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The Journal of Clinical Endocrinology & Metabolism 92(3):1145–1154
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,
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 obesity. However, the genetic background of obesity still remains unclear.
Objective: Our objective was to search for obesity-related genes using
a large number of gene-based single-nucleotide polymorphisms (SNPs).
Design and Setting: We conducted case-control association analyses
using 94 obese patients and 658 controls with 62,663 SNPs selected
from the SNP database. SNPs that possessed P ⱕ 0.02 were further
analyzed using 796 obese and 711 control subjects. One SNP
(rs3764220) in the secretogranin III (SCG3) gene showed the lowest
P value (P ⫽ 0.0000019). We sequenced an approximately 300-kb
genomic region around rs3764220 and discovered SNPs for haplotype
analyses. SCG3 was the only gene within a haplotype block that
contained rs3764220. The functions of SCG3 were studied.
Results: Twelve SNPs in the SCG3 gene including rs3764220 were
in almost complete linkage disequilibrium and significantly associated with an obesity phenotype. Two SNPs (rs16964465, rs16964476)
affected the transcriptional activity of SCG3, and subjects with the
minor allele seemed to be resistant to obesity (odds ratio, 9.23; 95%
confidence interval, 2.77–30.80; ␹2 ⫽ 19.2; P ⫽ 0.0000067). SCG3
mRNA and immunoreactivity were detected in the paraventricular
nucleus, lateral hypothalamic area, and arcuate nucleus, and the
protein coexisted with orexin, melanin-concentrating hormone, neuropeptide Y, and proopiomelanocortin. SCG3 formed a granule-like
structure together with these neuropeptides.
Conclusions: Genetic variations in the SCG3 gene may influence the
risk of obesity through possible regulation of hypothalamic neuropeptide secretion. (J Clin Endocrinol Metab 92: 1145–1154, 2007)
Patients: Obese subjects (body mass index ⱖ 30 kg/m2, n ⫽ 890) and
control subjects (general population; n ⫽ 658, body mass index ⱕ
25kg/m2; n ⫽ 711) were recruited for this study.
O
BESITY HAS BECOME one of the major issues in
public health, medicine, and the economy (1). In
particular, visceral obesity is considered to be important
due to its relation to various complications such as diabetes mellitus, dyslipidemia, and hypertension. A combi-
nation of these dysfunctions is now defined as the metabolic syndrome (2), which significantly increases the risk
of cardiovascular disease. Adipose tissue secretes various
adipokines, and an increase in adipose tissue mass affects
First Published Online January 2, 2007
Abbreviations: ARC, Arcuate nucleus; BMI, body mass index; CHG,
chromogranin; CI, confidence interval; IMS, Institute of Medical Science;
JST, Japan Science and Technology; LD, linkage disequilibrium; LHA, lateral hypothalamic area; MCH, melanin-concentrating hormone; NPY, neu-
ropeptide Y; POMC, proopiomelanocortin; PVN, paraventricular nucleus;
SFA, sc fat area; SNP, single-nucleotide polymorphism; VFA, visceral fat area.
JCEM is published monthly by The Endocrine Society (http://www.endosociety.org), the foremost professional society serving the endocrine
community.
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J Clin Endocrinol Metab, March 2007, 92(3):1145–1154
the level of adipokines, resulting in the development of
dyslipidemia, hypertension, and insulin resistance (3, 4).
Both genetic and environmental factors contribute to the
development of obesity. In epidemiological studies, heritability of body weight is estimated to be approximately 70%
(5, 6). Genetic studies in mice suggested that mutations in
several genes, such as leptin, proopiomelanocortin (POMC),
and melanocortin-4 receptor, were implicated in a monogenic form of inherited obesity, whereas mutations in such
genes were also reported in human subjects with obesity (6,
7). However, the most prevalent MC4R gene mutations have
been found in only 3–5% of obese patients with a body mass
index (BMI) of more than 40 kg/m2. In general, the vast
majority of obesity is considered to be caused by a polygenic
disorder, and its genetic susceptibility is likely to differ
among various ethnic groups (6, 7). A large number of manuscripts concerning obesity-related genes have been reported
(7). However, because there are also many papers reporting
controversial results at these candidate loci, the genetic background of obesity still remains unclear.
