Cross-platform expression profiling demonstrates that SV40 small tumor antigen activates Notch, Hedgehog, and Wnt signaling in human cells
© Ali-Seyed et al; licensee BioMed Central Ltd. 2006
Received: 08 August 2005
Accepted: 07 March 2006
Published: 07 March 2006
We previously analyzed human embryonic kidney (HEK) cell lines for the effects that simian virus 40 (SV40) small tumor antigen (ST) has on gene expression using Affymetrix U133 GeneChips. To cross-validate and extend our initial findings, we sought to compare the expression profiles of these cell lines using an alternative microarray platform. METHODS: We have analyzed matched cell lines with and without expression of SV40 ST using an Applied Biosystems (AB) microarray platform that uses single 60-mer oligonucleotides and single-color quantitative chemiluminescence for detection. RESULTS: While we were able to previously identify only 456 genes affected by ST with the Affymetrix platform, we identified 1927 individual genes with the AB platform. Additional technical replicates increased the number of identified genes to 3478 genes and confirmed the changes in 278 (61%) of our original set of 456 genes. Among the 3200 genes newly identified as affected by SV40 ST, we confirmed 20 by QRTPCR including several components of the Wnt, Notch, and Hedgehog signaling pathways, consistent with SV40 ST activation of these developmental pathways. While inhibitors of Notch activation had no effect on cell survival, cyclopamine had a potent killing effect on cells expressing SV40 ST. CONCLUSIONS: These data show that SV40 ST expression alters cell survival pathways to sensitize cells to the killing effect of Hedgehog pathway inhibitors.
DNA microarray technology enables the investigator to quantitate gene expression of hundreds or thousands of genes from a single RNA sample. However, the various types of commercially available microarray technologies have different genomic targets, use different probes design methodologies, and different detection chemistries making cross-platform comparisons difficult. A number of studies comparing spotted cDNA two-color technology with the Affymetrix single-color technology have found fairly poor correlation of data between these two approaches [1–3]. Other studies comparing oligonucleotide platforms such as Agilent and Affymetrix have found higher correlations . An important difference between Affymetrix GeneChips and other oligonucleotide platforms is that Affymetrix uses multiple 25-mer probe pairs, while all other oligonucleotide microarrays use a single probe per gene, varying in length from 50 to 70 bases.
A relatively new platform for microarray analysis developed by Applied Biosystems (AB) employs single 60-mer oligonucleotides, similar in length to Agilent, but uses single-color chemiluminescence detection technology as opposed to two-color Cy3/Cy5 labeling and laser fluorescence scanning. To compare the AB Expression Array System platform with Affymetrix, we analyzed RNA samples that we had previously analyzed with Affymetrix U133AB GeneChips . Here we show that the AB platform has substantially higher sensitivity, detecting four times as many gene changes in an identical experimental design, and over seven times as many genes when additional technical replicates were included. Moreover, the AB microarray data was well correlated with QRTPCR validation data (R2 = 0.71) while Affymetrix data had lower correlation with QRTPCR results (R2 = 0.47).
In addition, the genes that were identified solely with the AB technology provided insights into the mechanisms by which simian virus 40 small tumor antigen (SV40 ST) affects transformation of human cells that were not apparent in our earlier analyses. We show that SV40 ST induces expression of several key components of the Notch, Wnt, and Hedgehog signaling pathways. While inhibitors of Notch activation had little effect on cell survival, the Hedgehog inhibitor cyclopamine had >50% killing effect on cells expressing SV40 ST, suggesting that SV40 ST makes cells dependent on Hedgehog signaling for survival.
Stable human embryonic (HEK) cell lines HEK-TERST, HEK-TERV have been described previously . Briefly, cells were maintained in α-MEM, 10%FBS, 2 mM/L glutathione, 100 u/ml penicillin/streptomycin. Cells were serum starved in α-MEM, 0.1%FBS, 2 mM/L glutathione, 100 u/ml penicillin/streptomycin for 24 hours prior to preparation of total RNA for microarray analysis.
