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Human Molecular Genetics Advance Access originally published online on February 19, 2007
Human Molecular Genetics 2007 16(6):630-639; doi:10.1093/hmg/ddm005
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Functional SNP in an Sp1-binding site of AGTRL1 gene is associated with susceptibility to brain infarction

Jun Hata1,2,3, Koichi Matsuda3,*, Toshiharu Ninomiya1,2, Koji Yonemoto1, Tomonaga Matsushita2,3, Yozo Ohnishi4, Susumu Saito4, Takanari Kitazono2, Setsuro Ibayashi2, Mitsuo Iida2, Yutaka Kiyohara1, Yusuke Nakamura3 and Michiaki Kubo1,2,3

1 Department of Environmental Medicine and 2 Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan, 3 Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato, Tokyo 108-8639, Japan and 4 Laboratory for Genotyping, SNP Research Center, The Institute of Physical and Chemical Research (RIKEN), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

* To whom correspondence should be addressed at: Tel: +81 354495376; Fax: +81 354495123; Email: koichima{at}ims.u-tokyo.ac.jp

Received January 24, 2007; Accepted January 26, 2007


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Brain infarction is one of the common causes of death and also a major cause of severe disability. To identify a gene(s) susceptible to brain infarction, we performed a large-scale association study of Japanese patients with brain infarction, using 52608 gene-based single nucleotide polymorphism (SNP) markers. Comparison of allele frequencies between 1112 cases with brain infarction and age- and sex-matched control subjects of the same number found an SNP in the 5'-flanking region of angiotensin receptor like-1 (AGTRL1) gene (rs9943582, – 154G/A) to have a significant association with brain infarction [odds ratio = 1.30, 95% confidence interval (CI) = 1.14–1.47, P = 0.000066]. We also found the binding of Sp1 transcription factor to the region including the susceptible G allele, but not the non-susceptible A allele. Luciferase assay and RT–PCR analysis demonstrated that exogenously introduced Sp1 induced transcription of AGTRL1 and its ligand, apelin, as well, indicating direct regulation of apelin/APJ pathway by Sp1. Furthermore, a 14 year follow-up cohort study in a Japanese community in Hisayama town, Japan revealed that the homozygote of the susceptible G allele of this particular SNP had significantly higher risk of brain infarction (hazard ratio = 2.00, 95% CI = 1.22–3.29, P = 0.006). Our results indicate that the SNP in the AGTRL1 gene is associated with the susceptibility to brain infarction.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Stroke is one of the leading causes of death as well as severe physical disability and cognitive dysfunction. In Japan, the mortality rate from stroke has decreased significantly in the last three decades as a result of improvement in medicine and public health, but the incidence of stroke remained still high, especially in the elderly (1). Since we are facing a rapid increase of the elderly population, prevention and better treatment of stroke are becoming more important. For such purpose, identification of genetic and environmental risk factors for stroke is one of the critical steps. Stroke is classified into three major types, namely brain infarction, brain haemorrhage and subarachnoid haemorrhage. Brain infarction is the most common type of stroke and usually occurs because of atherosclerosis of small or large arteries in the brain or because of thromboembolism developed in the heart. Hypertension, diabetes, dyslipidaemia and smoking are well-known risk factors for brain infarction (2,3). In addition, case–control and cohort studies have indicated that a positive family history is a risk factor for brain infarction (4), suggesting involvement of genetic components in the aetiology. However, genes susceptible to brain infarction are still not well understood.

