0021-972X/07/$15.00/0 The Journal of Clinical Endocrinology & Metabolism 92(3):1145–1154 Printed in U.S.A. Copyright © 2007 by The Endocrine Society doi: 10.1210/jc.2006-1808

Functional Single-Nucleotide Polymorphisms in the Secretogranin III (SCG3) Gene that Form Secretory Granules with Appetite-Related Neuropeptides Are Associated with Obesity

Atsushi Tanabe, Takahiro Yanagiya, Aritoshi Iida, Susumu Saito, Akihiro Sekine, Atsushi Takahashi, Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021 Takahiro Nakamura, Tatsuhiko Tsunoda, Seika Kamohara, Yoshio Nakata, Kazuaki Kotani, Ryoya Komatsu, Naoto Itoh, Ikuo Mineo, Jun Wada, Tohru Funahashi, Shigeru Miyazaki, Katsuto Tokunaga, Kazuyuki Hamaguchi, Tatsuo Shimada, Kiyoji Tanaka, Kentaro Yamada, Toshiaki Hanafusa, Shinichi Oikawa, Hironobu Yoshimatsu, Toshiie Sakata, Yuji Matsuzawa, Naoyuki Kamatani, Yusuke Nakamura, and Kikuko Hotta Laboratories for Obesity (A.Tan., T.Y., K.Ho.), Pharmacogenetics (A.I.), SNP Analysis (S.S.), SNP Genotyping (A.S.), Statistical Analysis (A.Tak., T.N., N.K.), and Medical Informatics (T.T.), SNP Research Center, RIKEN, Kanagawa 230-0045, Japan; Medicine and Health Science Institute (S.K.), Tokyo Medical University, Tokyo 101-0062, Japan; Institute of Health and Sport Sciences (Y.Nakat., K.T.), University of Tsukuba, Ibaraki 305-8574, Japan; Department of Internal Medicine and Molecular Science (K.K., T.F., Y.M.), Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan; Rinku General Medical Center (R.K.), Osaka 598-8577, Japan; Toyonaka Municipal Hospital (N.I.), Osaka 560-8565, Japan; Otemae Hospital (I.M.), Osaka 540-0008, Japan; Department of Medicine and Clinical Science (J.W.), Okayama University Graduate School of Medicine and Dentistry, Okayama 700-8558, Japan; Tokyo Postal Services Agency Hospital (S.M.), Tokyo 102-8798, Japan; Itami City Hospital (K.T.), Hyogo 664-8540, Japan; Department of Community Health and Gerontological Nursing (K.Ha.), Faculty of Medicine, Department of Health Sciences (T.S.), School of Nursing, and Department of Anatomy, Biology, and Medicine (H.Y., T.S.), Faculty of Medicine, Oita University, Oita 879-5593, Japan; Division of Endocrinology and Metabolism, Department of Medicine (K.Y.), Kurume University, Fukuoka 830-0011, Japan; First Department of Internal Medicine (T.H.), Osaka Medical College, Osaka 569-8686, Japan; Division of Endocrinology and Metabolism (S.O.), Department of Medicine, Nippon Medical School, Tokyo 113-8603, Japan; and Laboratory for Molecular Medicine (Y.Nakam.), Human Genome Center, The Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan

Context: Genetic factors are important for the development of obe- Results: Twelve SNPs in the SCG3 gene including rs3764220 were sity. However, the genetic background of obesity still remains unclear. in almost complete linkage disequilibrium and significantly associ- ated with an obesity phenotype. Two SNPs (rs16964465, rs16964476) Objective: Our objective was to search for obesity-related genes using affected the transcriptional activity of SCG3, and subjects with the a large number of gene-based single-nucleotide polymorphisms (SNPs). minor allele seemed to be resistant to obesity (odds ratio, 9.23; 95% confidence interval, 2.77–30.80; ␹2 ϭ 19.2; P ϭ 0.0000067). SCG3 Design and Setting: We conducted case-control association analyses mRNA and immunoreactivity were detected in the paraventricular using 94 obese patients and 658 controls with 62,663 SNPs selected nucleus, lateral hypothalamic area, and arcuate nucleus, and the from the SNP database. SNPs that possessed P Յ 0.02 were further protein coexisted with orexin, melanin-concentrating hormone, neu- analyzed using 796 obese and 711 control subjects. One SNP ropeptide Y, and proopiomelanocortin. SCG3 formed a granule-like (rs3764220) in the secretogranin III (SCG3) gene showed the lowest structure together with these neuropeptides. P value (P ϭ 0.0000019). We sequenced an approximately 300-kb genomic region around rs3764220 and discovered SNPs for haplotype Conclusions: Genetic variations in the SCG3 gene may influence the analyses. SCG3 was the only gene within a haplotype block that risk of obesity through possible regulation of hypothalamic neuropep- contained rs3764220. The functions of SCG3 were studied. tide secretion. (J Clin Endocrinol Metab 92: 1145–1154, 2007)

Patients: Obese subjects (body mass index Ն 30 kg/m2,nϭ 890) and control subjects (general population; n ϭ 658, body mass index Յ 25kg/m2;nϭ 711) were recruited for this study.

