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Human Molecular Genetics Advance Access originally published online on September 12, 2007
Human Molecular Genetics 2007 16(24):3017-3026; doi:10.1093/hmg/ddm260
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Association of single-nucleotide polymorphisms in MTMR9 gene with obesity

Takahiro Yanagiya1, Atsushi Tanabe1, Aritoshi Iida4, Susumu Saito2, Akihiro Sekine2, Atsushi Takahashi5, Tatsuhiko Tsunoda3, Seika Kamohara6, Yoshio Nakata7, Kazuaki Kotani8, Ryoya Komatsu9, Naoto Itoh10, Ikuo Mineo11, Jun Wada12, Hiroaki Masuzaki13, Masato Yoneda14, Atsushi Nakajima14, Shigeru Miyazaki15, Katsuto Tokunaga16, Manabu Kawamoto17, Tohru Funahashi8, Kazuyuki Hamaguchi18, Kiyoji Tanaka7, Kentaro Yamada20, Toshiaki Hanafusa21, Shinichi Oikawa22, Hironobu Yoshimatsu19, Kazuwa Nakao13, Toshiie Sakata19, Yuji Matsuzawa8, Naoyuki Kamatani5,17, Yusuke Nakamura23,24 and Kikuko Hotta1,*

1 Laboratory for Obesity, 2 Laboratory for Genotyping, 3 Laboratory for Medical Informatics, SNP Research Center, RIKEN, Kanagawa 230-0045, Japan, 4 Laboratory for Pharmacogenetics, 5 Laboratory for Statistical Analysis, SNP Research Center, RIKEN, Tokyo 108-8639, Japan, 6 Medicine and Health Science Institute, Tokyo Medical University, Tokyo 163-1307, Japan, 7 Institute of Health and Sport Sciences, University of Tsukuba, Ibaraki 305-8574, Japan, 8 Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan, 9 Rinku General Medical Center, Osaka 598-0048, Japan, 10 Toyonaka Municipal Hospital, Osaka 560-8565, Japan, 11 Otemae Hospital, Osaka 540-0008, Japan, 12 Department of Medicine and Clinical Science, Okayama University Graduate School of Medicine and Dentistry, Okayama 700-8558, Japan, 13 Department of Medicine and Clinical Science, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan, 14 Division of Gastroenterology, Yokohama City University Graduate School of Medicine, Yokohama 236-0004, Japan, 15 Tokyo Postal Services Agency Hospital, Tokyo 102-8798, Japan, 16 Itami City Hospital, Hyogo 664-8540, Japan, 17 Institute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, 18 Department of Community Health and Gerontological Nursing, 19 Department of Anatomy, Biology and Medicine, Faculty of Medicine, Oita University, Oita 879-5593, Japan, 20 Division of Endocrinology and Metabolism, Department of Medicine, Kurume University, Fukuoka 830-0011, Japan, 21 First Department of Internal Medicine, Osaka Medical College, Osaka 569-8686, Japan, 22 Division of Endocrinology and Metabolism, Department of Medicine, Nippon Medical School, Tokyo 113-8603, Japan, 23 SNP Research Center, RIKEN, Kanagawa 230-0045, Japan, 24 Laboratory for Molecular Medicine, Human Genome Center, The Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan and

* To whom correspondence should be addressed at: Laboratory for Obesity, SNP Research Center, RIKEN 1-7-22 Suehiro, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. Tel: +81-45-503-9589; Fax: +81-45-503-9566; Email: kikuko{at}src.riken.jp