To clarify genetic factors that increase the risk of brain infarction, a number of candidate genes involved in haemostasis, renin angiotensin system and lipid metabolism have been investigated, but their associations with brain infarction are still controversial (5). Recently, genome-wide approach has been applied to screen genes that were involved in complex traits, and two novel genes, PDE4D and ALOX5AP, were identified to be associated with brain infarction in the Icelandic population (6,7). Through a large-scale case–control association study in a Japanese population, using 52608 gene-based single nucleotide polymorphism (SNP) markers, we recently reported non-synonymous SNP (Val374Ile) in protein kinase C-eta (PRKCH) to be associated with the susceptibility to brain infarction (8). In the present study, using the same approach, we demonstrate that a functional SNP in the 5'-flanking region (SNP30, rs9943582, – 154G/A) of angiotensin receptor like-1 gene (AGTRL1) is significantly associated with brain infarction.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Case–control association study
To screen a gene(s) involved in susceptibility to brain infarction, we performed a large-scale association study using gene-based SNPs in a step-wise manner. We enrolled 1112 Japanese subjects with brain infarction as well as 1112 age- and sex-matched control subjects in this study. All case subjects were diagnosed by stroke neurologists on the basis of clinical information and brain imaging. First, we genotyped 52608 gene-based tag-SNPs selected from JSNP database (9), using 188 cases and 188 controls, and identified 1098 SNPs that revealed P-values of 0.01 or smaller in a comparison of allele frequencies. These 1098 SNPs were further genotyped for the remaining 924 cases and 924 controls. Among these SNPs, SNP32 (rs948847) showed a P-value of 0.0061 in the first-step screening, and a P-value of 0.0011 in the second screening. Statistical analysis of the combined samples indicated a significant association of SNP32 with brain infarction in the allele frequency model (P = 0.000043). This association remained significant after a permutation test for multiple testing (P = 0.036), suggesting that this SNP was a good candidate marker associated with brain infarction.

Linkage disequilibrium (LD) analysis of the data from 44 unrelated Japanese individuals in the International HapMap Project (10) revealed that the marker SNP32 was located in an LD block spanning 230 kb on chromosome 11q12 (data not shown). This large LD block included five genes, P2RX3, SSRP1, TNKS1BP1, AGTRL1 and LRRC55. To further define the region of interest, we selected 49 tag-SNPs near and within this LD block from the JSNP and the HapMap databases (Supplementary Material, Table S1) and genotyped these SNPs in 1112 cases and 1112 controls. We constructed a pairwise LD map on the basis of genotype data of 48 SNPs that had minor allele frequencies of 0.15 or higher in 1112 case subjects (Fig. 1A). According to Gabriel's criteria (11), the 230 kb LD block could be divided into six small blocks, and SNP32 was located in block 4. When we compared the allele frequencies of 49 genotyped SNPs, we found that SNP25 in block 3 showed the strongest association with brain infarction (P = 0.000011). Among the 10 SNPs in block 3 (SNP16 to SNP25), six SNPs (SNP20–SNP25), including SNP25, were absolutely linked (D' > 0.99, r2 > 0.96) and located in the intergenic region between the two genes, TNKS1BP1 and AGTRL1, where no putative gene was predicted by the GENSCAN program. Other four SNPs in block 3 were located in the TNKS1BP1 gene (SNP16–SNP18) or in its 3'-flanking region (SNP19), but their associations with brain infarction were less significant (P = 0.0017–0.0058). Therefore, we excluded these SNPs for further functional analysis and focused on SNPs in block 4, where only AGTRL1 gene is present. Since we previously identified 10 SNPs (SNP27–SNP36) in the AGTRL1 locus by direct sequencing of a region from 2 kb upstream to the last exon, using 48 Japanese individuals (Fig. 2A) (12), we additionally genotyped these SNPs for our case–control subjects (Table 1). Among 11 SNPs in block 4, SNP29 showed the strongest association with brain infarction (P = 0.000037) when we compared allele frequencies between cases and controls (Fig. 1B and Table 1). A detailed LD analysis indicated that five SNPs in block 4 (SNP26, SNP28, SNP29, SNP30 and SNP32) were almost absolutely linked to each other (D' > 0.96, r2 > 0.87) and showed similar P-values with brain infarction as that of SNP29. Of these five SNPs, four SNPs (SNP28, SNP29, SNP30 and SNP32) were located in the AGTRL1 gene. APJ, the product of AGTRL1, is a G protein-coupled receptor and had been reported to be expressed in the cardiovascular and the central nervous system (13–15). Apelin (APLN), the endogenous ligand of APJ, had been reported to have some function in the control of blood pressure (16,17). Therefore, we considered that AGTRL1 might be a good candidate gene related to brain infarction.


