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A Common Haplotype in the G-Protein–Coupled Receptor Gene GPR74 Is Associated with Leanness and Increased Lipolysis

2007; Elsevier BV; Volume: 80; Issue: 6 Linguagem: Inglês

10.1086/518445

1537-6605

Ingrid Dahlman, Andrea Dicker, Hong Jiao, Juha Kere, Lennart Blomqvist, Vanessa van Harmelen, Johan Hoffstedt, Knut Borch‐Johnsen, Torben Jørgensen, Torben Hansen, Oluf Pedersen, Markku Laakso, Peter Arner,

Pharmacological Effects and Assays

The G-protein–coupled receptor GPR74 is a novel candidate gene for body weight regulation. In humans, it is predominantly expressed in brain, heart, and adipose tissue. We report a haplotype in the GPR74 gene, ATAG, with allele frequency ∼4% in Scandinavian cohorts, which was associated with protection against obesity in two samples selected for obese and lean phenotypes (odds ratio for obesity 0.48 and 0.62; nominal P=.0014 and .014; n=1,013 and 1,423, respectively). In a population-based sample, it was associated with lower waist (P=.02) among 3,937 men and with obesity protection (odds ratio 0.36; P=.036) among those selected for obese or lean phenotypes. The ATAG haplotype was associated with increased adipocyte lipid mobilization (lipolysis) in vivo and in vitro. In human fat cells, GPR74 receptor stimulation and inhibition caused a significant and marked decrease and increase, respectively, of lipolysis, which could be linked to catecholamine stimulation of adipocytes through β-adrenergic receptors. These findings suggest that a common haplotype in the GPR74 gene protects against obesity, which, at least in part, is caused by a relief of inhibition of lipid mobilization from adipose tissue. The latter involves a cross-talk between GPR74 and β-adrenoceptor signaling to lipolysis in fat cells. The G-protein–coupled receptor GPR74 is a novel candidate gene for body weight regulation. In humans, it is predominantly expressed in brain, heart, and adipose tissue. We report a haplotype in the GPR74 gene, ATAG, with allele frequency ∼4% in Scandinavian cohorts, which was associated with protection against obesity in two samples selected for obese and lean phenotypes (odds ratio for obesity 0.48 and 0.62; nominal P=.0014 and .014; n=1,013 and 1,423, respectively). In a population-based sample, it was associated with lower waist (P=.02) among 3,937 men and with obesity protection (odds ratio 0.36; P=.036) among those selected for obese or lean phenotypes. The ATAG haplotype was associated with increased adipocyte lipid mobilization (lipolysis) in vivo and in vitro. In human fat cells, GPR74 receptor stimulation and inhibition caused a significant and marked decrease and increase, respectively, of lipolysis, which could be linked to catecholamine stimulation of adipocytes through β-adrenergic receptors. These findings suggest that a common haplotype in the GPR74 gene protects against obesity, which, at least in part, is caused by a relief of inhibition of lipid mobilization from adipose tissue. The latter involves a cross-talk between GPR74 and β-adrenoceptor signaling to lipolysis in fat cells. Although it is generally accepted that heredity has a strong influence on body weight, the resolution of the genetic components underlying variability in BMI (calculated as body weight in kilograms divided by the square of height in meters) among the general population is incomplete. In particular, the genetic factors that protect against obesity are not well understood.1Bulik CM Allison DB The genetic epidemiology of thinness.Obes Rev. 2001; 2: 107-115Crossref PubMed Scopus (51) Google Scholar The G-protein–coupled receptor 74 (GPR74 [MIM 607449]) is a novel candidate gene for regulation of BMI. In humans, the receptor is predominantly expressed in the brain, heart, and adipose tissue.2Parker RM Copeland NG Eyre HJ Liu M Gilbert DJ Crawford J Couzens M Sutherland GR Jenkins NA Herzog H Molecular cloning and characterisation of GPR74 a novel G-protein coupled receptor closest related to the Y-receptor family.Brain Res Mol Brain Res. 2000; 77: 199-208Crossref PubMed Scopus (29) Google Scholar, 3Elshourbagy NA Ames RS Fitzgerald LR Foley JJ Chambers JK Szekeres PG Evans NA Schmidt DB Buckley PT Dytko GM et al.Receptor for the pain modulatory neuropeptides FF and AF is an orphan G protein-coupled receptor.J Biol Chem. 2000; 275: 25965-25971Crossref PubMed Scopus (231) Google Scholar The true endogenous ligand for the receptor is not known, but GPR74 has a high affinity for neuropeptides, such as neuropeptide FF (NPFF).4Vyas N Mollereau C Cheve G McCurdy CR Structure-activity relationships of neuropeptide FF and related peptidic and non-peptidic derivatives.Peptides. 