Mol Genet Genomics DOI 10.1007/s00438-015-0996-8

ORIGINAL PAPER

Associations between TRPV4 genotypes and body mass index in Taiwanese subjects De‑Min Duan · Semon Wu · Lung‑An Hsu · Ming‑Sheng Teng · Jeng‑Feng Lin · Yu‑Chen Sun · Ching‑Feng Cheng · Yu‑Lin Ko 

Received: 27 June 2014 / Accepted: 17 January 2015 © Springer-Verlag Berlin Heidelberg 2015

Abstract  Body weight regulation is influenced neuronally via the hypothalamus, which strongly expresses TRPV4. TRPV4 deficiency in mice confers resistance against diet-induced obesity. We investigated the association between TRPV4 gene variants and body mass index (BMI) in Taiwanese subjects. A sample population of 617 Taiwanese subjects was enrolled, and ten TRPV4 gene polymorphisms were selected and genotyped. After adjusting for clinical covariates, significant associations were observed between three studied polymorphisms and BMI using a dominant model (P  = 4.83 × 10−4, P  = 1.17 × 10−4, and P  = 3.37 × 10−4 for rs3742037, rs10735104, and rs3742035, respectively). Obesity as Communicated by S. Hohmann. Electronic supplementary material  The online version of this article (doi:10.1007/s00438-015-0996-8) contains supplementary material, which is available to authorized users.

defined according to both the Asian and National Institutes of Health (NIH) criteria was significantly associated with rs10735104 (P = 0.003 and P = 0.037, respectively) in a dominant model. Genotypes at the TRPV4 locus independently affect BMI and obesity status in Taiwanese subjects. This association may broaden our understanding of the role of neuronal influence on body weight regulation. The regulation of TRPV4 channels in skeletal muscle and adipose tissue could also be a new therapeutic target for preventing the development of obesity. Keywords  Body mass index · Single nucleotide polymorphism · Genetics · Obesity · Population studies Abbreviations SNPs Single nucleotide polymorphisms BMI Body mass index GWAS Genome-wide association studies

D.‑M. Duan · J.‑F. Lin · Y.‑L. Ko (*)  Division of Cardiology, Department of Internal Medicine and The Cardiovascular Medical Center, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 289 Jianguo Road, Xindian Dist, New Taipei City 231, Taiwan, ROC e-mail: [email protected]

L.‑A. Hsu  Chang Gung University College of Medicine, Taipei, Taiwan

S. Wu · M.‑S. Teng · Y.‑L. Ko  Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 289 Jianguo Road, Xindian Dist, New Taipei City 231, Taiwan, ROC

C.‑F. Cheng  Department of Pediatrics, Buddhist Tzu Chi General Hospital, Hualien, Taiwan

S. Wu  Department of Life Science, Chinese Culture University, Taipei, Taiwan

Y.‑C. Sun  Department of Laboratory Medicine, Chang Gung Memorial Hospital, Taipei, Taiwan

C.‑F. Cheng · Y.‑L. Ko  Tzu Chi University College of Medicine, Hualien, Taiwan

L.‑A. Hsu  The First Cardiovascular Division, Department of Internal Medicine, Chang Gung Memorial Hospital, Taipei, Taiwan

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TRP Transient receptor potential TRPV4 Transient receptor potential vanilloid subtype 4 NIH National Institutes of Health ELISA Enzyme-linked immunosorbent assay HDL High-density lipoprotein LDL Low-density lipoprotein

Introduction Obesity has reached epidemic proportions, resulting in increased morbidity and mortality as well as severe economic burdens on health-care systems (Flegal et al. 2007; Finkelstein et al. 2008). Obesity is caused by calorie intake that exceeds the body’s requirements in association with diminished physical activity. Excess accumulation of adipose tissue, particularly visceral adipose tissue, is associated with a large number of metabolic perturbations including type 2 diabetes and cardiovascular disease. Obesity is a multifactorial disease; interactions between environmental and genetic factors play key roles. Twin, adoption, and family studies demonstrate that genetic factors account for 40–70 % of the population variation in body mass index (BMI) (Maes et al. 1997; Atwood et al. 2002), which is the most commonly used quantitative measure of adiposity. Earlier studies revealed that BMI is influenced by mutations in several genes that cause rare, often severe monogenic syndromes with obesity as the main feature (Farooqi 2006). Mutations in these genes are thought to act via the central nervous system, particularly the hypothalamus, to influence the energy balance and appetite, thereby leading to obesity. Recent genome-wide association studies (GWAS) yielded sequence variants at several gene loci associated with measures of obesity including BMI and waist circumference (Cecil et al. 2008; Cho et al. 2009; Lindgren et al. 2009; Heid et al. 2010; Scherag et al. 2010; den Hoed et al. 2010; Wang et al. 2011). In an association analysis of nearly 250,000 individuals, Speliotes et al. (2010) revealed 18 new loci associated with BMI; furthermore, by pathway-based analyses, they found evidence of the enrichment of pathways involved in platelet-derived growth factor signaling, translation elongation, hormone and nuclear hormone receptor binding, homeobox transcription, regulation of cellular metabolism, neurogenesis and neuronal differentiation, and protein phosphorylation as well as numerous other pathways related to growth, metabolism, immune, and neuronal processes (Yang et al. 2009). These results suggest that genetic heterogeneity is involved in obesity. The transient receptor potential (TRP) superfamily, located in the chromosome 12, consists of a large number of cation channels that can be divided into six subfamilies: TRPC, TRPV, TRPM, TRPP, TRPML, and TRPA. TRP