Received June 15, 2007; Accepted September 6, 2007


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Genetic factors are clearly involved in the development of obesity, but the genetic background of obesity remains largely unclear. Starting from 62 663 gene-based single-nucleotide polymorphisms (SNPs) in three sequential case–control association studies, we identified a replicated association between the obesity phenotype (BMI ≥30 kg/m2) and a SNP (rs2293855) located in the myotublarin-related protein 9 (MTMR9) gene in the chromosomal segment 8p23–p22. P-values (minor allele dominant model) of the first set (93 cases versus 649 controls) and the second set (564 cases versus 562 controls) were 0.008 and 0.0002, respectively. The association was replicated in the third set [394 cases versus 958 controls, P=0.005, odds ratio (95% CI) =1.40 (1.11–1.78)]. The global P-value was 0.0000005. A multiple regression analysis revealed that gender, age BMI and rs2293855 genotype (minor allele dominant model) were significantly associated with both systolic and diastolic blood pressures. MTMR9 was shown to be the only gene within the haplotype block that contained SNPs associated with obesity. Both the transcript and protein of MTMR9 were detected in the rodent lateral hypothalamic area as well as in the arcuate nucleus, and the protein co-existed with orexin, melanin concentrating hormone, neuropeptide Y and proopiomelanocortin. The levels of MTMR9 transcript in the murine hypothalamic region increased after fasting and were decreased by a high-fat diet. Our data suggested that genetic variations in MTMR9 may confer a predisposition towards obesity and hypertension through regulation of hypothalamic neuropeptides.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Due to its increasing prevalence throughout the developed world, obesity has become a major issue in public health, medicine and the economy (1). In particular, visceral fat obesity is important due to its relation to various complications, such as diabetes mellitus, dyslipidemia and hypertension. A combination of these metabolic disorders is now defined as the metabolic syndrome that significantly increases the risk of cardiovascular disease (2). Adipose tissue secretes various adipocytokines, and an increase in adipose tissue mass alter the plasma level of adipocytokines resulting in the development of dyslipidemia, hypertension and insulin resistance (3,4).

In epidemiological studies based on the comparison of monozygotic and heterozygotic twins as well as biological parent–offspring and adoptive relative twins, heritability of body weight is estimated to be between 60 and 70% (5,6). Obesity is considered to be a polygenic disorder and its genetic susceptibility is likely to differ among various ethnic groups. Common obesity arises when an individual's genetic background is susceptible to an environment that promotes energy consumption over energy expenditure. Many cases of monogenic obesity (obesity associated with a single-gene mutation) have been also reported. These mutations lie in one of 11 genes, such as leptin, proopiomelanocortin (POMC) and melanocortin 4 receptor (6,7). These cases are rare and characterized by obesity more severe than the common form of obesity. In common polygenic obesities, numerous genes make minor contributions in determining the obese phenotype. Indeed, a large number of manuscripts concerning obesity-related genes have been published (7). However, there are many papers reporting conflicting results at these candidate loci and the genetic background of obesity still remains unclear.

Most of the genes causing monogenic obesity are expressed in the hypothalamus and have been indicated to have important roles in the regulation of food intake, therefore, the genes which are expressed in the hypothalamus are likely to be good candidates for susceptibility to obesity. We have reported that single-nucleotide polymorphisms (SNPs) in the secretoganin III (SCG3) gene were associated with obesity in the Japanese and that SCG3 was mainly expressed in the hypothalamus and may be involved in the regulation of appetite-related peptide secretion (8).

In the study reported here, we performed an association study using a large number of gene-based SNPs (JSNP database) and found that the myotublarin-related protein 9 (MTMR9) gene is associated with obesity. MTMR9 is located on segment 8p23–p22 where several researchers reported the linkage to obese phenotypes (9). MTMR9 was expressed in the lateral hypothalamic area (LHA), paraventricular nucleus (PVN) and arcuate nucleus (ARC) of the hypothalamus, the center for food intake regulation, and MTMR9 expression was regulated by diet. Our data suggest that MTMR9 is likely to contribute to genetic susceptibility to obesity.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Case–control association studies
A total of 62 663 IMS-JST SNPs covering 11 932 gene loci were successfully genotyped in 94 obese subjects (BMI ≥30 kg/m2, case-1) and 658 subjects randomly selected from the Japanese general population (control-1). The National Nutrition Survey of Japan reported that the prevalence of subjects with a BMI of ≥30 kg/m2 is 2.3% in men and 3.4% in women aged 20 years and over (10), and the mean BMI was ~23 kg/m2 for ages 15–84 years (11). Therefore, control-1 was appropriate as a control for the initial analysis because the subjects were randomly selected from the Japanese population. From 62 663 genotyped SNPs, we selected SNPs that possessed P-values <0.01 by a test of independence using either a minor allele recessive model, minor allele dominant model or allele frequency model. We excluded the SNPs that had either minor allele frequencies (MAF) in control subjects <10% or P-values <0.01 using the Hardy–Weinberg equilibrium. A total 1078 SNPs satisfied the conditions and were further analyzed using another set of 564 obese (case-2, BMI ≥30 kg/m2) and 564 normal weight control subjects (control-2, BMI <25 kg/m2). In Japan, there is a nation-wide screening system for common disease, such as stomach cancer, colon cancer, diabetes mellitus, hypertension, dyslipidemia and so on. Every adult Japanese has a chance to take a medical examination for the common disease screening once a year. Using this screening system, we collected normal weight control subjects. Among the 1078 SNPs, we successfully completed genotyping of 1061 SNPs and only one SNP (rs2293855), located in intron 9 of the MTMR9 gene, showed a P-value of less than 0.001 (Table 1, Table 2). Through the first and the second case–control studies, we identified a SNP that showed a significant association between the obesity phenotype and the genotype. The association was the most significant in the minor allele dominant model.