As one of the Japanese Millennium Projects, a large-scale
collaborative effort performed a search for gene-based single-nucleotide polymorphisms (SNPs) in a group of Japanese
subjects and discovered approximately 190,000 genetic variations (JSNP database) (8), and subsequently our center developed a high-throughput SNP genotyping system that uses
a combination of multiplex PCR and the Invader assay (9, 10)
to effectively determine these variations’ frequencies in the
Japanese population. We performed an association study
using a large number of SNPs selected from the JSNP database (62,663 SNPs in 11,932 genes, covering approximately
30% of the human genome) by genotyping Japanese obese
and lean subjects and found that one SNP (SNP-1, rs3764220)
showed the smallest P value and was significantly associated
with obesity. This SNP existed in the 5⬘-flanking region of the
secretogranin III (SCG3) gene. SCG3 belongs to a family of
acidic secretory proteins, known as granins, which are
widely expressed in endocrine and neuronal cells (11). SCG3
has been cloned from brain- and pituitary-specific mRNA
and is expressed in the paraventricular nucleus (PVN) of the
hypothalamus (12), which is known to be an important region for appetite regulation. SCG3 is also expressed in pancreatic ␤-cells and participates in insulin secretion together
with chromogranin (CHG) A (13). Interestingly, SCG3 is located on chromosome 15q21, on which association with obesity has been previously indicated (14). Data from the Framingham Heart Study suggested a moderate linkage of the
metabolic syndrome to this general region on chromosome
15q (15) on which the presence of a susceptibility gene for
type 2 diabetes in the Japanese population has also been
indicated (16).
In the present study, we demonstrate a significant association between functional SNPs in the SCG3 gene and obesity. We found that SCG3 was expressed together with appetite-regulating peptides such as orexin and melaninconcentrating hormone (MCH) in the lateral hypothalamic
area (LHA) and neuropeptide Y (NPY) and POMC in the
arcuate nucleus (ARC), suggesting that SCG3 is a good candidate as an obesity-related gene.
Tanabe et al. • SNPs in SCG3 Gene and Obesity
Subjects and Methods
Subjects
The sample size of the first set of Japanese obese subjects (BMI ⱖ 30
kg/m2) was 94 (case 1; male to female ratio 39:55; age 47 ⫾ 17 yr; BMI
36.3 ⫾ 5.0 kg/m2). The sample size of the first set of control individuals
(control 1) was 658 and consisted of the Japanese general population as
described in JSNP database [Institute of Medical Science (IMS)-Japan
Science and Technology (JST) Agency Japanese SNP database] (8). The
sample size of the second set of Japanese obese subjects (BMI ⱖ 30
kg/m2) was 796 (case 2; male to female ratio 379:417; age 49 ⫾ 14 yr; BMI
34.3 ⫾ 5.5 kg/m2), whereas that of the second set of Japanese normalweight controls (BMI ⱕ 25 kg/m2) was 711 (control 2; male to female
ratio 267:444; age 52 ⫾ 16 yr; BMI 21.6 ⫾ 2.2 kg/m2). Secondary obesity
and obesity-related Mendelian disorders were excluded in this study.
Patients with obesity caused by medications were also excluded. Control
2 subjects were Japanese normal-weight volunteers collected from subjects who had undergone a medical examination for common disease
screening. We further collected 403 Japanese subjects with various BMIs
[male to female ratio 144:259 females; age 48 ⫾ 12 yr; BMI 29.7 ⫾ 7.0
kg/m2; visceral fat area (VFA) 126 ⫾ 81 cm2; sc fat area (SFA) 248 ⫾ 117
cm2] who agreed to undergo computed tomography examinations to
measure the VFA and SFA. All subjects except control 1 were newly
recruited for this study. Written informed consent was obtained from
each subject, and the protocol was approved by the ethics committee of
each institution and that of RIKEN.
DNA preparation and SNP genotyping
Genomic DNA was prepared from each blood sample according to
standard protocols. Approximately 100,000 Invader probes (Third Wave
Technologies, Madison, WI) could be made for SNPs of IMS-JST (8), and
the SNPs were genotyped in case 1 by Invader assays as described
previously (9, 17). Genotype and allele frequencies of these SNPs were
compared with control 1. The SNPs selected by association study using
case 1 and control 1 were submitted for further examination using
independent case 2 and control 2 groups.
SNP discovery in around SNP-1
To identify additional variations in the genomic region around
SNP-1, we generated a reference sequence of approximately 300 kb by
assembling the relevant regions from the sequences with GenBank accession no. AC066613, AC020892, AC026770, and AC090971. We amplified appropriate fragments of genomic DNA by PCR and sequenced
the products to identify SNPs within 300 kb genomic region using
previously described methods (10, 18).
