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Nutritional regulation of genome-wide association obesity genes in a tissue-dependent manner Yoganathan, Piriya; Karunakaran, Subashini; Ho, Maggie M; Clee, Susanne M Jul 10, 2012

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RESEARCH Open AccessNutritional regulation of genome-wideassociation obesity genes in a tissue-dependentmannerPiriya Yoganathan1, Subashini Karunakaran1, Maggie M Ho1 and Susanne M Clee1,2*AbstractBackground: Genome-wide association studies (GWAS) have recently identified several new genetic variantsassociated with obesity. The majority of the variants are within introns or between genes, suggesting they affectgene expression, although it is not clear which of the nearby genes they affect. Understanding the regulation ofthese genes will be key to determining the role of these variants in the development of obesity and will providesupport for a role of these genes in the development of obesity.Methods: We examined the expression of 19 GWAS obesity genes in the brain and specifically the hypothalamus,adipose tissue and liver of mice by real-time quantitative PCR. To determine whether these genes are nutritionallyregulated, as may be expected for genes affecting obesity, we compared tissues from fasting and non-fastinganimals and tissues from mice consuming a high fat high sucrose diet in comparison to standard rodent chow.Results: We found complex, tissue-dependent patterns of nutritional regulation of most of these genes. Forexample, Bat2 expression was increased ~10-fold in the brain of fed mice but was lower or unchanged in thehypothalamus and adipose tissue. Kctd15 expression was upregulated in the hypothalamus, brain and adiposetissue of fed mice and downregulated by high fat feeding in liver, adipose tissue and the hypothalamus but notthe remainder of the brain. Sh2b1 expression in the brain and Faim2 expression in adipose tissue were specificallyincreased >20-fold in fed mice. Tmem18 expression in adipose tissue but not the brain was reduced 80% by highfat feeding. Few changes in the expression of these genes were observed in liver.Conclusions: These data show nutritional regulation of nearly all these GWAS obesity genes, particularly in thebrain and adipose tissue, and provide support for their role in the development of obesity. The complex patterns ofnutritional and tissue-dependent regulation also highlight the difficulty that may be encountered in determininghow the GWAS genetic variants affect gene expression and consequent obesity risk in humans where access totissues is constrained.Keywords: Obesity genes, Genome-wide association, Gene expression, High fat diet, Feeding and fasting,Gene-diet interaction, Adipose tissue, Brain* Correspondence: susanne.clee@ubc.ca1Department of Cellular and Physiological Sciences, University of BritishColumbia, Vancouver, BC, Canada2Life Sciences Institute, Department of Cellular and Physiological Sciences,University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T1Z3, Canada© 2012 Yoganathan et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of theCreative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.Yoganathan et al. Nutrition & Metabolism 2012, 9:65http://www.nutritionandmetabolism.com/content/9/1/65BackgroundAdvances in our understanding of the genetic contribu-tion to obesity have come from recent genome wideassociation studies (GWAS). GWAS have identifiedseveral new genetic variants associated with obesityand its related traits such as body mass index (BMI).Most prominent amongst these are SNPs within FTO,which have the strongest effects on obesity risk [1-5].Additional studies, including large scale meta-analyses,discovered additional variants associated with obesity-related traits that have now been replicated [5-7].These include SNPs within or near MC4R, CTNNBL1,NPC1, MAF, PTER, PRL, SH2B1, NEGR1, the SEC16B-RASAL2 region, TMEM18, the SFRS10-ETV5-DGKGregion, BDNF, FAIM2, KCTD15, the NCR3-AIF1-BAT2region, GNPDA2, and MTCH2 [8-11].Alterations in a few of these genes have previouslybeen shown to affect body weight [12-16]. Melanocortin4 receptor, MC4R, mutations are the most commoncause of monogenic severe obesity and missense variantswithin the gene have been associated with altered riskfor common obesity [16,17]. Brain derived neurotrophicfactor, BDNF, regulates feeding, its expression is alteredby consumption of high fat diet, and variants within thegene have previously been associated with obesity[13,14,16,18]. Sh2b1 knockouts are obese, and this geneis known to play important roles in leptin signalling[15,19]. Understanding the site of action and function ofthese new obesity genes may provide valuable insightinto the pathogenesis of obesity. However, despite theextensive investments in GWAS studies, to date studiesof the biology of these genes have been limited. Little isknown about most of these GWAS obesity genes andtheir role in the regulation of body weight is unknown.A better understanding of these genes is needed tocapitalize on the information generated by the GWAS.The SNPs identified by the GWAS generally do notaffect the amino acid sequence of the mature protein.They are typically intronic or intergenic. The location ofthese SNPs suggests they likely affect the regulation ofthe gene they are located within or of nearby genes, andin some cases it is not clear which of the nearby genesthey may be affecting [7,8,11,20]. Regulation of a genemay also be tissue specific, or occur in response to cer-tain physiological states. Many genes involved in theregulation of energy homeostasis are metabolically regu-lated [21-24]. Expression of FTO was shown to be regu-lated by feeding and fasting in the hypothalamus [25].Thus we hypothesize that if these GWAS genes affectthe development of obesity they may be nutritionallyregulated.Knowledge of the regulation of the GWAS obesitygenes is critical for understanding how the associatedSNPs alter the function of their cognate genes and thushow these genes may affect the development of obesity.This requires knowledge of the tissues where the gene isnormally expressed and an understanding of the physio-logical processes that regulate it. We sought to deter-mine whether metabolic factors affect the regulation ofthe newly discovered obesity genes. We examined theregulation of these genes by feeding/fasting status andby the consumption of a diet high in fat and sugar. Herewe show that most of the recently identified obesitygenes are regulated by dietary status, providing supportfor their role in processes relevant to the developmentof obesity. These patterns of regulation were oftentissue-dependent and largely unique for each gene, sug-gesting that many of these genes affect distinct pathwaysin the development of obesity and that analysis of theeffects of the GWAS SNPs on gene expression may needto be performed in all physiologically relevant tissuesand under