Affymetrix genechip expression analysis
Total RNA was prepared from two independent biological replicates of the HEK-TERV and HEK-TERST cell lines and used for whole genome expression analysis as previously described . Data from Affymetrix CEL files was then normalized using the robust multiarray average (RMA) method . Briefly in the original analysis of Affymetrix data, genes called Absent by the Microarray Suite 5.0 software in all hybridizations and genes that were called no change (NC) in more than one ST-TERV Affymetrix comparison file were filtered out leaving 2545 probes for Significance Analysis of Microarrays (SAM) analysis . After data normalization, SAM analysis was performed on the remaining 2545 probe sets using the following relevant parameters: Δ = 0.26, fold-change = 1.5, number permutations = 1000, RNG seed = 1234567, median FDR < 3%, significant probes = 555, predicted false positives = 17. For the revised Affymetrix analysis presented here, all probe sets with at least one present call (n = 15229) were included in the SAM analysis using the following relevant parameters: Δ = 0.59, fold-change = 1.5, number permutations = 500, RNG seed = 1234567, median FDR < 3%, significant probes = 668, predicted false positives = 16.
AB Expression array system analysis
The quality of the RNA from the samples was evaluated using the Agilent Bioanalyzer 2100 (Agilent Technologies, Palo Alto, CA). A minimum Bioanalyzer RNA Integrity Number (RIN) value of 8 was required prior to RNA labeling. One μg of total RNA from each sample was used to synthesize DIG-labeled cRNA as described by the Applied Biosystems Chemiluminescent RT-IVT Labeling protocol (Applied Biosystems, Foster City, CA). A total of 36 μg of labeled cRNA (18 μg for each process replicate) from each RT-IVT reaction was hybridized onto two Applied Biosystems Human Genome Survey Microarrays following the manufacturer's recommendations. Microarrays were analyzed using the AB1700 Chemiluminescent Microarray Analyzer. Two technical replicates were produced for each biological replicate, for a total of eight hybridizations.
For each gene, the expression values were normalized across arrays by quantile normalization . "Detectable" calls for gene expression were based on Applied Biosystems recommendations (signal/noise ratio >3.0 and FLAG values <5000). A gene had to be called "detectable" in 3 out of 4 replicates in at least one tested sample group (either HEK-TERST or HEK-TERV) to be retained for further analysis. From the initial dataset of 33096 probes, 17710 probes passed this filter and were used in subsequent SAM analyses. Significance analysis using the SAM software was performed on the four hybridizations (excluding technical replicates) using the following relevant parameters: Δ = 0.77, fold-change = 1.5, number permutations = 500, RNG seed = 1234567, median FDR < 3%, significant probes = 1554, predicted false positives = 51. For analysis of all eight hybridizations including technical replicates, SAM analysis was performed with the following parameters: Δ = 0.84, fold-change = 1.5, number permutations = 500, RNG seed = 1234567, median FDR < 3%, significant probes = 3663, predicted false positives = 100. Expression fold-changes between TERST and TERV groups were derived from the mean expression level of a gene in all four replicates. A complete dataset for the AB microarray data has been deposited in the ArrayExpress database, accession number E-MEXP-519. The Affymetrix dataset is available at ArrayExpress, accession number E-MEXP-156.
Gene network pathway analysis
Gene Ontology (GO) annotations were analyzed using the GOstat software [9, 10] and with the Panther Protein Classification System  to identify functional annotations that were significantly enriched in this gene set compared to the entire human genome. Gene lists altered by SV40 ST were mapped onto biological pathways that were significantly represented. Lists of significantly changed genes were similarly analyzed using Ingenuity Pathway Analysis . Gene lists were mapped to canonical pathways and relationships were extracted from the Ingenuity Pathways Knowledgebase. Lists were also loaded and analyzed with GenMAPP  and genes altered by SV40 ST were analyzed for interaction with the Wnt signaling pathway.