2006; 27: 990-996Crossref PubMed Scopus (21) Google Scholar This neuropeptide has been investigated in laboratory animals. Although the opioid modulating effects of NPFF are the best explored,5Mollereau C Roumy M Zajac JM Opioid-modulating peptides: mechanisms of action.Curr Top Med Chem. 2005; 5: 341-355Crossref PubMed Scopus (83) Google Scholar NPFF is also involved in cardiovascular regulation6Jhamandas JH Harris KH Petrov T Yang HY Jhamandas KH Activation of neuropeptide FF neurons in the brainstem nucleus tractus solitarius following cardiovascular challenge and opiate withdrawal.J Comp Neurol. 1998; 402: 210-221Crossref PubMed Scopus (25) Google Scholar and response to stress7Cador M Marco N Stinus L Simonnet G Interaction between neuropeptide FF and opioids in the ventral tegmental area in the behavioral response to novelty.Neuroscience. 2002; 110: 309-318Crossref PubMed Scopus (24) Google Scholar or reward.8Huang EY Li JY Wong CH Tan PP Chen JC Dansyl-PQRamide, a possible neuropeptide FF receptor antagonist, induces conditioned place preference.Peptides. 2002; 23: 489-496Crossref PubMed Scopus (17) Google Scholar As summarized elsewhere,9Nicklous DM Simansky KJ Neuropeptide FF exerts pro- and anti-opioid actions in the parabrachial nucleus to modulate food intake.Am J Physiol Regul Integr Comp Physiol. 2003; 285: R1046-R1054PubMed Google Scholar NPFF has been reported to have effects on food intake. In addition, NPFF has effects on rodent fat cells, which involve interactions with β-adrenergic receptors.10Lefrere I De Coppet P Camelin JC Le Lay S Mercier N Elshourbagy N Bril A Berrebi-Bertrand I Feve B Krief S Neuropeptide AF and FF modulation of adipocyte metabolism: primary insights from functional genomics and effects on β-adrenergic responsiveness.J Biol Chem. 2002; 277: 39169-39178Crossref PubMed Scopus (34) Google Scholar Catecholamines are the most important lipolytic hormones in man, and they regulate lipolysis through two major stimulatory adrenoceptors, β1and β2 (β3 is less effective), and through one inhibitory receptor (α2).11Arner P Human fat cell lipolysis: biochemistry, regulation and clinical role.Best Pract Res Clin Endocrinol Metab. 2005; 19: 471-482Abstract Full Text Full Text PDF PubMed Scopus (261) Google Scholar Taken together, these data suggest that GPR74 could be a gene of importance for body weight regulation, maybe by having effects mediated on adipocyte lipolysis. We therefore did a comprehensive analysis of genetic variance in the GPR74 gene, using a large cohort of Swedish subjects with well-defined criteria for either long-standing leanness or severe obesity. The most important polymorphisms were reinvestigated in another large cohort of lean and obese Swedes, and results were confirmed in a large population-based Danish cohort. Finally, studies on lipolysis were performed, first, to find a link between GPR74 polymorphisms and lipid mobilization and, second, to study the mechanisms of action for GPR74 on human fat-cell lipolysis. Sample 1 was recruited for a study of genes underlying susceptibility to obesity, either from an outpatient center for treatment of obesity or through local advertisement (fig. 1 and table 1). The subjects were carefully selected for a lean or obese phenotype, and were at least 2nd-generation Scandinavians living in Sweden. The obese subjects were either 30 or any age with BMI >40 (morbid obesity). The lean subjects were >45 years old and had never had BMI >25 according to self-report. The aim of selecting subjects with an extreme BMI phenotype in sample 1 was to enrich for a genetic impact on obesity or leanness.12Bell CG Walley AJ Froguel P The genetics of human obesity.Nat Rev Genet. 2005; 6: 221-234Crossref PubMed Scopus (470) Google Scholar This was also the purpose of recruiting young obese adults, since early onset of this disorder is believed to have a stronger genetic component because of the reduced time of environmental impact.12Bell CG Walley AJ Froguel P The genetics of human obesity.Nat Rev Genet. 