Biomedical subjects

Ran Elkon

Publications and source records attributed to Ran Elkon.

9 recordsLinked to original sources

PubMed2006

E2F1 identified by promoter and biochemical analysis as a central target of glioblastoma cell-cycle arrest in response to Ras inhibition.

Active Ras contributes to the malignant phenotype of glioblastoma multiforme. Recent studies showed that the Ras inhibitor farnesyl thiosalicylic acid downregulates the transcription factor hypoxia-inducible factor-1alpha, causing shutdown of glycolysis in U87 glioblastoma cells. Farnesyl thiosalicylic acid also inhibited the growth of U87 cells. The way in which Ras inhibition affects U87 cell proliferation was not clear. Here we applied a computational method in which gene expression profile clustering is combined with promoter sequence analysis to obtain global dissection of the transcriptional response to farnesyl thiosalicylic acid in U87 cells. The analysis revealed a prominent Ras-dependent cell-cycle arrest response, in which a major component is highly enriched for the binding-site signature of the transcription factor E2F1. Electrophoretic mobility shift assays together with E2F-luciferase reporter assays showed that E2F1 was inactivated by the Ras inhibitor. Inhibition of Ras by farnesyl thiosalicylic acid promoted proteasomal degradation of cyclin D1, with a concomitant decrease in phosphorylated retinoblastoma protein accompanied by downregulation of E2F1 and decreased expression of key E2F1-regulated genes critical for cell-cycle progression. U87 cell growth arrest induced by farnesyl thiosalicylic acid was overridden by constitutive expression of E2F1. Thus, downregulation of E2F1 and of hypoxia-inducible factor-1alpha represents 2 distinct arms of the antioncogenic effect of Ras inhibitors in glioblastoma.

Cell Cycle↗

PubMed2006

Comparative gene expression profiling reveals partially overlapping but distinct genomic actions of different antiestrogens in human breast cancer cells.

Antiestrogens used for breast cancer (BC) treatment differ among each other for the ability to affect estrogen receptor (ER) activity and thereby inhibit hormone-responsive cell functions and viability. We used high-density cDNA microarrays for a comprehensive definition of the gene pathways affected by 17beta-estradiol (E2), ICI 182,780 (ICI), 4OH-tamoxifen (Tamoxifen), and raloxifene (RAL) in ER-positive ZR-75.1 cells, a suitable model to investigate estrogen and antiestrogen actions in hormone-responsive BC. The expression of 601 genes was significantly affected by E2 in these cells; in silico analysis reveals that 86 among them include one or more potential ER binding site within or near the promoter and that the binding site signatures for E2F-1, NF-Y, and NRF-1 transcription factors are significantly enriched in the promoters of genes induced by estrogen treatment, while those for CAC-binding protein and LF-A1 in those repressed by the hormone, pointing to novel transcriptional effectors of secondary responses to estrogen in BC cells. Interestingly, expression of 176 E2-regulated mRNAs was unaffected by any of the antiestrogens tested, despite the fact that under the same conditions the transcriptional and cell cycle stimulatory activities of ER were inhibited. On the other hand, of 373 antiestrogen-responsive genes identified here, 52 were unresponsive to estrogen and 25% responded specifically to only one of the compounds tested, revealing non-overlapping and clearly distinguishable effects of the different antiestrogens in BC cells. As some of these differences reflect specificities of the mechanism of action of the antiestrogens tested, we propose to exploit this gene set for characterization of novel hormonal antagonists and selective estrogen receptor modulators (SERMs) and as a tool for testing new associations of antiestrogens, more effective against BC.

Breast Neoplasms↗

PubMed2005

Deciphering transcriptional regulatory elements that encode specific cell cycle phasing by comparative genomics analysis.

