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

Beck, Dominik; Thoms, Julie A.I.; Perera, D. S.; Schütte, Judith; Unnikrishnan, Ashwin; Knezevic, Kathy; Kinston, Sarah; Wilson, Nicola K.; O’Brien, Tracey; Göttgens, Berthold; Wong, Jason; Pimanda, John E.

doi:10.1182/blood-2013-03-490425

Cited by 133 publications

(180 citation statements)

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“…23,24 Due to the rapid decline in sequencing costs, ChIP-Seq technology is now readily available to individual research laboratories, and data integration across laboratories has generated a publicly accessible resource of several hundred TF ChIP-Seq studies across a wide range of hematopoietic cell types. 25 However, although ChIP-Seq experiments are increasingly used to complement gene expression profiling, [26][27][28][29][30][31][32][33][34] no major progress has yet been reported toward the generation of validated HSPC regulatory network models. This is at least partly due to the observation that TFs are commonly bound to thousands if not tens of thousands of sites in the genome, often near genes that would not be affected by ablation of the factor in question.…”

Section: Impact Of Genome-wide Tf Binding Maps For Network Reconstrucmentioning

confidence: 99%

Regulatory network control of blood stem cells

Göttgens

2015

Blood

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Hematopoietic stem cells (HSCs) are characterized by their ability to execute a wide range of cell fate choices, including selfrenewal, quiescence, and differentiation into the many different mature blood lineages. Cell fate decision making in HSCs, as indeed in other cell types, is driven by the interplay of external stimuli and intracellular regulatory programs. Given the pivotal nature of HSC decision making for both normal and aberrant hematopoiesis, substantial research efforts have been invested over the last few decades into deciphering some of the underlying mechanisms. Central to the intracellular decision making processes are transcription factor proteins and their interactions within gene regulatory networks. More than 50 transcription factors have been shown to affect the functionality of HSCs. However, much remains to be learned about the way in which individual factors are connected within wider regulatory networks, and how the topology of HSC regulatory networks might affect HSC function. Nevertheless, important progress has been made in recent years, and new emerging technologies suggest that the pace of progress is likely to accelerate. This review will introduce key concepts, provide an integrated view of selected recent studies, and conclude with an outlook on possible future directions for this field. (Blood. 2015;125(17):2614-2620 Building blocks of transcriptional regulatory networksThe primary components of transcriptional regulatory networks are transcription factor (TF) proteins and the gene regulatory DNA sequences that they bind to.1 By binding to specific DNA sequence motifs within gene regulatory regions, TF proteins are central players for this primary step of decoding gene regulatory instructions. TF proteins typically contain a number of distinct modules, such as DNA binding, transcriptional activation, and protein/protein interaction domains, with the latter 2 being essential for the recruitment of the basal transcriptional machinery and the assembly of higherorder TF complexes. Based on sequence similarity within the DNA binding domain, TF proteins can be categorized into distinct families, such as homeobox, basic helix-loop-helix, or zinc finger TFs. Members of a given TF family often bind to similar DNA sequences, and proteinprotein interactions are common both within and between the different TF families.Individual TFs bind short sequence motifs that are often no longer than 4 to 6 bp. Any given 6-bp sequence will occur on average approximately once every 4000 bp. Consequently, there will be ;750 000 occurrences just by chance within the 3 000 000 000 bp of the human genome. The number of possible binding sites therefore far exceeds the 20 000 or so human genes, and it has long been assumed that only a small minority of all motif instances play a role in transcriptional regulation. Functional gene regulatory regions should therefore display characteristics that go beyond the mere occurrence of a specific sequence motif. One such characteristic is the presence of clusters of b...