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  		Table 1. Genotype and allele frequencies of SNP (rs2293855) in the MTMR9 gene

 


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  		Table 2. Association of SNP (rs2293855) in the MTMR9 gene with obesity in the first (case-1 versus control-1), the second (case-2 versus control-2) and third (case-3 versus control-3) sets of experiments

 
To confirm this association, we subjected this SNP to an additional independent case–control. In the third case–control study [394 obese subjects (BMI ≥30 kg/m2) versus 958 normal weight controls (BMI <25 kg/m2)], the previous conclusion was successfully replicated [P = 0.005, odds ratio (OR) 1.40, 95% CI = 1.11–1.78] (Table 2). Although the risk allele frequencies were different among cases in different studies, it is most likely to be attributable to the fluctuation in the sampling.

To calculate the global P-value through the three association studies, we first got the P-value of each stage by a simulation as described in Materials and Methods. In each stage, the tests were performed in the three different models (dominant, recessive and allele frequency model), and the P-values that were the lowest among the three models were used. By this calculation, the P-values were 0.0283, 0.00160 and 0.0110 for the first, second and the third stage, respectively. If we consider the three stages as independent trials, then the global P-value for a single SNP is calculated to be 0.0283 x 0.00160 x 0.00110 = 4.98 x 10–7. After the Bonferroni's correction, the corrected P-value is 0.031. Thus, rs2293855 was significantly associated with obesity.

Since the effect size (for example, OR) is likely to be overestimated in the first and the second sets of the studies, we estimated the OR using only the third set. As expected, the estimated OR decreased from 1.80 (first set), to 1.57 (second set) and to 1.40 (third set) (Table 2). We propose that the OR estimated in the third set is the most accurate, since it is generally known that ORs tend to converge to the true value when the selection of the candidate SNPs proceeds. The power to identify SNPs associated with the obesity in the present study was estimated at 0.35 when the genotype relative risk is 1.4 and the minor allele frequency is 0.3 according to Monte–Carlo simulation (Supplementary Fig. S1).

Analysis of various quantitative phenotypes with SNP rs2293855
To investigate whether the genotypes of SNP rs2293855 are associated with the phenotypes of the metabolic disorders, we first compared by Student's t-test, BMI, fasting plasma glucose, hemoglobin A1c (HbA1c), total cholesterol, triglycerides or HDL cholesterol, and blood pressure between the different genotypes (AA versus AG+GG) in cases, controls and the combined group. We detected no significant association between the quantitative phenotypes of BMI, glucose, HbA1c, total cholesterol, triglycerides or HDL cholesterol, and the genotypes at SNP rs2293855 in either the case or control groups (Table 3). Although there was no significant difference in BMI values between the AA and AG + GG genotypes in either the control or the obese group, the direction of the difference (AA > AG + GG) was in accord with the association between the qualitative obesity phenotype and the genotype as shown in the present study. In the obese group, systolic and diastolic blood pressures were higher in the AA homozygotic patients compared with the other genotype (Table 3), however, the significance was not present when the multiple comparison problem was taken into account (significant P-value is 0.0028).