Figure 1
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  		Figure 1. Case–control association study and LD map. (A) Pairwise LD map around AGTRL1 gene, as measured by D' (lower left triangle) and r2 (upper right triangle) in the case subjects with brain infarction. Among 49 genotyped SNPs, SNP14 was excluded because of minor allele frequency <15%. An LD block spanning 230 kb defined by the Japanese data from the HapMap Project (SNP06–SNP54) was divided into six blocks according to Gabriel's criteria (11). The marker SNP32 was located in block 4. (B) Case–control association plots for 57 genotyped SNPs. In each SNP, allele frequency between 1112 cases and 1112 control subjects was compared using chi-square test, and –log10 P-values are plotted. Blue dots indicate six SNPs in block 3 that revealed the highest association and are located in the intergenic region between TNKS1BP1 and AGTRL1 (SNP20–SNP25). Green dots indicate other four SNPs in block 3 that showed less significant association and are located in TNKS1BP1 gene (SNP16–SNP18) and in its 3'-flanking region (SNP19). Red dots indicate 11 SNPs in block 4 that are located in the intergenic region (SNP26) and in AGTRL1 gene (SNP27–SNP36). All genotype data were evaluated Hardy–Weinberg equilibrium and no significant deviation (P > 0.01) was found.

 


Figure 2
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  		Figure 2. Genomic structure and expression of AGTRL1 gene. (A) Alternative splicing variants and SNPs of AGTRL1 gene (3.8 kb of V1, NM_005161.3 and 1.8 kb of V2, X89271.1 in GenBank database). ATG indicates the initiation codon. TAG indicates the stop codon. (B) Multiple tissue northern blotting for human normal tissues. Arrows show two splicing variants of AGTRL1.

 


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  		Table 1. Case–control association study of 10 SNPs in AGTRL1 gene

 
Expression of AGTRL1 mRNA in human normal organs
We subsequently examined the expression of AGTRL1 in normal human tissues by northern blot analysis and found a high level of a 3.8 kb AGTRL1 transcript (V1 mRNA) and a less amount of a 1.8 kb transcript (V2 mRNA) in heart, placenta, spleen and spinal cord (Fig. 2B). These results indicated that AGTRL1 was highly expressed in the cardiovascular system and the central nervous system consistent with the previous reports (13–15). Although two splicing variants of AGTRL1 transcript were registered in the GenBank database of the National Center of Biotechnology Information (NCBI) (Fig. 2A), we considered the longer transcript (V1, 3.8 kb) to be the major transcript in normal human tissues.

Sp1 regulates transcription of AGTRL1 at SNP30
Among the 10 SNPs genotyped in AGTRL1, four SNPs, three in the 5'-flanking region (SNP28–SNP30) and one in the coding region (SNP32), showed the strong association with brain infarction. Since SNP32 in the coding region was synonymous (Gly45Gly), we considered that one or a combination of these SNPs might have functional significance to alter the quantity of APJ, the gene product of AGTRL1.