2005; 6: 221-234Crossref PubMed Scopus (470) Google Scholar A total of 59 obese subjects had oral treatment for type 2 diabetes, but otherwise the subjects in sample 1 were healthy according to self-report.Table 1Characteristics of the CohortsSample, Country, Body Weight Status, and SexnAgeaValues are mean ± SD (range) (years)BMIaValues are mean ± SD (range)1: Sweden: Obese: F48742 ± 13 (16–77)44 ± 4 (30–64) M12445 ± 13 (16–73)45 ± 5 (32–58) Lean: F33850 ± 4 (46–68)22 ± 2 (15–25) M6457 ± 8 (46–79)23 ± 2 (18–25)2: Sweden: Obese: F68844 ± 11 (19–75)36 ± 4 (30–58) M12245 ± 13 (18–75)44 ± 5 (31–66) Lean: F52539 ± 6 (25–61)22 ± 2 (17–25) M8836 ± 11 (26–49)23 ± 1 (19–25)3: Sweden: Nonobese: F9938 ± 10 (18–60)23 ± 2 (18–27) M5141 ± 11 (26–77)23 ± 2 (19–25)4: Denmark: AnybAscertainment was population based.: F3,85448 ± 10 (22–88)26 ± 5 (15–56) M3,93749 ± 10 (19–89)27 ± 4 (17–57)a Values are mean ± SD (range)b Ascertainment was population based. Open table in a new tab Sample 2, used for confirmation of genetic associations, was recruited from the same sources but according to less stringent definitions for lean and obese phenotypes than those used for sample 1 (fig. 1 and table 1). The obese subjects had BMI >30 at any age, and the lean subjects were >25 years old and had BMI 5% and (2) one SNP with frequency >2% every 5,000 bp. In selecting between different SNPs, we prioritized SNPs with Golden-Gate–validated assays or with a high score according to Illumina,24Fan JB Oliphant A Shen R Kermani BG Garcia F Gunderson KL Hansen M Steemers F Butler SL Deloukas P et al.Highly parallel SNP genotyping.Cold Spring Harb Symp Quant Biol. 2003; 68: 69-78Crossref PubMed Scopus (503) Google Scholar which indicates that the designed Illumina genotyping assays are likely to work. Subjects in sample 1 and 3 were genotyped using Illumina.24Fan JB Oliphant A Shen R Kermani BG Garcia F Gunderson KL Hansen M Steemers F Butler SL Deloukas P et al.Highly parallel SNP genotyping.Cold Spring Harb Symp Quant Biol. 2003; 68: 69-78Crossref PubMed Scopus (503) Google Scholar Samples 2 and 4 were genotyped using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (SEQUENOM) as described elsewhere.25Dahlman I Eriksson P Kaaman M Jiao H Lindgren CM Kere J Arner P α2-Heremans-Schmid glycoprotein gene polymorphisms are associated with adipocyte insulin action.Diabetologia. 2004; 47: 1974-1979Crossref PubMed Scopus (55) Google Scholar For both genotyping platforms, the overall genotype call rate was 96% and the accuracy was 99.99%, according to duplicate analysis of, on average, 2% of the total genotypes. Primers are available on request. Two independent scorers confirmed all genotypes. Hardy-Weinberg calculations were performed to ensure that each marker was in population equilibrium. To identify promoter or coding SNPs carried by the ATAG haplotype, we sequenced all GPR74 exons, the exon-intron borders, and 1,730 bp 5′ of exon 1 in five subjects homozygous for ATAG and in three subjects who did not carry ATAG. We failed to sequence further 5′ because of a repeat region. Staden software was used for sequence assembly. All sequences were scored manually for the presence of SNPs. Haploview was employed to calculate Hardy-Weinberg P values and linkage disequilibrium, to infer haplotypes, and to test for allelic and haplotype association with obesity. Linkage disequilibrium was calculated as D′. For defining haploblocks, 95% confidence bounds on D′ were generated, and each comparison was classified as "strong LD," "inconclusive," or "strong recombination." A block was created when 95% of informative (i.e., not inconclusive) comparisons showed "strong LD." This method, by default, ignores markers with minor-allele frequency 50% missing genotypes were excluded from analysis. It is not possible in Haploview to visualize the probabilities of each individual's estimated haplotypes. Therefore, SNPHAP, which uses a method similar to Haploview to infer haplotypes, was used to assign probabilities to each individual's estimated haplotypes. Odds ratios (ORs) were calculated using the Finetti software (Hardy-Weinberg equilibrium Web site). We did not adjust for hypertension, type 2 diabetes, or dyslipidemia because, in obese subjects, these phenotypes are usually the consequence of obesity. In samples 1 and 2, which were ascertained on the basis of obesity status, waist circumference was analyzed by Mann-Whitney U test (Statview). In sample 4, which was population-based and thus not ascertained on the basis of analyzed phenotypes, we applied an analysis of the quantitative trait waist or BMI by using analysis of covariance (ANCOVA) with age as covariate. HOMAIR, plasma lipid, plasma glucose, and serum insulin are dependent on adiposity and age. In samples 1 and 2, these phenotypes were analyzed by ANCOVA, with