TaqMan assay validation
Validation of microarray results was done using Applied Biosystems TaqMan Gene Expression Assays . Total RNA from the original samples was converted to cDNA using the Applied Biosystems High Capacity cDNA Archive Kit. Ten ng of cDNA was used per TaqMan reagent based Reaction. Each TaqMan assay was run on an Applied Biosystems 7900HT FAST Real-Time PCR system in quadruplicates and expression fold-changes (normalized to the endogenous control GAPDH) between HEK-TERST and HEK-TERV were derived using the comparative CT method . Concordance of fold-change comparability of selected genes between microarray and TaqMan data was determined by the Pearson correlation coefficient.
Immunoblots were performed as previously described . The human activated Notch 2 antibody (Abcam Inc., Cambridge, MA, cat# Ab8926) was derived using a synthetic peptide CRDASNHKRREPVGQD, corresponding to amino acids 1719–1733 of Human activated Notch2 and used at 1:500.
Promoter Analysis was performed using our automated CONFAC software, which identifies promoters sequences within 3 kb upstream of transcription start sites and the first intron of each gene that are conserved between human and mouse genomes . The position weight matrices are those from the MATCH software using the TRANSFAC Professional 8.3 database.
Drug sensitivity assays
HEKTER-LUK and HEKTER-ST cells were treated with appropriate concentrations of vehicle (Ethanol or DMSO), 2.4μM cyclopamine (C-8700, LC laboratories, Woburn, MA, USA), or 1 nM γ-secretase inhibitor (S-1120, A.G. Scientific, Inc. San Diego, CA, USA). Cells were initially incubated with the compounds for 48 hours and the incubation was extended for another 24 hours with fresh media containing vehicle or drug. Cells were plated at 2 × 104/well in a 96-well plate and viability was determined by the MTT assay. Twenty μl of 3 mg/mL 3- [4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) was added to each well and incubated for 4 hours at 37°C. Elution of the precipitate was performed with 100 μL of DMSO and 10μL of Tris-Glycine buffer. Cell viability was calculated from the absorption values obtained at 570 nm using an automated enzyme-linked immunosorbent assay (ELISA) reader. In a separate set of experiments, both HEK-TERV and HEK-TERST cells were plated at 1 × 105/well in a 6 well plate containing α-MEM media (Cellgro Mediatec Inc, Herndon, VA, USA) supplemented with 10% FBS and allowed to attach overnight. Media was then replaced with fresh medium containing either vehicle or drug. Cell viability was determined after 72 hrs by Trypan blue exclusion. Fifty μl of cell suspension was mixed with 50 μl of Trypan blue isotonic solution (0.2%; w/v), and cell viability was determined on a hemocytometer by phase contrast microscopy.
Cross-platform microarray analysis
Because our original SAM analysis of our Affymetrix data was performed on a prefiltered set of only 2545 probe sets, we performed another SAM analysis on a larger set of Affymetrix probes. For the revised SAM analysis, we included all of the 15,229 Affymetrix probe sets with a present call in at least one sample. Despite the larger number of probe sets included in the analysis, the number of significant genes identified increased by only 92 genes for a total of 548 significant genes [see Additional file 2]. Of those 548 genes, 202 (37%) overlapped with the 1927 AB genes identified without technical replicates (Figure 1C). When compared to the 3478 genes identified on the AB platform with technical replicates, there were 306 genes (56%) that overlapped (Figure 1D). These data show that the AB platform was able to measure significant changes in many more genes than the Affymetrix platform and that additional technical replicates nearly doubled the number of detectable differences. A total of 178 genes called significant by the Affymetrix platform were not confirmed using the AB1700 [see Additional file 3].