We prepared 32P-labelled oligonucleotide probes corresponding to each allele of these four candidate polymorphism loci and performed the gel-shift assay using nuclear extract of SBC-3 cells, in which AGTRL1 was expressed abundantly. The gel-shift assay demonstrated a shifted band of a DNA– protein complex, with a very strong intensity in a lane corresponding to the G allele of SNP30 (rs9943582, – 154G/A), whereas that to the A allele was very weak (Fig. 3A); the G allele was considered to be the risk allele and the A allele to be the protective allele in the association study [Table 1, odds ratio = 1.30, 95% confidence interval (CI) = 1.14–1.47, P = 0.000066]. The competition assay with the unlabelled oligonucleotides demonstrated that the self (G allele) oligonucleotide inhibited DNA–protein complex formation in a dose-dependent manner but the non-self oligonucleotide (A allele) did not (Fig. 3B), suggesting that some nuclear protein(s) specifically bound to the DNA fragment corresponding to the G allele. The computer simulation using MATCH program based on TRANSFAC database indicated that Sp1 transcription factor was likely to bind to the G allele of SNP30 (Fig. 3C). Unlabelled Sp1-binding consensus oligonucleotide also inhibited the formation of the DNA–protein complex (Fig. 3B). Moreover, when we added anti-Sp1 antibody to the mixture, the band was further shifted to a higher molecular position, indicating the specific binding of the Sp1 protein to the at-risk G allele of SNP30 (Fig. 3B). We also carried out the chromatin immunoprecipitation (ChIP) assay, using HEK293T cells, which were found to be the heterozygote at SNP30. We transfected HEK293T cells with HA-tagged Sp1 expression vector (pCAGGS-Sp1-HA) and then DNA–protein complex were precipitated using anti-HA antibody. Subsequent PCR experiments indicated that Sp1 bound to a genomic fragment corresponding to SNP30 in vivo (Fig. 3D). To evaluate allele-specific binding of Sp1 in the ChIP assay, we subcloned the PCR product from anti-HA ChIP sample into pCR2.1-TOPO vector and transformed Escherichia coli competent cells. Then, we genotyped SNP30 in 20 colonies of these transformed cells and found all colonies had the G allele, indicating the specific binding of Sp1 to the genomic region including the G allele, but not the A allele. Furthermore, we found that introduction of Sp1 expression vector to HEK293T cells remarkably induced transcription of AGTRL1 mRNA (Fig. 3E).


Figure 3
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  		Figure 3. Sp1 regulates transcription of AGTRL1 gene at the G allele of SNP30. (A) Gel-shift assay using end-labelled 25 bp probes around each allele of four SNPs of AGTRL1 gene and nuclear extract of SBC-3 cells. A solid arrow indicates the shifted band that shows tighter binding of a nuclear factor to the G allele of SNP30 than to the A allele. A broken arrow indicates the shifted band that shows tighter binding of a nuclear factor to the A allele of SNP32 than to the C allele. (B) Gel-shift assay using end-labelled probes around the G allele of SNP30 and nuclear extract of SBC-3 cells. DNA–protein complex (solid arrow) was competed by unlabelled oligonucleotide with the G allele but not by oligonucleotide with the A allele. This complex was also strongly competed by unlabelled Sp1-consensus oligonucleotide. When we added anti-Sp1 antibody to the mixture, additional shifted band was observed in a long exposure image (broken arrow). (C) DNA sequences of oligonucleotide used in gel-shift assay. The sequence around the G allele of SNP30 is more similar to GC box (GGGCGG), the Sp1-consensus sequence, than the A allele. The core match scores were calculated using MATCH program. (D) ChIP, using formaldehyde-treated HEK293T cells that were ectopically expressed HA-tagged Sp1 protein. DNA was immunoprecipitated with (anti-HA) or without (no antibody) anti-HA antibody. DNA sample before precipitation was used as a control (input). For each sample, PCR for genomic fragment around SNP30 was performed. PCR for the 3'-flanking region of AGTRL1 was also performed as a negative control that had no putative Sp1-binding site. (E) Exogenously introduced Sp1 induced AGTRL1 mRNA. HEK293T cells were transfected with mock pCAGGS or HA-tagged Sp1 expression vector (pCAGGS-Sp1-HA). RNAs were extracted in various time spans, and semi-quantitative (upper) and real-time quantitative (lower) RT–PCR for AGTRL1 and B2M mRNA were performed. (F) Luciferase assay. Eighty base pair fragments around each allele of SNP30 in the 5'-flanking region of AGTRL1 were inserted into pGL3-basic vector. Luciferase assay was performed using U-2OS cells with co-transfection of mock pCAGGS or pCAGGS-Sp1-HA vector. Luciferase activity is indicated relative to the activity of pGL3-basic vector. Each sample was studied in triplicate and data shown are the mean ± SD (*P < 0.01).