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Mol Genet Genomics

channels play a general role as cellular sensors (Clapham et al. 2003; Voets et al. 2005). TRP channel malfunction is linked to a growing number of human diseases. The initial focus of research was on TRP channels that are expressed on nociceptive neurons. There has been a recent upsurge in the amount of work that expands TRP channel drug discovery efforts into new disease areas such as asthma, cancer, anxiety, cardiac hypertrophy, as well as obesity and metabolic disorders (Kaneko and Szallasi 2014). Transient receptor potential vanilloid subtype 4 (TRPV4) is a broadly expressed nonselective Ca2+-permeable cation channel in the vanilloid subfamily of TRP channels that is activated by a wide variety of physical and chemical stimuli (Nilius et al. 2004). TRPV4 protein expression has been detected in the epithelial cells of the renal distal convoluted tubule, trachea, and submucosal glands; neutrophils; and autonomic nerve fibers (Delany et al. 2001). In situ hybridization studies also demonstrate TRPV4 mRNA expression in the hair cells of the inner ear, peripheral sensory ganglia, and osmoregulation-related brain structures including the vascular organ of the lamina terminalis and hypothalamic median preoptic region (Liedtke et al. 2000; Schumacher et al. 2000). The TRPV4 gene is also an ion channel gene highly expressed in the hypothalamus—a region that plays an important role in the neuronal influence on body weight regulation (Thaler et al. 2012). Recent studies have shown that TRPV4−/− mice confer resistance against diet-induced obesity (Kusudo et al. 2012; Li et al. 2012). Kusudo et al. (2012) revealed that obesity phenotypes induced by a high-fat diet were significantly improved in TRPV4−/− mice. Li et al. (2012) also revealed that TRPV4 acts as a cell-autonomous mediator for both the thermogenic and proinflammatory programs in adipocytes, and TRPV4−/− mice are protected from obesity and adipose tissue inflammation, with improved insulin sensitivity on exposure to high-fat diet. These results are the first to demonstrate the anti-obesity phenotype induced by TRPV4 deficiency in mice. The present study aimed to investigate the associations between TRPV4 genotypes and BMI and obesity status in Taiwanese subjects.

Materials and methods Subjects A total of 617 Han Chinese subjects, age older than 20 years (327 men with a mean age of 45.2 ± 10.5 years and 290 women with a mean age of 46.8 ± 10.1 years), were enrolled. They responded to a questionnaire about their medical history and lifestyle characteristics. The subjects were recruited during routine health examinations between October 2003 and September 2005 at the Chang Gung Memorial Hospital. The subjects underwent physical

Mol Genet Genomics Table 1  Clinical and biochemical characteristics of the study participants

Obesity was defined as BMI ≥ 25 kg/m2  according to the Asian criteria (WHO expert consultation 2004) BP blood pressure, HDL highdensity lipoprotein, LDL lowdensity lipoprotein

Number, n (%) Age (years)

Total

Obese

Non-obese

617

245 (39.7 %)

372 (60.3 %)

P value

Male, n (%) Systolic BP (mmHg) Diastolic BP (mmHg) Total cholesterol (mg/dL) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) Triglyceride (mg/dL) Body mass index (kg/m2) Diabetes mellitus (%) Current smokers (%)

49.95 ± 10.34 327 (53.0 %)

46.72 ± 10.34 155 (63.3 %)

45.44 ± 10.33 172 (46.2 %)

0.130 <0.001

115.08 ± 17.60 75.95 ± 10.58 198.07 ± 36.62 55.12 ± 12.24 115.58 ± 32.88 142.12 ± 117.56 24.33 ± 3.47 31 (5.0 %) 153 (24.8 %)

119.40 ± 17.11 78.99 ± 10.36 200.76 ± 36.87 50.69 ± 11.43 117.89 ± 32.41 168.55 ± 126.02 27.70 ± 2.43 16 (6.5 %) 78 (31.8 %)