The difference in the range of the data from the two expression platforms in Figures 2A and 2B is worth noting. In general, the dynamic range of the Affymetrix data was smaller (from 2-5 to 23) than the AB dynamic range (from 2-9 to 25). This may account for the greater number of genes that were significantly altered on the AB platform due to the nature of chemiluminescence with higher signal range and lower background. In addition, the potential for greater sensitivity of 60-mer oligonucleotides compared to 25-mer oligonucleotides, as well as the sequence specificity of the 60-mers may also account for the greater number of genes that were significantly altered on the AB platform.
QRTPCR taqman validation
TaqMan assay based QRTPCR and Microarray Fold changes for 23 genes. Genes that were not significant on the Affymetrix column are indicated (n.s.). Twenty genes were validated and three showed little change (BIRC5, WNT7A, and ETS1).
Expression Array SystemFold Change
TaqMan Fold Change
Affymetrix Fold Change
Pathway annotation of ST-regulated genes
GOStat analysis of Biological Processes that are significantly enriched in the set of 3478 genes found with the AB platform. Of those genes only 2476 had recognizable HUGO symbols and 1811 were annotated in the GO database. Shown here is a subset of 20 representative significant GO annotations. The complete set of 135 significant GO categories together with the identities of the genes in each category is included in the supplementary data.
response to wounding
response to pathogen
response to stress
regulation of cell cycle
protein kinase cascade
positive regulation of I-kappaB kinase/NF-kappaB cascade
Notch signaling pathway
Significantly overrepresented GO categories from the Panther annotation database using the same gene set as Table 1.
Cell structure and motility
Immunity and defense
Cell proliferation and differentiation
Protein metabolism and modification
Cell cycle control
Extracellular matrix protein-mediated signaling
Intracellular signaling cascade
Other polysaccharide metabolism
Receptor protein tyrosine kinase signaling pathway
Nucleoside, nucleotide and nucleic acid metabolism
Cell adhesion-mediated signaling
Lipid, fatty acid and steroid metabolism
SV40 ST may increase notch signaling
Among the genes we observed to be upregulated by SV40 ST were Delta-like ligand 1 (DLL1, up 5.3 fold), a human homolog of the D. melanogaster ligand delta, and Jagged 1 (JAG1, up 1.6 fold) a homolog of the Notch ligand serrate. To determine whether the Notch receptor was expressed in HEK-TERST cells, we examined the expression profiling data from both our Affymetrix and AB expression studies. Data from both platforms indicated that while the Notch 1, Notch 3, and Notch 4 homologs had very low expression levels, the Notch 2 receptor was highly expressed in both the HEK-TERV and the HEK-TERST cell lines. Moreover, both platforms indicated that there was little if any change in Notch 2 expression between the HEK-TERV and HEK-TERST cells.
In a recent study , mesothelial cells required both SV40 ST and SV40 LT for induction of Notch receptor expression, and required activation of the MAPK-ERK pathway. Since the HEK-TERV cells express both SV40 LT and mutant H-Ras-V12, they have constitutive activation of the MAPK-ERK pathway even in the absence of SV40 ST. Thus, our data are consistent with the observation that LT and ERK activation are sufficient to activate Notch expression. However, the mesothelial cells also constitutively expressed DLL1 and JAG1 ligands with or without expression of SV40 ST. Thus, in HEK cells, it appears that ST increases DLL1 and JAG1 expression, which are already expressed in mesothelial cells. In light of these data, we predict that enhanced expression of both DLL1 and Notch2 on the HEK-TERST cells would increase activation of Notch signaling via cell-cell interactions between adjacent HEK-TERST cells.