 
To further evaluate the promoter or enhancer activity of the genomic region around this SNP, we performed a luciferase assay using U-2OS cells, which expressed Sp1 at a very low level (18). An 80 bp DNA fragment (–202/–123) including the SNP30 locus was inserted into the pGL3-basic vector. Exogenously introduced Sp1 enhanced the luciferase activity in the cells transfected with the reporter vector containing the G allele, but the enhancement was relatively low in the cells transfected with the A allele vector (Fig. 3F). These findings implied that Sp1 enhanced the transcription of AGTRL1 through the binding to the G allele of SNP30. As Sp1 is abundantly expressed in multiple tissues, the subjects with the disease-susceptible G allele are expected to have higher expression of APJ and might result in the higher activity in the apelin/APJ-signalling pathway.

We also found a DNA–protein complex formation specific to the A-allele oligonucleotide of SNP32 (rs948847, +570A/C) (Fig. 3A), but no transcription factor was predicted to bind to a region corresponding to this SNP. We also constructed luciferase vector containing an 18 bp DNA fragment (+566/+583) corresponding to each allele of SNP32, but no difference in the reporter activities was found (data not shown).

The association of SNP30 with brain infarction susceptibility in the prospective cohort study
We then examined the effect of SNP30 on the incidence of brain infarction, using the data obtained from the population-based prospective cohort study in Hisayama town (1). During a 14 year follow-up period, 67 events of first-ever brain infarction were observed among 1659 subjects without a history of stroke at baseline examination. The 14 year cumulative incidence of brain infarction was 5.58% in the subjects with GG genotype and 2.79% in the other genotypes (GA and AA) (Fig. 4). Age- and sex-adjusted risk of brain infarction was significantly higher in the GG genotype than in the other genotypes (hazard ratio = 2.00, 95% CI = 1.22–3.29, P = 0.006).


Figure 4
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  		Figure 4. Kaplan–Meier estimates of incidence of brain infarction during 14 years of follow-up in the Hisayama study, stratified by the GG genotype and the other genotypes (GA and AA).

 
Transcription of apelin, the ligand of APJ, was also activated by Sp1
Since MATCH program also predicted Sp1-binding motif (GC box) in the 5'-flanking region of APLN gene (Fig. 5A, –147/–142), we examined the effect of Sp1 on APLN transcription. Introduction of Sp1 expression vector to HEK293T cells significantly increased APLN mRNA (Fig. 5B). Furthermore, we constructed a 468 bp DNA fragment (–336/+132) including this putative Sp1-binding site into the upstream of the luciferase gene. The reporter-gene assay by co-transfection of this clone with Sp1 expression vector in U-2OS cells indicated remarkable enhancement of the luciferase activity (Fig. 5C), indicating APLN was also a transcriptional target of Sp1. These results demonstrated that Sp1 is a key regulator of the apelin/APJ-signalling pathway.


Figure 5
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  		Figure 5. Transcriptional regulation of APLN gene by Sp1. (A) Genomic structure of APLN. A putative Sp1-binding site (GC box) was located in the upstream (–147/–142) of the transcription-initiation site based on NM_017413.3. ATG indicates the initiation codon. TGA indicates the stop codon. (B) Exogenously introduced Sp1 induced APLN mRNA. HEK293T cells were transfected with mock pCAGGS or HA-tagged Sp1 expression vector (pCAGGS-Sp1-HA). RNAs were extracted in various time spans, and semi-quantitative (upper) and real-time quantitative (lower) RT–PCR for APLN mRNA and B2M were performed. (C) Luciferase assay. A 468 bp fragment around the GC box of APLN was inserted into pGL3-basic vector. Luciferase assay was performed using U-2OS cells with co-transfection of mock pCAGGS or pCAGGS-Sp1-HA vector. Luciferase activity is indicated relative to the activity of pGL3-basic vector. Each sample was studied in triplicate and data shown are the mean ± SD (*P < 0.01).

