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Human Molecular Genetics Advance Access originally published online on August 4, 2005
Human Molecular Genetics 2005 14(18):2641-2648; doi:10.1093/hmg/ddi298
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org

Functional promoter SNPs in cell cycle checkpoint genes

Hélène Bélanger1, Patrick Beaulieu1, Claudia Moreau1, Damian Labuda1,2, Thomas J. Hudson3 and Daniel Sinnett1,2,*

1Division of Hematology–Oncology, Research Center, Sainte-Justine Hospital, 3175 chemin de la Côte-Sainte-Catherine, Montreal, Canada QC H3T 1C5, 2Department of Pediatrics, University of Montreal, 3175 Côte-Sainte-Catherine, Montreal, Canada QC H3T 1C5 and 3McGill University and Genome Quebec Innovation Centre, 740 Drive Penfield Avenue, Montreal, Canada QC H3A 1A4

* To whom correspondence should be addressed. Tel: +1 5143454931 ext. 2990; Fax: +1 5143454731; Email: daniel.sinnett{at}umontreal.ca

Received June 2, 2005; Revised August 1, 2005; Accepted August 1, 2005


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
A substantial number of genes mutated in human cancers encode components of the cell cycle processes. As the G1/S transition in the cell cycle is a finely regulated biological process, we hypothesized that sequence variations in the promoter region of the related genes might indeed lead to abnormal expression, thus predisposing the individuals carrying these genetic variants to cancer. In this report, we screened the promoter regions of 16 cell cycle checkpoint genes for DNA variants and assessed the functional impact of these promoter region single nucleotide polymorphisms (pSNPs) by combining in silico analysis and in vitro functional assays. We identified 127 pSNPs including 90 with predicted impact on putative binding sites of known transcription factors. Eleven pSNPs were selected for electrophoresis mobility shift assays because of their association with predicted gains of binding sites, and nine pSNPs showed differential allelic shifts in at least one cell line tested. Following the subcloning of the promoter regions into a gene reporter system, we found that at least four promoter haplotypes associated with CCND1, E2F1, HDAC1 and RB1 significantly influenced transcriptional activity in an allele-specific manner. Although the biological significance of these observations still remains to be demonstrated, the expected variability of expression levels in key cell cycle components might influence individual's risk of cancer.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
The cell has developed a series of checkpoints to ensure the quality control of the cell cycle processes, particularly the fidelity of replication and accurate chromosomal segregation. The G1/S cell cycle stage represents a critical period for cells to commit to growth arrest or proliferation (1Go). Understanding the regulation of the G1/S transition is central to the study of many diseases, particularly cancers (2Go,3Go). For instance, changes in protein activity in positive regulators such as CYCLIN D1, CYCLIN E, CDK4 and CDK6 and/or inactivation of repressors such as CDKN1A, CDKN1B, CDKN2A, CDKN2B and RB1 can lead to the development of tumours (4Go–8Go). It is therefore reasonable to consider that common variants in G1/S cell cycle genes might explain some of the inter-individual variability in the risk of cancer. Until recently, the search for functional polymorphisms has been predominantly focused on single nucleotide polymorphisms in the coding region of genes (cSNPs) because of their putative direct effect on the protein structure and function. Polymorphism in non-coding DNA, particularly regulatory regions, has been studied less intensively, despite the suggestion that at least 25% of relevant mutations reside in regulating regions (9Go,10Go). As protein levels regulate many biological pathways, regulatory polymorphisms can indeed influence biological processes (11Go) and modify disease risk as suggested by several studies (12Go–20Go). Here, we propose that polymorphisms in promoter regions of G1/S cell cycle checkpoint genes could modify transcription factor binding sites (TFBSs) and thus give rise to differences in expression. To address this hypothesis, we have combined in silico and in vitro approaches to detect and to validate the promoter SNPs (pSNPs) found in the regulatory region of 16 G1/S genes. We have found 127 pSNPs including 90 with predicted impact on putative TFBSs. We are aware that systematic effort should be made to identify and to annotate these pSNPs (21Go), but we decided to focus on those leading to putative gains of binding sites that cannot be further investigated with phylogenic tools. Using gene reporter and electrophoretic mobility shift assays (EMSAs), we found that four such pSNPs/haplotypes associated with the genes E2F1, HDAC1, RB1 and CCND1 influenced transcriptional activity in an allele-specific manner.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
A panel of 40 unrelated individuals from different ethnicity was screened by dHPLC to detect the presence of variants in the proximal promoter region that might alter the expression in 16 G1/S checkpoint genes: CCND1, CCNE1, CDC25A, CDK4, CDK6, CDKN1A, CDKN1B, CDKN2A, CDKN2B, E2F1, HDAC1, MADH3, MADH4, RB1, SKP2 and TFDP1 (Supplementary Material). The targeted promoter region was arbitrarily defined as the 2 kb sequence upstream of the transcriptional initiation site, because most of the validated regulatory SNPs were found within this interval (22Go). Amplimers were designed to direct amplification of five to eight overlapping PCR fragments to cover the entire 2 kb genomic segment. Despite the high GC-content of the promoters, only 9% of fragments failed to amplify, indicating the absence of significant bias due to PCR. We identified 127 pSNPs, including 104 (82%) that were not present in public databases. These pSNPs were equally distributed throughout the 2 kb region with an average density of 4.4 pSNPs/kb. Of all DNA variants identified, 17 (13%) were length polymorphisms, 65 (51%) were transition and 45 (35%) were transversion, thus giving a ratio of transition on transversion of 1.4, which is similar to published figures (23Go).