To directly test whether Notch-2 is activated in HEK-TERST cells, we probed immunoblots of whole cell lysates from HEK-TERV and HEK-TERST cells using polyclonal antibodies specific to activated human Notch-2 (Figure 4C). Notch is translated as a ~300 kD full length preprotein (Notch-FL) that is proteolytically cleaved to produce a mature ~100 kD transmembrane/cytosolic C-terminal fragment (Notch-TM) and an extracellular N-terminal fragment . Upon binding of the extracellular fragment to the DLL1 ligand, the Notch-TM is cleaved by gamma-secretase [20, 21] to produce the intracellular fragment (Notch-IC) that translocates to the nucleus to activate transcription . Short exposures of Activated Notch-2 immunoblots indicate no change in activation of Notch-2 between HEK-TERV and HEK-TERST cells, but longer exposures demonstrated a shorter isoform of Notch-2 present in HEK-TERST cells that is not present in HEK-TERV cells (Figure 4C). Thus, while there may be increased Notch signaling in cells expressing SV40 ST, it is clear that Notch-2 is also activated in the HEK-TERV cells that do not express SV40 ST.
SV40 ST also activates components of the Wnt and Hedgehog pathways
Hedgehog inhibitors, but not Notch inhibitors, kill cells expressing SV40 ST
We have used a relatively new commercial DNA microarray platform to analyze the genes that are affected by expression of SV40 ST in HEK cells and to compare the performance of two single-color, oligonucleotide-based technologies. In general, the AB microarray platform outperformed the Affymetrix platform in both sensitivity and accuracy. The AB platform identified over 3.5 times as many genes (1927 vs. 548) using equivalent replicates and analyses, and over six times as many genes (3478 vs. 548) with additional technical replicates. The AB data was highly reproducible, had a greater dynamic range than the Affymetrix data, and correlated much better with TaqMan assay based QRTPCR validation data in our experiments.
The fact that the AB microarray uses 60-mer oligonucleotides compared to the 25-mer probe sets used in Affymetrix microarrays likely contributed greatly to the differences in specificity, sensitivity, and accuracy. Apart from the difference in the length of the oligonucleotides, the actual sequences that were queried were different between the two platforms, making cross-platform annotation somewhat difficult. The TaqMan assay based QRTPCR assays used were not designed biased to any particular region of each target transcript, while the probes on the AB microarray were biased to the 3' UTR (within 1500 bp of the 3' UTR), making these assays independent for mRNA quantitation. However, each probe on the AB microarray queries sequences in common to all verified alternative splice forms of each gene, while the TaqMan assays can measure a subset of specific splice forms. Thus, in the few instances in which the TaqMan and AB microarray data did not agree, it is likely to be either a function of higher sensitivity of TaqMan or that the TaqMan assays are targeting a subset of spliced transcripts that the microarrays measures. Moreover, it is entirely possible that the Affymetrix and AB platforms can be measuring alternative splice forms of the same gene.
The classes of genes that were altered based on GO ontologies were in general the same as that observed previously in our original analysis . Specifically, we once again observed large decreases in expression of immune response genes, and changes in genes involved in cell cycle regulation, oncogenesis, cellular adhesion, and signal transduction. However, the statistical significance of these observations was increased due to the greater number of genes detected. For example, we found 70 genes annotated for wounding response were affected (p = 9.63E-12), while our earlier study found 48 genes (p = 3.46E-4). In addition, a number of new, biologically significant observations became apparent from the AB expression data that we did not observe previously.
In our earlier study , we also determined that cell-cell adhesion and integrin signaling were altered in cells that express SV40 ST. The larger dataset of altered genes from the AB platform provides additional insights into these changes. In addition to our earlier observations on ICAM1, VCAM1, junction plakoglobin (JUP), proto-cadherins, collagens, and laminins, the AB expression data showed decreases in integrins α2, α3, αV, αX, and β8 and increases in integrins α4 and α7. Integrins α3, α4 and α7 mediate binding to laminins, while α2 integrin binds to collagen, and integrin αVβ8 is an RGD receptor (reviewed in ). We also observed alterations in both directions for seven laminin genes and seven collagen genes, suggesting that, in addition to modulating cellular adhesion in favor of laminin-binding receptors, SV40 ST is altering the composition of the extracellular matrix. The AB data also provided additional evidence for decreased cell-cell adhesion through downregulation of neural cell adhesion molecule 1 (NCAM1), L1 cell adhesion molecule (L1CAM), claudin 1 (CLDN1), and junction adhesion molecule 2 (JAM2).