multiple physiological contexts.MethodsAnimalsAnimals were housed in an environmentally controlledfacility with 14 hour light cycles (7 am – 9 pm). FemaleC57BL/6 J mice were given free access to water and ei-ther a standard rodent chow (LabDiet 5010, PMI Nutri-tion, St. Louis MO, USA) or a diet containing 60%calories from fat (primarily lard) and 20% calories fromsugar (sucrose and maltodextrin; D12492, ResearchDiets, New Brunswick NJ, USA) from weaning. Thishigh fat diet has a cholesterol content of 300.8 mg/kgfrom the lard. The mice were sacrificed by CO2 asphyxi-ation at 8 weeks of age. Tissues were rapidly dissectedand immediately placed in liquid nitrogen then stored at−80°C. Prior to sacrifice, animals were either fasted 4hours (9 am – 1 pm) or dissections were performed at9 am without prior removal of food (“fed”). All proce-dures were approved by the UBC Committee on AnimalCare and were performed according to Canadian Coun-cil on Animal Care guidelines.RNA extraction and cDNA synthesisPrior to extraction, tissue samples were cut into smallpieces and placed in RNA Later Ice (Applied Biosystems,Carlsbad CA, USA) at −20°C for at least 24 hrs. RNA wasextracted using a commercially available kit (E.Z.N.A.™Total RNA II, Omega Biotek, Norcross GA, USA). Theintegrity of the RNA was verified by performing formal-dehyde denaturing agarose gel electrophoresis for allsamples except the hypothalamus, for which too littleRNA was obtained. Following DNAse I digestion (Fer-mentas, Burlington ON, Canada) to remove any contam-inating genomic DNA, cDNA synthesis (RevertAid FirstStrand cDNA Synthesis Kit, Fermentas, Burlington ON,Canada) was performed using 1 μg of RNA with mixedYoganathan et al. Nutrition & Metabolism 2012, 9:65 Page 2 of 10http://www.nutritionandmetabolism.com/content/9/1/65oligo-dT and hexamer primers (20 ng/μL oligo-dT,3.6 ng/μL random hexamer final concentration) accord-ing to the manufacturer’s directions. RNA was subse-quently removed with RNase H digestion (Fermentas,Burlington ON, Canada). Successful cDNA synthesis wasverified by PCR amplification of our control genes andvisualization of a band of the appropriate size on an agar-ose gel.Gene expressionWe examined the dietary regulation of Aif1, Bat2,C10orf97, Ctnnbl1, Dgkg, Etv5, Faim2, Gnpda2, Kctd15,Maf, Mtch2, Negr1, Npc1, Pter, Rasal2, Sec16b, Sfrs10,Sh2b1, and Tmem18. Primer sequences are provided inAdditional file 1 Table S1. When there were possible al-ternatively spliced forms identified in the UCSC genomebrowser (www.genome.ucsc.edu), we designed the pri-mers in regions common to all isoforms. All primerpairs spanned an intron. Ncr3 is a pseudogene in mice.It does not encode a functional protein due to the pres-ence of premature stop codons [26] and was not exam-ined in our studies.Relative gene expression was measured using a Ste-pOne Plus real time thermocycler (Applied BiosystemsInc., Carlsbad CA, USA), using SYBR green I for detec-tion (PerfeCTa SYBR Green FastMix, VWR Inter-national, Edmonton AB, Canada). Forty cycles ofamplification were performed followed by melt-curveanalysis for verification that only a single product wasamplified from each sample. This verification was alsoperformed by direct visualization of PCR amplificationproducts on an agarose gel. Baseline correction, thresh-old setting, and calculation of the Ct value were per-formed automatically by the StepOne software (version2.1, Applied Biosystems, Carlsbad CA, USA).Cyclophilin (Ppib) was chosen as the reference gene,as it proved most robust to changes in expression by ex-perimental condition across each of the tissues in com-parison to beta-actin (Actb), Gapdh, and Arbp. Thiscontrol gene was included on several plates contempor-aneous with the genes being assessed, and for each sam-ple the average value of this gene across its multiplereplicates was used for normalization of that sample.Delta Ct (ΔCt) values were calculated by subtracting thecycle threshold (Ct) value for each gene from this aver-age value. Delta delta Ct (ΔΔCt) values were calculatedfor each sample by subtracting its ΔCt value from theaverage ΔCt for the chow-fed, fasted controls in thesame experiment. Initially, single measurements weremade for each sample (n = 5 per dietary group). Whenthere was a potential difference between groups, repli-cate experiments were performed, including additionalsamples (up to 10 animals per group). The numbers varyfor each tissue and gene because of poor-quality or lowyields of RNA obtained from some samples and thenumber of samples with sufficient RNA available at thetime for analysis. For each sample, the average ΔΔCt ofall its replicates across experiments was used for statis-tical analysis.Statistical analysisOur study was comprised of three groups: chow-fedmice fasted prior to tissue collection (controls); chow-fed mice not fasted prior to tissue collection, “fed”; andhigh fat diet-fed mice that were fasted prior to tissuecollection. The average ΔΔCt values of the high fat-fedand fed groups were compared to that of the fastedchow-fed control group and the significance of eachassessed by a non-parametric Mann–Whitney U test be-cause of the small sample size. Because this results intwo comparisons for each gene (chow-fed, fasted vs.non-fasted mice; fasted chow-fed vs. fasted high fat diet-fed mice), the resulting P-values were subsequently Bon-ferroni corrected by multiplying them by 2. Thisapproach was chosen over a Kruskal-Wallis test becausethe comparison between chow-fed “fed” mice and fastedhigh fat diet-fed mice that is accounted for in theKruskal-Wallis test is not meaningful. The averagefold change for each group shown in the figures wascalculated as 2-average ΔΔCt, with the error bars as2-average ΔΔCt ± its SE [27].ResultsRegulation of GWAS obesity genes by dietary status inhypothalamusAll 19 of the GWAS genes we examined were expressedin the hypothalamus. Many were regulated by eitherfeeding/fasting or by consumption of the high fat diet(Figure 1), but only two genes, Kctd15 and likely Mtch2,were regulated by both conditions. The expression ofmany genes was increased in non-fasted animals. Kctd15expression was increased 2.1-fold in fed animals,C10orf97 expression was increased 2.3-fold, and expres-sion of Sfrs10 was also increased an average of 2.2-fold.The expression of Pter was increased 1.6-fold, and wedetected a 1.8-fold increase in Gnpda2 expression. Inaddition, Ctnnbl1 expression was increased an averageof 1.5-fold, although this did not reach statistical signifi-cance. Similarly, the expression of Mtch2, Faim2 andDgkg were also increased in the fed mice compared tofasted, however due to the large variability in expressionlevels, these were not significant.In contrast to the small differences in gene expressionobserved in fed compared to fasted animals, chronicfeeding of a high fat diet resulted in substantial changesin expression (Figure 1). Kctd15 was decreased a marked20-fold