112.24 ± 17.37 73.94 ± 10.24 196.31 ± 36.39 58.04 ± 15.14 114.05 ± 33.14 124.72 ± 108.33 22.12 ± 1.92 15 (4.0 %) 75 (20.2 %)

<0.001 <0.001 0.137 <0.001 0.108 <0.001 <0.001 0.190 0.001

C-reactive protein (mg/L)

1.68 ± 6.17

2.05 ± 4.11

1.44 ± 7.21

examinations including the measurement of height, weight, waist and hip circumference, and blood pressure in a sitting position after 15 min of rest. Fasting blood samples were obtained from each subject. The exclusion criteria included a history of myocardial infarction, stroke, transient ischemic attack, or cancer and current renal or liver disease. The clinical and biometrical features of the study population are summarized in Table 1. Obesity was defined as BMI ≥ 25 kg/m2 according to the Asian criteria (WHO expert consultation 2004) and BMI ≥ 30 kg/m2 according to the National Institutes of Health (NIH) criteria (National Institute of Health 1998). Current smokers were defined as those who smoked at least one cigarette per day at the time of survey. The Ethics Committee of the Buddhist Tzu Chi General Hospital, Taipei Branch, approved the investigation.

0.220

rs3742037, and rs1861810 were typed by the TaqMan assay. Genotyping data are shown in Table 2. Assays C-reactive protein was measured using a sandwich enzymelinked immunosorbent assay (ELISA) developed in-house. The in-house kit was compared with commercially available ELISA kits and showed good to excellent correlation (Wu et al. 2002). Total cholesterol and triglyceride concentrations were measured by automatic enzymatic colorimetry. High-density lipoprotein (HDL) cholesterol levels were measured enzymatically after phosphotungsten/magnesium precipitation. Low-density lipoprotein (LDL) cholesterol was either calculated from the Friedewald formula or detected with commercial reagents using standard protocol in patients with triglycerides >400 mg/dL.

Genomic DNA extraction and genotyping Statistical analysis Genomic DNA was extracted as described previously (Hsu et al. 2002; Ko et al. 2004). Oligonucleotide primers were generated to amplify fragments of genomic DNA containing single nucleotide polymorphisms (SNPs) as reported on the NCBI SNP database (http://www.ncbi.nlm.nih.gov/ SNP). The tag SNP was selected based on its ability to tag surrounding variants (chr12:108699994.0.108760877) in the Han Chinese panel (Beijing, China) of the International HapMap project, NCBI build B36 assembly HapMap phase III (http://www.hapmap.org). We selected the tag SNPs by running the tagger program implemented in Haploview. The criteria for r2 and the minor allele frequency were set at 0.95 and 0.05, respectively. Ten tagSNPs with TRPV were chosen in this study. SNPs rs6606743, rs7971845, rs3742030, rs10850783, rs3742034, rs16940583, and rs10735104 were genotyped by PCR with restriction enzyme digestion, while other SNPs including rs3742035,

The power and sample size of this study was evaluated by SPSS software. The analysis using alpha = 0.05 was performed to detect the power of the study. The study sample size produced a power of 0.960 for all SNPs and BMI. The χ2 test or χ2 test for trends was used to examine differences in categorical data distribution, such as diabetes mellitus and current smoker status. The clinical characteristics of the continuous variables are expressed as mean ± SD and were tested by two-sample t test or ANOVA. The explained variance for BMI was calculated as 2 × f × (1 − f) × β2, where f is the minor allele frequency and β is the per-allele effect on standardized values of BMI (Park et al. 2011). A generalized linear model was used to analyze BMI with respect to the predictors of the investigated genotypes and confounders. Two-sided P values <0.05 were considered statistically significant. Deviation from the

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Mol Genet Genomics

Table 2  Linkage disequilibrium between TRPV4 genetic polymorphisms in all subjects rs6606743 rs7971845 rs3742030 rs10850783 rs3742034 rs16940583 rs3742035 rs10735104 rs3742037 rs1861810 rs6606743 rs7971845 rs3742030 rs10850783

– – – –

0.68 – – –

0.52 0.94 – –

0.84 0.49 0.67 –

0.47 0.82 0.85 0.54

0.49 0.85 0.88 0.56

0.64 0.87 0.88 0.97

0.41 0.46 0.79 0.95

0.33 0.51 0.76 0.38

0.06 0.34 0.89 0.81

rs3742034 rs16940583 rs3742035 rs10735104 rs3742037

– – – – –

– – – – –

– – – – –

– – – – –

– – – – –

0.98 – – – –

1.00 1.00 – – –

0.98 0.99 0.85 – –

0.99 0.99 0.88 0.98 –

0.90 0.90 0.16 0.50 0.98

rs1861810

–

–

–

–

–

–

–

–

–

–

The values represent D′

Hardy–Weinberg equilibrium, an estimation of linkage disequilibrium between polymorphisms, was analyzed using the Golden Helix SVS Win32 7.3.1 software.