Depending on the position in the regulatory region, a pSNP has no effect, or may lead to a loss of a TF site (24Go,25Go) or results in the formation of a novel TFBS (26Go,27Go). Therefore, pSNPs located within sequence motifs predicting TFBSs may have putative effects on the expression of the corresponding gene (28Go). In the search for SNPs with putative functional impact, the proximal promoter region of the selected genes was screened for the presence of pSNPs located within predicting TFBSs. In silico analysis showed that 71% (90/127) of the pSNPs found in this study had a predicted impact on TFBSs: 30 (23.6%) and 24 (18.9%) variants were associated with a gain and a loss of a binding site, respectively, whereas 36 (28.3%) variants were expected to lead to a combination of loss/gain of binding sites (available upon request). We decided to further analyse polymorphisms in which the major allele had no known TFBSs, whereas the minor allele containing the discovered pSNP had a predicted gain of a binding site. Consequently, a panel of 11 pSNPs/haplotypes (Table 1) were validated with functional in vitro assays.


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  		Table 1. List of the promoter SNPs associated with predicted gains of TFBSs and the corresponding oligonucleotides used in EMSAs
 
We assessed by EMSA whether a pSNP in a predicted TF binding site would modify the binding of protein to the polymorphic DNA sequences. In these assays, double-stranded oligonucleotide (ds-oligo) probes corresponding to the sequence surrounding the polymorphic sites (Table 1) were radiolabelled and allowed to interact with nuclear extracts prepared from HepG2, Jeg-3 and HeLa cells. Representative data for CCND1 are shown in Fig. 1. Using ds-oligo probes spanning each form of the –568A<C pSNPs, we observed a slow migrating DNA–protein complex when the allele –568C probe was incubated in the presence of HeLa nuclear extracts (Fig. 1, lane 7), whereas no complexes were observed with the variant –568A (Fig. 1, lane 5). To confirm specificity of binding to the –568C cis-element, competition EMSAs were performed. The –568C related DNA–protein complex was competed with a 50-fold excess of the corresponding unlabelled –568C ds-oligo probe (Fig. 1, lane 8). The nuclear protein bound to the –568C probe in HeLa cells, thus represents a specific DNA–protein interaction. Similar results were obtained with Jeg-3 and HepG2 nuclear extracts (Table 2). The other pSNPs were processed similarly, and we found that nine of them showed differential allelic shifts in at least one cell line tested (Table 2).



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  		Figure 1. Representative EMSA illustrating allelic DNA–protein interactions in the promoter of CCND1. Labelled ds-oligo probe corresponding to –568A<C alleles when incubated with HeLa nuclear extracts reveals the position of the fast migrating unbound probe at the bottom of the gel and DNA–protein complexes of slower mobility (see arrow). Lanes 1–4 represent control ds-oligos using HeLa nuclear extracts. Lane 1, negative control; lane 2, positive control (labelled SP1 ds-oligo); lane 3, labelled SP1 ds-oligo plus unlabelled SP1 probe (competitor) and lane 4, labelled SP1 ds-oligo plus unlabelled AP1 probe (non-competitor). Lanes 5–8 represent differential binding of alleles –568A and –568C. Lane 5, labelled –568A ds-oligo; lane 6, labelled –568A ds-oligo plus unlabelled –568A probe (competitor); lane 7, labelled –568C ds-oligo and lane 8, labelled minus; 568C ds-oligo plus unlabelled –568C probe (competitor).