BESITY HAS BECOME one of the major issues in nation of these dysfunctions is now defined as the meta- O public health, medicine, and the economy (1). In bolic syndrome (2), which significantly increases the risk particular, visceral obesity is considered to be important of cardiovascular disease. Adipose tissue secretes various due to its relation to various complications such as dia- adipokines, and an increase in adipose tissue mass affects betes mellitus, dyslipidemia, and hypertension. A combi-

First Published Online January 2, 2007 ropeptide Y; POMC, proopiomelanocortin; PVN, paraventricular nucleus; Abbreviations: ARC, Arcuate nucleus; BMI, body mass index; CHG, SFA, sc fat area; SNP, single-nucleotide polymorphism; VFA, visceral fat area. chromogranin; CI, confidence interval; IMS, Institute of Medical Science; JCEM is published monthly by The Endocrine Society (http://www.endo- JST, Japan Science and Technology; LD, linkage disequilibrium; LHA, lat- society.org), the foremost professional society serving the endocrine eral hypothalamic area; MCH, melanin-concentrating hormone; NPY, neu- community.

1145 1146 J Clin Endocrinol Metab, March 2007, 92(3):1145–1154 Tanabe et al. • SNPs in SCG3 Gene and Obesity the level of adipokines, resulting in the development of Subjects and Methods dyslipidemia, hypertension, and insulin resistance (3, 4). Subjects Both genetic and environmental factors contribute to the The sample size of the first set of Japanese obese subjects (BMI Ն 30 development of obesity. In epidemiological studies, herita- kg/m2) was 94 (case 1; male to female ratio 39:55; age 47 Ϯ 17 yr; BMI bility of body weight is estimated to be approximately 70% 36.3 Ϯ 5.0 kg/m2). The sample size of the first set of control individuals (5, 6). Genetic studies in mice suggested that mutations in (control 1) was 658 and consisted of the Japanese general population as several genes, such as leptin, proopiomelanocortin (POMC), described in JSNP database [Institute of Medical Science (IMS)-Japan and melanocortin-4 receptor, were implicated in a mono- Science and Technology (JST) Agency Japanese SNP database] (8). The sample size of the second set of Japanese obese subjects (BMI Ն 30 genic form of inherited obesity, whereas mutations in such kg/m2) was 796 (case 2; male to female ratio 379:417; age 49 Ϯ 14 yr; BMI genes were also reported in human subjects with obesity (6, 34.3 Ϯ 5.5 kg/m2), whereas that of the second set of Japanese normal- 7). However, the most prevalent MC4R gene mutations have weight controls (BMI Յ 25 kg/m2) was 711 (control 2; male to female Ϯ Ϯ 2 been found in only 3–5% of obese patients with a body mass ratio 267:444; age 52 16 yr; BMI 21.6 2.2 kg/m ). Secondary obesity 2 and obesity-related Mendelian disorders were excluded in this study. Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021 index (BMI) of more than 40 kg/m . In general, the vast Patients with obesity caused by medications were also excluded. Control majority of obesity is considered to be caused by a polygenic 2 subjects were Japanese normal-weight volunteers collected from sub- disorder, and its genetic susceptibility is likely to differ jects who had undergone a medical examination for common disease among various ethnic groups (6, 7). A large number of manu- screening. We further collected 403 Japanese subjects with various BMIs Ϯ Ϯ scripts concerning obesity-related genes have been reported [male to female ratio 144:259 females; age 48 12 yr; BMI 29.7 7.0 kg/m2; visceral fat area (VFA) 126 Ϯ 81 cm2; sc fat area (SFA) 248 Ϯ 117 (7). However, because there are also many papers reporting cm2] who agreed to undergo computed tomography examinations to controversial results at these candidate loci, the genetic back- measure the VFA and SFA. All subjects except control 1 were newly ground of obesity still remains unclear. recruited for this study. Written informed consent was obtained from As one of the Japanese Millennium Projects, a large-scale each subject, and the protocol was approved by the ethics committee of collaborative effort performed a search for gene-based sin- each institution and that of RIKEN. gle-nucleotide polymorphisms (SNPs) in a group of Japanese subjects and discovered approximately 190,000 genetic vari- DNA preparation and SNP genotyping ations (JSNP database) (8), and subsequently our center de- Genomic DNA was prepared from each blood sample according to veloped a high-throughput SNP genotyping system that uses standard protocols. Approximately 100,000 Invader probes (Third Wave a combination of multiplex PCR and the Invader assay (9, 10) Technologies, Madison, WI) could be made for SNPs of IMS-JST (8), and to effectively determine these variations’ frequencies in the the SNPs were genotyped in case 1 by Invader assays as described Japanese population. We performed an association study previously (9, 17). Genotype and allele frequencies of these SNPs were compared with control 1. The SNPs selected by association study using using a large number of SNPs selected from the JSNP data- case 1 and control 1 were submitted for further examination using base (62,663 SNPs in 11,932 genes, covering approximately independent case 2 and control 2 groups. 30% of the human genome) by genotyping Japanese obese and lean subjects and found that one SNP (SNP-1, rs3764220) SNP discovery in around SNP-1 showed the smallest P value and was significantly associated with obesity. This SNP existed in the 5Ј-flanking region of the To identify additional variations in the genomic region around secretogranin III (SCG3) gene. SCG3 belongs to a family of SNP-1, we generated a reference sequence of approximately 300 kb by acidic secretory proteins, known as granins, which are assembling the relevant regions from the sequences with GenBank ac- cession no. AC066613, AC020892, AC026770, and AC090971. We am- widely expressed in endocrine and neuronal cells (11). SCG3 plified appropriate fragments of genomic DNA by PCR and sequenced has been cloned from brain- and pituitary-specific mRNA the products to identify SNPs within 300 kb genomic region using and is expressed in the paraventricular nucleus (PVN) of the previously described methods (10, 18). hypothalamus (12), which is known to be an important re- gion for appetite regulation. SCG3 is also expressed in pan- Cell culture creatic ␤-cells and participates in insulin secretion together with chromogranin (CHG) A (13). Interestingly, SCG3 is lo- SH-SY5Y and BE(2)-C neuroblastoma cells and HIT-T15 cells were purchased from the American Type Culture Collection (Manassas, VA). cated on chromosome 15q21, on which association with obe- Cells were cultured in advanced DMEM (Invitrogen, Carlsbad, CA) with sity has been previously indicated (14). Data from the Fra- 2mm glutamine, 5% fetal bovine serum, 100 U/ml penicillin, and 100 mingham Heart Study suggested a moderate linkage of the ␮g/ml streptomycin. metabolic syndrome to this general region on chromosome 15q (15) on which the presence of a susceptibility gene for Luciferase assay type 2 diabetes in the Japanese population has also been indicated (16). We synthesized double-stranded oligonucleotides containing either In the present study, we demonstrate a significant asso- a single copy or four concatenated copies of either the major or minor allele for a 19-bp region centered on SNP-1, SNP-2, SNP-5, SNP-9, ciation between functional SNPs in the SCG3 gene and obe- SNP-11, or SNP-12 (Fig. 1B), with an NheI restriction site at the 5Ј end sity. We found that SCG3 was expressed together with ap- and an XboI restriction site at the 3Ј end. We constructed luciferase petite-regulating peptides such as orexin and melanin- reporter plasmids by cloning the oligonucleotides into the pGL3-pro- concentrating hormone (MCH) in the lateral hypothalamic moter vector (Promega, Madison, WI) upstream of the Simian virus 40 promoter. pGL3-promoter vectors containing oligonucleotides were area (LHA) and neuropeptide Y (NPY) and POMC in the transfected into SH-SY5Y neuroblastoma cells together with the arcuate nucleus (ARC), suggesting that SCG3 is a good can- phRL-TK vector (Promega), an internal control for transfection effi- didate as an obesity-related gene. ciency, using lipofectamine 2000 reagent (Invitrogen). After 24 h, we Tanabe et al. • SNPs in SCG3 Gene and Obesity J Clin Endocrinol Metab, March 2007, 92(3):1145–1154 1147 Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021