Transcriptional regulation is a major tier in the periodic engine that mobilizes cell cycle progression. The availability of complete genome sequences of multiple organisms holds promise for significantly improving the specificity of computational identification of functional elements. Here, we applied a comparative genomics analysis to decipher transcriptional regulatory elements that control cell cycle phasing. We analyzed genome-wide promoter sequences from 12 organisms, including worm, fly, fish, rodents and human, and identified conserved transcriptional modules that determine the expression of genes in specific cell cycle phases. We demonstrate that a canonical E2F signal encodes for expression highly specific to the G1/S phase, and that a cis-regulatory module comprising CHR-NF-Y elements dictates expression that is restricted to the G2 and G2/M phases. B-Myb binding site signatures occur in many of the CHR-NF-Y target genes, suggesting a specific role for this triplet in the regulation of the cell cycle transcriptional program. Remarkably, E2F signals are conserved in promoters of G1/S genes in all organisms from worm to human. The CHR-NF-Y module is conserved in promoters of G2/M regulated genes in all analyzed vertebrates. Our results reveal novel modules that determine specific cell cycle phasing, and identify their respective putative target genes with remarkably high specificity.

Animals↗

PubMed2005

EXPANDER--an integrative program suite for microarray data analysis.

BACKGROUND: Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. RESULTS: Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. CONCLUSION: EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users at http://www.cs.tau.ac.il/~rshamir/expander.

Algorithms↗

PubMed2005

Impaired genomic stability and increased oxidative stress exacerbate different features of Ataxia-telangiectasia.

Ataxia-telangiectasia (A-T) is a multisystem, cancer-predisposing genetic disorder caused by deficiency of the ATM protein. To dissect the A-T phenotype, we augmented specific features of the human disease by generating mouse strains that combine Atm deficiency with dysfunction of other proteins. Increasing oxidative stress by combining deficiencies in Atm and superoxide dismutase 1 (Sod1) exacerbated growth retardation and markedly reduced the mean survival time following ionizing radiation. In contrast, increasing genomic instability by combining deficiencies of Atm and the mismatch repair protein Mlh1 caused a moderate increase in radiation sensitivity and dramatic increase in aggressive lymphomas, compared with thes Atm-/- single knockout. Remarkably, Atm, Mlh1 or Mlh1/Atm single or double heterozygosity did not significantly affect the life span of the various genotypes. Mlh1/Atm double null tumors were polyclonal, whereas the tumors in other genotypes were mono- or oligoclonal, demonstrating the high predisposition of thymocytes with this genotype to become malignant. Chromosomal aberrations in the tumors were localized mainly in chromosomes 12 and 15. The genomic region on chromosome 15, which contains the gene for the c-Myc oncoprotein, was commonly amplified, and elevated levels of the c-Myc protein were subsequently observed in the tumors. Our data suggest that impaired genomic instability is an important contributing factor to cancer predisposition in A-T, whereas oxidative stress is more important in the radiation sensitivity and growth retardation facets of this disease.

Animals↗

PubMed2005

Apolipoprotein E4 enhances brain inflammation by modulation of the NF-kappaB signaling cascade.

Apolipoprotein E4 (apoE4), the major genetic risk factor of Alzheimer's disease (AD), is associated with enhanced brain inflammation. Genome-wide gene expression profiling was employed to study the effects of apoE genotype on hippocampal gene expression in LPS-treated mice, transgenic for either apoE4 or the AD benign allele, apoE3. This revealed that the expression of inflammation-related genes following intracerebroventricular injection of LPS was significantly higher and more prolonged in apoE4 than in apoE3 transgenic mice. Clustering analysis revealed gene clusters which responded differently in apoE4 and apoE3 mice and were significantly enriched in NF-kappaB response elements. Direct measurement of NF-kappaB-regulated genes revealed that their extent of activation was greater in the apoE4 mice. Immunohistochemistry experiments revealed that microglial and NF-kappaB activation were more pronounced in apoE4 than in apoE3 mice. These findings suggest that the increased brain inflammation in apoE4 mice is related to disregulation of NF-kappaB signaling pathway.

Alzheimer Disease↗

PubMed2005

Dissection of a DNA-damage-induced transcriptional network using a combination of microarrays, RNA interference and computational promoter analysis.