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Section: Impact Of Genome-wide Tf Binding Maps For Network Reconstrucmentioning

confidence: 99%

Regulatory network control of blood stem cells

Göttgens

2015

Blood

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“…35,[42][43][44] Details of transcriptional factor binding sites, primer design, and ChIP/ChIP-seq protocols are provided in the supplemental Methods. The ChIP-seq data were deposited in National Center for Biotechnology Information's Gene Expression Omnibus 41 (accession no.…”

Section: Chip and Chip-seqmentioning

confidence: 99%

Opposing regulation of BIM and BCL2 controls glucocorticoid-induced apoptosis of pediatric acute lymphoblastic leukemia cells

Jing

Bhadri

Beck

et al. 2015

Blood

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Key Points• The glucocorticoid receptor coordinately regulates the antiapoptotic BCL2 and proapoptotic BIM genes in pediatric ALL cells in vivo.• GR binding at a novel intronic region is associated with BIM transcription and dexamethasone sensitivity in pediatric ALL cells in vivo.Glucocorticoids are critical components of combination chemotherapy regimens in pediatric acute lymphoblastic leukemia (ALL). The proapoptotic BIM protein is an important mediator of glucocorticoid-induced apoptosis in normal and malignant lymphocytes, whereas the antiapoptotic BCL2 confers resistance. The signaling pathways regulating BIM and BCL2 expression in glucocorticoid-treated lymphoid cells remain unclear. In this study, pediatric ALL patient-derived xenografts (PDXs) inherently sensitive or resistant to glucocorticoids were exposed to dexamethasone in vivo. Microarray analysis showed that KLF13 and MYB gene expression changes were significantly greater in dexamethasonesensitive than -resistant PDXs. Chromatin immunoprecipitation (ChIP) analysis detected glucocorticoid receptor (GR) binding at the KLF13 promoter to trigger KLF13 expression only in sensitive PDXs. Next, KLF13 bound to the MYB promoter, deactivating MYB expression only in sensitive PDXs. Sustained MYB expression in resistant PDXs resulted in maintenance of BCL2 expression and inhibition of apoptosis. ChIP sequencing analysis revealed a novel GR binding site in a BIM intronic region (IGR) that was engaged only in dexamethasone-sensitive PDXs. The absence of GR binding at the BIM IGR was associated with BIM silencing and dexamethasone resistance. This study has identified novel mechanisms of opposing BCL2 and BIM gene regulation that control glucocorticoid-induced apoptosis in pediatric ALL cells in vivo. (Blood. 2015;125(2):273-283)

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“…The ChIP-seq data from human HSC and SEM cells (GSE45144, Beck et al 2013;GSE42075, Wilkinson et al 2013; originally mapped to hg18) were reanalyzed starting from raw reads. See Supplemental Material for details of processing and visualizing the GRO and ChIP sequencing reads.…”

Section: Gro-seq Assay and Processing Of Gro-seq And Chip Sequencing mentioning

confidence: 99%

Genome-wide repression of eRNA and target gene loci by the ETV6-RUNX1 fusion in acute leukemia

et al. 2016

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Approximately 20%-25% of childhood acute lymphoblastic leukemias carry the ETV6-RUNX1 (E/R) fusion gene, a fusion of two central hematopoietic transcription factors, ETV6 (TEL) and RUNX1 (AML1). Despite its prevalence, the exact genomic targets of E/R have remained elusive. We evaluated gene loci and enhancers targeted by E/R genome-wide in precursor B acute leukemia cells using global run-on sequencing (GRO-seq). We show that expression of the E/R fusion leads to widespread repression of RUNX1 motif-containing enhancers at its target gene loci. Moreover, multiple super-enhancers from the CD19 + /CD20 + -lineage were repressed, implicating a role in impediment of lineage commitment. In effect, the expression of several genes involved in B cell signaling and adhesion was down-regulated, and the repression depended on the wild-type DNA-binding Runt domain of RUNX1. We also identified a number of E/R-regulated annotated and de novo noncoding genes. The results provide a comprehensive genome-wide mapping between E/R-regulated key regulatory elements and genes in precursor B cell leukemia that disrupt normal B lymphopoiesis.

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

Abstract: Key Points Genome-wide binding profiles of FLI1, ERG, GATA2, RUNX1, SCL, LMO2, and LYL1 in human HSPCs reveals patterns of combinatorial TF binding. Integrative analysis of transcription factor binding reveals a densely interconnected network of coding and noncoding genes in human HSPCs.

Cited by 133 publications

References 55 publications

Regulatory network control of blood stem cells

Regulatory network control of blood stem cells

Opposing regulation of BIM and BCL2 controls glucocorticoid-induced apoptosis of pediatric acute lymphoblastic leukemia cells

Genome-wide repression of eRNA and target gene loci by the ETV6-RUNX1 fusion in acute leukemia

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