FIG. 1. LD mapping, polymorphisms, and P values identified around the SCG3 gene. A, LD mapping around the SCG3 gene. LD coefficients (⌬) between every pair of SNPs around SNP-1 (rs3764220, Ϫ1492A3G) were calculated. Minor allele frequencies of all SNPs used in this analysis are greater than 10%. Genomic structure is shown at the bottom. SNP rs2124879, SNP-1, SNP-29, and SNP-40 are indicated. B, Genetic variations and P values in the SCG3 gene. #, SNP-1; *, insertion/deletion polymorphisms; †, SNPs analyzed in the first screening; no symbol, SNPs identified in the extensive search of the gene’s genomic sequence. P values are represented as Ϫlogarithm of P values of genotype mode. Each SNP is labeled with its rs number, except for novel SNPs, which are indicated by JSNP ID (ssj0011008-0011013). collected the cells and measured luciferase activity with the dual-lucif- mg/kg), mice were perfused with 10% neutral buffered formalin. The erase reporter assay system (Promega). hypothalamic region was dissected from the brain, further fixed with tissue fixative (Genostaff, Tokyo, Japan), embedded in paraffin, and Gel-shift assay sectioned. Tissue sections (4 ␮m) were dewaxed and incubated at 4 C overnight with polyclonal goat anti-SCG3 (1:200; Santa Cruz Biotech- We prepared nuclear extract from SH-SY5Y cells using NE-PER ex- nology, Santa Cruz, CA) together with either rabbit polyclonal antibody traction reagents (Pierce, Rockford, IL) and then incubated the extracts to orexin B (1:500; Chemicon, Temecula, CA), MCH (1:500; Phoenix with 33-bp double-stranded oligonucleotides containing SNP-1, SNP-2, Pharmaceuticals, Belmont, CA), NPY (1:200; Chemicon), or POMC (1: SNP-5, SNP-9, SNP-11, or SNP-12 (Fig. 1B) labeled with digoxigenin- 5000; Phoenix Pharmaceuticals). After washing, the sections were in- 11-ddUTP using the digoxigenin gel-shift kit (Roche Diagnostics, Indi- cubated at room temperature for 2 h with Alexa Fluor 568 donkey anapolis, IN). For competition studies, we incubated nuclear extract with antigoat IgG (1:2000; Molecular Probes, Eugene, OR) and Alexa Fluor unlabeled oligonucleotides (100-fold excess before adding digoxigenin- 488 donkey antirabbit IgG (1:2000; Molecular Probes) secondary anti- labeled oligonucleotide). Protein-DNA complexes were separated on a bodies. Double-immunofluorescence detection was carried out using a 5% nondenaturing polyacrylamide gel in 0.5 ϫ Tris-borate-EDTA buffer. BX51 microscope (Olympus, Tokyo, Japan). The gel was transferred to nylon membrane, and the signal was detected with a chemiluminescent detection system (Roche Diagnostics) accord- Expression of SCG3, orexin, MCH, NPY, and POMC in ing to the manufacturer’s instructions. BE(2)-C neuroblastoma Double-labeling immunohistochemistry for SCG3, orexin, The coding sequence of human SCG3 was amplified by RT-PCR from MCH, NPY, and POMC hypothalamus cDNA using primers with an added N-terminal PstI restriction site located before the start codon and a C-terminal SalI Male mice (B57BL/6, 8 wk old) were purchased from CLEA Japan restriction site located after the stop codon. The PCR product was cloned (Tokyo, Japan). After being anesthetized with sodium pentobarbital (100 between the PstI and SalI sites of the pBI vector (CLONTECH, Palo Alto, 1148 J Clin Endocrinol Metab, March 2007, 92(3):1145–1154 Tanabe et al. • SNPs in SCG3 Gene and Obesity