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  		Table 3. Comparison of various quantitative phenotypes between different genotypes at SNP rs2293855

 
In the next step, we attempted to perform linear multiple regression analysis with systolic and diastolic blood pressures as the dependent variables. The rs2293855 genotype was transformed to a multi-dichotomous variable; i.e. A-allele homozygotes versus the other genotypes or G-allele homozygotes versus the other genotypes and used as an explanatory variable. Table 4 shows the data of multiple regression analysis using gender, age, BMI and genotype as explanatory variables. Stepwise multiple regression analysis (both forward selection and backward elimination) revealed that gender, age BMI and genotype (A-allele homozygotes) were significantly associated with both systolic and diastolic blood pressures. Although, G-allele homozygotes was not significantly associated with systolic or diastolic blood pressure, A-allele homozygotes was significantly associated with increases in both systolic and diastolic pressures even after aging, male gender and increase in BMI were removed from the model. These data suggested that the rs2293855 genotype might affect blood pressure independently of the other explanatory variables.


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  		Table 4. Multiple linear regression analysis for systolic blood pressure or diastolic blood pressure using SNP rs2293855 and other features as explanatory variables

 
Finally, we examined the BMI distribution of rs2293855 in the Japanese general population. The subjects were randomly recruited using the screening system for common disease. The mean BMI of A-allele homozygote was significantly higher than that of G-allele homozygote and heterozygote (Table 5). This result would confirm the association of rs2293855 with obesity.


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  		Table 5. Comparison of BMI between different genotypes at SNP rs2293855 in 2459 general population

 
LD blocks of the MTMR9 locus
We have searched dbSNP with MAF >0.15 around SNP rs2293855. A total of 67 SNPs covering the ~570-kb genomic region were successfully genotyped by Invader assay. LD analysis revealed that SNP rs2293855 in the MTMR9 gene was located in a 70-kb LD block (Fig. 1), which contains acyl-malonyl-condensing enzyme 1-like 2 (AMAC1L2) and L-threonine dehydrogenase (TDH) as well as MTMR9. We sequenced to screen SNPs in the genomic region covering MTMR9, AMAC1L2 and TDH except for those corresponding to repetitive sequences. We discovered five novel SNPs in this region. Using the SNPs around rs2293855, we performed tests of independence between the obesity phenotype and the genotypes at various SNPs using the combined second and third sets (958 cases and 1520 controls). For each SNP, the lowest P-value among the three different modes was selected as the minimum P-value. Although we could find many SNPs near rs2293855 that showed significant associations with the obesity phenotype, most of them were in or around MTMR9 gene and the P-value for rs2293855, which is in the MTMR9 gene, showed the lowest (Fig. 1). The P-values of SNPs found in TDH gene were >0.01. Human TDH is not capable of encoding a functional TDH protein; the predicted protein lacks most of the C-terminus and parts of the NAD+-binding motif compared with other species (12). AMAC1L2 is similar to mouse AMAC1, one of the enzymes in the biosynthesis of long-chain fatty acids. AMAC1L2 consists of a single exon and a 3'-polyadeneylation tract present in the genomic DNA adjacent to AMAC1L2, which are characteristics of pseudogenes. In the human genome, three additional sequences coding for elements of AMAC1L2 are located on 17p13.1, 18p11.2 and 17q11.2. Transcript of AMAC1L2 is detected in various tissues: however, whether AMAC1L2 is translated into protein has not been elucidated (13). Therefore, MTMR9 is most likely to be functionally associated with obesity. However, we could not exclude the possibility of AMAC1L2 and TDH as susceptibility genes for obesity.


Figure 1
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  		Figure 1. LD mapping, polymorphisms and P-values obtained by the test of independence between the obesity phenotype and the genotypes at various SNPs around rs2293855. P-values are expressed as –log of the minimum P-values of the three models (allele frequency model, minor allele dominant model, and minor allele recessive model) using the combined second and third set. LD coefficients (D') between every pair of SNPs around SNP rs2293855 were calculated and shown as the strand of the LD blocks. Minor allele frequencies of all SNPs used in this analysis are ≥15%. Each SNP is labeled with its rs number, except for four novel SNPs. Genomic structure is shown in the middle. *, rs2293855. P-value of rs2293855 is shown as a red circle.