Results Clinical and biochemical characteristics and biomarkers The demographic features and clinical and lipid profiles of the study participants stratified by obesity are summarized in Table 1. No significant deviations from the Hardy–Weinberg equilibrium were detected for the studied polymorphisms (the P values were 0.99, 0.70, 1.0, 1.0, 0.83, 0.83, 0.89, 0.60, 0.98, and 0.88 for SNPs rs6606743, rs7971845, rs3742030, rs10850783, rs3742034, rs16940583, rs3742035, rs10735104, rs3742037, and rs1861810, respectively). The pairwise linkage disequilibrium of all SNPs is shown in Table 2. Associations between TRPV4 gene polymorphisms and BMI and obesity status The effect of each allele of the studied SNPs on BMI quantum was analyzed and shown in Table 3. After adjusting for age, sex, and current smoker status, rs3742037, rs10735104, and rs3742035 were associated with BMI using a dominant model (P  = 4.83 × 10−4, P  = 1.17 × 10−4, and P  = 3.37 × 10−4 for rs3742037, rs10735104, and rs3742035, respectively) and an additive model (P  = 5.05 × 10−4, P  = 1.06 × 10−4, and P  = 3.04 × 10−4, respectively) (Table 3). The maximum value of explained variance for BMI is 0.4 % for SNP rs10735104. Obesity, defined as BMI ≥ 25 kg/m2 (Asian criteria) and BMI ≥ 30 kg/m2 (NIH criteria), was significantly associated with rs10735104 (P  = 0.003 and P = 0.037, respectively) in the dominant model (Fig. 1).

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Associations between TRPV4 gene polymorphisms and obesity‑related phenotypes and adipokine levels There were no significant differences among the TRPV4 genotypes with respect to other baseline characteristics including age, total cholesterol levels, LDL and HDL cholesterol levels, triglyceride levels, fasting plasma glucose levels, serum insulin level, HOMA-IR, and various adipokine levels. Moreover, there were no significant differences between different genotypes with respect to various electrocardiographic parameters including heart rate and corrected QT interval (supplementary Table 3).

Discussion Our data revealed that TRPV4 genotypes are significantly associated with BMI. In addition, obesity status defined according to both the Asian and NIH criteria was significantly associated with TRPV4 genotypes. Therefore, our findings indicate that the genotypes at the TRPV4 locus independently affect BMI and obesity status in Taiwanese subjects. To our knowledge, this is the first report detailing the association between TRPV4 and obesity in humans. This association may broaden our understanding of the genetic determinants of body weight regulation. In humans, the TRPV4 gene is located on chromosome 12q24.1 (Gao and Wu 2003). Recent investigations using TRPV4−/− mice revealed the involvement of TRPV4 channels in sensing mechanical pressure (Suzuki et al. 2003; Liedtke and Friedman 2003), osmolality (Liedtke and Friedman 2003), and warmth (Todaka et al. 2004; Lee et al. 2005) in vivo. Gevaert et al. (Gevaert et al. 2007) revealed that the deletion of the TRPV4 channel impairs murine bladder voiding. Mutations in TRPV4 are reported to cause autosomal-dominant brachyolmia in gain-of-function mutations, an unusual spectrum of neuropathies in dominant

Mol Genet Genomics Table 3  Body mass index analysis according to the TRPV4 genotype Total TRPV4

Genotype

BMI (kg/m2)

P value

B (95 % CI)

4.83 × 10−4

0.099 (0.044–0.154)

5.05 × 10−4

0.997 (0.437–1.558)

1.17 × 10−4

0.105 (0.052–0.159)

1.06 × 10−4

1.083 (0.538–1.628)

3.37 × 10−4

0.097 (0.044–0.150)

3.04 × 10−4

1.002 (0.461–1.544)

0.792

−0.017 (−0.143–0.109)

0.07

−0.476 (−1.033–0.080)

0.879

0.007 (−0.087–0.102)

0.235

−0.334 (−0.882–0.213)

Mean ± SD (N) rs3742037

rs10735104

rs3742035

rs7971845

rs6606743

rs3742030

rs10850783

rs3742034

TT

23.57 ± 2.46 (27)

TC CC TT + TC CC CC CT TT CC + CT TT AA AG GG AA + AG GG CC CG GG CC CG + GG AA AG GG AA AG + GG TT TC CC TT + TC CC AA AC CC AA + AC CC TT TC CC TT TC + CC

