Associating genetic variation at Perilipin 1 ...

Gene 610 (2017) 80–89

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Research paper

Associating genetic variation at Perilipin 1, Complement Factor D and Adiponectin loci to the bone health status in North Indian population Harkirat Singh Sandhu a,b, Sanjeev Puri b,c, Rubina Sharma a, Jasmine Sokhi a, Gagandeep Singh d, Kawaljit Matharoo a,⁎, AJS Bhanwer a,⁎ a

Department of Human Genetics, Guru Nanak Dev University, Amritsar 143005, India Centre for Stem Cell and Tissue Engineering, Panjab University, Chandigarh 160014, India Biotechnology Branch, UIET, Panjab University, Chandigarh 160014, India d Department of Anthropology, Panjab University, Chandigarh 160014, India b c

a r t i c l e

i n f o

Article history: Received 15 June 2016 Received in revised form 9 January 2017 Accepted 6 February 2017 Available online 09 February 2017 Keywords: Perilipin 1 Complement Factor D Adiponectin Genetic variants Haplotype North India

a b s t r a c t Osteoporosis, the most common bone metabolic disease affecting nearly 200 million people worldwide is under the strong influence of genetic components. Simultaneously, adipogenesis and osteogenesis are two highly coordinated processes imperative for the maintenance of bone quality and quantity, where any perturbation leads to pathological conditions of obesity, osteopenia and osteoporosis. To delineate this adipogenic-osteogenic connection, a total of 254 cases (T-score b −1.0 SD) and 250 age, gender and ethnicity matched healthy controls (Tscore ≥ −1.0 SD) were recruited from North India after analyzing bone health status employing quantitative ultrasound (QUS) bone densitometer. The genetic variants of Perilipin 1 (PLIN1), Complement Factor D (CFD) and Adiponectin (ADIPOQ) were genotyped using the PCR-RFLP/ARMS-PCR approach. Subjects with CC + CT (PLIN1 rs2304795) and CC + CG (CFD rs1683563) genotypes conferred nearly 1.54–1.87 fold increased risk towards bone deterioration. Predicted RNA secondary structures of rs2304795 corroborated the risk associated with wild type C allele. G allele carriers at the ADIPOQ locus (rs1501299) were more likely to have a lower bone health (1.57-fold). Haplotype analysis revealed the ADIPOQ variants rs1501299 and rs3774261 in slight linkage disequilibrium (LD), nonetheless G/G haplotype was associated with increased risk. 3-locus and 5-locus gene-gene interaction models revealed a greater likelihood of bone deterioration. In conclusion, certain variants of adipogenic genes might serve as potential biomarkers for determining the genetic predisposition towards bone loss in the North Indian population, further, emphasizing the role of impaired metabolism in bone health. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Osteoporosis is a multifactorial skeletal disorder marked by bone microarchitectural defects and low bone mineral density (BMD) resulting in increased bone fragility and subsequent risk of fractures (Kawalkar, 2014). It involves a crosstalk between various environmental, nutritional, hormonal and genetic factors (Cohen and Roe, 2000). Among these, the genetic component accounts for ~ 50–80% of the

Abbreviations: PLIN1, Perilipin 1; CFD, Complement Factor D; ADIPOQ, Adiponectin; QUS, quantitative ultrasound; BMD, bone mineral density; MSC, mesenchymal stem cell; T2D, type 2 diabetes; HWE, Hardy-Weinberg Equilibrium; OR, odds ratio; LD, linkage disequilibrium; MDR, Multifactor Dimensionality Reduction; CVC, cross-validation consistency; TBA, testing balance accuracy; MFE, minimum free energy; BMI, body mass index; WHR, waist hip ratio; WHtR, waist height ratio; TC, total cholesterol; TGL, triglycerides; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein; VLDL-C, very low-density lipoprotein. ⁎ Corresponding authors. E-mail addresses: [email protected] (K. Matharoo), [email protected] (A.J.S. Bhanwer).

http://dx.doi.org/10.1016/j.gene.2017.02.009 0378-1119/© 2017 Elsevier B.V. All rights reserved.