in animals consuming a high fat diet (i.e. expres-sion was ~5% of that seen in chow-fed, fasted controlYoganathan et al. Nutrition & Metabolism 2012, 9:65 Page 3 of 10http://www.nutritionandmetabolism.com/content/9/1/65mice). Bat2 and Sh2b1 mRNA levels were decreased~10-fold in animals fed the high fat diet. Npc1 gene ex-pression was decreased by ~4-fold. Maf expression was~3-fold lower, but due to the high variability in the con-trol group did not quite statistical significance after Bon-ferroni correction. Expression of Etv5 and Negr1 wasonly 15% and 19%, respectively, of that observed in con-trols, although neither reached statistical significance.Mtch2 was the only gene that had increased expressionwith high fat feeding in the hypothalamus. We did notobserve any significant changes in hypothalamic geneexpression with feeding or consumption of a high fatdiet for Aif1, Dgkg, Faim2, Rasal2, Sec16b or Tmem18(not shown).Regulation of GWAS genes by dietary status in wholebrain excluding hypothalamusMany regions of the brain outside the hypothalamus arealso important for the regulation of food intake and en-ergy balance [28]. We detected expression of all 19 genesin these other regions of the brain. Similar to the hypo-thalamus, the expression of many genes differed betweenthe fasted and fed groups (Figure 2). In contrast to thehypothalamus, however, the changes in expression wereoften much larger and occurred primarily in non-fastedanimals. Most notably, expression of Kctd15 and Sh2b1were increased >20-fold, and expression levels of Bat2,Etv5, Negr1and Tmem18 were increased ~10-fold in thefed group. Changes in Kctd15 expression likely did notreach statistical significance due to a large variability inthe increase in expression. While the magnitude wasvariable, most samples had at least 15-fold increasedKctd15 expression. Feeding was also associated with a 2to 4 fold increase in the expression of Sec16b, Rasal2,and Npc1. We observed similar trends in the expressionof Maf that did not reach statistical significance.In marked contrast to what was observed in the hypo-thalamus where several genes were substantially downre-gulated by high fat feeding, the expression of only asingle gene in the remainder of the brain was altered byhigh fat feeding. Expression of Faim2 was reduced ~50%in the high fat-fed group compared to the chow-fed mice(Figure 2). We did not observe any differences in the ex-pression of Aif1, Mtch2, Dgkg, Sfrs10, Pter, Ctnnbl1,Gnpda2 or C10orf97 under any condition in the remain-der of the brain (not shown).Figure 1 Regulation of GWAS obesity genes in the hypothalamus in the fed state and by high fat feeding. Open bars represent thechow-fed, fasted controls; red bars represent the chow-fed non-fasted group; and blue bars represent the high fat fed group (fasted prior totissue collection). The number of mice in the chow fasted, chow fed, and HFD fasted groups were: Bat2 (10, 5, 7), C10orf97 (10, 7, 5), Dgkg(4, 5, 5), Etv5 (10, 7, 7) Faim2 (4, 5, 5), Gnpda2 (9, 9, 6), Kctd15 (7, 5, 6), Maf (7, 7, 7), Mtch2 (10, 9, 7), Negr1 (10, 7, 7), Npc1 (10, 5, 7), Pter (10, 8, 6),Sfrs10 (10, 7, 5) and Sh2b1 (9, 6, 7), respectively. P-values vs. the chow-fed fasted controls: * <0.05, ** <0.01, # <0.1.Figure 2 Regulation of GWAS obesity genes in the remainder of the brain in the fed state and by high fat feeding. Open bars representthe chow-fed, fasted controls; red bars represent the chow-fed non-fasted group; and blue bars represent the high fat fed group (fasted prior totissue collection). The number of mice in the chow fasted, chow fed, and HFD fasted groups were: Bat2 (7, 10, 5), Etv5 (7, 10, 7) Faim2 (7, 10, 7),Kctd15 (5, 10, 5), Maf (7, 10, 7), Negr1 (7, 10, 5), Npc1 (7, 10, 5), Rasal2 (7, 10, 6), Sec16b (7, 10, 5), Sh2b1 (7, 10, 7), and Tmem18 (7, 10, 5),respectively. *P <0.05 vs. the chow-fed fasted controls.Yoganathan et al. Nutrition & Metabolism 2012, 9:65 Page 4 of 10http://www.nutritionandmetabolism.com/content/9/1/65Regulation of GWAS genes by dietary status in adiposetissueAll the GWAS genes we assessed were also expressed inwhite adipose tissue. Several GWAS obesity genes werehighly differentially regulated in adipose tissue from highfat-fed compared to chow-fed mice. Many genes hadreduced expression in high fat diet-fed mice (Figure 3).Most strikingly, we observed a ~10-fold reduction in ex-pression of Negr1 in the high fat-fed mice compared tothe chow-fed controls. Etv5 and Kctd15 were also mark-edly downregulated in mice consuming the high fat diet(~8-fold and 5-fold, respectively). Decreased expressionof Maf, Npc1, Rasal2, Sec16b, Sh2b1, and Tmem18 inhigh fat diet-fed mice ranged from ~2.5-fold for Npc1to 6-fold for Rasal2. We observed a similar decrease inBat2 expression with high fat feeding (3-fold), althoughthis did not reach statistical significance. In contrast,Aif1 had a small but significant increase in gene expres-sion in adipose tissue of the mice fed a high fat diet(2.1-fold).We found the expression of three genes changed sig-nificantly by feeding status in adipose tissue. Faim2 wasupregulated a remarkable 20-fold in fed versus fastedmice consuming normal chow (Figure 3). Expression ofMtch2 was increased an average of 8-fold and Kctd15was increased ~5-fold in fed animals. A similar trendwas observed for Etv5, which had 4-fold higher expres-sion in adipose tissue of fed mice. Smaller increaseswere observed for Dgkg and Negr1 (3.5 and 2.3-fold, re-spectively), but these were not significant. We did notdetect any significant differences in gene expression forDgkg, Ctnnbl1, Sfrs10, Bat2, Pter, Gnpda2, or C10orf97in either group.Regulation by dietary status in liverOf the 19 genes we assessed, all except Faim2 and Negr1were expressed in the liver. In contrast to the tissuesabove, we observed few differences in gene expression inthe liver (Figure 4). Expression of Kctd15 was decreasedan average of 20-fold in high fat diet-fed mice comparedto chow-fed mice, and was the only gene for which sig-nificant differences were detected. Sh2b1 expression wasdecreased by a similar amount but was not significant(P = 0.14) likely due to the variability of the controls.Rasal2 expression was decreased an average 3.4-fold inthe high fat diet-fed mice. Expression of Gnpda2 wasdecreased 2-fold in the high fat-fed mice, and by a simi-lar amount in fed animals compared to fasted. Similarly,Npc1 expression was decreased an average 2.6-fold infed animals compared with fasted. Interestingly, expres-sion of Dgkg was low but detectable in both groups ofchow-fed mice, whereas expression could not bedetected in five of six high fat diet-fed mice. No differ-ences were observed in the expression of the other genesexpressed in liver (Sec16b, Maf, Mtch2, Tmem18, Aif1,Bat2, Sfrs10, Pter, Ctnnbl1, Etv5, and C10orf97) in re-sponse to fed status or consumption of the high fat diet.DiscussionGWAS have identified many new SNPs associated withthe development of