CA). The coding region of human preproorexin, pro-MCH, POMC, and Results pro-NPY were also amplified but with primers that included an N- Case-control association study terminal MluI site located before the start codon and a C-terminal EcoRV site located after the stop codon. The PCR products were cloned between A total of 62,663 IMS-JST SNPs covering 11,932 gene loci the MluI and EcoRV sites of the pBI-SCG3 plasmid. The pBI-SCG3- were successfully genotyped in 94 obese subjects (case-1). preproorexin, pBI-SCG3-pro-MCH, pBI-SCG3-POMC, and pBI-SCG3- The genotype and allele frequencies were compared with 658 pro-NPY were transfected using lipofectamine 2000 reagent (Invitrogen) into a previously established cell line of BE(2)-C cells containing the random Japanese subjects. According to the National Nu- pTet-Off vector (CLONTECH). For immunocytochemical detection, cells trition Survey, the proportion of the subjects with BMI of 30 were fixed with 4% paraformaldehyde for 15 min then treated with 0.5% kg/m2 or greater was estimated to be 0.023 in males and 0.034 Triton X-100. Cells were incubated with polyclonal goat anti-SCG3 (1: in females aged 20 yr and older (23), and the mean BMIs are 200; Santa Cruz Biotechnology) together with rabbit polyclonal antibody approximately 23 kg/m2 for ages 15–84 yr in Japan (24). to orexin B (1:500; Chemicon), MCH (1:500; Phoenix Pharmaceuticals), NPY (1:500; Progen Biotechnik, Heidelberg, Germany), or POMC (1: Therefore, control 1 that was randomly selected from the

5000; Phoenix Pharmaceuticals) in PBS containing 1% BSA overnight at Japanese subjects was not an inappropriate control for the Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021 4 C. We washed and incubated the cells at room temperature for 2 h with initial analysis. A total of 2261 SNPs that possessed P values Alexa Fluor 488 donkey antigoat IgG (1:2000; Molecular Probes) and less than or equal to 0.02 by a test of independence using Alexa Fluor 568 donkey antirabbit IgG (1:2000; Molecular Probes) sec- either genotype mode or allele frequency mode were further ondary antibodies. The cells were examined using an Olympus FV300 confocal laser-scanning microscope. analyzed using another set of obese (case 2) and control subjects (control 2). Among the 2261 SNPs, we successfully completed genotyping of 2115 SNPs and identified a strong Statistical analysis association with the obesity phenotype for SNP-1 (rs3764220, For each case-control study, the frequencies of the genotypes or the Ϫ1492A3G), which lies in the 5Ј flanking region of the SCG3 alleles were compared between cases and controls in four different gene (Table 1). There were no gender- or age-related differ- modes. In the first mode (allele frequency mode), allele frequencies were ences with respect to SNP-1 alleles. Because the P value of ϫ compared between cases and controls using a 2 2 contingency table, SNP-1 was the smallest (P ϭ 0.0000019, genotype mode) whereas in the second mode (genotype mode), frequencies of the three genotypes were compared between cases and controls using a 2 ϫ 3 among the 2115 SNPs, we considered this gene as a good contingency table. In the third mode (minor allele homozygotes mode), candidate for further investigation. the frequencies of the homozygous genotype for the minor allele were compared using a 2 ϫ 2 contingency table, whereas in the fourth mode (major allele homozygotes mode), the frequencies of the homozygotes LD blocks of the SCG3 locus for the major allele were compared using a 2 ϫ 2 contingency table. Odds ratio and its 95% confidence interval (CI) were calculated by Woolf’s We identified 112 genetic variations (107 SNPs and five method. Hardy-Weinberg equilibrium was assessed using the ␹2 test insertions/deletions) by sequencing in the approximately (19). We used the correlation coefficient ⌬, calculated as reported pre- 300-kb genomic region around SNP-1, of which 38 SNPs and viously (20), as the measure to evaluate the strength of linkage disequi- two insertions/deletions resided in the SCG3 gene. Among librium (LD). Haplotype phasing was estimated using the Expectation- the 107 SNPs, Invader probes could be synthesized for 81 Maximization algorithm (21). Haplotype blocks were estimated using Haploview 3.2 (22). Multiple linear regression analysis was performed SNPs, and 79 SNPs were successfully genotyped. Seven using StatView 5.0 (SAS Institute Inc., Cary, NC) to test an independent SNPs had minor allele frequency less than 5% and were effect of SNP-2 genotypes on SFA or VFA, considering the effects of other excluded from LD analysis, whereas 10 SNPs had minor variables (age, BMI, and gender) that were assumed to be independent allele frequency less than 10% and were excluded from case- of the effect of the SNP. The significance of the association between an control association study. LD analysis revealed that SNP-1 in independent variable and the dependent variable was tested by t test. The relative luciferase activities and clinical data are expressed as the SCG3 gene was located in a 40-kb LD block (block 2, Fig. mean Ϯ sd. Differences in luciferase activities were analyzed with the 1A), which did not contain any gene apart from SCG3. Be- unpaired t test. cause no association with obesity was observed for SNPs