Cell culture
SH-SY5Y and BE(2)-C neuroblastoma cells and HIT-T15 cells were
purchased from the American Type Culture Collection (Manassas, VA).
Cells were cultured in advanced DMEM (Invitrogen, Carlsbad, CA) with
2 mm glutamine, 5% fetal bovine serum, 100 U/ml penicillin, and 100
␮g/ml streptomycin.
Luciferase assay
We synthesized double-stranded oligonucleotides containing either
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,
SNP-11, or SNP-12 (Fig. 1B), with an NheI restriction site at the 5⬘ end
and an XboI restriction site at the 3⬘ end. We constructed luciferase
reporter plasmids by cloning the oligonucleotides into the pGL3-promoter vector (Promega, Madison, WI) upstream of the Simian virus 40
promoter. pGL3-promoter vectors containing oligonucleotides were
transfected into SH-SY5Y neuroblastoma cells together with the
phRL-TK vector (Promega), an internal control for transfection efficiency, 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
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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, ⫺1492A3 G) 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 ⫺logarithm 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-luciferase reporter assay system (Promega).
Gel-shift assay
We prepared nuclear extract from SH-SY5Y cells using NE-PER extraction reagents (Pierce, Rockford, IL) and then incubated the extracts
with 33-bp double-stranded oligonucleotides containing SNP-1, SNP-2,
SNP-5, SNP-9, SNP-11, or SNP-12 (Fig. 1B) labeled with digoxigenin11-ddUTP using the digoxigenin gel-shift kit (Roche Diagnostics, Indianapolis, IN). For competition studies, we incubated nuclear extract with
unlabeled oligonucleotides (100-fold excess before adding digoxigeninlabeled oligonucleotide). Protein-DNA complexes were separated on a
5% nondenaturing polyacrylamide gel in 0.5 ⫻ Tris-borate-EDTA buffer.
The gel was transferred to nylon membrane, and the signal was detected
with a chemiluminescent detection system (Roche Diagnostics) according to the manufacturer’s instructions.
Double-labeling immunohistochemistry for SCG3, orexin,
MCH, NPY, and POMC
Male mice (B57BL/6, 8 wk old) were purchased from CLEA Japan
(Tokyo, Japan). After being anesthetized with sodium pentobarbital (100
mg/kg), mice were perfused with 10% neutral buffered formalin. The
hypothalamic region was dissected from the brain, further fixed with
tissue fixative (Genostaff, Tokyo, Japan), embedded in paraffin, and
sectioned. Tissue sections (4 ␮m) were dewaxed and incubated at 4 C
overnight with polyclonal goat anti-SCG3 (1:200; Santa Cruz Biotechnology, Santa Cruz, CA) together with either rabbit polyclonal antibody
to orexin B (1:500; Chemicon, Temecula, CA), MCH (1:500; Phoenix
Pharmaceuticals, Belmont, CA), NPY (1:200; Chemicon), or POMC (1:
5000; Phoenix Pharmaceuticals). After washing, the sections were incubated at room temperature for 2 h with Alexa Fluor 568 donkey
antigoat IgG (1:2000; Molecular Probes, Eugene, OR) and Alexa Fluor
488 donkey antirabbit IgG (1:2000; Molecular Probes) secondary antibodies. Double-immunofluorescence detection was carried out using a
BX51 microscope (Olympus, Tokyo, Japan).