obesity. Most of these SNPs arelocated in introns or intergenic regions, suggesting theyaffect the regulation of the corresponding gene ornearby gene(s), but which gene or genes they affect isunknown. Little is known about most of these genes orhow they may affect the development of obesity, butunderstanding their regulation is a key step that mayprovide important clues. Many genes involved in metab-olism and maintaining energy balance are regulated inresponse to feeding and fasting or by dietary compo-nents. Identification of such regulatory patterns wouldprovide support for a potential role in the developmentof obesity. To begin to understand the regulation of theGWAS obesity genes and if they are nutritionally regu-lated we examined how changes in dietary conditionsaffect their expression.Figure 3 Regulation of GWAS obesity genes in adipose tissue in the fed state and by high fat feeding. Open bars represent thechow-fed, fasted controls; red bars represent the chow-fed non-fasted group; and blue bars represent the high fat fed group (fasted prior totissue collection). The number of mice in the chow fasted, chow fed, and HFD fasted groups were: Aif1 (7, 8, 9), Bat2 (7, 6, 9), C10orf97 (7, 8, 5),Dgkg (7, 6, 4), Etv5 (7, 4, 8), Faim2 (7, 6, 7), Kctd15 (7, 4, 9), Maf (7, 7, 9), Mtch2 (7, 8, 5), Negr1 (7, 4, 8), Npc1 (7, 8, 9), Rasal2 (7, 7, 9), Sec16b (7, 5, 8),Sfrs10 (7, 8, 5), Sh2b1 (7, 5, 9), and Tmem18 (7, 6, 9), respectively. P-values vs. the chow-fed fasted controls: * <0.05, ** <0.01, # <0.1.Yoganathan et al. Nutrition & Metabolism 2012, 9:65 Page 5 of 10http://www.nutritionandmetabolism.com/content/9/1/65We observed complex, tissue-dependent regulation ofmost of these new obesity genes. Only one gene, dia-cylglceryol kinase gamma (Dgkg), did not significantlychange under any condition tested. However, given therole of this kinase in second messenger signalling, it islikely that it may not be physiologically regulated at thelevel of transcription but rather by activity of the en-zyme. Note that this does not preclude the possibilitythat the GWAS SNPs may affect its basal transcriptionlevels. The regulation of some genes was highly tissuespecific. Four transcripts, Sfrs10, C10orf97, Pter, andCtnnbl1 were increased in hypothalami of fed animalscompared to fasted, but were not changed by this condi-tion in other regions of the brain, liver or adipose tissue,or by high fat feeding in any tissue. Thus these genesmay have unique functions in the hypothalamus regulat-ing energy balance and the development of obesity.Similarly, Aif1 (allograft inflammatory factor 1) expres-sion was increased by high fat feeding only in adiposetissue, whereas no differences in its expression wereobserved in the other tissues or in fed animals. It is un-clear whether this reflects increased inflammation of adi-pose tissue in the high fat-fed mice (e.g. expression fromincreased amounts of inflammatory cells in the tissue),or whether this suggests that the role of Aif1 in obesitymay be mediated via specific actions in adipose tissue.In contrast to the above genes whose expression wasaffected by a single condition in a single tissue, the ex-pression of other genes was more complex and alteredin multiple tissues or by both conditions. Gnpda2 ex-pression was increased in the hypothalamus anddecreased in the liver of fed animals, whereas its expres-sion did not differ in other regions of the brain or adi-pose tissue in fed vs. fasted animals. We did not detectany significant changes of this gene in response to highfat diet consumption, although another group has re-cently reported decreased expression of this gene in highfat diet-fed rats [29]. Several transcripts were decreasedin the hypothalamus by high fat feeding and increased inthe remainder of the brain in the fed state, with variablepatterns in adipose tissue. Kctd15 regulation was themost consistent across tissues, with its expression beingincreased with feeding in hypothalamus, brain and adi-pose tissue (but not liver) and decreased by high fat dietconsumption in hypothalamus, adipose tissue and liver(but not brain). A recent study has also found decreasedKctd15 expression in hypothalamus and adipose tissueof high fat-fed rats, although they did not observereduced expression in liver [29].We also observed a large range in the magnitude ofthe changes in gene expression, and for each transcriptthe magnitude of the change in expression was not thesame in each tissue. For example, expression of Kctd15was increased >20-fold in the brain of fed animals, butonly by 2- to 4-fold in the hypothalamus and adipose tis-sue. These complex and tissue-dependent patterns ofregulation in response to nutritional status suggest thatthese genes have important and perhaps distinct roles inseveral tissues.Although each gene showed distinct patterns and mag-nitude of regulation across tissues and nutritional condi-tions, we observed striking consistency in the overallpattern of regulation by feeding and chronic high fat dietconsumption. With few exceptions (Npc1 and Gnpda2 inliver, Figure 4), all genes regulated by feeding/fastingwere upregulated in the non-fasted animals. This sug-gests that most transcripts respond to nutrient intake orincreased nutrient levels, rather than acting as cues initi-ating food intake or promoting the mobilization of en-ergy stores. Several transcription factors, co-activators,and factors affecting chromatin structure are known toincrease expression of target genes in response tochanges in cellular nutrient levels and energy status andthus may be important for the regulation of these genes[21-23]. Regulation of gene expression by feeding wasprominent in the hypothalamus and remainder of brain,and also for some transcripts in adipose tissue. In fact,changes in response to feeding-fasting status accountedFigure 4 Regulation of GWAS obesity genes in the liver in the fed state and by high fat feeding. Open bars represent the chow-fed,fasted controls; red bars represent the chow-fed non-fasted group; and blue bars represent the high fat fed group (fasted prior to tissuecollection). The number of mice in the chow fasted, chow fed, and HFD fasted groups were: Dgkg (5, 4, 6), Etv5 (7, 5, 3), Gnpda2 (7, 5, 6),Kctd15 (5, 4, 4), Npc1 (7, 5, 5), Rasal2 (7, 5, 6), and Sh2b1 (7, 5, 6), respectively. P-values vs. the chow-fed fasted controls: * <0.05, # <0.1. ND=notdetected.Yoganathan et al. Nutrition & Metabolism 2012, 9:65 Page 6 of 10http://www.nutritionandmetabolism.com/content/9/1/65for nearly all the expression differences we observed inthe brain; only one gene (Faim2) was regulated by highfat feeding in the brain. Interestingly, however, there waslittle overlap in the genes regulated by feeding betweenthe hypothalamus and the remainder of the brain, sug-gesting that distinct nutrient-responsive regulatory path-ways operate in various regions of the brain. In contrast,consumption of a high fat diet was associated with