Regarding cellular proliferation, we had previously reported increased levels of myc and src activation. The additional expression data described here show increased levels of epidermal growth factor receptor (EGFR) and insulin-like growth factor 2 (IGF2), providing potential mechanisms by which growth-factor signaling cascades can be stimulated by SV40 ST. Moreover, we observed activation of the Wnt signaling pathway, providing another mechanism that may explain the observed increases in myc expression, since myc is a known downstream target of β-catenin  and Wnt signaling provides resistance to myc-induced apoptosis . We also observed increased expression of the Wnt5b ligand, as well as DKK1, a recently validated downstream target of Wnt signaling . At least fourteen known Wnt downstream targets  were affected by SV40 ST, further supporting the evidence that Wnt signaling is activated by SV40 ST.
The observation that cyclopamine killed 50–60% of HEK-TERST cells, but had little effect on HEK-TERV cells suggests that SV40 ST exerts changes within the cell that makes it dependent on hedgehog signals for survival. However, the lack of an effect of gamma-secretase inhibitors suggests that Notch signaling is not essential for survival in cells expressing SV40 ST.
Our data show that the AB platform had substantially higher sensitivity than the Affymetrix platform in these experiments, detecting four times as many gene changes. Moreover, the AB data was highly correlated with QRTPCR validation data (R2 = 0.71) while Affymetrix data had lower correlation with QRTPCR results (R2 = 0.47).
Among the Wnt target genes that were upregulated by SV40 ST, we observed changes in key developmental regulators, including the DLL1 and JAG1 ligands for Notch. However, activated Notch-2 was detected in both cell lines, suggesting that its activation, while possibly increased by SV40 ST, was not dependent on SV40 ST. Finally, the increased levels of the Smoothened receptor (SMO) and three Hedgehog target genes (GLI2, HIP, and Wnt5A), suggest that Hedgehog pathways are also activated by SV40 ST, and the cell death induced by cyclopamine shows that Hedgehog activation is critical to survival for cells expressing SV40 ST. These data support a model in which Hedgehog signaling activates Wnt signaling, which in turn activates Notch signaling (Figure 5). The activation of all three of these developmental pathways provides a mechanism for our earlier conclusions  that SV40 ST induces a de-differentiated phenotype in transformed human cells. Together, the changes in adhesion, proliferation, apoptosis, and differentiation orchestrated by SV40 ST provide several insights into the pathways that are disrupted upon transformation of human cells.
- SV40 ST:
Simian Virus 40 Small Tumor Antigen, AB: Applied Biosystems, RNG: Random number generator, FDR: False Discovery Rate, RT-IVT: Reverse Transcription-In Vitro Transcription.
The authors would like to thank Jacob Kagey (Emory University Graduate Program in Genetics and Molecular Biology) for technical assistance, Dr. William C. Hahn (Dana Farber Cancer Institute, Broad Institute) for the generous gift of a HEK-TERV and HEK-TERST cell lines, and Dr. David C. Pallas (Emory University) for critical reading of the manuscript and helpful discussions. This research was supported in part by NIH grants K22-CA96560 and R01-CA106826 to CSM and the Department of Pathology & Laboratory Medicine, Emory University School of Medicine.