Expression of SCG3, orexin, MCH, NPY, and POMC in
BE(2)-C neuroblastoma
The coding sequence of human SCG3 was amplified by RT-PCR from
hypothalamus cDNA using primers with an added N-terminal PstI
restriction site located before the start codon and a C-terminal SalI
restriction site located after the stop codon. The PCR product was cloned
between the PstI and SalI sites of the pBI vector (CLONTECH, Palo Alto,
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J Clin Endocrinol Metab, March 2007, 92(3):1145–1154
Tanabe et al. • SNPs in SCG3 Gene and Obesity
Results
Case-control association study
CA). The coding region of human preproorexin, pro-MCH, POMC, and
pro-NPY were also amplified but with primers that included an Nterminal MluI site located before the start codon and a C-terminal EcoRV
site located after the stop codon. The PCR products were cloned between
the MluI and EcoRV sites of the pBI-SCG3 plasmid. The pBI-SCG3preproorexin, pBI-SCG3-pro-MCH, pBI-SCG3-POMC, and pBI-SCG3pro-NPY were transfected using lipofectamine 2000 reagent (Invitrogen)
into a previously established cell line of BE(2)-C cells containing the
pTet-Off vector (CLONTECH). For immunocytochemical detection, cells
were fixed with 4% paraformaldehyde for 15 min then treated with 0.5%
Triton X-100. Cells were incubated with polyclonal goat anti-SCG3 (1:
200; Santa Cruz Biotechnology) together with rabbit polyclonal antibody
to orexin B (1:500; Chemicon), MCH (1:500; Phoenix Pharmaceuticals),
NPY (1:500; Progen Biotechnik, Heidelberg, Germany), or POMC (1:
5000; Phoenix Pharmaceuticals) in PBS containing 1% BSA overnight at
4 C. We washed and incubated the cells at room temperature for 2 h with
Alexa Fluor 488 donkey antigoat IgG (1:2000; Molecular Probes) and
Alexa Fluor 568 donkey antirabbit IgG (1:2000; Molecular Probes) secondary antibodies. The cells were examined using an Olympus FV300
confocal laser-scanning microscope.
A total of 62,663 IMS-JST SNPs covering 11,932 gene loci
were successfully genotyped in 94 obese subjects (case-1).
The genotype and allele frequencies were compared with 658
random Japanese subjects. According to the National Nutrition Survey, the proportion of the subjects with BMI of 30
kg/m2 or greater was estimated to be 0.023 in males and 0.034
in females aged 20 yr and older (23), and the mean BMIs are
approximately 23 kg/m2 for ages 15– 84 yr in Japan (24).
Therefore, control 1 that was randomly selected from the
Japanese subjects was not an inappropriate control for the
initial analysis. A total of 2261 SNPs that possessed P values
less than or equal to 0.02 by a test of independence using
either genotype mode or allele frequency mode were further
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
association with the obesity phenotype for SNP-1 (rs3764220,
⫺1492A3 G), which lies in the 5⬘ flanking region of the SCG3
gene (Table 1). There were no gender- or age-related differences with respect to SNP-1 alleles. Because the P value of
SNP-1 was the smallest (P ⫽ 0.0000019, genotype mode)
among the 2115 SNPs, we considered this gene as a good
candidate for further investigation.
Statistical analysis
For each case-control study, the frequencies of the genotypes or the
alleles were compared between cases and controls in four different
modes. In the first mode (allele frequency mode), allele frequencies were
compared between cases and controls using a 2 ⫻ 2 contingency table,
whereas in the second mode (genotype mode), frequencies of the three
genotypes were compared between cases and controls using a 2 ⫻ 3
contingency table. In the third mode (minor allele homozygotes mode),
the frequencies of the homozygous genotype for the minor allele were
compared using a 2 ⫻ 2 contingency table, whereas in the fourth mode
(major allele homozygotes mode), the frequencies of the homozygotes
for the major allele were compared using a 2 ⫻ 2 contingency table. Odds
ratio and its 95% confidence interval (CI) were calculated by Woolf’s
method. Hardy-Weinberg equilibrium was assessed using the ␹2 test
(19). We used the correlation coefficient ⌬, calculated as reported previously (20), as the measure to evaluate the strength of linkage disequilibrium (LD). Haplotype phasing was estimated using the ExpectationMaximization algorithm (21). Haplotype blocks were estimated using
Haploview 3.2 (22). Multiple linear regression analysis was performed
using StatView 5.0 (SAS Institute Inc., Cary, NC) to test an independent
effect of SNP-2 genotypes on SFA or VFA, considering the effects of other
variables (age, BMI, and gender) that were assumed to be independent
of the effect of the SNP. The significance of the association between an
independent variable and the dependent variable was tested by t test.
The relative luciferase activities and clinical data are expressed as
mean ⫾ sd. Differences in luciferase activities were analyzed with the
unpaired t test.
LD blocks of the SCG3 locus
We identified 112 genetic variations (107 SNPs and five
insertions/deletions) by sequencing in the approximately
300-kb genomic region around SNP-1, of which 38 SNPs and
two insertions/deletions resided in the SCG3 gene. Among
the 107 SNPs, Invader probes could be synthesized for 81
SNPs, and 79 SNPs were successfully genotyped. Seven
SNPs had minor allele frequency less than 5% and were
excluded from LD analysis, whereas 10 SNPs had minor
allele frequency less than 10% and were excluded from casecontrol association study. LD analysis revealed that SNP-1 in
the SCG3 gene was located in a 40-kb LD block (block 2, Fig.