a re-duction in expression of all of the regulated transcriptswith the exception of Mtch2 in hypothalamus and Aif1 inadipose tissue. High fat diet consumption affected geneexpression mainly in adipose tissue and the hypothal-amus but not the rest of the brain, suggesting energystorage and homeostatic regulation of energy balancemay be preferentially affected by today’s obesogenic diets.This also suggests that high fat diets may predominantlydown-regulate pathways important for normal adiposetissue function. Previous studies have shown reduced adi-pogenic gene expression in obese individuals [30-32].Our findings further suggest that the effects of thesegenes on the development of obesity may occur in tissuesin addition to the brain, which was previously suggestedto be their likely site of action [8,11]. Consistent withthis, Negr1 has recently been shown to be expressed inadipose tissue and to be associated with gene expressionnetworks [33]. Mtch2 expression in adipose tissue isincreased in obese individuals [34] and has been shownto be increased in high fat-fed rats [29], although we didnot observe changes in its expression in adipose tissue inresponse to high fat feeding (Figure 3). Somewhat sur-prisingly given its key role in whole-body metabolismand that high fat diets have previously been shown toregulate the hepatic expression of many genes [32], veryfew of the GWAS obesity genes had altered expressionin the liver, suggesting that the metabolic processes ofthe liver may not play a significant role in the mainten-ance of whole body energy homeostasis. We also foundthat most of these genes except Etv5, Mtch2, Pter andTmem18 had relatively low to no detectable expressionin soleus muscle (not shown). A recent study examiningthe expression of a partially-overlapping subset ofGWAS obesity genes also found modest changes in ex-pression of only a few genes in liver and muscle [29].The nutritional regulation of these new obesity genesprovides support for their potential role in the regulationof energy balance. These data have several importantimplications for studies examining the effects of theGWAS SNPs on gene regulation and ultimately themechanisms by which these genes affect the developmentof obesity. The highly tissue-dependent patterns of regu-lation suggest that all tissues where a gene is expressedwill need to be examined to determine whether the par-ticular SNPs identified by the GWAS affect the expres-sion of a gene. This is further highlighted by the complexpatterns and variability in magnitude of expressionchanges for a transcript in different tissues. Analysis ofthe effects of the GWAS SNPs on gene expression inlymphocytes has been suggested since this is a readily ac-cessible source of RNA, and differences in lymphocytegene expression between lean and obese individuals havebeen detected [35]. This may be useful if the SNPs affectbasal transcription of a gene, but will not be useful if theSNPs affect enhancer elements. Indeed, in a study exam-ining the effects of diabetes-associated SNPs on gene ex-pression, many correlations between genotype andexpression were found in muscle and adipose tissue thatwere not observed in lymphocytes [36]. Similarly, SNPsaffecting gene expression in lymphocytes were only asso-ciated with inflammatory and auto-immune diseases notwith metabolic diseases [37]. Thus it may be very difficultto pinpoint the effects of these obesity GWAS SNPs ontranscript levels. To understand the role of these genes inthe development of obesity it will be necessary to fullyunderstand the role of each gene in each physiologicallyrelevant tissue, as they are regulated and thus may be-have differently depending upon the tissue beingexamined.Transcriptional regulatory elements can be located be-fore, within, or even following a gene, and can be locatedat substantial distance (up to 1 Mb) from the gene [38].Thus it is possible that the variants detected by theGWAS studies affect the regulation of other nearbygenes, not just those that are closest to the SNP. Wechose to focus our analyses on the genes reported by theGWAS studies as the most likely genes affected by theassociated variants. The functions of many of thesegenes are not well described, and it is not clear how theymay affect the development of obesity. We performedthese studies to learn more about these genes, determinewhether their expression patterns were consistent withthem having a role in the regulation of body weight, andexamine whether their expression is affected by knownmetabolic regulatory factors. While our data providesupport for all the genes having a potential role in thedevelopment of obesity, they do not confirm that theseare “the genes” nor do they exclude the possibility thatthe GWAS SNPs affect the regulation of other nearbygenes.Three of the obesity SNPs identified by Thorliefssonand colleagues [8] were found in gene dense regionswhere the SNP was not clearly more closely related to asingle gene, and were consequently identified as beingwithin gene clusters: SEC16B-RASAL2, SFRS10-ETV5-DGKG, and NCR3-AIF1-BAT2. We examined the ex-pression of the genes in each cluster with the hope ofdiscovering regulatory patterns that may point to themore likely causative gene in each cluster. Unfortunatelythese studies provide little support favoring one of theYoganathan et al. Nutrition & Metabolism 2012, 9:65 Page 7 of 10http://www.nutritionandmetabolism.com/content/9/1/65genes over the other(s). All the genes were expressed intissues relevant to the regulation of body weight. For thefirst cluster, both Sec16b and Rasal2 were increased inthe fed state in the brain and decreased by high fat feed-ing in adipose tissue. Neither was altered in the hypo-thalamus, while Rasal2 expression was also decreased inresponse to high fat feeding in the liver. The similar ex-pression patterns make it difficult to prioritize one overthe other as likely being the causative gene and also sug-gest that there may in fact be common regulatory ele-ments affecting multiple genes in the region. The genesin the second and third clusters showed very differentexpression patterns from each other. In the second clus-ter, Sfrs10 expression was increased in the hypothalamusin fed mice but did not change in any of the other tis-sues. Etv5 expression in the hypothalamus was decreasedby high fat feeding and its expression increased in thebrain and adipose tissue of fed animals. Dgkg expression,although relatively high in the hypothalamus and re-mainder of the brain, was not affected by fasting or theconsumption of a high fat diet. For the third cluster, dif-ferential expression of Aif1 was detected in adipose tis-sue, where it was the only gene to increase in responseto a high fat diet. In contrast, Bat2 expression wasdecreased by the high fat diet in hypothalamus,increased by feeding in brain, and was not changed inliver or adipose tissue. The expression of the Ncr3pseudogene was not analyzed. Thus