 

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  		Table 2. Summary of the EMSA results for the chosen pSNP
 
To assess the functional significance of these 11 pSNPs on promoter activity, different haplotypes of 2.0 kb of promoter sequences containing the pSNP were subcloned into the promoterless pGL3 vector. Then, transient transfection experiments were carried out for each of the resulting 20 allelic constructs (Table 3). Assuming a conservative definition of promoter activity as ~10-fold increase in the activity of the ‘highly expressed allele’ over the expression of the pGL3-basic vector (29Go), all constructs showed promoter activity in at least one of the cell lines tested (Table 4). Significant 1.5-fold differences (P≤0.05) in at least one cell line system combined with EMSA validation were considered as criteria for functional pSNP, at least in vitro. We have found putative functional SNPs/haplotypes in the HDAC1, E2F1, RB1 and CCND1 genes (Table 4). For HDAC1, the luciferase gene expression controlled by HDAC1 –905A>C promoter variants showed differential expression when transfected into HepG2, HeLa and JEG-3 cells (representative data shown in Fig. 2). The luciferase level driven by the –905A HDAC1 constructs (pHDAC1–905A) was 2-fold higher than the luciferase levels produced by the –905C allelic counterpart (Fig. 2 and Table 4). Combined with differential DNA–protein binding detected in HeLa nuclear extract (Table 2), this supports the functional significance of the pSNP/haplotype.


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  		Table 3. Promoter haplotypes studied in gene reporter assays
 

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  		Table 4. Results of the gene reporter assays for the promoter haplotype tested
 


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  		Figure 2. Assessment of the functional impact of the HDAC1 –905A<C variant on the promoter activity using a gene reporter assay. (A) A 2 kb regulatory haplotype inserted in the promoterless pGL3-basic vector containing the luciferase reporter gene. (B) Relative luciferase levels for the allelic constructs associated either with pSNP –905A (white) or with pSNP –905C (black) following transient transfection of HeLa, Jeg-3 and HepG2 cells. The C at position –905 decreases the promoter activity in all three cell lines. The luciferase activity is indicated by the ratio of firefly luciferase activity on the Renilla luciferase activity multiplied by 100.

 
In E2F transcription factor 1 (E2F1), the –897T variant significantly decreased the promoter activity when compared with the C allele, at least in HeLa cells, whereas specific differential binding was observed in HeLa and HepG2 nuclear extracts (gain of binding in the T allele). For retinoblastoma 1 (RB1), the insertion CTGATA at position –1733 (located on haplotype D) showed significant difference in promoter activity in HepG2 cells. This insertion led to a predicted gain of binding sites for at least three transcription factors (Table 1). For Cyclin D1 (CCND1), we observed significant differences in reporter gene levels between –568A>C derived haplotypes in HeLa and Jeg-3 (Table 4), which is supported by the observation of differential protein binding in all three cell lines with EMSA (Table 2). Statistically significant differences were also observed between other promoter allelic constructs, but did not meet our 1.5-fold threshold criteria. Although these allelic differences could have an important biological relevance, they will not be further discussed in this report.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Cell cycle is a finely regulated process that depends on combinations of cell cycle activators and inhibitors (30Go). It is thus plausible that changes in protein levels of key components might have an important functional impact. Thus, differential allelic expression of the corresponding genes might lead to inter-individual variability in the cell cycle function. In this report, we have detected variants in the proximal promoter regions of 16 G1/S checkpoint genes because of their putative impact on the protein product levels. We found 127 promoter SNPs (pSNPs) with a density of 4.4 SNPs/kb, which is similar to the sequence diversity reported for other genes (31Go). The scope of this report was directed towards the pSNPs leading to predicted gain of binding sites because of the lack of validated data for this type of pSNPs. This could be partly explained by the inapplicability of evolution-based tools such as phylogenetic footprinting analysis to select putative functional SNPs (32Go). By combining in silico and experimental approaches, we provided evidence that promoter SNPs/haplotypes significantly change promoter activity of the genes E2F1, HDAC1, pRB1 and CCND1. A similar strategy has been used by others to show the presence of functional promoter variants in 10–18% of some of the genes tested (29Go,33Go,34Go) but not all of them (35Go). This is an important observation because these pSNPs in vivo may lead to deregulation in the G1/S machinery, promote genomic instability and can give rise to increased individual susceptibility to develop cancer and other complex diseases (36Go).