variance in peak BMD and other osteoporotic phenotypes such as bone geometry and ultrasound properties of bone (Arden et al., 1996; Heaney et al., 2000). The polygenic character of osteoporosis renders multiple genes to exert small, individualistic effects, but only their combinatorial effect results in the manifestation of pathological symptoms (Guéguen et al., 1995; Duncan and Brown, 2008). For identifying the susceptibility genes and loci for osteoporosis, several approaches including the genetic linkage and candidate gene approach have been used, yet leading to inconclusive results (Mohammadi et al., 2014). This necessitates the need for studying the interaction between various genes and their variants involved in the etiology of bone loss. A common cellular origin of the bone and fat tissue represents commonality among the genes regulating one over the other. This reciprocricity is detrimental to the pathophysiology of the bone or fat metabolism (Pei and Tontonoz, 2004; Tian and Yu, 2015). The observations of Gharavi (2002) and Pirih et al. (2012) indeed lend credibility, wherein it was shown that secretory adipokines exert deleterious effects on the function and differentiation of osteoblasts, thus, suggesting that bone marrow adipocytes might play a pivotal role in modulating

H.S. Sandhu et al. / Gene 610 (2017) 80–89

marrow niche during the remodeling process. This directed the focus of the present study to introspect the role of the genes regulating adipose metabolism. Perilipin 1 (PLIN1), located on chromosome 15 is a major gene involved in modulating the lipolysis of cytoplasmic triacylglycerol deposits in adipocytes and steroidogenic cells (Londos et al., 1995). Its expression levels in animals and humans are elevated upon obesity, and any ablation confers a lean phenotype, making it a potential marker of terminal adipocytes (Brasaemle et al., 2000; Tansey et al., 2003). These observations have encouraged studying its association with various metabolic conditions resulting in obesity and type 2 diabetes (T2D), involving a dysregulation of the adipogenic pathway (Qi et al., 2008; Smith and Ordovás, 2012; Yu et al., 2013). Furthermore, studies have revealed the implication of genetic variation in PLIN1 with bone-related phenotypes in Japanese and Caucasian populations (Cusano et al., 2012; Yamada et al., 2006). This provided a valid explanation for evaluating the role of this adipogenic marker in the etiology of low bone mass. The cross-communication between bone and compartments like fat tissue and other surrounding organs for the allocation of resources involves the coordination of the endocrine system, inflammatory components and adipokines (Palermo et al., 2016). The adipose tissue, which is an active endocrine organ, secretes various circulating adipokines such as complement Factor D (CFD, mouse homolog: adipsin), adiponectin (ADIPOQ), leptin and estrogen (Gimble et al., 1996; reviewed in Rosen and Bouxsein, 2006; Yadav et al., 2013). CFD is a serine protease secreted by CFD gene present on chromosome 19 and is involved in the activation of the alternate complement pathway. Plasma CFD is majorly secreted by the adipose tissue and its levels get elevated during increased adiposity and obesity (Cook et al., 1985; Bahceci et al., 2007). Its role in regulating energy balance has been justified by its increased expression levels during overfeeding (Ukkola et al., 2003). Moreover, accumulating evidence has indicated the localization of complement protein receptors on osteoblasts and their involvement during endochondral bone formation (Ignatius et al., 2011; Schoengraf et al., 2013). This prompts to the involvement of complement proteins like CFD, secreted by the adipocytes, in various bone-associated disorders such as osteoporosis. Earlier groups have, however, investigated the association of its genetic variation with age-related macular degeneration (Kijlstra and Berendschot, 2015; Stanton et al., 2011; Zeng et al., 2010), but further research with implication to skeletal health will provide an interesting aspect of study. Another adipokine whose role in bone homeostasis has been well documented includes adiponectin (ADIPOQ). This is a 30 KDa protein encoded by the ADIPOQ gene located on chromosome 3q27.3 (Takahashi et al., 2000; Prakash et al., 2015). Its association with the susceptibility loci for various metabolic syndromes like T2D and CAD suggests its contribution in modulating the phenotypic characteristics of bone metabolic diseases like osteoporosis (Hara et al., 2002; K et al., 2009; Hascoet et al., 2013). In support of this, various clinical studies have attempted to relate plasma ADIPOQ levels with bone parameters. This correlation is of much relevance as the receptors for ADIPOQ viz. ADIPOR1 and ADIPOR2 have been identified on cells involved in the bone remodeling process (Lee et al., 2006; K et al., 2009; Kim et al., 2012). Regardless of the factuality that experimental and clinical studies have highlighted the potential of various factors including microRNAs (miR-146a, miR-146b, miR-433, miR-2861 etc.) through their binding at the 3′UTR sites of osteoblast genes and bone morphogenetic proteins (BMPs) e.g. BMP-2, 4, 6 and 7 for regulating the bone remodeling process (Kanakaris et al., 2009; Lei et al., 2011; McGee-Lawrence and Westendorf, 2011; Dole et al., 2015), the role of adipogenic markers still remains critical. The strong association of PLIN1, CFD and ADIPOQ genes with adipogenesis prompted us to decipher the role of genetic variants rs2304796 and rs2304795 (PLIN1), rs1683563 (CFD) and, rs1501299 and rs3774261 (ADIPOQ) that might influence the bone