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
In spite of the recent advances in medicine, stroke is still one of the leading causes of death as well as severe physical disability. A genome-wide approach is believed to be useful to find novel genes underlying complex traits. In fact, genetic variations in several genes enhancing susceptibility to common diseases such as myocardial infarction (19), IgA nephropathy (20,21), Crohn's disease (22) and osteoarthritis (23) were successfully identified. We recently reported that a non-synonymous SNP in PRKCH gene was associated with susceptibility to brain infarction through the large-scale gene-based SNP analysis (8). Using the same approach, the present study identified an SNP in the 5'-flanking region of the AGTRL1 gene (SNP30, – 154G/A) to be significantly associated with brain infarction. We demonstrated that the genomic fragment including the G allele of this particular SNP had much higher binding affinity to Sp1 transcription factor and higher enhancer activity than that of the A allele. It is also found that the subjects with homozygote of the G allele of SNP30 had higher incidence of brain infarction in the 14 year follow-up study of the population-based cohort. Furthermore, we showed that both AGTRL1 and APLN genes were likely to be regulated by Sp1. From these results, it is reasonable to consider that the apelin/APJ-signalling pathway might be highly activated in the subjects with the G allele of SNP30 through the binding of Sp1 and might contribute to the pathogenesis of brain infarction.

APJ was first identified as a novel orphan G protein-coupled receptor in 1993 (24), and the endogenous ligand for APJ, apelin, was found in 1998 (25). Recent accumulated lines of evidence revealed that the apelin/APJ signalling plays an important role to maintain homeostasis of cardiovascular system. APJ is abundantly expressed in hypothalamus and medulla oblongata, which play key roles in cardiovascular regulation (14–17). It was recently reported that mean arterial pressure was increased after administration of apelin into cerebral ventricle or medulla oblongata in rats (16,17). Furthermore, apelin/APJ signalling was shown to activate the phosphatidylinositol 3-kinase (PI3K)/Akt pathway (26), which was indicated to promote the development of atherosclerotic lesion (27). Thus, we considered that disease-susceptible SNP in the AGTRL1 gene might be associated with the disorder of blood pressure and the progression of atherosclerosis, which are the major causes of brain infarction, although the relationship between this SNP and cardiovascular diseases should be examined in the future studies.

In conclusion, we identified AGTRL1 as a susceptible gene to brain infarction by means of a large-scale SNP screening. A functional SNP in the 5'-flanking region of AGTRL1 gene appears likely to regulate its transcription level through the allele-specific binding of Sp1 transcription factor. We also found that this functional SNP increased the risk of brain infarction in the population-based prospective cohort study. Despite the recent advances in medicine, brain infarction is still one of the major causes of death. Prediction of brain infarction risk by the analysis of AGTRL1 genotypes may be useful for the prevention and primary care of stroke. Although the mechanisms of apelin/APJ signalling in the pathogenesis of brain infarction are still to be elucidated, our finding might contribute to a better understanding of brain infarction in the future.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
SUPPLEMENTARY MATERIAL
 REFERENCES
 
Study populations
For the large-scale case–control association study, we registered patients with brain infarction from seven medical centres in and around Fukuoka City, Japan (Kyushu University Hospital, National Hospital Organization Kyushu Medical Center, National Hospital Organization Fukuoka Higashi Medical Center, Fukuoka Red Cross Hospital, Hakujuji Hospital, Imazu Red Cross Hospital and Seiai Rehabilitation Hospital) in 2004. Details of the registration were described previously (8). In brief, all case subjects were diagnosed by stroke neurologists on the basis of detailed clinical features and brain imaging including computed tomography and magnetic resonance imaging. Control subjects were enrolled from the participants of the Hisayama study, a population-based cohort study for cardiovascular diseases in Hisayama Town, started in 1961. Details of this study have been described previously (1,3,8). Between 2002 and 2003, we performed a screening examination for Hisayama residents, and 3328 individuals of 40 years or higher (78% of the total population of this age group) participated in the examination. After excluding the subjects with a history of stroke or coronary heart disease, we randomly selected age- and sex-matched control subjects by 1:1, using random numbers. Mean age ± SD was 70 ± 10 years and 60.7% of subjects were male in both case and control groups.