 
Expression of MTMR9 in the hypothalamus
MTMR9 was reported to be expressed in the brain, but its physiologic roles have not yet been clarified (14). To further elucidate this role, we performed in situ hybridization for MTMR9 in the murine hypothalamus and found MTMR9 to be expressed in LHA, PVN, ARC and supraoptic nucleus (SON) (Supplementary Fig. S2). MTMR9 immunoreactivity was also observed in various other regions of the rat brain, however, the most intense immunoreactivities were observed in the PVN, ARC, LHA and SON (Fig. 2). We used rat brain for immunohistochemistry, since anti-MTMR9 antibody did not cross-react with mouse MTMR9. MTMR9 is belongs to myotublarin family that possesses protein-tyrosine phosphatase activities. MTMR9, however, lacks phosphatase activity (15). MTMR9 interacts with active phosphatases MTMR7 and MTMR6 and regulates MTMR7 phosphatase activity (16,17). Thus, we also examined the distribution of MTMR6 and MTMR7 by in situ hybridization. MTMR6 and MTMR7 transcripts were also detected at PVN, ARC, LHA and SON (Supplementary Fig. S2). MTMR9 and MTMR7 proteins were expressed in the same cells (Fig. 2), suggesting that MTMR9 could act in the hypothalamic region together with MTMR7. Specific anti-MTMR6 antibody were not available, thus, we could not confirm whether or not MTMR6 and MTMR9 are co-expressed. The ARC neurons that express and secrete neuropeptide Y (NPY) and POMC are regulated by leptin and transfer their neuronal signal to orexin-expressing neurons in the LHA and PVN (18). To investigate the relation between MTMR9 and these neuronal peptides, we performed double-labeling immunohistochemical analysis and found that MTMR9 was co-expressed with NPY and POMC in ARC cells in the rat brain (Fig. 2). We also examined the relation between MTMR9 and two major neuropeptides in the LHA that stimulate food intake, orexin and melanin concentrating hormone (MCH), and we found that orexin- and MCH-expressing neurons are also co-expressed MTMR9 (Fig. 2).


Figure 2
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  		Figure 2. Co-localization of MTMR9 with MCH, orexin B, NPY, POMC or MTMR7 in rat hypothalamus. After being anesthetized, rats were perfused with 10% neutral-buffered formalin. The hypothalamic region was dissected from the brain, further fixed with Tissue Fixative, embedded in paraffin, and sectioned. Tissue sections were de-waxed and incubated with polyclonal goat anti-MTMR9 antibody together with rabbit polyclonal antibody against MCH, orexin B, NPY, POMC, or MTMR7. Scale bar = 50 µm.

 
Regulation of MTMR9 expression in hypothalamus
To examine the role of MTMR9 in the murine hypothalamus, we examined the effects of fasting and a high-fat diet on MTMR9 regulation. The mRNA levels of MTMR9 were increased after 24 h of fasting. Administration of a high-fat diet for one month increased the body weight of mice slightly; the mean body weight of control mice was 25.6±0.3 g and that of mice given the high-fat diet was 26.5±0.7 g. The mRNA levels of MTMR9 were significantly decreased in the mice given the high-fat diet. The mRNA levels of orexigenic peptides agouti-related protein (AGRP) and NPY were increased by fasting and decreased by a high-fat diet, as reported previously (19–23) (Fig. 3). In contrast, the mRNA levels of anorectic peptides POMC were decreased by fasting and increased by a high-fat diet, although the difference was not significant. These results suggest that NPY, AGRP and MTMR9 are downregulated by high-fat diet, whereas the anorexigenic POMC is upregulated.


Figure 3
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  		Figure 3. Effect of fasting (A) and high-fat diet (B) on the mRNA expression of MTMR9, NPY, AGRP and POMC. Seven-week-old mice (n = 4) were deprived of food (free access to water) overnight after being provided with regular laboratory chow for a week. Mice (seven weeks old) were provided with regular laboratory chow (n = 6) or with a high-fat diet (n = 6) for four weeks. Relative amounts of MTMR9, NPY, AGRP, POMC and ß-actin mRNA were determined by quantitative real-time PCR. The quantities were normalized to mRNA for ß-actin as an internal control. Data are mean±SE. *P < 0.05; **P < 0.01 (Student's t-test).

