E2F-mediated Growth Regulation Requires Transcription Factor Cooperation

Ginkel, Paul R. van; Hsiao, Kuang Ming; Schjerven, Hilde; Farnham, Peggy J.

doi:10.1074/jbc.272.29.18367

Cited by 64 publications

(55 citation statements)

References 36 publications

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“…However, the presence of E2F and Sp1/Sp3 in cells is not sufficient to activate a transcriptionally silent MYCN gene. This is a surprising result given that the transactivation domain of E2F-1 can stimulate both transcriptional initiation and elongation (51) and can confer cell-cycle control when brought to a promoter by the Gal4 DNA-binding domain (48). In fact, based on this observation, it was proposed that cooperating transcription factors such as Sp1 are mainly required to achieve stable interaction of E2F with the target promoter (48).…”

Section: Discussionmentioning

confidence: 97%

“…This is a surprising result given that the transactivation domain of E2F-1 can stimulate both transcriptional initiation and elongation (51) and can confer cell-cycle control when brought to a promoter by the Gal4 DNA-binding domain (48). In fact, based on this observation, it was proposed that cooperating transcription factors such as Sp1 are mainly required to achieve stable interaction of E2F with the target promoter (48). This raises the question of why overexpressed E2F-1 in combination with endogenous Sp1/Sp3 failed to bind to the inactive MYCN promoter, while at the same time it bound to other target genes.…”

Section: Discussionmentioning

confidence: 99%

“…Included in this group are genes that share a role in the cell cycle, such as cyclin D1 and dhfr (46,47). Several lines of evidence suggest that cooperation between E2F and Sp1 is required to achieve cell cycle-dependent regulation of these target promoters, entailing both an activating mode during late G 1 and S phases and a histone deacetylase-dependent repressing mode during G 0 and early G 1 (27,47,48). Sp1 and E2F cooperate by means of direct physical interaction (49).…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

E2F and Sp1/Sp3 Synergize but Are Not Sufficient to Activate the MYCN Gene in Neuroblastomas

Kramps

Strieder

Sapetschnig

et al. 2004

Journal of Biological Chemistry

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The transcription factors encoded by the MYC genes control diverse tumorigenesis-relevant processes such as cell-cycle progression, growth factor dependence, and response to anti-mitogenic signals (for a review, see Refs. 1 and 2). Overexpression of one of the MYC genes as a result of chromosomal translocation, gene amplification, or loss of negative transcriptional control plays a prominent role in the etiology of many types of tumors (for a review, see Refs. 3 and 4). The MYCN gene is found amplified in several tumors of mostly neuroendocrine origin including about 25% of neuroblastomas, the most common solid tumor in childhood (for a review, see Refs. 5 and 6). Whereas many neuroblastomas regress spontaneously or can be cured with minimal therapy, MYCN-amplified tumors have a poor prognosis. The treatment of these patients has not improved significantly in the course of the last two decades. Recently, the comparison of the gene expression profiles of MYCN-expressing versus non-expressing neuroblastoma cells as well as MYCNamplified versus non-amplified primary tumors have begun to address the functional consequences of the massive overexpression of MYCN resulting from gene amplification (7,8).Several mouse models of Myc-induced tumorigenesis suggest that Myc is not only required for the initiation but also for the maintenance of the tumorigenic state supporting the value of the MYC genes as targets of tumor therapy (9 -11). Roughly half of the drugs that are in clinical use currently act as inhibitors of enzymes. In transcriptional regulation, enzymes are involved mainly as components of signal transduction cascades that relay information to the promoter and as transcriptional co-regulators that modulate local chromatin structure either by covalent modification of histones or by an ATPase-dependent remodeling of nucleosomes (for a review, see Ref. 12). The efficacy of histone deacetylase inhibitors against leukemia has proven the principle of treating cancer by the selective pharmacological modulation of the transcription machinery (13). Application of this concept to the MYCN gene requires a detailed knowledge of the molecular basis of the transcriptional activation of MYCN in neuroblastomas.Toward this goal, we have recently identified the activating members of the E2F family of transcription factors (E2F-1, E2F-2, and E2F-3) as regulators of MYCN expression in neuroblastomas (14). E2F proteins are important regulators of cell-cycle progression, and their activity is negatively controlled by the p16/Rb pathway, which is inactivated in the majority of human cancers (for a review, see Refs. 15 and 16). Although genetic defects of the p16/Rb pathway are rare in neuroblastomas, there is some evidence for a loss of normal control of E2F activity in neuroblastomas by epigenetic means (17).In addition to E2F binding sites, the MYCN promoter contains several putative binding sites for Sp1 and related zinc finger transcription factors (18). One of these, a non-consensus binding site, the CT-box, was previously impli...