23.75 ± 3.46 (197) 24.71 ± 3.51 (384) 23.72 ± 3.35 (224) 24.71 ± 3.51 (384) 24.09 ± 2.87 (51) 23.71 ± 3.31 (224) 24.86 ± 3.62 (328) 23.78 ± 3.23 (275) 24.86 ± 3.62 (328) 24.11 ± 2.84 (70) 23.84 ± 3.32 (262) 24.89 ± 3.70 (276) 23.89 ± 3.23 (332) 24.89 ± 3.70 (276) 24.57 ± 3.57 (361) 24.00 ± 3.37 (219) 24.44 ± 2.97 (25) 24.57 ± 3.57 (361) 24.05 ± 3.32 (244) 24.53 ± 3.52 (307) 24.11 ± 3.47 (249) 24.60 ± 3.31 (48) 24.53 ± 3.52 (307) 24.19 ± 3.45 (297) 31.21 (1) 24.48 ± 3.55 (54) 24.32 ± 3.47 (553) 24.60 ± 3.63 (55) 24.32 ± 3.47 (553) 24.28 ± 0.06 (2) 24.11 ± 2.96 (64) 24.39 ± 3.55 (539) 24.11 ± 2.91 (66) 24.39 ± 3.55 (539) 24.36 ± 3.50 (545) 24.20 ± 3.24 (59) 31.21 (1) 24.36 ± 3.50 (545) 24.32 ± 3.34 (60)

0.534

−0.030 (−0.125–0.065)

0.577

−0.290 (−1.241–0.660)

0.488

0.031 (−0.057–0.118)

0.484

0.313 (−0.565–1.191)

0.191

0.343 (−0.171–0.856)

0.99

0.006 (−0.910–0.921)

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Mol Genet Genomics

Table 3  continued Total TRPV4

Genotype

BMI (kg/m2)

P value

B (95 % CI)

Mean ± SD (N) rs16940583

rs1861810

GG GA AA GG + GA AA AA AC CC AA + AC

31.21 (1) 24.19 ± 3.24 (60) 24.37 ± 3.51 (542) 24.30 ± 3.34 (61) 24.37 ± 3.51 (542) 24.67 ± 3.37 (98) 24.12 ± 3.22 (301) 24.58 ± 3.86 (206) 24.25 ± 3.27 (399)

CC

24.58 ± 3.86 (206)

0.923

0.004 (−0.86–0.095)

0.905

0.055 (0.855–0.966)

0.383

0.025 (−0.031–0.081)

0.328

0.288 (−0.290–0.867)

P adjusted for age, sex, and current smoker status, P interaction P value for interaction between genotype and sex BMI body mass index

Fig. 1  The associations between obesity defined as BMI ≥25 and ≥30 kg/m2 and rs10735104 genotype. The allele frequency of rs10735104 was significantly different between obese and non-obese subjects with both the (a) Asian and (b) NIH criteria. NIH National Institutes of Health, BMI body mass index

mutations, and hyponatremia in a loss-of-function nonsynonymous polymorphism (Rock et al. 2008; Zimon´ et al. 2010; Tian et al. 2009). Furthermore, TRPV4 genotypes are associated with chronic obstructive pulmonary disease (Zhu et al. 2009). These results suggest that TRPV4

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has multiple functions involving the central and peripheral nervous systems as well as pulmonary diseases. It is possible that the association between genetic variants of TRPV4 and obesity may act via TRPV4 channels in skeletal muscle and adipose tissue. Using gene knockout mice as a model, Kusudo et al. (2012) found no difference in body weight between wild-type and TRPV4−/− mice fed a standard chow diet. However, obesity phenotypes induced by a high-fat diet were significantly improved in TRPV4−/− mice without any change in food intake. Low adiposity and hepatic steatosis were also noted in TRPV4−/− mice. Histological analysis of adipose tissues also indicates small adipocytes in mutant mice. The inactivation of TRPV4 is suggested to induce compensatory increases in TRPC3 and TRPC6 production and increase calcineurin activity; this affects energy metabolism via the increased expressions of genes involved in fuel oxidation in skeletal muscle, thereby contributing to increased energy expenditure and protection from diet-induced obesity in mice. Li et al. (2012) revealed that TRPV4, a member of a family of chemically tractable ion channels, is a negative regulator of PGC-1α and the thermogenic program. TRPV4 positively regulates a host of proinflammatory genes in white adipocytes. Genetic ablation and pharmacological inhibition of TRPV4 in mice modulate both thermogenic and proinflammatory pathways in fat, resulting in a robust resistance to obesity and insulin resistance. In contrast, O’ Conor et al. (2013) found increased obesity status and more severe obesityinduced osteoarthritis for TRPV4−/− mice with very highfat diet for a long duration of high-fat feeding. However, the unmeasured food consumption and energy expenditure and reduced cage activity of high-fat-fed TRPV4−/− mice could partly explain the result compared to the previous