BACKGROUND: Gene-expression microarrays and RNA interferences (RNAi) are among the most prominent techniques in functional genomics. The combination of the two holds promise for systematic, large-scale dissection of transcriptional networks. Recent studies, however, raise the concern that nonspecific responses to small interfering RNAs (siRNAs) might obscure the consequences of silencing the gene of interest, throwing into question the ability of this experimental strategy to achieve precise network dissections. RESULTS: We used microarrays and RNAi to dissect a transcriptional network induced by DNA damage in a human cellular system. We recorded expression profiles with and without exposure of the cells to a radiomimetic drug that induces DNA double-strand breaks (DSBs). Profiles were measured in control cells and in cells knocked-down for the Rel-A subunit of NFkappaB and for p53, two pivotal stress-induced transcription factors, and for the protein kinase ATM, the major transducer of the cellular responses to DSBs. We observed that NFkappaB and p53 mediated most of the damage-induced gene activation; that they controlled the activation of largely disjoint sets of genes; and that ATM was required for the activation of both pathways. Applying computational promoter analysis, we demonstrated that the dissection of the network into ATM/NFkappaB and ATM/p53-mediated arms was highly accurate. CONCLUSIONS: Our results demonstrate that the combined experimental strategy of expression arrays and RNAi is indeed a powerful method for the dissection of complex transcriptional networks, and that computational promoter analysis can provide a strong complementary means for assessing the accuracy of this dissection.

Ataxia Telangiectasia Mutated Proteins↗

PubMed2004

In silico identification of transcriptional regulators associated with c-Myc.

The development of powerful experimental strategies for functional genomics and accompanying computational tools has brought major advances in the delineation of transcriptional networks in organisms ranging from yeast to human. Regulation of transcription of eukaryotic genes is to a large extent combinatorial. Here, we used an in silico approach to identify transcription factors (TFs) that form recurring regulatory modules with c-Myc, a protein encoded by an oncogene that is frequently disregulated in human malignancies. A recent study identified, on a genomic scale, human genes whose promoters are bound by c-Myc and its heterodimer partner Max in Burkitt's lymphoma cells. Using computational methods, we identified nine TFs whose binding-site signatures are highly overrepresented in this promoter set of c-Myc targets, pointing to possible functional links between these TFs and c-Myc. Binding sites of most of these TFs are also enriched on the set of mouse homolog promoters, suggesting functional conservation. Among the enriched TFs, there are several regulators known to control cell cycle progression. Another TF in this set, EGR-1, is rapidly activated by numerous stress challenges and plays a central role in angiogenesis. Experimental investigation confirmed that c-Myc and EGR-1 bind together on several target promoters. The approach applied here is general and demonstrates how computational analysis of functional genomics experiments can identify novel modules in complex networks of transcriptional regulation.

Animals↗

PubMed2003

Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells.

Dissection of regulatory networks that control gene transcription is one of the greatest challenges of functional genomics. Using human genomic sequences, models for binding sites of known transcription factors, and gene expression data, we demonstrate that the reverse engineering approach, which infers regulatory mechanisms from gene expression patterns, can reveal transcriptional networks in human cells. To date, such methodologies were successfully demonstrated only in prokaryotes and low eukaryotes. We developed computational methods for identifying putative binding sites of transcription factors and for evaluating the statistical significance of their prevalence in a given set of promoters. Focusing on transcriptional mechanisms that control cell cycle progression, our computational analyses revealed eight transcription factors whose binding sites are significantly overrepresented in promoters of genes whose expression is cell-cycle-dependent. The enrichment of some of these factors is specific to certain phases of the cell cycle. In addition, several pairs of these transcription factors show a significant co-occurrence rate in cell-cycle-regulated promoters. Each such pair indicates functional cooperation between its members in regulating the transcriptional program associated with cell cycle progression. The methods presented here are general and can be applied to the analysis of transcriptional networks controlling any biological process.

Binding Sites↗