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health status as evaluated by quantitative ultrasound (QUS) bone densitometer taking a North Indian cohort. Interrogation of the interaction among these variants was required to delineate any probable relationship. The North Indian population serves as a suitable model for elucidating this relationship between adipose metabolism and bone health pertaining to the greater prevalence of obesity prevailing here (Pradeepa et al., 2015). Moreover, this concern dwells from the growing prevalence of osteoporosis with 50% of Indians being either osteoporotic or having low bone mass (Mithal and Kaur, 2012). The present study not only aids in evaluating this relationship but at the same time it also takes into consideration the complex genetic heterogeneity persisting among the Indian subcontinent. Panorama of social and racial diversities in India is reflected in the genetic variation that exists, especially between the North Indian and South Indian populations (Tripathi et al., 2008). Hence, to enrich the existing knowledge regarding the fat and bone connection, a case-control approach from North India was employed. Moreover, this is the first study from India to report the influence of these genetic markers and their interactive impact on bone health.

2. Materials and methods 2.1. Study design and sample collection A total of 638 subjects were enrolled from the region of North India. After excluding the patients suffering from various diseases known to affect bone metabolism such as diabetes mellitus, coronary heart disease, chronic liver and kidney problems, thyroidism and other bone disorders, and those subjected to glucocorticoid medications, a total of 504 samples were used for subsequent analysis. These included 254 cases (62.20% osteopenic, 37.80% osteoporotic) and 250 age, gender and ethnicity matched healthy controls. 3–5 ml of venous blood was collected from all subjects and transferred to a pre-labeled vial containing 0.5 M EDTA as an anticoagulant. A written and well-informed consent was taken from all the participants enrolled in the study prior to sample collection. The study is approved by the Institutional Ethics Committee of Guru Nanak Dev University, Amritsar. The samples were collected from hospital-based bone check-up camps. All the QUS scans for measuring bone density at the distal radius and calcaneus were carried out by a trained medical technician, with calibrations made periodically. This was followed by diagnosis from an orthopedician according to the World Health Organization (WHO) guidelines. According to these guidelines, individuals with a T-score of ≤−2.5 SD at the distal radius and calcaneus were considered as osteoporotic, − 1.0 to − 2.5 SD as osteopenic and ≥− 1.0 SD as normal (WHO, 2003). However, for the simplicity of the analysis, cases were categorized as having a T-score b− 1.0 SD and controls with T-score ≥− 1.0 SD, assessed using QUS method. Both males and females in the age group of 20–80 years were included in the study. A comprehensive proforma was scored for their demographic, lifestyle, nutritional and medical histories. Details regarding the family history, previous fractures and medication were also recorded. The anthropometric parameters such as body weight, height, waist circumference (WC) and hip circumference (HC) to the nearest 0.1 units were taken using standard protocols for assessment of body mass index (BMI), waist hip ratio (WHR) and waist height ratio (WHtR). Fresh plasma samples were subjected to biochemical analysis [triglyceride (TGL), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)] using Erba-Mannheim kits (Trans Asia Bio-medicals Ltd., India) according to manufacturer's instructions. The derived parameters of LDL-C and VLDL-C were calculated by the FriedewaldFredrickson method (Friedewald et al., 1972). Cut-off values for the normal anthropometric measurements among Asian Indian adults were as follows: BMI (≤23 kg/m2) (Snehalatha et al., 2003), WC (M: ≤85 cm, F: ≤80 cm) (Hsieh and Muto, 2005), WHR (M: ≤0.89, F: ≤0.81) (Hsieh and Muto, 2005), WHtR (≤0.5) (Hsieh and Muto, 2005), TGL (≤150 mg/dl)