- Kothapalli R, Yoder SJ, Mane S, Loughran TPJ: Microarray results: how accurate are they?. BMC Bioinformatics. 2002, 3: 22-10.1186/1471-2105-3-22.PubMed CentralView ArticlePubMed
- Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics. 2002, 18: 405-412. 10.1093/bioinformatics/18.3.405.View ArticlePubMed
- Barczak A, Rodriguez MW, Hanspers K, Koth LL, Tai YC, Bolstad BM, Speed TP, Erle DJ: Spotted long oligonucleotide arrays for human gene expression analysis. Genome Res. 2003, 13: 1775-1785. 10.1101/gr.1048803.PubMed CentralView ArticlePubMed
- Jarvinen AK, Hautaniemi S, Edgren H, Auvinen P, Saarela J, Kallioniemi OP, Monni O: Are data from different gene expression microarray platforms comparable?. Genomics. 2004, 83: 1164-1168. 10.1016/j.ygeno.2004.01.004.View ArticlePubMed
- Moreno CS, Ramachandran S, Ashby D, Laycock N, Plattner CA, Chen W, Hahn WC, Pallas DC: Signaling and Transcriptional Changes Critical for Transformation of Human Cells by SV40 Small Tumor Antigen or PP2A B56gamma Knockdown. Cancer Res. 2004, 64: 6978-6988. 10.1158/0008-5472.CAN-04-1150.View ArticlePubMed
- Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19: 185-193. 10.1093/bioinformatics/19.2.185.View ArticlePubMed
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001, 98: 5116-5121. 10.1073/pnas.091062498.PubMed CentralView ArticlePubMed
- Bioconductor: [http://www.bioconductor.org].
- Beissbarth T, Speed T: GOstat: Find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics. 2004, 20: 1464-1465. 10.1093/bioinformatics/bth088.View ArticlePubMed
- GOstat: [http://gostat.wehi.edu.au].
- Panther: [http://www.pantherdb.org/].
- Ingenuity: [http://www.ingenuity.com/].
- GenMAPP: [http://www.genmapp.org].
- AppliedBiosystems: [http://www.appliedbiosystems.com].
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25: 402-408. 10.1006/meth.2001.1262.View ArticlePubMed
- Karanam S, Moreno CS: CONFAC: Automated Application of Comparative Genomic Promoter Analysis to DNA Microarray Datasets. Nucleic Acids Res. 2004, 32: W475-84.PubMed CentralView ArticlePubMed
- Bocchetta M, Miele L, Pass HI, Carbone M: Notch-1 induction, a novel activity of SV40 required for growth of SV40-transformed human mesothelial cells. Oncogene. 2003, 22: 81-89. 10.1038/sj.onc.1206097.View ArticlePubMed
- Iso T, Kedes L, Hamamori Y: HES and HERP families: multiple effectors of the Notch signaling pathway. J Cell Physiol. 2003, 194: 237-255. 10.1002/jcp.10208.View ArticlePubMed
- Blaumueller CM, Qi H, Zagouras P, Artavanis-Tsakonas S: Intracellular cleavage of Notch leads to a heterodimeric receptor on the plasma membrane. Cell. 1997, 90: 281-291. 10.1016/S0092-8674(00)80336-0.View ArticlePubMed
- De Strooper B, Annaert W, Cupers P, Saftig P, Craessaerts K, Mumm JS, Schroeter EH, Schrijvers V, Wolfe MS, Ray WJ, Goate A, Kopan R: A presenilin-1-dependent gamma-secretase-like protease mediates release of Notch intracellular domain. Nature. 1999, 398: 518-522. 10.1038/19083.View ArticlePubMed
- Ray WJ, Yao M, Mumm J, Schroeter EH, Saftig P, Wolfe M, Selkoe DJ, Kopan R, Goate AM: Cell surface presenilin-1 participates in the gamma-secretase-like proteolysis of Notch. J Biol Chem. 1999, 274: 36801-36807. 10.1074/jbc.274.51.36801.View ArticlePubMed