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Although many quantitative trait loci of obese phenotype (e.g. BMI, plasma leptin and fat mass) identified by linkage studies have been reported, few have been replicated. Meta-analysis of five genome-wide linkage studies has revealed that chromosome 8, especially 8p, is the most likely region influencing BMI in various populations (9). Linkage studies of at least four different populations (American, Amish, European and African) reported that 8p23–22 was linked with an obesity-related phenotype (BMI, fat mass and hypertension) (7,9). Through case–control association studies using gene-based SNPs, we identified a SNP rs2293855 associated with the quantitative obesity phenotype (BMI and hypertension), which was located in the MTMR9 gene. The association was replicated in an independent study. MTMR9 is located on chromosome 8p23–p22 (14). Therefore, MTMR9 is likely to be one of the genes assigned to obesity and obesity-related phenotypes.

MTMR9 is belongs to the myotublarin family that constitutes one of the largest and most highly conserved protein-tyrosine phosphatase subfamilies (15). In eight of the family members, the active site motif CSDGWDR is absolutely conserved. Six members contain mutations within this sequence, which render them catalytically inactive phosphatase-like molecule. MTMR9 is one of the inactive phosphatase-like molecules. However, MTMR9 interacts with active phosphatases, MTMR7 and MTMR6 (16,17), and regulates MTMR7 phosphatase activity (16). MTMR7 and MTMR6 dephosphorylate phosphatidylinositol 3-phosphate (PtdIns3P) (15,16,24). PtdIns3P is highly enriched on early endosomes and on the internal vesicles of multi-vesicular endosomes. PtdIns3P is important in endosome function and recruits a number of effector proteins to the endosomal membrane (24,25). NPY receptors Y1 and Y2 exist in ARC, PVN and LHA, Y4 and Y5 in LHA and ARC (18,26,27), which are internalized after NPY binding (28). MTMR7 was expressed restrict in brain and co-expressed with MTMR9 in ARC, LHA and PVN. MTMR9 was mainly expressed in ARC, LHA and PVN in hypothalamus and co-expressed with MTMR7 and maybe with MTMR6. Therefore, MTMR9 may act together with MTMR7 and MTMR6, and may regulate the internalization of NPY receptors through changes in the intracellular concentration of PtdIns3P. Alteration of the internalization of NPY receptors could be related to the development of obesity.

MTMR9 would be involved in appetite regulation since its mRNA levels were increased by fasting and decreased by a high-fat diet in parallel with NPY and AGRP, although it is necessary to elucidate whether the MTMR9 mRNA levels changes by fasting in response to other factors, such as reproduction and growth hormone, besides those influencing food intake.

MTMR7 also dephosphorylates inositol 1,3-bisphosphate (Ins(1,3)P2) as well as PtdIns3P (16). The function of Ins(1,3)P2 is yet unknown. However, there is a possibility that a complex of MTMR9 and MTMR7 may participate in insulin signaling since MTMR7 is involved in the inositol metabolism as is tensin homologue on chromosome 10 (PTEN) and SRC homology (SH2)-containing inositol 5'-phosphatase protein 2 (SHIP2), which are inositol phosphatases and involved in insulin signaling (24,29,30). MTMR9 may play some roles in regulation of appetite-related neuropeptides through insulin signaling in hypothalamus. SNP rs2293855 in MTMR9 gene did not associate fasting glucose and HbA1c. This may be due to that MTMR7 is expressed in restricted in brain and not in muscle, liver or adipose tissue (16).