TABLE 1. Association of SNP-1 (rs3764220, 5Ј flanking Ϫ1492) in the SCG3 gene with obesity in the first (case 1 vs. control 1) and the second (case 2 vs. control 2) set of experiments

No. of subjects (%) No. of chromosomes (%) HWE testa Population AA AG GG A G ␹2 P value Case 1 (n ϭ 94) 81 (86.2) 13 (13.8) 0 (0.0) 175 (93.1) 13 (6.9) 0.5 0.47 Control 1 (n ϭ 634) 486 (76.7) 134 (21.1) 14 (2.2) 1106 (87.2) 162 (12.8) 1.7 0.19 Case 2 (n ϭ 796) 639 (80.3) 154 (19.3) 3 (0.4) 1432 (89.9) 160 (10.1) 3.9 0.05 Control 2 (n ϭ 711) 522 (73.4) 164 (23.1) 25 (3.5) 1208 (85.0) 214 (15.0) 6.8 0.009

Genotype modeb Allele frequency modeb Major allele homozygotes modeb Minor allele homozygotes modeb ␹2 Pc ␹2 P OR (95% CI) ␹2 P OR (95% CI) ␹2 Pc OR (95% CI) Case 1 vs. control 1 5.2 0.09 5.3 0.02 1.97 (1.10–3.54) 4.3 0.04 1.90 (1.03–3.51) 2.1 0.24 ND Case 2 vs. control 2 24.7 0.000002 17.3 0.00003 1.59 (1.27–1.97) 9.9 0.002 1.47 (1.16–1.88) 20.3 0.000004 9.63 (2.90–32.05) The position of SNP in the 5Ј flanking region is counted from the transcription initiation site. OR, Odds ratio; ND, not determined. a Hardy-Weinberg equilibrium test. b Association test was performed in four different modes as described in Subjects and Methods, and the results in the three modes are shown. c Fisher’s exact test. Tanabe et al. • SNPs in SCG3 Gene and Obesity J Clin Endocrinol Metab, March 2007, 92(3):1145–1154 1149 located outside this LD block (Fig. 1B), we judged SCG3 to allele (A allele) (Fig. 2B), indicating that some nuclear fac- be a candidate susceptibility gene for obesity. P values of 39 tor(s) has higher binding affinity to the minor allele. Al- SNPs located in block 2 and the adjacent blocks 1 and 3 are though we observed shifted bands for the oligonucleotides indicated in Fig. 1B. Among 40 genetic polymorphisms corresponding to SNP-5 and SNP-12, no significant differ- within the SCG3 gene that we found and genotyped, 11 SNPs ence in the intensity of the bands between the major and [SNP-2 (rs16964465), 5Ј flanking Ϫ1203; SNP-5 (rs3809498), 5Ј minor alleles was observed (data not shown). No shifted Ϫ ϩ flanking 65; SNP-9 (rs16964476), intron 1 190; SNP-11 band was observed in the case of SNP-1 and SNP-11. In the ϩ ϩ (ssj0011012), intron 1 478; SNP-12 (rs3214014), intron 1 case of SNP-9, the band corresponding to the minor allele ϩ 605; SNP-16 (rs2305709), exon 4 351(I117I); SNP-17 was more intense than that corresponding to the major allele, ϩ ϩ (rs3816544), intron 4 127; SNP-20 (rs2305715), intron 5 as observed in SNP-2 (Fig. 2B). The combination of the results ϩ 677; SNP-26 (rs2305719), intron 6 2677; SNP-27 of the luciferase assay and the gel-shift assay suggested that (ssj0011013), intron 8 ϩ 25; SNP-29 (rs3765067), intron 9 ϩ 52]