- Radtke F, Raj K: The role of Notch in tumorigenesis: oncogene or tumour suppressor?. Nat Rev Cancer. 2003, 3: 756-767. 10.1038/nrc1186.View ArticlePubMed
- Lum L, Beachy PA: The Hedgehog response network: sensors, switches, and routers. Science. 2004, 304: 1755-1759. 10.1126/science.1098020.View ArticlePubMed
- Regl G, Neill GW, Eichberger T, Kasper M, Ikram MS, Koller J, Hintner H, Quinn AG, Frischauf AM, Aberger F: Human GLI2 and GLI1 are part of a positive feedback mechanism in Basal Cell Carcinoma. Oncogene. 2002, 21: 5529-5539. 10.1038/sj.onc.1205748.View ArticlePubMed
- Sheng T, Li C, Zhang X, Chi S, He N, Chen K, McCormick F, Gatalica Z, Xie J: Activation of the hedgehog pathway in advanced prostate cancer. Mol Cancer. 2004, 3: 29-10.1186/1476-4598-3-29.PubMed CentralView ArticlePubMed
- Reddy S, Andl T, Bagasra A, Lu MM, Epstein DJ, Morrisey EE, Millar SE: Characterization of Wnt gene expression in developing and postnatal hair follicles and identification of Wnt5a as a target of Sonic hedgehog in hair follicle morphogenesis. Mech Dev. 2001, 107: 69-82. 10.1016/S0925-4773(01)00452-X.View ArticlePubMed
- Niida A, Hiroko T, Kasai M, Furukawa Y, Nakamura Y, Suzuki Y, Sugano S, Akiyama T: DKK1, a negative regulator of Wnt signaling, is a target of the beta-catenin/TCF pathway. Oncogene. 2004, 23: 8520-8526. 10.1038/sj.onc.1207892.View ArticlePubMed
- Li CM, Kim CE, Margolin AA, Guo M, Zhu J, Mason JM, Hensle TW, Murty VV, Grundy PE, Fearon ER, D'Agati V, Licht JD, Tycko B: CTNNB1 mutations and overexpression of Wnt/beta-catenin target genes in WT1-mutant Wilms' tumors. Am J Pathol. 2004, 165: 1943-1953.PubMed CentralView ArticlePubMed
- Willert J, Epping M, Pollack JR, Brown PO, Nusse R: A transcriptional response to Wnt protein in human embryonic carcinoma cells. BMC Dev Biol. 2002, 2: 8-10.1186/1471-213X-2-8.PubMed CentralView ArticlePubMed
- Yue TL, Ni J, Romanic AM, Gu JL, Keller P, Wang C, Kumar S, Yu GL, Hart TK, Wang X, Xia Z, DeWolf WEJ, Feuerstein GZ: TL1, a novel tumor necrosis factor-like cytokine, induces apoptosis in endothelial cells. Involvement of activation of stress protein kinases (stress-activated protein kinase and p38 mitogen-activated protein kinase) and caspase-3-like protease. J Biol Chem. 1999, 274: 1479-1486. 10.1074/jbc.274.3.1479.View ArticlePubMed
- Ichijo H, Nishida E, Irie K, ten Dijke P, Saitoh M, Moriguchi T, Takagi M, Matsumoto K, Miyazono K, Gotoh Y: Induction of apoptosis by ASK1, a mammalian MAPKKK that activates SAPK/JNK and p38 signaling pathways. Science. 1997, 275: 90-94. 10.1126/science.275.5296.90.View ArticlePubMed
- Hynes RO: Integrins: bidirectional, allosteric signaling machines. Cell. 2002, 110: 673-687. 10.1016/S0092-8674(02)00971-6.View ArticlePubMed
- He TC, Sparks AB, Rago C, Hermeking H, Zawel L, da Costa LT, Morin PJ, Vogelstein B, Kinzler KW: Identification of c-MYC as a target of the APC pathway. Science. 1998, 281: 1509-1512. 10.1126/science.281.5382.1509.View ArticlePubMed
- You Z, Saims D, Chen S, Zhang Z, Guttridge DC, Guan KL, MacDougald OA, Brown AM, Evan G, Kitajewski J, Wang CY: Wnt signaling promotes oncogenic transformation by inhibiting c-Myc-induced apoptosis. J Cell Biol. 2002, 157: 429-440. 10.1083/jcb.200201110.PubMed CentralView ArticlePubMed
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/6/54/prepub
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