E2F factors are involved in the control of multiple biological processes including cell cycle, replication, DNA synthesis and DNA repair (37Go). In particular, E2F1 not only plays a role as a growth-promoting factor but also promotes apoptosis depending on the cell type and the Rb status (38Go). E2F1 has been shown to be amplified in human erythroleukaemia cells (39Go) as well as up-regulated in thyroid carcinogenesis (40Go), whereas the loss of E2F1 significantly reduces the frequency of hyperplasic lesions in the thyroid glands of Rb +/– mice (41Go). Given the fact that E2F1 activity is often deregulated in human tumours, one might suggest that the functional C to T substitution at position –897 leads to a reduction in expression, thus modifying the risk of cancer among carriers. Of note, this pSNP leads to the gain of a predicted site for the binding of the cut-like homeodomain protein that has been shown to act as negative regulator of gene expression (42Go,43Go).

HDAC1 is part of the E2F-RB repression complex that is critical in evoking growth arrest and can also be recruited to specific genomic region by a variety of factors to mediate the repression of corresponding genes (44Go). Furthermore, overexpression of HDAC1 has been shown in hormone refractory prostate cancer (45Go). Therefore, it is reasonable to propose that the C-905A derived allele, which leads to the creation of predicted binding sites for three different transcription factors (C-Abl, SRY and HNF-3/fkh homologue 2) and shows significant differences in promoter activity, might modify ones risk of developing certain types of cancers.

CCND1 is a regulator that is able to drive cells through the restriction point in the G1 phase where these cells will be committed to divide (46Go). Overexpression of this gene occurs in many types of cancer, including breast and colorectal cancers, and it has been associated with an increase in cell proliferation (47Go,48Go). In this context, the functional haplotype B carrying the pSNP –568A<C could be involved in the susceptibility to cancer.

Mutations disrupting the Rb pathway lead to inappropriate proliferation and contribute to mitotic alterations and chromosome instability (49Go) and they are common in human cancers (50Go). Nonsense or frameshift mutations are the most frequent RB1 alterations, followed by point mutations in intronic or exonic sequences that cause aberrant splicing (51Go). Of note, two distinct point mutations in the core promoter region of RB1 gene at an ATF site and a retinoblastoma binding factor 1 site caused a large reduction of the promoter activity (52Go). Therefore, the allelic insertion of CTGATA at position –1733 (haplotype D), which leads to the predicted gain of three putative transcription binding sites [ecotropic viral integration site 1 factor (EVI-1), GATA-1 and RBP-Jkappa/CBF1] and associated with an increased promoter activity, might indeed play a role in cancer susceptibility. EVI-1 works as a transcriptional activator (53Go), though it has been shown to repress expression in some cases (54Go). GATA-1 is considered a transcriptional activator of erythroid-specific transcripts, but recent studies demonstrated that GATA-1 can also act as a repressor and is likely to play a major role in haematopoiesis (55Go–57Go). Finally, RBP-Jkappa/CBF1 has been recognized to act as a transcription factor that represses mammalian gene expression (58Go,59Go).

It is very likely that distinct functional pSNPs/haplotypes might have a synergistic interaction because of the interconnected nature of cell cycle regulation. For instance, members of the pRB family bind to E2F transcription factors causing them to repress transcription of the genes required for cell cycle progression. This transcriptional inhibition is also done through the recruitment, via the RB proteins, of histone deacetylases, thus leading to chromatin compaction and decreased access to transcription factory.

In conclusion, we showed that sequence variants in the proximal promoter regions of cell cycle genes could result in abnormal transcriptional regulation. Because of the possibilities that pSNPs/haplotypes may modify susceptibility in cancers, association studies in patient cohorts will further determine their importance in the clinical setting.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
SUPPLEMENTARY MATERIAL
 REFERENCES
 
SNP detection in promoter regions
Detection of DNA variants in a population panel consisting of 40 unrelated individuals (eight Africans, eight Europeans, eight Asians, eight Middle Easterners and eight Amerindians) was performed by PCR-based dHPLC analysis (Transgenomic WAVE Nuclei Acid Fragment Analysis System, Omaha, USA), followed by direct sequencing as described by Sinnett et al. (submitted for publication). The promoter region arbitrarily defined as the 2 kb sequence upstream of the transcription start site (based on RefSeq mRNA) of each gene was amplified sequentially in four to eight ~350 bp overlapping fragments. The heterozygous samples were sequenced to confirm the nature and the position of the DNA variants. Promoter positions were numbered with respect to the first nucleotide of the first exon as +1 and the nucleotide immediately upstream as –1. The primer sequences and detailed information on the reaction conditions are available upon request.