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(NCEP, 2001), TC (≤200 mg/dl) (NCEP, 2001) and HDL-C (≥40 mg/dl) (NCEP, 2001). 2.2. SNP selection and genotyping Whole genomic DNA was extracted from peripheral blood using the standard phenol-chloroform method (Adeli and Ogbonna, 1990) with slight modifications according to laboratory conditions. The selection of the variants for PLIN1 (rs2304796 and rs2304795), CFD (rs1683563) and ADIPOQ (rs1501299 and rs3774261) was made based on previous literature and the ones having a minor allele frequency (MAF) N 1% in the associated population upon referring to the public databases. All other genetic variants except rs1683563 (CFD) have been reported to be associated with various bone parameters and other metabolic disorders. Genotyping of rs2304795 (PLIN1), rs1683563 (CFD) and rs3774261 (ADIPOQ) was done using Amplification-Refractory Mutation System-Polymerase Chain Reaction (ARMS-PCR) for which the allele-specific primers were designed through the web-based allelespecific primer (WASP) software (Wangkumhang et al., 2007). On the other hand, screening of rs2304796 (PLIN1) and rs1501299 (ADIPOQ) was done employing the Restriction Fragment Length PolymorphismPolymerase Chain Reaction (RFLP-PCR) method with primer sequences and restriction conditions for rs2304796 adapted from Yu et al. (2013) and those for rs1501299 were designed using Primer3 software (http://bioinfo.ut.ee/primer3-0.4.0/) and NEBcutter online tool (http:// nc2.neb.com/NEBcutter2/). All sequences were matched using the bioinformatics algorithm, Basic Local Alignment Search Tool (BLAST). A list of the primer sequences and restriction enzymes used is given in Table 1. For quality control and authenticity of results, negative and positive controls were incorporated during each reaction. At least 10% of the samples were blindly re-analyzed. 2.3. Statistical analyses The statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) software for Windows version 20 (SPSS Inc., Chicago, Illinois, USA). The power of the study was computed using the QUANTO software (version 1.2) (http://hydra.usc.edu/gxe/) with a significance level of 0.05 (Gauderman, 2002). Mean ± standard deviation (SD) was derived for the continuous variables such as age, QUS Tscore, BMI, WHR, WHtR, TC, TGL, HDL-C, LDL-C and VLDL-C using independent samples t-test after applying Bonferroni correction for multiple hypothesis testing. For obtaining the significance values of mean differences, Levene's test for measuring the equality of variances was applied. Allele and genotype frequencies were calculated using gene-counting