the genetic variations corresponding to SNP-2 and -9 were Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021 were in almost complete linkage disequilibrium (⌬ϭ0.99– the most likely candidates to affect the transcriptional activ- 1.0) with SNP-1 and also revealed significant associations ity of SCG3 and perhaps susceptibility to the development of with obesity (Fig. 1B). For example, the frequency of the obesity. subjects with the C/C genotype at SNP-2 was significantly lower in the obesity group than the control group (odds ratio 9.23; 95% CI 2.77–30.80, ␹2 19.2, P ϭ 0.0000067) (Supplemen- Expression of SCG3 in the hypothalamus tal Table 1, published as supplemental data on The Endocrine Society’s Journals Online Web site at http://jcem.endojour- SCG3 was reported to be expressed in the hypothalamus, nals.org). The remaining SNPs showed no significant asso- but its physiological roles have not yet been clarified (12). To ciation with obesity. SNPs in the 5Ј-flanking region are further elucidate this role, we performed in situ hybridization counted from the transcription initiation site. For SNPs in and immuohistochemical analysis for SCG3 in the murine introns, nucleotide positions are counted from the first in- hypothalamus and observed that SCG3 was expressed in the tronic nucleotide at the exon-intron junction and for SNPs in LHA, PVN, ventromedial hypothalamus, and ARC (data not exon regions, from the first exonic nucleotide (transcription shown). SCG3 immunoreactivity was also observed in var- initiation site) according to sequence accession no. ious other regions of the mouse brain as reported previously AC020892.7 and NM_013243.2. (12); however, the most intense immunoreactivities were observed in the ARC and LHA as well as the PVN and ventromedial hypothalamus (Fig. 3). The ARC neurons that Regulatory effect of SNPs on SCG3 expression express and secrete NPY and POMC are regulated by leptin Three SNPs (SNP-1, SNP-2, SNP-5) were located in the 5Ј and transfer their neuronal signal to orexin-expressing neu- flanking region and three SNPs (SNP-9, SNP-11, SNP-12) rons in the LHA (26). To investigate the relationship between were located in intron 1 of SCG3 gene, regions that could SCG3 and these neuronal peptides, we performed double- putatively affect transcriptional activity. To examine labeling immunohistochemical analysis and found that whether these six SNPs would affect the transcriptional ac- SCG3 was coexpressed with POMC and NPY in ARC cells tivity, we performed a luciferase assay using the neuroblas- (Fig. 3). We also examined the relationship between SCG3 toma cell-line SH-SY5Y, which has previously been shown to and two major neuropeptides in the LHA that inhibit food express SCG3 (25). Between the major and minor alleles at intake, orexin and MCH, and detected that many orexin- each locus, only the clones containing SNP-2 or SNP-9 expressing neurons and MCH-expressing neurons coex- showed significant differences in transcriptional activity pressed SCG3 (Fig. 3). (Fig. 2A), and these differences were enhanced using the Granins, such as CHGA, CHGB, and secretogranin II, form plasmids containing four concatenated copies of these DNA granule-like structure when they are expressed in cultured fragments, suggesting that SNP-2 and SNP-9 were able to cells (11, 27). To examine whether SCG3 would also form affect the transcriptional activity of the SCG3 gene. SCG3 was granule-like structures and interact with each of these neu- also reported to be expressed in pancreatic ␤-cells (13); thus, ropeptides, we transfected pBI-SCG3-preproorexin, pBI- we performed the same experiments using the hamster pan- SCG3-pro-MCH, pBI-SCG3-POMC, and pBI-SCG3-pro-NPY creatic ␤-cell line, HIT-T15 (Fig. 2A). We observed similar results, although the differences in the transcriptional activ- into established BE(2)-C cell lines that were stably transfected ity between SNPs using HIT-T15 cells were smaller than with the pTet-Off vector system. The results indicated that those seen in the SH-SY5Y cells, probably due to the species SCG3 formed granule-like structures, like other granins, and difference. colocalized with orexin, MCH, NPY, and POMC (Fig. 4). To further investigate whether the regions containing each Immunoelectron microscopic analysis revealed that the of these six SNPs can act as target binding sites of nuclear granules were detected in BE(2)-C cells transfected with protein(s), we performed a gel-shift assay using SH-SY5Y cell SCG3 but not in those transfected with vector alone (data not extract and oligonucleotides corresponding to genomic se- shown). The granules stained with anti-SCG3 antibody (data quences that included major or minor alleles of each of the not shown), suggesting that SCG3 forms secretory granules six SNPs (SNP-1, SNP-2, SNP-5, SNP-9, SNP-11, and SNP- in neuroblastoma cells. These in vivo and in vitro data suggest 12). The band corresponding to the minor allele (C allele) of that SCG3 may play some role in the secretion of neuropep- SNP-2 was more intense than that corresponding to the major tides that are related to appetite. 1150 J Clin Endocrinol Metab, March 2007, 92(3):1145–1154 Tanabe et al. • SNPs in SCG3 Gene and Obesity Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021

FIG. 2. Transcriptional activities affected by SNPs. A, Comparison of allelic variants of SCG3 analyzed by relative luciferase activity in SH-SY5Y cells and HIT-T15 cells. The val- ues are mean Ϯ SD. pGL3-promoter, the empty vector. The gray boxes indicate the oligonucle- otide unit around the SNPs, and white and black small boxes represent major and minor allele of each SNP, respectively. SV40, Simian virus 40. B, Binding of unknown nuclear fac- tor(s) to the SCG3 gene. Gel-shift assay was performed with digoxigenin-labeled 33-bp oli- gonucleotides corresponding to two SCG3 polymorphic sites (SNP-2 and SNP-9) in SH- SY5Y cells. An arrow indicates the band that shows binding of nuclear proteins to the oli- gonucleotides containing minor alleles of SNP-2 (C allele, left panel) and SNP-9 (G al- lele, right panel).