In summary, we identified the genetic variations in MTMR9 that may influence the risk of obesity and hypertension in a large-scale case–control association study. We have demonstrated the possibility that MTMR9 could be involved in appetite regulation. Our present data suggest that MTMR9 is a candidate target for the development of new medicine to aid in the prevention and treatment of obesity.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
SUPPLEMENTARY MATERIAL
 REFERENCES
 
Study subjects
The sample size of the first set of Japanese obese subjects (BMI ≥30 kg/m2) was 94 (case-1; male:female ratio 39:55; age 47±2 years; BMI 36.3±0.5 kg/m2) as reported previously (8). The sample size of the first set of control individuals (control-1) was 658, and it consisted of the Japanese general population as described in the JSNP database (IMS-JST: Institute of Medical Science-Japan Science and Technology Agency Japanese SNP database) (8,31). The sample size of the second set of Japanese obese subjects (BMI ≥30 kg/m2) was 564 (case-2; male:female ratio 289:275; age 50±1 years; BMI 34.4±0.2 kg/m2), while that of the second set of Japanese normal weight controls (BMI <25 kg/m2) was 564 (control-2; male:female ratio 231:333; age 56±1 years; BMI 21.6±0.1 kg/m2). The sample size of the third set of Japanese obese subjects (BMI ≥30 kg/m2) was 394 (case-3; male:female ratio 156:238; age 48±1 years; BMI 34.5±0.3 kg/m2) and that of the third set of Japanese normal weight controls (BMI <25 kg/m2) was 958 (control-3; male:female ratio 454:504; age 44±1 years; BMI 21.7±0.1 kg/m2). Case-1, case-2 and case-3 subjects were recruited from the outpatients of medical institutes. Patients with secondary obesity and obesity-related hereditary disorders were excluded from this study. Patients with obesity caused by medications were also excluded. A total of 2459 subjects of the Japanese general population was recruited (male:female ratio 1301:1158; age 44±1 years; BMI 22.6±0.1 kg/m2). Control-2, control-3 and the subjects of general population were collected from subjects who had undergone a medical examination for common disease screening. Written informed consent was obtained from each subject and the protocol was approved by the ethics committee of each institution and by 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 (31), and the SNPs were genotyped in case-1 by Invader assays as described previously (32,33).

SNP discovery in around rs2293855
To identify additional variations in the genomic region around rs2293855, we amplified appropriate fragments of genomic DNA by PCR and sequenced the products to identify SNPs within a 100 kb of MTMR9, AMAC1L2 and TDH (GenBank accession number AF131216 [GenBank] ) using previously described methods (34).

Double-labeling immunohistochemistry for MTMR9, MTMR7, OREXIN, MCH, NPY and POMC
Male rats (Wistar, six weeks old) were purchased from CLEA Japan (Tokyo, Japan). After being anesthetized with sodium pentobarbital (100 mg/kg), rats 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 (6 µm) were de-waxed and incubated at 4°C overnight with polyclonal goat anti-MTMR9 antibody (1:200; Abcam, Cambridge Science Park, UK) together with either rabbit polyclonal antibody to MCH (1:500; Phoenix Pharmaceuticals, Belmont, CA), orexin B (1:500; Chemicon, Temecula, CA), NPY (1:500; PROGEN Bioteknik, Heidelberg, Germany), POMC (1:5000; Phoenix Pharmaceuticals) or MTMR7 (1:200; ABGENT San Diego, CA). After washing, the sections were incubated at room temperature for 2 h with Alexa Fluor® 488 donkey anti-goat IgG (1:2000; Molecular Probes, Eugene, OR) and Alexa Fluor® 568 donkey anti-rabbit IgG (1:2000; Molecular Probes) secondary antibodies. Double immunofluorescence detection was carried out using an Olympus BX51 microscope.

Quantitative analysis of RNA expression
Male C57BL/6J mice (six weeks old) were purchased from CLEA Japan. Mice were kept at constant room temperature and provided with regular laboratory chow (CRF-1, Oriental Yeast Co., Ltd., Tokyo, Japan). Seven-week-old mice were deprived of food (free access to water) overnight after being provided with regular laboratory chow for a week. Seven-week-old mice were provided with regular laboratory chow (CRF-1, Oriental Yeast Co) or a high-fat diet (CLEA Rodent Diet Quick Fat, CLEA Japan Inc.) for four weeks. Diet composition, calculated as a percentage of total kilocalories, was as follows: regular laboratory chow (14% fat, 61% carbohydrate, 25% protein) with 3.59 kcal/g and high-fat diet (33% fat, 44% carbohydrate, 23% protein) with 4.25 kcal/g. The mice were anesthetized with pentobarbital (10 mg/100 g of body weight) and the hypothalamic regions were obtained and immediately frozen for RNA preparation.