each member of thelast two groups has a differential regulation in responseto changing dietary conditions, which suggests that thecausative variant(s) at these loci may affect just one ofthe genes rather than the coordinate regulation of mul-tiple genes. However, for each cluster all the genes hadpotentially relevant changes in expression and it is un-clear which of the genes is most likely to be affected bythe GWAS SNP based on these analyses.Recently other studies have reported expression pat-terns of some of these genes. One of these has shownthat Tmem18 is expressed in most neurons [39]. Thesestudies found no change in its expression in the hypo-thalamus or brainstem with prolonged (16 or 24 hour)fasting, 48 hour sucrose or fat ingestion or in responseto chronic addition of 10% sucrose to the diet comparedto ad-lib chow-fed mice [39]. Consistent with these find-ings, we saw no evidence of altered Tmem18 expressionin response to chronic consumption of a high fat-sucrose diet. In contrast, however, we found ~8-foldhigher levels of Tmem18 in the brain of non-fasted ani-mals (Figure 2). A recent study reproduced our findingof reduced expression of Etv5 and Sh2b1 in the hypo-thalamus and Kctd15 in the hypothalamus and adiposetissue of high fat-fed animals [29]. In contrast, this studyalso found decreased expression of Kctd15 and Gnpda2in the hypothalamus and Tmem18 in the liver,hypothalamus and soleus muscle, and increased expres-sion of Mtch2 in adipose tissue and Pter in adipose tis-sue and liver of high fat-fed rats, which we did notobserve [29]. The reasons for these discrepancies mayrelate to experimental design, such as differences in thespecies and strain studied; the diet used and its duration;the time of day of sacrifice; and the fasting conditionsused, and require further investigation.ConclusionsThese studies have shown that the regulation of thesenew GWAS obesity genes in response to dietary condi-tions is complex and often tissue-dependent. Knowledgeof the regulation of these genes will help in determiningtheir role in obesity. These data provide supportive evi-dence for a role of these genes in the regulation of en-ergy metabolism, but suggest that discovery of themechanism by which the regulatory SNPs identified inthe GWAS affect these genes will require tissue andcontext specific analysis.Additional fileAdditional file 1: Table S1. Primer sequences used in these studies.AbbreviationsAif1: Allograft inflammatory factor 1; BMI: Body mass index; Ctnnbl1: Catenin,beta-like 1; C10orf97: Chromosome 10 open reading frame 97 a;Fam188a: Which is now known as Family with sequence similarity 188member A; Ct: Cycle threshold; Dgkg: Diacylglycerol kinase gamma; Etv5: Etsvariant 5; Faim2: Fas apoptotic inhibitory molecule 2; GWAS: Genome-wideassociation study; Gnpda2: Glucosamine 6 phosphate deaminase 2;HFD: High fat diet; Bat2: HLA-B associated transcript 2; Mb: Million base pairs;Mtch2: Mitochondrial carrier homolog 2; Negr1: Neuronal growth regulator 1;Npc1: Niemann Pick type C1; Pter: Phosphotriesterase related;Kctd15: Potassium channel tetramerisation domain containing 15; Rasal2: Rasprotein activator like 2; Sec16b: SEC16 homolog b from S. cerevisiae;SNP: Single nucleotide polymorphism; qPCR: Real-time quantitative reversetranscription PCR; Tra2b originally named Sfrs10: Transformer 2 betahomolog; Sh2b1: Sh2b adaptor protein 1; Tmem18: Transmembrane protein18; Maf: v-maf musculoaponeurotic fibrosarcoma oncogene homolog.Competing interestsThe authors confirm that they have no competing interests to disclose.Authors' contributionsPY, SK and MMH conducted the study and participated in writing themanuscript. SMC designed the study, analyzed and interpreted the resultsand wrote the manuscript. All authors read and approved the finalmanuscript.AcknowledgementsThe authors would like to thank Ms. Shadi Mahmoodi and Mr. Akiff Manji forassistance with tissue collection and Dr. James D. Johnson for the use of thereal time PCR equipment. These studies were supported by funding fromthe Michael Smith Foundation for Health Research (CI-SCH-01423), theAmerican Heart Association (0635234 N), the Canadian Diabetes Association(OG-2-08-2600-SC) and the Heart and Stroke Foundation of BC & Yukon(SMC). SMC is Canada Research Chair in the Genetics of Obesity andDiabetes and a Michael Smith Foundation for Health Research Scholar.Yoganathan et al. Nutrition & Metabolism 2012, 9:65 Page 8 of 10http://www.nutritionandmetabolism.com/content/9/1/65Received: 7 December 2011 Accepted: 21 April 2012Published: 10 July 2012References1. Dina C, Meyre D, Gallina S, Durand E, Korner A, Jacobson P, Carlsson LM,Kiess W, Vatin V, Lecoeur C, Delplanque J, Vaillant E, Pattou F, Ruiz J, Weill J,Levy-Marchal C, Horber F, Potoczna N, Hercberg S, Le Stunff C, Bougneres P,Kovacs P, Marre M, Balkau B, Cauchi S, Chevre JC, Froguel P: Variation inFTO contributes to childhood obesity and severe adult obesity. NatGenet 2007, 39:724–726.2. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM,Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC,Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA,Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D,Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR,Walker M, Hitman GA, Palmer CN, Doney AS, Morris AD, Smith GD,Hattersley AT, McCarthy MI: A common variant in the FTO gene isassociated with body mass index and predisposes to childhood andadult obesity. Science 2007, 316:889–894.3. Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, Najjar S, Nagaraja R,Orru M, Usala G, Dei M, Lai S, Maschio A, Busonero F, Mulas A, Ehret GB,Fink AA, Weder AB, Cooper RS, Galan P, Chakravarti A, Schlessinger D,Cao A, Lakatta E, Abecasis GR: Genome-Wide Association Scan ShowsGenetic Variants in the FTO Gene Are Associated with Obesity-RelatedTraits. PLoS Genet 2007, 3:e115.4. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU,Allen HL, Lindgren CM, Luan J, Magi R, Randall JC, Vedantam S, Winkler TW,Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, WeedonMN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segre AV, Estrada K,Liang L, Nemesh J, Park JH, Gustafsson S, Kilpelainen TO, Yang J,Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S,Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N,Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V,Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF,Myers RH, Narisu N, Perry JR, Peters MJ, Preuss M, Ripatti S, Rivadeneira F,Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM,White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO,Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H,Alibrandi MT, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B,Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S,Biebermann H, Blakemore AI, Boes T, Bonnycastle LL, Bornstein SR,Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP,Cavalcanti-Proenca C, Chen YD, Chen CM, Chines PS, Clarke