Analysis of various quantitative phenotypes with SNP-2 genotype was transformed to a multidichotomous variable, and SNP-9 i.e. homozygosity with the A alleles vs. the other genotypes, Because SCG3 is expressed in pancreatic ␤-cells and in- heterozygosity vs. the other genotypes, or homozygosity volved in insulin secretion (13), SCG3 may play a role in with the C alleles vs. the other genotypes. Stepwise multiple metabolic disorders as well as in obesity. Therefore, to in- regression analysis (both forward selection and backward vestigate whether the genotypes of SNP-2 and SNP-9 are elimination) revealed that gender, age, and BMI were sig- related to the phenotypes of the metabolic disorders, we nificantly associated with VFA. However, no genotypes were compared BMI, blood insulin, glucose, cholesterol, triglyc- significantly associated with VFA. In contrast, gender, BMI, erides, and high-density lipoprotein-cholesterol, and blood and genotype (homozygosity with the A allele or heterozy- pressure among the different genotypes in cases and con- gosity with A and C alleles) were significantly associated trols. We detected no relationship between these quantitative with SFA. Neither age nor homozygosity with the C allele phenotypes and the genotypes at SNP-2 and SNP-9 in either was significantly associated with SFA. Table 2 shows the data the case or control groups. of multiple regression analysis using gender, age, BMI, and The most important phenotype of the metabolic syndrome genotype as independent variables. Among the independent is visceral fat accumulation. Thus, we performed multiple variables, homozygosity with the A allele, female gender, linear regression analysis to further define the role of this and increase in BMI were significantly associated with in- gene in the amount of visceral and/or sc fat. The SNP-2 creases in SFA. In concordance, each of the three parameters, Tanabe et al. • SNPs in SCG3 Gene and Obesity J Clin Endocrinol Metab, March 2007, 92(3):1145–1154 1151 Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021

FIG. 3. Colocalization of SCG3 with POMC, NPY, MCH, or orexin in mouse hypothalamus. Immunostained tissue sections were double labeled for SCG3 with POMC, NPY, MCH, or orexin B. Scale bar,50␮m.

heterozygosity with A and C alleles, male gender, and de- Map phase II SNPs that are: in LD (r2 Ͼ 0.5) at least with one crease in BMI, was significantly associated with a decrease in SNP in our set, with minor allele frequencies greater than SFA. Because the number of homozygotes with the C allele 0.05, and at distances less than 500 kb from at least one SNP was very small (n ϭ 9), we were unable to validate its as- in our set. Because of this low genomic coverage for studies sociation with either SFA or VFA. SNP-2 and SNP-9 were in up to now, further investigations will be necessary as high- complete linkage disequilibrium (⌬ϭ1.0); thus, the same throughput genotyping products achieve higher SNP results were observed. These data suggested that the geno- densities. types of SNP-2 and SNP-9 have an effect on the amount of Intracellular granins are costored and cosecreted with pep- SFA independent of the effects of the other independent tide hormones (11). Our results suggest that SCG3 forms variables. secretory granules together with orexin, MCH, NPY, and POMC in the hypothalamus. We demonstrated that SNP-2 Discussion and SNP-9 might have an effect on the transcriptional activity Epidemiological studies have provided evidence indicat- of the SCG3 gene. Transcriptional activity of the major allele, ing the involvement of genetic factors in the development of the frequencies of which were higher in obese subjects than obesity (5, 6). Through case-control association studies using normal controls, was shown to be lower, which indicates that gene-based SNPs, our center has successfully discovered decreased SCG3 expression levels may increase the risk of candidate genes that confer susceptibility to various common obesity. These results seem to be complicated. Many granins diseases (myocardial infarction, diabetic nephropathy, type are known to work as inhibitors of endocrine secretion (11); 2 diabetes mellitus) (9, 28–30). Using this approach, we iden- for example, extracellular CHGA undergoes proteolytic pro- tified novel functional SNPs associated with obesity, which cessing into several bioactive peptides such as pancreastatin are located in the SCG3 gene. Our approach should prove and catestatin (11). Pancreastatin inhibits insulin secretion effective and useful in searching for genes related to common from pancreatic ␤-cells (32), and catestatin inhibits the release diseases; however, the set of SNPs that we used covered only of catecholamines from sympathoadrenal chromaffin cells 11,932 gene loci. Recently a haplotype map of the human (33). CHGA also inhibits POMC-derived peptide secretion genome has been constructed (31). Despite the relatively high (34). CHGB-derived peptides inhibit the secretion of PTH SNP density in genomic region, our SNP set only covered and insulin (35, 36). A secretogranin II-derived peptide, se- approximately 30% of the human genome by counting Hap- cretoneurin, inhibits serotonin and melatonin release from 1152 J Clin Endocrinol Metab, March 2007, 92(3):1145–1154 Tanabe et al. • SNPs in SCG3 Gene and Obesity Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021

FIG. 4. Granular accumulation of SGG3 pro- tein overexpressed in BE(2)-C cells. BE(2)-C cells expressed SCG3 (green stain) and POMC (red stain), NPY (red stain), orexin (red stain), or MCH (red stain). Scale bars,10␮m.