For the prospective cohort study, we used a cohort population of the Hisayama study established in 1988 (1,8). In this cohort, 2637 Hisayama residents aged 40 years or over without a history of stroke or coronary heart disease were enrolled in 1988 and continuously followed up for 14 years until the occurrence of cardiovascular diseases or death. Among them, the 1683 subjects participated in the examination between 2002 and 2003 were used in the present study. Mean age ± SD at baseline was 56 ± 10 years and 40.3% of subjects were male in this cohort.

All subjects were Japanese and provided written informed consent to participate in the study. This study was approved by human ethics committees of Graduate School of Medical Sciences, Kyushu University and Institute of Medical Science, the University of Tokyo.

SNP genotyping
We extracted genomic DNA from peripheral blood leukocytes by a standard method. We genotyped SNPs, using the multiplex PCR-based invader assay (Third Wave Technologies), as described previously (28), or TaqMan SNP genotyping assays (Applied Biosystems).

Northern blotting
cDNA probe was constructed from full-length coding region of AGTRL1 gene. The probe was labelled with [{alpha}-32P]-CTP (GE Healthcare), using Megaprime DNA labelling systems (GE Healthcare), and hybridized with Human Multiple Tissue Northern Blot I, II and III membranes (Takara Bio), using a standard protocol.

Cell culture
Human lung cancer SBC-3 cells were grown in RPMI medium 1640 (Invitrogen) with 10% fetal bovine serum (FBS). Human embryonic kidney fibroblasts HEK293T were grown in Dulbecco's modified Eagle's medium (Invitrogen) with 10% FBS. Human osteosarcoma U-2OS cells were grown in McCoy's 5a medium (Invitrogen) with 15% FBS. All cells were incubated in a humidified atmosphere with 5% CO2 at 37°C.

Gel-shift assay
The sequence of each probe is listed in Supplementary Material, Table S2. Oligonucleotides were annealed and end-labelled with [{gamma}-32P]-ATP (GE Healthcare), using T4-polynucleotide kinase (Toyobo). We prepared nuclear extract from SBC-3 cells, as described previously (29). Ten micrograms of nuclear extract was incubated for 30 min at room temperature with 500 000 c.p.m. of labelled probe in a reaction mixture of 15 mM Tris–HCl (pH 7.5), 6.5% glycerol, 50 mM KCl, 0.7 mM EDTA-2Na (pH 8.0), 0.2 mM dithiothreitol, 0.1% bovine serum albumin, 1 µg of poly(dI–dC) and 0.1 µg of salmon sperm DNA. For competition assays, 10-, 30- or 100-fold molar excess of unlabelled oligonucleotide was added and incubated for another 30 min at room temperature. For supershift assay, 2 µg of goat polyclonal anti-human Sp1 antibody (SantaCruz, sc-59X) was added and incubated for another 30 min at room temperature. The mixture was subjected to electrophoresis on a 4% polyacrylamide gel in 0.5 x Tris–Borate–EDTA buffer. The gel was dried up before exposure to X-ray film.

ChIP assay
We subcloned the full-length human Sp1 cDNA with HA tag in the C-terminus into pCAGGS expression vector (pCAGGS-Sp1-HA). HEK293T cells were transfected with the vector using FuGENE 6 Transfection Reagent (Roche). Forty-eight hours later, cells were treated with 1% formaldehyde and immunoprecipitated by rabbit polyclonal anti-HA antibody (SantaCruz, sc-805), using Chromatin Immunoprecipitation Assay Kit (Upstate) according to the manufacturer's protocol. We also performed the same protocol without antibody. Precipitated DNAs were analysed via PCR using primer pairs listed in Supplementary Material, Table S3. To evaluate allele-specific binding of Sp1, we subcloned the PCR product from anti-HA ChIP sample into pCR2.1-TOPO vector (Invitrogen) and transformed E. coli competent cells (DH10B strain) with this vector by electroporation. Then we incubated these E. coli on a Luria Bertani agar plate overnight and picked up 20 colonies from the plate. For each colony, we performed PCR, using the same primer sets, and genotyped SNP30 by direct sequencing, using ABI 3700 (Applied Biosystems).

