial binding patterns for key transcription factors (TFs) have not been reported for primary human hematopoietic stem and progenitor cells (HSPCs), and have constrained analysis of the global architecture of molecular circuits controlling these cells. Here we provide high-resolution genome-wide binding maps for a heptad of key TFs (FLI1, ERG, GATA2, RUNX1, SCL, LYL1, and LMO2) in human CD34(+) HSPCs, together with quantitative RNA and microRNA expression profiles. We catalog binding of TFs at coding genes and microRNA promoters, and report that combinatorial binding of all 7 TFs is favored and associated with differential expression of genes and microRNA in HSPCs. We also uncover a previously unrecognized association between FLI1 and RUNX1 pairing in HSPCs, we establish a correlation between the density of histone modifications that mark active enhancers and the number of overlapping TFs at a peak, we demonstrate bivalent histone marks at promoters of heptad target genes in CD34(+) cells that are poised for later expression, and we identify complex relationships between specific microRNAs and coding genes regulated by the heptad. Taken together, these data reveal the power of integrating multifactor sequencing of chromatin immunoprecipitates with coding and noncoding gene expression to identify regulatory circuits controlling cell identity.

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Year:  2013        PMID: 23974199     DOI: 10.1182/blood-2013-03-490425

Source DB:  PubMed          Journal:  Blood        ISSN: 0006-4971            Impact factor:   22.113


  63 in total

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Journal:  Stem Cell Res       Date:  2015-02-18       Impact factor: 2.020

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Review 10.  Understanding transcriptional regulatory networks using computational models.

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