1A), which did not contain any gene apart from SCG3. Because no association with obesity was observed for SNPs
TABLE 1. Association of SNP-1 (rs3764220, 5⬘ flanking ⫺1492) 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 (%)
HWE testa
No. of chromosomes (%)
Population
AA
AG
GG
A
G
␹2
P value
Case 1 (n ⫽ 94)
Control 1 (n ⫽ 634)
Case 2 (n ⫽ 796)
Control 2 (n ⫽ 711)
81 (86.2)
486 (76.7)
639 (80.3)
522 (73.4)
13 (13.8)
134 (21.1)
154 (19.3)
164 (23.1)
0 (0.0)
14 (2.2)
3 (0.4)
25 (3.5)
175 (93.1)
1106 (87.2)
1432 (89.9)
1208 (85.0)
13 (6.9)
162 (12.8)
160 (10.1)
214 (15.0)
0.5
1.7
3.9
6.8
0.47
0.19
0.05
0.009
Genotype modeb
␹
P
5.2
24.7
0.09
0.000002
2
Case 1 vs. control 1
Case 2 vs. control 2
c
Allele frequency modeb
␹
2
5.3
17.3
Major allele homozygotes modeb
Minor allele homozygotes modeb
P
OR (95% CI)
␹
P
OR (95% CI)
␹
0.02
0.00003
1.97 (1.10 –3.54)
1.59 (1.27–1.97)
4.3
9.9
0.04
0.002
1.90 (1.03–3.51)
1.47 (1.16 –1.88)
2.1
20.3
2
2
Pc
OR (95% CI)
0.24
ND
0.000004 9.63 (2.90 –32.05)
The position of SNP in the 5⬘ 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
located outside this LD block (Fig. 1B), we judged SCG3 to
be a candidate susceptibility gene for obesity. P values of 39
SNPs located in block 2 and the adjacent blocks 1 and 3 are
indicated in Fig. 1B. Among 40 genetic polymorphisms
within the SCG3 gene that we found and genotyped, 11 SNPs
[SNP-2 (rs16964465), 5⬘ flanking ⫺1203; SNP-5 (rs3809498), 5⬘
flanking ⫺65; SNP-9 (rs16964476), intron 1 ⫹ 190; SNP-11
(ssj0011012), intron 1 ⫹ 478; SNP-12 (rs3214014), intron 1 ⫹
605; SNP-16 (rs2305709), exon 4 ⫹ 351(I117I); SNP-17
(rs3816544), intron 4 ⫹ 127; SNP-20 (rs2305715), intron 5 ⫹
677; SNP-26 (rs2305719), intron 6 ⫹ 2677; SNP-27
(ssj0011013), intron 8 ⫹ 25; SNP-29 (rs3765067), intron 9 ⫹ 52]
were in almost complete linkage disequilibrium (⌬ ⫽ 0.99 –
1.0) with SNP-1 and also revealed significant associations
with obesity (Fig. 1B). For example, the frequency of the
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 ⫽ 0.0000067) (Supplemental Table 1, published as supplemental data on The Endocrine
Society’s Journals Online Web site at http://jcem.endojournals.org). The remaining SNPs showed no significant association with obesity. SNPs in the 5⬘-flanking region are
counted from the transcription initiation site. For SNPs in
introns, nucleotide positions are counted from the first intronic nucleotide at the exon-intron junction and for SNPs in
exon regions, from the first exonic nucleotide (transcription
initiation site) according to sequence accession no.
AC020892.7 and NM_013243.2.
Regulatory effect of SNPs on SCG3 expression
Three SNPs (SNP-1, SNP-2, SNP-5) were located in the 5⬘
flanking region and three SNPs (SNP-9, SNP-11, SNP-12)
were located in intron 1 of SCG3 gene, regions that could
putatively affect transcriptional activity. To examine
whether these six SNPs would affect the transcriptional activity, we performed a luciferase assay using the neuroblastoma cell-line SH-SY5Y, which has previously been shown to
express SCG3 (25). Between the major and minor alleles at
each locus, only the clones containing SNP-2 or SNP-9
showed significant differences in transcriptional activity
(Fig. 2A), and these differences were enhanced using the
plasmids containing four concatenated copies of these DNA
fragments, suggesting that SNP-2 and SNP-9 were able to
affect the transcriptional activity of the SCG3 gene. SCG3 was
also reported to be expressed in pancreatic ␤-cells (13); thus,
we performed the same experiments using the hamster pancreatic ␤-cell line, HIT-T15 (Fig. 2A). We observed similar
results, although the differences in the transcriptional activity between SNPs using HIT-T15 cells were smaller than
those seen in the SH-SY5Y cells, probably due to the species
difference.