Semi-quantitative RT–PCR and real-time quantitative RT–PCR
We transfected pCAGGS-Sp1-HA or mock pCAGGS vector to HEK293T cells. Total RNAs were collected in various time spans, using RNeasy Mini Kit and RNase-free DNase Set (Qiagen). cDNAs were synthesized by SuperScript II Reverse Transcriptase (Invitrogen). Expression levels of human AGTRL1, APLN and housekeeping gene B2M were determined by semi-quantitative RT–PCR, using primer pairs listed in Supplementary Material, Table S3. For real-time quantitative RT–PCR, cDNAs were amplified using SYBR Premix ExTaq (Takara Bio) and analysed by ABI PRISM 7700 (Applied Biosystems), using primer pairs listed in Supplementary Material, Table S3. The normalized amount of AGTRL1 or APLN expression was obtained by dividing the AGTRL1 or APLN value, respectively, by the B2M value.

Luciferase assay
DNA fragment corresponding to –202 to –123 of AGTRL1, including either allele of SNP30 and DNA fragment corresponding to –336 to +132 of APLN were subcloned into pGL3-basic luciferase vector (Promega). We transfected U-2OS cells with 90 ng of each reporter construct, 10 ng of pRL-TK vector (Promega) and 100 ng of either pCAGGS-Sp1-HA or mock pCAGGS vector, using FuGENE 6 Transfection Reagent (Roche). After 48 h, we collected the cells and measured luciferase activities, using Dual Luciferase Assay System (Toyo B-Net).

Statistical analysis
We assessed case–control association and Hardy–Weinberg equilibrium by chi-square test and Fisher's exact test. For adjustment of multiple testing, we performed a random permutation test with 10 000 replications, using MULTTEST procedure of SAS software version 9.1.2 (SAS Institute). LD coefficients (D' and r2) were calculated using the expectation-maximization algorithm, and haplotype blocks were defined by Gabriel's criteria (11), using Haploview version 3.32 (Broad Institute). In the prospective cohort study, the cumulative incidence of brain infarction was estimated by Kaplan–Meier product limit method, and age- and sex-adjusted hazard ratio and its 95% CI were estimated by Cox proportional hazards model using SAS software. The relative luciferase activities were compared using t-test.

URLs
The JSNP database can be found at http://snp.ims.u-tokyo.ac.jp/. The International HapMap Project can be found at http://www.hapmap.org/. The dbSNP and GenBank databases provided by the NCBI of the USA can be found at http://www.ncbi.nlm.nih.gov/. The Haploview software (Broad Institute) can be found at http://www.broad.mit.edu/mpg/haploview/. The GENSCAN program (Stanford University) can be found at http://genes.mit.edu/GENSCAN.html. The MATCH program (Biobase) can be found at http://www.gene-regulation.com/.


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


   ACKNOWLEDGEMENTS
 
We thank all the participated physicians and staff in the following hospitals for collecting subjects with brain infarction: Kyushu University Hospital, National Hospital Organization Kyushu Medical Center, National Hospital Organization Fukuoka Higashi Medical Center, Fukuoka Red Cross Hospital, Hakujuji Hospital, Imazu Red Cross Hospital and Seiai Rehabilitation Hospital. This study was supported in part by a grant from the Special Coordination Fund for Promoting Science to M.I. and a grant from the Technology and Innovative Development Project in Life Sciences (Ministry of Education, Culture, Sports, Science and Technology of Japan) to Y.K.

Conflict of Interest statement. None declared.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 

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