method. Hardy-Weinberg Equilibrium (HWE) at each variant locus was tested using the chi-square test. Assessment of the differences in frequency distribution among all the genotypes as estimated by pvalues, and odds ratio (OR) at 95% confidence interval (CI) was carried out using 3 × 3 and 2 × 2 chi-square contingency tables. Binary logistic regression under the dominant, recessive and co-dominant models was applied to correct the data for confounding factors such as age, gender, BMI, WHR and WHtR. Haplotype frequencies and pairwise linkage disequilibrium (LD) among the two polymorphisms each of PLIN1 and ADIPOQ were determined using the Haploview 4.2 software (www. hapmap.org). Interaction analysis for the various studied SNPs was performed using the Multifactor Dimensionality Reduction (MDR) software (http://www.multifactordimensionalityreduction.org) with an 8/ 10–10/10 cross-validation consistency (CVC) and at least 50% testing balance accuracy (TBA). A graphical model and dendrogram based on the color-coding scheme was generated depicting entropy (Hahn et al., 2003). The structural changes in RNA caused by the variant and wild type alleles of the exonic polymorphisms of PLIN1 viz. rs2304796 and rs2304795 were evaluated using the online tool RNAFold (http:// rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi) (Michael Zuker, 1984). The minimum free energy (MFE) in terms of ΔG was predicted for the transcript sequences, and changes in the RNA structure (ΔΔG) were calculated from the differences in the MFE using the formula ΔΔG = | ΔGmut − ΔGwt |, where ΔGmut was the MFE of the mutant allele and ΔGwt of the wild type allele. p-Values b 0.05 were considered statistically significant. 3. Results 3.1. Clinical characteristics A comparison of the various anthropometric and clinical parameters among cases and controls is represented in Table 2. The mean QUS Tscore for cases and controls was −2.16 ± 0.95 and − 0.14 ± 1.07, respectively. No statistically significant difference was observed for age; however, significant differences were prevalent in the case of BMI (p = 0.001), WHtR (p = 0.000), TGL (p = 0.002) and VLDL-C (p = 0.003) after applying Bonferroni correction for multiple comparisons. Interestingly, the mean values of TGL were observed to be higher in the control group whereas all other parameters were higher in cases. 3.2. Genetic association analyses The genotype frequencies of all studied genetic variants in controls were found to be in accordance with HWE, with only rs3774261

Table 1 List of polymorphism details, primer sequences and restriction enzymes. Gene

Polymorphism rs id

Allele change

Functional consequence

Primer sequence

Annealing temperature (°C)

PCR product (bp)

Restriction enzyme

Restriction digestion fragment size (bp)

PLIN1

rs2304796

CNT

Synonymous codon

F- 5′ AAGGGTCAGGGGAGTTACCA 3′ R- 5′ AATGTTGCCAGGGCACTGAG 3′

61

451

BsmAI

rs2304795

CNT

Synonymous codon

59

210

–

CFD

rs1683563

GNC

Intron

57

217

–

–

ADIPOQ

rs1501299

GNT

Intron

F- 5′ GAGTTACAAGAGGAGGCACT 3′ R1- 5′ CCCTTGGTTGAGGAGACAGAG 3′ R2- 5′ CCCTTGGTTGAGGAGACAGAA 3′ F- 5′ CCACGTCGCAGAGAGTTC 3′ R1- 5′ GCACTGAGACCCACGCTG 3′ R2- 5′ GCACTGAGACCCACGCTC 3′ F- 5′ CCTGGTGAGAAGGGTGAGAA 3′ R- 5′ AGATGCAGCAAAGCCAAAGT 3′

Wt- 210,193,48 Het- 403, 48 Mut- 403,210,193,48 –

58

241

BsmI

rs3774261

GNA

Intron

53

222

–

Wt- 148,93 Het- 241,148,93 Mut- 241 –

F- 5′ CTTAACTTCCATTTCACCCA 3′ R1- 5′ TCAGGTCCACGGTGAGTTT 3′ R2- 5′ TCAGGTCCACGGTGACTTC 3′

R1- reverse primer for wild type allele, R2- reverse primer for mutant allele. Wt- wild type, Het- heterozygous, Mut- mutant.

H.S. Sandhu et al. / Gene 610 (2017) 80–89 Table 2 Comparison of baseline parameters among cases and controls. Variables

Cases (n = 504)

Control (n = 500)

p-Value

Age (years) QUS T-score (SD) BMI (kg/m2) WHR WHtR TC (mg/dl) TGL (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) VLDL-C (mg/dl)

45.54 ± 10.08 −2.16 ± 0.95 27.58 ± 4.70 0.97 ± 0.07 0.60 ± 0.07 160.15 ± 21.21 133.35 ± 77.39 37.68 ± 16.13 96.68 ± 48.72 26.87 ± 16.04

43.60 ± 12.00 −0.14 ± 1.07 26.25 ± 4.08 0.96 ± 0.08 0.57 ± 0.07 159.72 ± 51.57 158.25 ± 107.23 38.32 ± 18.64 94.18 ± 50.06 31.62 ± 21.27