Mol Genet Genomics

studies. In addition, to further examine the obese phenotype of TRPV4−/− mice, histological sections were taken of the inguinal fat pad of 10-week-old normally fed mice and showed that even prior to high-fat feeding, TRPV4−/− mice may possess larger adipocytes than TRPV4+/+ controls. Another possible explanation for the association between TRPV4 variants and obesity involves the effect of TRPV4 channels in the hypothalamic area. TRPV4 channels are highly expressed in the hypothalamic area. In rodent models of high-fat diet-induced obesity, hypothalamic inflammatory signaling is noted in both rats and mice prior to substantial weight gain (Thaler et al. 2012). Evidence of increased gliosis in the mediobasal hypothalamus of obese humans shown by MRI further suggests that obesity is associated with neuronal injury in a brain area crucial for body weight control in both humans and rodent models (Thaler et al. 2012). The neuronal influence on body weight regulation in both the monogenic and polygenic forms of obesity is considered to act via the central nervous system, particularly the hypothalamus. In their meta-analysis of 15 GWAS, including a total of 32,387 individuals, Willer et al. (2009) found six new loci associated with BMI. This highlights the neuronal influence on body weight regulation, similar to the rare monogenic form of obesity. Central, but not peripheral effects of diet-induced obesity have also been demonstrated in mice with central nervous system-specific knockdown of cannabinoid receptor CB1 and neuronal PGC-1α; these results indicate the physiological role of the central regulation of energy balance (Ma et al. 2010; Pang et al. 2011). Further, two candidate gene loci on chromosome 12q23.1, near the TRPV4 loci on chromosome 12q24.1, have recently been linked to BMI in GWAS. In a GWAS of anthropometric traits in Filipino women, Croteau-Chonka et al. (2011) found ANKS1B gene variant rs2373011 associated with BMI. By SNP and copy number variants analysis in GWAS, Wheeler et al. (2013) identified RMST gene variant rs11109072 associated with severer early-onset obesity in UK children of European ancestry. We examined the amount of LD between the five SNPs (TRPV4 rs3743037, rs10735104, rs3742035, ANKS1B gene variant rs2373011 and RMST gene variant rs11109072). The SNPs exhibited markedly low LD between each TRPV4 SNP and the ANKS1B gene variant rs2373011 or RMST gene variant rs11109072, the average D’ estimate was 0.05, ranging from 0.02 to 0.08, suggesting that TRPV4 genotypes associated with BMI due to linkage disequilibrium with ANKS1B gene variant rs2373011 or RMST gene variant rs11109072 is less favored. We further examined the potential function of the studies’ SNPs. By screening the sequence of the three most significant TRPV4 SNPs for putative gene regulatory factor and miRNA using in silico analysis (available at http://genepipe.ncgm.sinica.edu.tw/variowatch/main.do

and http://www.mirbase.org/), a sequence that overlaps rs10735104 and is highly homologous to hsa-miR-5187-5p was identified. Using the online miRDB software (available at http://mirdb.org/miRDB/index.html), we found klotho beta (KLB), a gene related to Kl protein, as a predicted target gene for hsa-miR-5187-5p, and Ohnishi et al. (2011) showed that high-fat diet fed to Kl−/− mice did not lead to any gain in body weight compared with a standardfat diet. These results suggested the possibility of an epigenetic mechanism as a molecular basis for the association between TRPV4 SNP rs10735104 and obesity. Obesity is associated with various BMI-related traits including metabolic, hemodynamic, and inflammatory traits as well as adipokine levels. The present study shows no association between TRPV4 SNPs and any BMI-related trait or adipokine levels. Furthermore, the six genes linked to obesity reported by Willer et al. (2009) were not associated with BMI-related traits. The most likely explanations for this may be the weak ability of SNPs to detect very small effects in public datasets as well as the incomplete correlation between BMI and these traits. If substantial, modifying effects such as interactions with environmental factors, other genetic variants, age, sex, and other variables may also diminish apparent effect sizes. Therefore, further detailed analyses of interactions with validated variants may be informative. One limitation of the present study is the association between genetic variants and obesity in a single modest sample, which was not analyzed in any functional study; this only demonstrates an arguable relationship with the disease. Replication in a second cohort would improve the strength of the present study. When a Bonferroni correction was stringently applied for multiple tests, the statistical significance of some of our results with the adjustment of the ten SNPs studied failed to suggest that the association between TRPV4 polymorphisms and BMI was due to chance. The examined subjects were ethnically Chinese and different allele frequencies were noted when compared with Caucasians (supplementary Table 1); and caution should be exercised when extrapolating our results to other ethnic groups. Another limitation of this study is its crosssectional design, which only allows it to draw limited inference regarding the relationships between TRPV4 variants and BMI-related traits and adipokine levels. To our knowledge, our results demonstrate for the first time that TRPV4 is a candidate gene for obesity in humans. Several possible mechanisms have been proposed. Both the high prevalence of TRPV4 in the hypothalamus and the demonstration of the anti-obesity phenotypes induced by TRPV4 deficiency in mice with skeletal muscle and adipose tissue as the target site suggest the involvement of central or peripheral effects. The regulation of TRPV4 channels in the peripheral tissue or the hypothalamus could