Electrophoretic mobility shift assay
DNA–protein interactions were assessed by incubating 5' end radiolabelled ds-oligo probes corresponding to each allelic variants (top strand sequences for the binding sites are listed in Table 1) in the presence of HeLa, JEG-3 and HepG2 cell nuclear extracts (60Go) according to the manufacturer's protocol (Promega gel shift assay system). Protein was quantified with the Bradford protein assay (BioRad). Specifically, nuclear extracts (5 µg) were incubated with 35 fmol radiolabelled double-stranded DNA probes and with a buffer containing 50 mM Tris–HCl (pH 7.5), 5 mM MgCl2, 2.5 mM EDTA, 2.5 mM DTT, 250 mM NaCl, 0.25 µg/µl poly (deoxyinosinate-deoxycytidylate) and 20% glycerol in a total volume of 10 µl, for 20 min at room temperature. Complexes were separated in a non-denaturing polyacrylamide gel (6%, acrylamide:bisacrylamide, 60:1) in 0.5x Tris–borate–EDTA buffer (190 V at 4°C). For competition EMSA, 50-fold molar excess competitors (excess of unlabelled probe oligonucleotide or corresponding mutant oligonucleotide) were included as described in Fig. 1.

Gene reporter assay
Constructs
Allele/haplotype-specific fragments corresponding to ~2.0 kb of the proximal promoter region were amplified from genomic DNA of known heterozygotes and cloned into the promoterless pGL3-basic firefly luciferase reporter vector (Promega). Constructs corresponding to the major haplotypes were sequenced to confirm the presence of the expected polymorphic sites and then purified on QIAquick PCR purification columns prior transfection.

Transfection
Approximately 3–4x104 cells (JEG-3, HeLa and HepG2) were plated-out and grown in 96-well plates (30 mm2) to reach 80–90% confluent at the time of transfection with lipofectamine according to the manufacturer's protocol (Invitrogen). These cell lines were co-transfected with 100 ng of each allelic construct and 0.5 ng of SV40-driven (ratio 200 : 1) Renilla luciferase pRL-SV40 plasmid to control for transfection efficiency. Similar experiments were performed with a negative control consisting of the empty promoterless pGL3-basic plasmid (Promega). Transfected cells were harvested 48 h following transfection and luciferase reporter gene activity was measured with the Dual-Luciferase Reporter Assay System according to the manufacturer's instructions (Promega), in a Spectra Max 190 luminometer (Molecular Devices). The Renilla luciferase activity of the control pRL-SV40 was used to normalize the results of the firefly luciferase activity of the allelic constructs. After background correction (subtraction of the activity in untreated cells), the results were expressed as the ratio of firefly luciferase activity divided by the pRL-CMV internal control pRL-SV40 activity and expressed as relative luciferase (means±SD) of five replicates. Statistical significance (P-value) was determined using unpaired Student's t-test.

In silico prediction of putative TFBSs
We used the MatInspector program from Genomatix Software GmbH (Germany) to determine the presence of putative binding sites for known transcription factors. The predicted gain and/or loss of a putative TFBS due to a given SNP was assessed by the optimized matrix threshold as defined for each individual matrix in the MatInspector program. For each SNP, major and minor alleles, together with 50 bp of surrounding sequence, were sent for analysis using the optimized matrix similarity thresholds.


   SUPPLEMENTARY MATERIAL
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
REFERENCES
 
Supplementary Material is available at HMG Online.


   ACKNOWLEDGEMENTS
 
This study was supported by research funds provided by Genome Quebec and Genome Canada. T.J.H. is supported by a Clinician-Scientist Award in Translational Research by the Burroughs Wellcome Fund and by an Investigator Award from the Canadian Institutes of Health Research. D.S. holds the François-Karl Viau Chair in Pediatric Oncogenomics and is a scholar of the Fonds de la Recherche en Santé du Québec (FRSQ).

Conflict of Interest statement. None declared.


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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 

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