pinealocytes (37). SCG3 also undergoes proteolytic process- also complicated, and the whole network is not well under- ing and is secreted from cells (38). It needs to be investigated stood. There have been no reports indicating the involve- whether the peptides derived from proteolytic processing of ment of SCG3 in appetite regulation, but in light of our data, SCG3 are bioactive and whether they may also inhibit the it is interesting to speculate that SCG3 may be a potential secretion of orexin, MCH and NPY, like other granins. Hence, factor in the regulation of food intake. Nevertheless, because we consider that increased expression of SCG3 in the subjects fat accumulation is also affected by other variables like phys- with the minor allele of SNP-2 and SNP-9 may result in a ical activity as well as food intake, it is also necessary to decrease in the secretion of orexin, MCH, and NPY and investigate whether SCG3 interacts with other variables. thereby inhibit food intake and accumulation of sc fat. Functional SNP-2 and SNP-9, which we have shown to be Food intake control is complicated (26) because in addition associated with obesity, are located on the chromosome to many neuropeptides in the central nervous system, pep- 15q21 locus in which a positive linkage to SFA was indicated tides secreted from other tissues, such as adipose tissue and using the Que´bec Family Study (39). In concordance with this gastrointestinal organs, participate in the control of food previous result, our study showed an association of SNP-2 intake. The neural circuits in the hypothalamic region are and SNP-9 with SFA. Tanabe et al. • SNPs in SCG3 Gene and Obesity J Clin Endocrinol Metab, March 2007, 92(3):1145–1154 1153

TABLE 2. Multiple linear regression analysis for VFA or SFA using SNP-2 (5Ј flanking Ϫ1203) and other features as independent variables

AA vs. the other genotype AC vs. the other genotype CC vs. the other genotype

Independent variables Regression SE P Regression SE P Regression SE P coefficient coefficient coefficient VFA (dependent variable) Gender (men/women, 1/0) 61.271 6.583 Ͻ0.0001 61.398 6.572 Ͻ0.0001 61.352 6.570 Ͻ0.0001 Age (yr) 1.064 0.264 Ͻ0.0001 1.063 0.264 Ͻ0.0001 1.06 0.264 Ͻ0.0001 BMI (kg/m2) 5.723 0.454 Ͻ0.0001 5.728 0.454 Ͻ0.0001 5.705 0.456 Ͻ0.0001 Genotype (1/0) 3.193 7.332 0.66 Ϫ2.293 7.577 0.76 Ϫ8.755 21.229 0.68 R2 41% 41% 41% SFA (dependent variable) Gender (men/women, 1/0) Ϫ69.550 7.167 Ͻ0.0001 Ϫ68.998 7.157 Ͻ0.0001 Ϫ67.623 7.244 Ͻ0.0001 Age (yr) Ϫ0.329 0.289 0.26 Ϫ0.326 0.289 0.26 Ϫ0.357 0.293 0.22 Downloaded from https://academic.oup.com/jcem/article/92/3/1145/2597987 by guest on 28 September 2021 BMI (kg/m2) 13.044 0.495 Ͻ0.0001 13.095 0.496 Ͻ0.0001 12.998 0.503 Ͻ0.0001 Genotype (1/0) 25.499 7.952 0.0015 Ϫ25.761 8.221 0.0019 Ϫ11.438 23.275 0.62 R2 67% 67% 66%

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BMC Genet Hotta, Laboratory for Obesity, SNP Research Center, RIKEN, 1-7-22, 4:S99–S103 Suehiro, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. E-mail: 16. Mori Y, Otabe S, Dina C, Yasuda K, Populaire C, Lecoeur C, Vatin V, Durand E, Hara K, Okada T, Tobe K, Boutin P, Kadowaki T, Froguel P 2002 Genome- [email protected]. wide search for type 2 diabetes in Japanese affected sib-pairs confirms sus- This work was supported by a grant from the Japanese Millennium ceptibility genes on 3q, 15q, and 20q and identifies two new candidate loci on Project, Takeda Science Foundation (to K.Ho.), and Chiyoda Mutual Life 7p and 11p. Diabetes 51:1247–1255 Foundation (to K.Ho.). 17. Takei T, Iida A, Nitta K, Tanaka T, Ohnishi Y, Yamada R, Maeda S, Tsunoda Disclosure statement: T.Y., A.I., S.S., A.S., A.Tak., T.N., T.T., Y.Nakat., T, Takeoka S, Ito K, Honda K, Uchida K, Tsuchiya K, Suzuki Y, Fujioka T, K.K., R.K., N.I., I.M., J.W., T.F., S.M., K.To., K.Ha., T.Sh., K.Tan., K.Y., Ujiie T, Nagane Y, Miyano S, Narita I, Gejyo F, Nihei H, Nakamaura Y 2002 T.H., S.O., H.Y., T.Sa., Y.M., N.K., Y.Nakam. have nothing to declare. S.K. Association between single-nucleotide polymorphisms in selectin genes and consults for DHC Corporation Laboratories. K.Ho. and A.Tan. are in- immunoglobulin A nephropathy. Am J Hum Genet 70:781–786 18. Iida A, Saito S, Sekine A, Mishima C, Kondo K, Kitamura Y, Harigae S, ventors on (Japan and PCT) (PCT/JP2005/023674). 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