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also be new therapeutic targets to prevent the development of obesity. Acknowledgments  This study was supported by a grant from the National Science Council, Taiwan (NSC101-2314-B-303-023-MY3), and a grant from the Buddhist Tzu Chi General Hospital (TCRDI100-01-01) to YL Ko. Conflict of interest None.

References Atwood LD, Heard-Costa NL, Cupples LA, Jaquish CE, Wilson PW, D’Agostino RB (2002) Genomewide linkage analysis of body mass index across 28 years of the Framingham Heart Study. Am J Hum Genet 71:1044–1050 Cecil JE, Tavendale R, Watt P, Hetherington MM, Palmer CN (2008) An obesity-associated FTO gene variant and increased energy intake in children. N Engl J Med 359:2558–2566 Cho YS, Go MJ, Kim YJ et al (2009) A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41:527–534 Clapham DE, Montell C, Schultz G, Julius D (2003) International Union of Pharmacology. XLIII. Compendium of voltage-gated ion channels: transient receptor potential channels. Pharmacol Rev 55:591–596 Croteau-Chonka DC, Marvelle AF, Lange EM, Lee NR, Adair LS (2011) Genome-wide association study of anthropometric traits and evidence of interactions with age and study year in Fiipino women. Obesity 19(5):1019–1027 Delany NS, Hurle M, Facer P et al (2001) Identification and characterization of a novel human vanilloid receptor-like protein, VRL2. Physiol Genomics 4:165–174 den Hoed M, Ekelund U, Brage S et al (2010) Genetic susceptibility to obesity and related traits in childhood and adolescence: influence of loci identified by genome-wide association studies. Diabetes 59:2980–2988 Farooqi IS (2006) Genetic aspects of severe childhood obesity. Pediatr Endocrinol Rev 3:528–536 Finkelstein EA, Trogdon JG, Brown DS, Allaire BT, Dellea PS, Kamal-Bahl SJ (2008) The lifetime medical cost burden of overweight and obesity: implications for obesity prevention. Obesity 16:1843–1848 Flegal KM, Graubard BI, Williamson DF, Gail MH (2007) Cause-specific excess deaths associated with underweight, overweight, and obesity. J Am Med Assoc 298:2028–2037 Gao X, Wu L, O’Neil RG (2003) Temperature-modulated diversity of TRPV4 channel gating activation by physical stresses and phorbol ester derivatives through protein kinase C-dependent and -independent pathways. J Biol Chem 278:27129–27137 Gevaert T, Vriens J, Segal A et al (2007) Deletion of the transient receptor potential cation channel TRPV4 impairs murine bladder voiding. J Clin Invest 117:3453–3462 Heid IM, Jackson AU, Randall JC et al (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42:949–960 Hsu LA, Ko YL, Hsu KH, Ko YH, Lee YS (2002) Genetic variations in the cholesteryl ester transfer protein gene and high density lipoprotein cholesterol levels in Taiwanese Chinese. Hum Genet 110:57–63 Kaneko Y, Szallasi A (2014) Transient receptor potential (TRP) channels: a clinical perspective. Br J Pharmacol 171:2474–2507