To further investigate whether the regions containing each
of these six SNPs can act as target binding sites of nuclear
protein(s), we performed a gel-shift assay using SH-SY5Y cell
extract and oligonucleotides corresponding to genomic sequences that included major or minor alleles of each of the
six SNPs (SNP-1, SNP-2, SNP-5, SNP-9, SNP-11, and SNP12). The band corresponding to the minor allele (C allele) of
SNP-2 was more intense than that corresponding to the major
J Clin Endocrinol Metab, March 2007, 92(3):1145–1154
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allele (A allele) (Fig. 2B), indicating that some nuclear factor(s) has higher binding affinity to the minor allele. Although we observed shifted bands for the oligonucleotides
corresponding to SNP-5 and SNP-12, no significant difference in the intensity of the bands between the major and
minor alleles was observed (data not shown). No shifted
band was observed in the case of SNP-1 and SNP-11. In the
case of SNP-9, the band corresponding to the minor allele
was more intense than that corresponding to the major allele,
as observed in SNP-2 (Fig. 2B). The combination of the results
of the luciferase assay and the gel-shift assay suggested that
the genetic variations corresponding to SNP-2 and -9 were
the most likely candidates to affect the transcriptional activity of SCG3 and perhaps susceptibility to the development of
obesity.
Expression of SCG3 in the hypothalamus
SCG3 was reported to be expressed in the hypothalamus,
but its physiological roles have not yet been clarified (12). To
further elucidate this role, we performed in situ hybridization
and immuohistochemical analysis for SCG3 in the murine
hypothalamus and observed that SCG3 was expressed in the
LHA, PVN, ventromedial hypothalamus, and ARC (data not
shown). SCG3 immunoreactivity was also observed in various other regions of the mouse brain as reported previously
(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
express and secrete NPY and POMC are regulated by leptin
and transfer their neuronal signal to orexin-expressing neurons in the LHA (26). To investigate the relationship between
SCG3 and these neuronal peptides, we performed doublelabeling immunohistochemical analysis and found that
SCG3 was coexpressed with POMC and NPY in ARC cells
(Fig. 3). We also examined the relationship between SCG3
and two major neuropeptides in the LHA that inhibit food
intake, orexin and MCH, and detected that many orexinexpressing neurons and MCH-expressing neurons coexpressed SCG3 (Fig. 3).
Granins, such as CHGA, CHGB, and secretogranin II, form
granule-like structure when they are expressed in cultured
cells (11, 27). To examine whether SCG3 would also form
granule-like structures and interact with each of these neuropeptides, we transfected pBI-SCG3-preproorexin, pBISCG3-pro-MCH, pBI-SCG3-POMC, and pBI-SCG3-pro-NPY
into established BE(2)-C cell lines that were stably transfected
with the pTet-Off vector system. The results indicated that
SCG3 formed granule-like structures, like other granins, and
colocalized with orexin, MCH, NPY, and POMC (Fig. 4).
Immunoelectron microscopic analysis revealed that the
granules were detected in BE(2)-C cells transfected with
SCG3 but not in those transfected with vector alone (data not
shown). The granules stained with anti-SCG3 antibody (data
not shown), suggesting that SCG3 forms secretory granules
in neuroblastoma cells. These in vivo and in vitro data suggest
that SCG3 may play some role in the secretion of neuropeptides that are related to appetite.
1150
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Tanabe et al. • SNPs in SCG3 Gene and Obesity
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 values are mean ⫾ SD. pGL3-promoter, the empty
vector. The gray boxes indicate the oligonucleotide 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 factor(s) to the SCG3 gene. Gel-shift assay was
performed with digoxigenin-labeled 33-bp oligonucleotides corresponding to two SCG3
polymorphic sites (SNP-2 and SNP-9) in SHSY5Y cells. An arrow indicates the band that
shows binding of nuclear proteins to the oligonucleotides containing minor alleles of
SNP-2 (C allele, left panel) and SNP-9 (G allele, right panel).