R, Coin L,Connell J, Day IN, Heijer MD, Duan J, Ebrahim S, Elliott P, Elosua R,Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB,Fischer-Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S,Gejman PV, Geus EJ, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H,Grassler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C,Hartikainen AL, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC,Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W,Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jorgensen T,Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S,Kettunen J, Kinnunen L, Knowles JW, Kolcic I, Konig IR, Koskinen S, Kovacs P,Kuusisto J, Kraft P, Kvaloy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ,Lecoeur C, Lehtimaki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben RN,Ludwig B, Manunta P, Marek D, Marre M, Martin NG, McArdle WL,McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K,Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M,Neville MJ, Nyholt DR, O'Donnell CJ, O'Rahilly S, Ong KK, Oostra B, Pare G,Parker AN, Perola M, Pichler I, Pietilainen KH, Platou CG, Polasek O, Pouta A,Rafelt S, Raitakari O, Rayner NW, Ridderstrale M, Rief W, Ruokonen A,Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S,Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H,Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J,Surakka I, Swift AJ, Tammesoo ML, Tardif JC, Teder-Laving M, Teslovich TM,Thompson JR, Thomson B, Tonjes A, Tuomi T, van Meurs JB,van Ommen GJ, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel CI, Voight BF,Waite LL, Wallaschofski H, Walters GB, Widen E, Wiegand S, Wild SH,Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A,Zitting P, Beilby JP, Farooqi IS, Hebebrand J, Huikuri HV, James AL,Kahonen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ,Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI,Caulfield MJ, Chanock SJ, Collins FS, Cupples LA, Smith GD, Erdmann J,Froguel P, Gronberg H, Gyllensten U, Hall P, Hansen T, Harris TB,Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin MR,Kaprio J, Karpe F, Khaw KT, Kiemeney LA, Krude H, Laakso M, Lawlor DA,Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A,Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ,Schwarz PE, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A,Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Wilson JF, WrightAF, Zillikens MC, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, VisscherPM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T,Hunter DJ, Kaplan RC, Mohlke KL, O'Connell JR, Peltonen L, Schlessinger D,Strachan DP, van Duijn CM, Wichmann HE, Frayling TM, Thorsteinsdottir U,Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE MIM,Hirschhorn JN, Ingelsson E, Loos RJ: Association analyses of 249,796individuals reveal 18 new loci associated with body mass index. NatGenet 2010, 42:937–948.5. Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, Inouye M,Freathy RM, Attwood AP, Beckmann JS, Berndt SI, Jacobs KB, Chanock SJ,Hayes RB, Bergmann S, Bennett AJ, Bingham SA, Bochud M, Brown M,Cauchi S, Connell JM, Cooper C, Smith GD, Day I, Dina C, De S,Dermitzakis ET, Doney AS, Elliott KS, Elliott P, Evans DM, Sadaf Farooqi I,Froguel P, Ghori J, Groves CJ, Gwilliam R, Hadley D, Hall AS, Hattersley AT,Hebebrand J, Heid IM, Lamina C, Gieger C, Illig T, Meitinger T,Wichmann HE, Herrera B, Hinney A, Hunt SE, Jarvelin MR, Johnson T,Jolley JD, Karpe F, Keniry A, Khaw KT, Luben RN, Mangino M, Marchini J,McArdle WL, McGinnis R, Meyre D, Munroe PB, Morris AD, Ness AR,Neville MJ, Nica AC, Ong KK, O'Rahilly S, Owen KR, Palmer CN, Papadakis K,Potter S, Pouta A, Qi L, Randall JC, Rayner NW, Ring SM, Sandhu MS,Scherag A, Sims MA, Song K, Soranzo N, Speliotes EK, Syddall HE,Teichmann SA, Timpson NJ, Tobias JH, Uda M, Vogel CI, Wallace C,Waterworth DM, Weedon MN, Willer CJ, Wraight, Yuan X, Zeggini E,Hirschhorn JN, Strachan DP, Ouwehand WH, Caulfield MJ, Samani NJ,Frayling TM, Vollenweider P, Waeber G, Mooser V, Deloukas P, McCarthy MI,Wareham NJ, Barroso I, Kraft P, Hankinson SE, Hunter DJ, Hu FB, Lyon HN,Voight BF, Ridderstrale M, Groop L, Scheet P, Sanna S, Abecasis GR, Albai G,Nagaraja R, Schlessinger D, Jackson AU, Tuomilehto J, Collins FS,Boehnke M, Mohlke KL: Common variants near MC4R are associated withfat mass, weight and risk of obesity. Nat Genet 2008, 40:768–775.6. Chambers JC, Elliott P, Zabaneh D, Zhang W, Li Y, Froguel P, Balding D,Scott J, Kooner JS: Common genetic variation near MC4R is associatedwith waist circumference and insulin resistance. Nat Genet 2008,40:716–718.7. Hofker M, Wijmenga C: A supersized list of obesity genes. Nat Genet 2009,41:139–140.8. Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P,Helgadottir A, Styrkarsdottir U, Gretarsdottir S, Thorlacius S, Jonsdottir I,Jonsdottir T, Olafsdottir EJ, Olafsdottir GH, Jonsson T, Jonsson F,Borch-Johnsen K, Hansen T, Andersen G, Jorgensen T, Lauritzen T, Aben KK,Verbeek AL, Roeleveld N, Kampman E, Yanek LR, Becker LC, Tryggvadottir L,Rafnar T, Becker DM, Gulcher J, Kiemeney LA, Pedersen O, Kong A,Thorsteinsdottir U, Stefansson K: Genome-wide association yields newsequence variants at seven loci that associate with measures of obesity.Nat Genet 2009, 41:18–24.9. Meyre D, Delplanque J, Chevre JC, Lecoeur C, Lobbens S, Gallina S,Durand E, Vatin V, Degraeve F, Proenca C, Gaget S, Korner A, Kovacs P,Kiess W, Tichet J, Marre M, Hartikainen AL, Horber F, Potoczna N,Hercberg S, Levy-Marchal C, Pattou F, Heude B, Tauber M, McCarthy MI,Blakemore AI, Montpetit A, Polychronakos C, Weill J, Coin LJ, Asher J,Elliott P, Jarvelin MR, Visvikis-Siest S, Balkau B, Sladek R, Balding D, Walley A,Dina C, Froguel P: Genome-wide association study for early-onset andmorbid adult obesity identifies three new risk loci in Europeanpopulations. Nat Genet 2009, 41:157–159.10. Liu YJ, Liu XG, Wang L, Dina C, Yan H, Liu JF, Levy S, Papasian CJ, Drees BM,Hamilton JJ, Meyre D, Delplanque J, Pei YF, Zhang L, Recker RR, Froguel P,Deng HW: Genome-wide association scans identified CTNNBL1 as anovel gene for obesity. Hum Mol Genet 2008, 17:1803–1813.11. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, Berndt SI,Elliott AL, Jackson AU, Lamina C, Lettre G, Lim N, Lyon HN, McCarroll SA,Papadakis K, Qi L, Randall JC, Roccasecca RM, Sanna S, Scheet P,Weedon MN, Wheeler E, Zhao JH, Jacobs LC, Prokopenko I, Soranzo N,Yoganathan et al. Nutrition & Metabolism 2012, 9:65 Page 9 of 10http://www.nutritionandmetabolism.com/content/9/1/65Tanaka T, Timpson NJ, Almgren P, Bennett A, Bergman RN, Bingham SA,Bonnycastle LL, Brown M, Burtt NP, Chines P, Coin L, Collins FS, Connell JM,Cooper C, Smith GD, Dennison EM, Deodhar P, Elliott P, Erdos MR, Estrada K,Evans DM, Gianniny L, Gieger C, Gillson CJ, Guiducci C, Hackett R, Hadley D,Hall AS, Havulinna AS, Hebebrand J, Hofman A, Isomaa B, Jacobs KB,Johnson T, Jousilahti P, Jovanovic Z, Khaw KT, Kraft P, Kuokkanen M,Kuusisto J, Laitinen J, Lakatta EG, Luan J, Luben RN, Mangino M,McArdle WL, Meitinger T, Mulas A, Munroe PB, Narisu N, Ness AR,Northstone K, O'Rahilly S, Purmann C, Rees MG, Ridderstrale M, Ring SM,Rivadeneira F, Ruokonen A, Sandhu MS, Saramies J, Scott LJ, Scuteri A,Silander K, Sims MA, Song K, Stephens J, Stevens S, Stringham HM, Tung YC,Valle TT, Van Duijn CM, Vimaleswaran KS, Vollenweider P, Waeber G,Wallace C, Watanabe RM, Waterworth DM, Watkins N, Witteman JC,Zeggini E, Zhai G, Zillikens MC, Altshuler D, Caulfield MJ, Chanock SJ,Farooqi IS, Ferrucci L, Guralnik JM, Hattersley AT, Hu FB, Jarvelin MR,Laakso M, Mooser V, Ong KK, Ouwehand WH, Salomaa V, Samani NJ,Spector TD, Tuomi T, Tuomilehto J, Uda M, Uitterlinden AG, Wareham NJ,Deloukas P, Frayling TM, Groop LC, Hayes RB, Hunter DJ, Mohlke KL,Peltonen L, Schlessinger D, Strachan DP, Wichmann HE, McCarthy MI,Boehnke M, Barroso I, Abecasis GR, Hirschhorn JN: Six new loci associatedwith body mass index highlight a neuronal influence on body weightregulation. Nat Genet 2009, 41:25–34.12. Walley AJ, Asher JE, Froguel P: The genetic contribution to non-syndromichuman obesity. Nat Rev Genet 2009, 10:431–442.13. Cordeira JW, Frank L, Sena-Esteves M, Pothos EN, Rios M: Brain-derivedneurotrophic factor regulates hedonic feeding by acting on themesolimbic dopamine system. J Neurosci 2010, 30:2533–2541.14. Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B,Perusse L, Bouchard C: The Human Obesity Gene Map: The 2005 Update.Obesity 2006, 14:529–644.15. Ren D, Li M, Duan C, Rui L: Identification of SH2-B as a key regulator ofleptin sensitivity, energy balance, and body weight in mice. Cell Metab2005, 2:95–104.16. Farooqi IS, O'Rahilly S: Mutations in ligands and receptors of theleptin-melanocortin pathway that lead to obesity. Nat Clin PractEndocrinol Metab 2008, 4:569–577.17. Stutzmann F, Vatin V, Cauchi S, Morandi A, Jouret B, Landt O, Tounian P,Levy-Marchal C, Buzzetti R, Pinelli L, Balkau B, Horber F, Bougneres P,Froguel P, Meyre D: Non-synonymous polymorphisms in melanocortin-4receptor protect against obesity: the two facets of a Janus obesity gene.Hum Mol Genet 2007, 16:1837–1844.18. Han JC, Liu QR, Jones M, Levinn RL, Menzie CM, Jefferson-George KS,Adler-Wailes DC, Sanford EL, Lacbawan FL, Uhl GR, Rennert OM, Yanovski JA:Brain-derived neurotrophic factor and obesity in the WAGR syndrome.N Engl J Med 2008, 359:918–927.19. Morris DL, Rui L: Recent advances in understanding leptin signalingand leptin resistance. Am J Physiol Endocrinol Metab 2009,297:E1247–E1259.20. Ragvin A, Moro E, Fredman D, Navratilova P, Drivenes O, Engstrom PG,Alonso ME, Mustienes Ede L, Skarmeta JL, Tavares MJ, Casares F,Manzanares M, van Heyningen V, Molven A, Njolstad PR, Argenton F,Lenhard B, Becker TS: Long-range gene regulation links genomic type 2diabetes and obesity risk regions to HHEX, SOX4, and IRX3. Proc NatlAcad Sci U S A 2010, 107:775–780.21. Canto C, Auwerx J: PGC-1alpha, SIRT1 and AMPK, an energy sensingnetwork that controls energy expenditure. Curr Opin Lipidol 2009,20:98–105.22. Gross DN, van den Heuvel AP, Birnbaum MJ: The role of FoxO in theregulation of metabolism. Oncogene 2008, 27:2320–2336.23. Moreno M, Lombardi A, Silvestri E, Senese R, Cioffi F, Goglia F, Lanni A,de Lange P: PPARs: Nuclear Receptors Controlled by, and Controlling,Nutrient Handling through Nuclear and Cytosolic Signaling. PPAR Res2010, 2010:43568924. Redinger RN: Fat storage and the biology of energy expenditure. TranslRes 2009, 154:52–60.25. Fredriksson R, Hagglund M, Olszewski PK, Stephansson O, Jacobsson JA,Olszewska AM, Levine AS, Lindblom J, Schioth HB: The obesity gene, FTO,is of ancient origin, up-regulated during food deprivation and expressedin neurons of feeding-related nuclei of the brain. Endocrinology 2008,149:2062–2071.26. Hollyoake M, Campbell RD, Aguado B: NKp30 (NCR3) is a pseudogene in12 inbred and wild mouse strains, but an expressed gene in Mus caroli.Mol Biol Evol 2005, 22:1661–1672.27. Yuan JS, Reed A, Chen F, Stewart CN Jr: Statistical analysis of real-time PCRdata. BMC Bioinformatics 2006, 7:85.28. Lenard NR, Berthoud HR: Central and peripheral regulation of food intakeand physical activity: pathways and genes. Obesity (Silver Spring) 2008,16(Suppl 3):S11–S22.29. Gutierrez-Aguilar R, Kim DH, Woods SC, Seeley RJ: Expression of new lociassociated with obesity in diet-induced obese rats: from genetics tophysiology. Obesity (Silver Spring) 2012, 20:306–312.30. Nadler ST, Stoehr JP, Schueler KL, Tanimoto G, Yandell BS, Attie AD: Theexpression of adipogenic genes is decreased in obesity and diabetesmellitus. Proc Natl Acad Sci U S A 2000, 97:11371–11376.31. Diraison F, Dusserre E, Vidal H, Sothier M, Beylot M: Increased hepaticlipogenesis but decreased expression of lipogenic gene in adiposetissue in human obesity. Am J Physiol Endocrinol Metab 2002, 282:E46–E51.32. Kim S, Sohn I, Ahn JI, Lee KH, Lee YS: Hepatic gene expression profiles ina long-term high-fat diet-induced obesity mouse model. Gene 2004,340:99–109.33. Walley AJ, Jacobson P, Falchi M, Bottolo L, Andersson JC, Petretto E,Bonnefond A, Vaillant E, Lecoeur C, Vatin V, Jernas M, Balding D, Petteni M,Park YS, Aitman T, Richardson S, Sjostrom L, Carlsson LM, Froguel P:Differential coexpression analysis of obesity-associated networks inhuman subcutaneous adipose tissue. Int J Obes (Lond) 2012, 36:137–147.34. Kulyte A, Ryden M, Mejhert N, Dungner E, Sjolin E, Arner P, Dahlman I:MTCH2 in human white adipose tissue and obesity. J Clin EndocrinolMetab 2011, 96:E1661–E1665.35. Ghosh S, Dent R, Harper ME, Gorman SA, Stuart JS, McPherson R: Geneexpression profiling in whole blood identifies distinct biologicalpathways associated with obesity. BMC Med Genomics 2010, 3:56.36. Sharma NK, Langberg KA, Mondal AK, Elbein SC, Das SK: Type 2 diabetes(T2D) associated polymorphisms regulate expression of adjacenttranscripts in transformed lymphocytes, adipose, and muscle fromCaucasian and African-American subjects. J Clin Endocrinol Metab 2011,96:E394–E403.37. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ: Trait-associated SNPs are more likely to be eQTLs: annotation to enhancediscovery from GWAS. PLoS Genet 2010, 6:e1000888.38. Maston GA, Evans SK, Green MR: Transcriptional regulatory elements inthe human genome. Annu Rev Genomics Hum Genet 2006, 7:29–59.39. Almen MS, Jacobsson JA, Shaik JH, Olszewski PK, Cedernaes J, Alsio J,Sreedharan S, Levine AS, Fredriksson R, Marcus C, Schioth HB: The obesitygene, TMEM18, is of ancient origin, found in majority of neuronal cells inall major brain regions and associated with obesity in severely obesechildren. BMC Med Genet 2010, 11:58.doi:10.1186/1743-7075-9-65Cite this article as: Yoganathan et al.: Nutritional regulation ofgenome-wide association obesity genes in a tissue-dependent manner.Nutrition & Metabolism 2012 9:65.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitYoganathan et al. 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