stratification for obesity status and with age as covariate. In sample 4, these phenotypes were analyzed by ANCOVA with age and BMI as covariates. To study in vivo lipolysis, which was already corrected for body fat, we employed ANCOVA with age as covariate. In vitro lipolysis phenotypes in the nonobese sample 3 were analyzed by ANCOVA with age and BMI as covariates. Sex has impact on the analyzed phenotypes; therefore, we repeated all ANCOVA analyses with stratification for sex. We report nonadjusted results of ANOVA because the different adjustments of P values described above did not, in any case, make an important change in the P values, which usually were identical with the P values from ANOVA. All reported P values are nominal, unless otherwise stated. A permutation test with 10,000 permutations was used to adjust significance in the initial analysis of association between the ATAG haplotype and obesity. For confirmation, when one hypothesis was tested, no correction for multiple comparisons was used. Bonferroni correction for the number of comparisons was applied when multiple phenotypes were analyzed—for example, in analysis of the adipocyte lipolysis data. The recruitment criteria for sample 1, which aimed to recruit subjects with extreme phenotypes and with high or no predisposition to obesity, resulted in obese subjects that were younger (mean age 44 years) than the lean subjects (mean age 52 years) (table 1). In addition, less stringent BMI recruitment criteria for sample 2 resulted in phenotypic differences between samples 1 and 2 (table 1). In sample 2, the mean age among obese subjects (44 years) was similar to that in sample 1, whereas the lean subjects were younger (mean age 39 years). The obese subjects in sample 1 had a mean BMI of 45, and, in sample 2, the obese subjects had a mean BMI of 38. In summary, sample 4 comprised 775 obese women and 793 obese men with BMI >30 (mean age 51 ± 9 years; BMI 34 ± 4) and 3,079 nonobese women and 3,144 nonobese men (mean age 48 ± 10 years; BMI 25 ± 3) (table 1). Thus, the subjects in the Danish sample 4 were older and less obese than those in Swedish samples 1 and 2. We initially investigated sample 1, which had the most-rigorous selection criteria for a lean or obese phenotype, for SNPs covering the GPR74 gene selected from the International HapMap Project (fig. 2). Of the 25 SNPs tested in genotyping assays, 3 failed to show association (12%), which is close to the expected 10% failure rate, and 3 were nonpolymorphic (table 2). Of the remaining 19 GPR74 SNPs, 9 were nominally associated with obesity in sample 1—2 of these, rs9637554 and rs9291171, were more strongly associated, with P values of .0058 and .0004, respectively (table 3). rs6446796 displayed a low call rate and was therefore excluded from analysis of allelic association. Single SNPs associated with obesity with P values <.01, as well as SNPs that defined obesity-associated haplotypes, were genotyped in a second cohort of lean or obese Swedes, sample 2, which had less rigorous selection criteria for leanness (age >25 years and BMI 30 at any age) than those of sample 1. In sample 2, no allelic difference between the lean and obese groups was observed (table 3). Since 80%–85% of subjects in samples 1 and 2 were women, no sex-specific analysis was performed.Table 2GPR74 SNPsSNPRegionPositionaPosition on chromosome 4, genome build 35.AllelesAllele FrequencybNP = nonpolymorphic. (%)Call Rate (%)HWE Prs68178455′73259416…NP97.7…rs68566515′73261492A→T29.0100.0.34rs6446796Intron 173264126T→C30.276.8.021cHWE among controls.rs6824342Intron 173268217T→C30.0100.0.39rs13150414Intron 173270871G→T30.299.7.47rs9637554Intron 173278192C→G1.694.7.42rs4694453Intron 173284045C→A.9100.0.19rs11938755Intron 173286432G→A1.798.5.51rs6446809Intron 173290529T→G.1100.01rs4353870Intron 173297930A→T1.799.9.5rs4694462Intron 173302773C→G1.7100.0.5rs12642985Intron 173312934C→T30.298.8.38rs9993413Intron 173319113C→T.299.91rs12510838Intron 173326573A→G17.399.5.78rs4129733Intron 173328055T→G35.798.0.42rs7662933Intron 173335910…NP98.0…rs13107347Intron 173339783…….0…rs4264803Intron 173343505…….0…rs9291171Intron 173346661A→G28.992.1.22rs11940192Intron 173356942…….0…rs7679840Intron 273362944C→T.1100.01rs12650900Intron 273367493…NP98.0…rs11940196Intron 273368604A→G38.797.6.27rs6826854Intron 373373464C→T.199.91rs177753093′73382993T→G32.487.9.13a Position on chromosome 4, genome build 35.b NP = nonpolymorphic.c HWE among controls. Open table in a new tab Table 3Association of GPR74 SNPs wit