Total RNA was prepared using TRIzol reagent (Invitrogen, Carlsbad, CA). Relative amounts of MTMR9, NPY, AGRP, POMC and ß-actin mRNA were determined by quantitative real-time PCR. One microgram of total RNA was reverse-transcribed using the Superscript III First-Strand Synthesis System (Invitrogen) according to the manufacturer's protocol. Quantification of PCR products was performed by measuring fluorescence from the progressive binding of SYBR green I dye to double-stranded DNA using the ABI PRISM 7000 Sequence Detection System (Applied Biosystems, Foster City, CA). The quantities were normalized to mRNA for ß-actin as an internal control. The sets of primers that we used were as follows:

MTMR9; 5'-GCTTATGCCTCACAGTTCGGGACA-3' and 5'-TGAACTTACTCAGCTCGCCGGGCCG-3'

NPY; 5'-GTTTGGGCATTCTGGCTGAGGG -3' and 5'-GTGTCTCAGGGCTGGATCTCTTGC-3'

AGRP; 5'-GTCTAAGTCTGAATGGCCTCAAG-3' and 5'-GACTCGTGCAGCCTTACACAG-3'

POMC; 5'-ATAGATGTGTGGAGCTGGTGCC-3' and 5'-TCATCTCCGTTGCCAGGAAACAC-3'

ß-actin; 5'-GAATGGGTCAGAAGGACTCCTATG-3' and 5'-CCAGTTGGTAACAATGCCATGT-3'.

Statistical analysis
For each case–control study, the frequencies of the genotypes or the alleles were compared between cases and controls in three different models. In the first model (allele frequency model), allele frequencies were compared between cases and controls using a 2x2 contingency table. In the second model (minor allele recessive model), the frequencies of the homozygous genotype for the minor allele were compared using a 2x2 contingency table, whereas in the third model (minor allele dominant model), the frequencies of the homozygotes for the major allele were compared using a 2x2 contingency table. Test of independence was performed using Pearson's {chi}2 method. OR and its 95% CI were calculated by Woolf's method. Hardy–Weinberg equilibrium was assessed using the {chi}2 test (35). Haplotype blocks were determined using Haploview 3.2 (36). Multiple linear regression analysis was performed using StatView 5.0 (SAS Institute Inc., Cary, NC). The significance of the association between an explanatory variable and the response variable was tested by Student's t-test. Simple comparison of the clinical data between the different genotypes and the relative mRNA levels were done with the unpaired t-test using StatView 5.0 (SAS Institute Inc.).

We calculated the global P-value by simulations. We first gave a population allele frequency and calculated the genotype frequencies assuming the Hardy–Weinberg's equilibrium. Then the numbers of the subjects with three different genotypes were sampled assuming the multinomial distributions. After the statistical tests in three different models (allele frequency model, minor allele recessive model and minor allele dominant model), the minimum P-values were used to test whether they were lower than the significance levels. The proportion of the trials that gave the test of the significance in the three stages was judged as the global type 1 error rate. The minimum P-value method was applied to the test of independence using 94 cases and 658 controls for the significance level of 0.01 for three different models. In the second stage, 564 cases versus 564 controls at the significant level of 0.001. In the third stage, 394 cases versus 958 controls were used for the significant level of 0.05.


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


   ACKNOWLEDGEMENTS
 
The authors express their appreciation to Drs Chisa Nakagawa (Otemae Hospital), Dr Hideki Asakawa (Itami City Hospital), Dr Shiro Maeda (Laboratory for Diabetic Nephropathy, SNP Research Center, RIKEN), Ms Kaoru Nakene, Ms Yuko Ohta, Mr Fumitaka Sakurai, Mr Michihiro Nakamura and Ms Chiaki Ohkura, and all the members of the SNP Research Center for their contribution to our study.

Conflict of Interest statement. None declared.


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 

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