Analysis of various quantitative phenotypes with SNP-2
and SNP-9
Because SCG3 is expressed in pancreatic ␤-cells and involved in insulin secretion (13), SCG3 may play a role in
metabolic disorders as well as in obesity. Therefore, to investigate whether the genotypes of SNP-2 and SNP-9 are
related to the phenotypes of the metabolic disorders, we
compared BMI, blood insulin, glucose, cholesterol, triglycerides, and high-density lipoprotein-cholesterol, and blood
pressure among the different genotypes in cases and controls. We detected no relationship between these quantitative
phenotypes and the genotypes at SNP-2 and SNP-9 in either
the case or control groups.
The most important phenotype of the metabolic syndrome
is visceral fat accumulation. Thus, we performed multiple
linear regression analysis to further define the role of this
gene in the amount of visceral and/or sc fat. The SNP-2
genotype was transformed to a multidichotomous variable,
i.e. homozygosity with the A alleles vs. the other genotypes,
heterozygosity vs. the other genotypes, or homozygosity
with the C alleles vs. the other genotypes. Stepwise multiple
regression analysis (both forward selection and backward
elimination) revealed that gender, age, and BMI were significantly associated with VFA. However, no genotypes were
significantly associated with VFA. In contrast, gender, BMI,
and genotype (homozygosity with the A allele or heterozygosity with A and C alleles) were significantly associated
with SFA. Neither age nor homozygosity with the C allele
was significantly associated with SFA. Table 2 shows the data
of multiple regression analysis using gender, age, BMI, and
genotype as independent variables. Among the independent
variables, homozygosity with the A allele, female gender,
and increase in BMI were significantly associated with increases 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
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 decrease in BMI, was significantly associated with a decrease in
SFA. Because the number of homozygotes with the C allele
was very small (n ⫽ 9), we were unable to validate its association with either SFA or VFA. SNP-2 and SNP-9 were in
complete linkage disequilibrium (⌬ ⫽ 1.0); thus, the same
results were observed. These data suggested that the genotypes of SNP-2 and SNP-9 have an effect on the amount of
SFA independent of the effects of the other independent
variables.
Discussion
Epidemiological studies have provided evidence indicating the involvement of genetic factors in the development of
obesity (5, 6). Through case-control association studies using
gene-based SNPs, our center has successfully discovered
candidate genes that confer susceptibility to various common
diseases (myocardial infarction, diabetic nephropathy, type
2 diabetes mellitus) (9, 28 –30). Using this approach, we identified novel functional SNPs associated with obesity, which
are located in the SCG3 gene. Our approach should prove
effective and useful in searching for genes related to common
diseases; however, the set of SNPs that we used covered only
11,932 gene loci. Recently a haplotype map of the human
genome has been constructed (31). Despite the relatively high
SNP density in genomic region, our SNP set only covered
approximately 30% of the human genome by counting Hap-
Map phase II SNPs that are: in LD (r2 ⬎ 0.5) at least with one
SNP in our set, with minor allele frequencies greater than
0.05, and at distances less than 500 kb from at least one SNP
in our set. Because of this low genomic coverage for studies
up to now, further investigations will be necessary as highthroughput genotyping products achieve higher SNP
densities.
Intracellular granins are costored and cosecreted with peptide hormones (11). Our results suggest that SCG3 forms
secretory granules together with orexin, MCH, NPY, and
POMC in the hypothalamus. We demonstrated that SNP-2
and SNP-9 might have an effect on the transcriptional activity
of the SCG3 gene. Transcriptional activity of the major allele,
the frequencies of which were higher in obese subjects than
normal controls, was shown to be lower, which indicates that
decreased SCG3 expression levels may increase the risk of
obesity. These results seem to be complicated. Many granins
are known to work as inhibitors of endocrine secretion (11);
for example, extracellular CHGA undergoes proteolytic processing into several bioactive peptides such as pancreastatin
and catestatin (11). Pancreastatin inhibits insulin secretion
from pancreatic ␤-cells (32), and catestatin inhibits the release
of catecholamines from sympathoadrenal chromaffin cells
(33). CHGA also inhibits POMC-derived peptide secretion
(34). CHGB-derived peptides inhibit the secretion of PTH
and insulin (35, 36). A secretogranin II-derived peptide, secretoneurin, inhibits serotonin and melatonin release from
1152
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Tanabe et al. • SNPs in SCG3 Gene and Obesity
FIG. 4. Granular accumulation of SGG3 protein 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 processing and is secreted from cells (38). It needs to be investigated
whether the peptides derived from proteolytic processing of
SCG3 are bioactive and whether they may also inhibit the
secretion of orexin, MCH and NPY, like other granins. Hence,
we consider that increased expression of SCG3 in the subjects
with the minor allele of SNP-2 and SNP-9 may result in a
decrease in the secretion of orexin, MCH, and NPY and
thereby inhibit food intake and accumulation of sc fat.