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

E2F and Sp1/Sp3 Synergize but Are Not Sufficient to Activate the MYCN Gene in Neuroblastomas

Kramps

Strieder

Sapetschnig

et al. 2004

Journal of Biological Chemistry

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“…Below we will use the data on the transcriptional activity r normalized with respect to the activity (32) Note that the function a(y) reflecting the relative transcriptional activity is independent of the model parameter and depends only on the three parameters q R , , and .…”

Section: Thermostatistics-based Metric Of Cell-to-cell Variabilitymentioning

confidence: 99%

Catching transcriptional regulation by thermostatistical modeling

et al. 2012

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Gene expression is frequently regulated by multiple transcription factors (TFs). Thermostatistical methods allow for a quantitative description of interactions between TFs, RNA polymerase, and DNA, and their impact on the transcription rates. We illustrate three different scales of the thermostatistical approach: the microscale of TF molecules, the mesoscale of promoter energy levels, and the macroscale of transcriptionally active and inactive cells in a cell population. We demonstrate versatility of combinatorial transcriptional activation by exemplifying logic functions, such as AND and OR gates. We discuss a metric for cell-to-cell transcriptional activation variability known as Fermi entropy. Suitability of thermostatistical modeling is illustrated by describing the experimental data on transcriptional induction of NF B and the c-Fos protein.2

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“…It is known that E2F, YY1, and c MYC are potential cooperating factors (41). Thus, in our simulation, motif sites were generated according to the weight matrices of these three TFs based on TRANSFAC (42) matrix accession numbers, V$E2F 03, V$YY1 02, and V$MYCMAX 02, respectively.…”

Section: Resultsmentioning

confidence: 99%

CisModule: De novo discovery of cis-regulatory modules by hierarchical mixture modeling

Zhou

Wong²

2004

Proc. Natl. Acad. Sci. U.S.A.

194

209

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The regulatory information for a eukaryotic gene is encoded in cis-regulatory modules. The binding sites for a set of interacting transcription factors have the tendency to colocalize to the same modules. Current de novo motif discovery methods do not take advantage of this knowledge. We propose a hierarchical mixture approach to model the cis-regulatory module structure. Based on the model, a new de novo motif-module discovery algorithm, CisModule, is developed for the Bayesian inference of module locations and within-module motif sites. Dynamic programminglike recursions are developed to reduce the computational complexity from exponential to linear in sequence length. By using both simulated and real data sets, we demonstrate that CisModule is not only accurate in predicting modules but also more sensitive in detecting motif patterns and binding sites than standard motif discovery methods are.T ranscription factors (TFs) regulate genes by binding to their recognition sites. The common pattern of the binding sites for a TF is called a motif, usually modeled by a position-specific weight matrix (PWM). Experimental methods such as DNase footprinting (1) and gel-mobility shift assay (2, 3) have allowed the determination of some binding sites for selected TFs. Because these procedures are time-consuming, several computational methods have been developed for de novo motif discovery, including progressive alignment (4, 5), the expectationmaximization algorithm (6, 7), the Gibbs sampler (8-12), word enumeration (13,14), and the dictionary model (15, 16). The propagation model (17) and the recursive Gibbs motif sampler (18) have been developed for locating multiple motifs simultaneously. In addition, methods also exist that combine motif discovery with gene expression data (19-21) or phylogenetic footprinting (22,23). These experimental and computational analyses have given us a good number of useful TF motifs. However, there are still many important TFs whose motifs remain to be characterized. What is more, molecular analyses have established that most eukaryotic genes are not controlled by a single site but by cis-regulatory modules (CRMs), each consisting of multiple TF-binding sites (TFBSs) that act in combination (24-27). It can be argued that motif discovery is but an intermediate step toward the characterization of CRMs. Current approaches on module prediction such as those based on logistic regression (28,29) or hidden Markov models (30, 31) depend on the availability of known motifs, i.e., PWMs for several TFs hypothesized to bind synergistically to regulatory modules. Clearly, we cannot apply these methods to the situations where no prior knowledge on the TFs is available, and in these cases we must resort to de novo motif discovery algorithms. We hypothesized that greater sensitivity and specificity can be achieved for motif discovery by considering the colocalization of different TFBSs and searched for modules and motifs simultaneously. It is clear that the task of module discovery and motif estimation is ...

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E2F-mediated Growth Regulation Requires Transcription Factor Cooperation

Cited by 64 publications

References 36 publications

E2F and Sp1/Sp3 Synergize but Are Not Sufficient to Activate the MYCN Gene in Neuroblastomas

E2F and Sp1/Sp3 Synergize but Are Not Sufficient to Activate the MYCN Gene in Neuroblastomas

Catching transcriptional regulation by thermostatistical modeling

CisModule: De novo discovery of cis-regulatory modules by hierarchical mixture modeling

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