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Mol Genet Genomics Ko YL, Hsu LA, Hsu KH, Ko YH, Lee YS (2004) The interactive effects of hepatic lipase gene promoter polymorphisms with sex and obesity on high-density-lipoprotein cholesterol levels in Taiwanese-Chinese. Atherosclerosis 172:135–142 Kusudo T, Wang Z, Mizuno A, Suzuki M, Yamashita H (2012) TRPV4 deficiency increases skeletal muscle metabolic capacity and resistance against diet-induced obesity. J Appl Physiol 112:1223–1232 Lee H, Iida T, Mizuno A, Suzuki M, Caterina MJ (2005) Altered thermal selection behavior in mice lacking transient receptor potential vanilloid 4. J Neurosci 25:1304–1310 Li Y, Sandra K, Jun W et al (2012) TRPV4 is a regulator of adipose oxidative metabolism, inflammation, and energy homeostasis. Cell 152:96–110 Liedtke W, Friedman JM (2003) Abnormal osmotic regulation in trpv4−/− mice. Proc Natl Acad Sci USA 100:13698–13703 Liedtke W, Choe Y, Marti-Renom MA et al (2000) Vanilloid receptor-related osmotically activated channel (VR-OAC), a candidate vertebrate osmoreceptor. Cell 103:525–535 Lindgren CM, Heid IM, Randall JC et al (2009) Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genet 5:e1000508 Ma D, Li S, Lucas EK, Cowell RM, Lin JD (2010) Neuronal inactivation of peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) protects mice from diet-induced obesity and leads to degenerative lesions. J Biol Chem 285:39087–39095 Maes HH, Neale MC, Eaves LJ (1997) Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 27:325–351 National Institute of Health (1998) National Heart Lung and Blood Institute: clinical guidelines on the identification, evaluation and treatment of overweight and obesity in adults: the evidence report. Obes Res 6:51S–209S Nilius B, Vriens J, Prenen J, Droogmans G, Voets T (2004) TRPV4 calcium entry channel: a paradigm for gating diversity. Am J Physiol Cell Physiol 286:C195–C205 O’ Conor C, Griffin T, Liedtke W, Guilak F (2013) Increased susceptibility of TRPV4 deficient mice to obesity and obesity-induced osteoarthritis with very high fat diet. Ann Rheu Dis 72:300–304 Ohnishi M, Kato S, Akiyoshi J, Atfi A, Razzaque MS (2011) Dietary and genetic evidence for enhancing glucose metabolism and reducing obesity by inhibiting Klotho functions. FASEB J 25:201–2039 Pang Z, Wu NN, Zhao W et al (2011) The central cannabinoid CB1 receptor is required for diet-induced obesity and rimonabant’s antiobesity effects in mice. Obesity 19:1923–1934 Park JH, Wacholder S, Gail MH et al (2011) Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet 42(7):570–575 Rock MJ, Prenen J, Funari VA et al (2008) Gain-of-function mutations in TRPV4 cause autosomal dominant brachyolmia. Nat Genet 40:999–1003 Scherag A, Jarick I, Grothe J et al (2010) Investigation of a genome wide association signal for obesity: synthetic association and haplotype analyses at the melanocortin 4 receptor gene locus. PLoS One 5:e13967 Schumacher MA, Jong BE, Frey SL, Sudanagunta SP, Capra NF, Levine JD (2000) The stretch-inactivated channel, a vanilloid receptor variant, is expressed in small-diameter sensory neurons in the rat. Neuro sci Lett 287:215–218 Speliotes EK, Willer CJ, Berndt SI et al (2010) Association analyses of 249,796 individuals reveal eighteen new loci associated with body mass index. Nat Genet 42:937–948 Suzuki M, Mizuno A, Kodaira K, Imai M (2003) Impaired pressure sensation in mice lacking TRPV4. J Biol Chem 278:22664–22668 Thaler JP, Yi CX, Schur EA et al (2012) Obesity is associated with hypothalamic injury in rodents and humans. J Clin Invest 122:153–162

Mol Genet Genomics Tian W, Fu Y, Garcia-Elias A et al (2009) A loss-of-function nonsynonymous polymorphism in the osmoregulatory TRPV4 gene is associated with human hyponatremia. Proc Natl Acad Sci USA 106:14034–14039 Todaka H, Taniguchi J, Satoh J, Mizuno A, Suzuki M (2004) Warm temperature-sensitive transient receptor potential vanilloid 4 (TRPV4) plays an essential role in thermal hyperalgesia. J Biol Chem 279:35133–35138 Voets T, Talavera K, Owsianik G, Nilius B (2005) Sensing with TRP channels. Nat Chem Biol 1:85–92 Wang K, Li WD, Zhang CK et al (2011) A genome-wide association study on obesity and obesity-related traits. PLoS One 6:e18939 Wheeler E, Huang N, Bochukova EG, Keogh JM, Lindsay S et al (2013) Genome-wide SNP and CNV analysis idenitifies common and low-frequency variants associated with severe early-onset obesity. Nat Genet 45:513–517 WHO expert consultation (2004) Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363:157–163

Willer CJ, Speliotes EK, Loos RJ et al (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41:25–34 Wu TL, Tsao KC, Chang CP, Li CN, Sun CF, Wu JT (2002) Development of ELISA on microplate for serum C-reactive protein and establishment of age-dependent normal reference range. Clin Chim Acta 322:163–168 Yang X, Deignan JL, Qi H et al (2009) Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat Genet 41:415–423 Zhu G, Gulsvik A, Bakke P, ICGN Investigators et al (2009) Association of TRPV4 gene polymorphisms with chronic obstructive pulmonary disease. Hum Mol Genet 18:2053–2062 Zimon´ M, Baets J, Auer-Grumbach M et al (2010) Dominant mutations in the cation channel gene transient receptor potential vanilloid 4 cause an unusual spectrum of neuropathies. Brain 133:1798–1809

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