Food intake control is complicated (26) because in addition
to many neuropeptides in the central nervous system, peptides secreted from other tissues, such as adipose tissue and
gastrointestinal organs, participate in the control of food
intake. The neural circuits in the hypothalamic region are
also complicated, and the whole network is not well understood. There have been no reports indicating the involvement of SCG3 in appetite regulation, but in light of our data,
it is interesting to speculate that SCG3 may be a potential
factor in the regulation of food intake. Nevertheless, because
fat accumulation is also affected by other variables like physical activity as well as food intake, it is also necessary to
investigate whether SCG3 interacts with other variables.
Functional SNP-2 and SNP-9, which we have shown to be
associated with obesity, are located on the chromosome
15q21 locus in which a positive linkage to SFA was indicated
using the Québec Family Study (39). In concordance with this
previous result, our study showed an association of SNP-2
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 (5⬘ flanking ⫺1203) and other features as
independent variables
AA vs. the other genotype
Independent variables
Regression
coefficient
VFA (dependent variable)
Gender (men/women, 1/0)
Age (yr)
BMI (kg/m2)
Genotype (1/0)
R2
SFA (dependent variable)
Gender (men/women, 1/0)
Age (yr)
BMI (kg/m2)
Genotype (1/0)
R2
SE
P
61.271
1.064
5.723
3.193
41%
6.583
0.264
0.454
7.332
⬍0.0001
⬍0.0001
⬍0.0001
0.66
⫺69.550
⫺0.329
13.044
25.499
67%
7.167
0.289
0.495
7.952
⬍0.0001
0.26
⬍0.0001
0.0015
In summary, we identified the genetic variations in SCG3
that may influence the risk of obesity (particularly sc fat
obesity) by a large-scale case-control association study. We
found that SNP-2 and SNP-9 posses moderate effect sizes
(supplemental Table 1) and affect the expression levels of
SCG3 and that SCG3 forms secretory granules with hypothalamic neuropeptides. Our present data suggest that SCG3
is a good target for the development of new medicine to aid
in the prevention and treatment of obesity.
Acknowledgments
The authors express their appreciation to Dr. Chisa Nakagawa, Dr.
Hideki Asakawa, Dr. Hiroaki Masuzaki, Dr. Kazuwa Nakao, Ms. Kaoru
Nakene, Ms. Taeko Okubayashi, Ms. Yuko Ohta, Ms. Emiko Takada, Mr.
Fumitaka Sakurai, and Mr. Masahiro Uchibatake and all the members
of the SNP Research Center for their contribution to our study. The
authors also thank Dr. Todd Johnson for critical reading of the
manuscript.
Received August 17, 2006. Accepted December 21, 2006.
Address all correspondence and requests for reprints to: Kikuko
Hotta, Laboratory for Obesity, SNP Research Center, RIKEN, 1-7-22,
Suehiro, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. E-mail:
[email protected].
This work was supported by a grant from the Japanese Millennium
Project, Takeda Science Foundation (to K.Ho.), and Chiyoda Mutual Life
Foundation (to K.Ho.).
Disclosure statement: T.Y., A.I., S.S., A.S., A.Tak., T.N., T.T., Y.Nakat.,
K.K., R.K., N.I., I.M., J.W., T.F., S.M., K.To., K.Ha., T.Sh., K.Tan., K.Y.,
T.H., S.O., H.Y., T.Sa., Y.M., N.K., Y.Nakam. have nothing to declare. S.K.
consults for DHC Corporation Laboratories. K.Ho. and A.Tan. are inventors on (Japan and PCT) (PCT/JP2005/023674).
AC vs. the other genotype
Regression
coefficient
SE
P
61.398
1.063
5.728
⫺2.293
41%
6.572
0.264
0.454
7.577
⬍0.0001
⬍0.0001
⬍0.0001
0.76
⫺68.998
⫺0.326
13.095
⫺25.761
67%
7.157
0.289
0.496
8.221
⬍0.0001
0.26
⬍0.0001
0.0019
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
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