0.026 0.000⁎ 0.001⁎ 0.070 0.000⁎ 0.462 0.002⁎ 0.340 0.286 0.003⁎

Data represented as mean ± SD. Independent samples t-test done for comparison of various parameters among cases and controls. BMI- body mass index, WHR- waist hip ratio, WHtR- waist height ratio, TC- total cholesterol, TGL- triglycerides, HDL-C- high-density lipoprotein-cholesterol, LDL-C- low-density lipoprotein, VLDL-C- very low-density lipoprotein. ⁎ Statistically significant p-value after Bonferroni correction (adjusted p-value at 0.05 significance is 0.025).

showing departure. The power of the study was calculated to be N80%. Table 3 documents the distribution of the allele and genotype frequencies of rs2304796 and rs2304795 (PLIN1), rs1683563 (CFD) and, rs3774261 and rs1501299 (ADIPOQ). The frequencies of the minor alleles were observed to be higher in cases than the control group for rs2304796 (PLIN1, p = 0.828) and rs1683563 (CFD, p = 0.002), whereas the major allele was conferring risk in case of rs2304795 (PLIN1, p = 0.018), rs1501299 (ADIPOQ, p = 0.004) and rs3774261 (ADIPOQ, p = 0.151). MAFs for the genetic variants examined were 0.36 for rs2304796, 0.44 for rs2304795, 0.33 for rs1683563, 0.41 for rs1501299 and 0.35 rs3774261 (Table 3). The present study suggests that subjects with the genotype CC + CT at the PLIN1 rs2304795 loci had 1.87-times increased risk of low bone mass as compared to those with the CC

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genotype. The CC + CG genotype rendered greater susceptibility in case of CFD rs1683563 (1.54-fold). However, for the rs1501299 locus of ADIPOQ, the G allele in the recessive combination exhibited 1.57fold greater likelihood of low bone mass than the T allele. Analysis of other genetic variants did not reveal any statistically significant association (Table 4). 3.3. Prediction of RNA secondary structures The relatedness of the secondary structure of RNA to its function directed the prediction of its secondary structure using a thermodynamic approach. The software RNAFold based on dynamic programming algorithms was used to predict structures with MFE and base-pairing probabilities. The MFE structures having the highest probabilities were generated for the wild type and variant alleles for SNPs rs2304796 and rs2304795 of PLIN1 gene (Fig. 3). While the red arrows denote the position of the two alleles, black arrows indicate the structural differences caused by the variant allele in comparison to the wild type allele. There was 1.4% increase in the energy of the rs2304796 variant T allele as compared to the wild type C allele and ΔΔG being 2.4 kcal/mol, indicating the wild type allele to be structurally more stable at thermodynamic equilibrium. However, the reverse scenario was prevalent for rs2304795, where a slight decrease in ΔG was observed for the variant T allele in contrast to wild type C allele (ΔΔG = 0.60 kcal/mol). This suggested the variant allele to be higher in stability. 3.4. Haplotype analyses To investigate the association of different haplotypes of PLIN1 and ADIPOQ with low bone mass characterized by QUS T-score, measures of LD and haplotype frequencies were calculated for the study participants (Table 5). Upon analyzing the D′ and r2 values calculated from haplotype frequencies, it could be discerned that the two genetic

Table 3 Distribution of allele and genotype frequencies of various genetics variants in the population of North India. Alleles PLIN1

rs2304796 Cases (n = 249) Controls (n = 248) p-Value OR (95% CI) rs2304795 Cases (n = 250) Controls (n = 249) p-Value OR (95% CI)

CFD

rs1683563 Cases (n = 249) Controls (n = 242) p-Value OR (95% CI)

ADIPOQ

rs1501299 Cases (n = 252) Controls (n = 238) p-Value OR (95% CI) rs3774261 Cases (n = 252) Controls (n = 244) p-Value OR (95% CI)

Allele and genotype frequencies tested using chi-square test. Odds ratio (OR) at 95% confidence interval (CI). ⁎ p-Value b 0.05 considered statistically significant.

C 317 (0.64) 319 (0.64) Ref.

C 183 (0.37) 219 (0.44) Ref.

G 287 (0.58) 326 (0.67) Ref.

G 160 (0.32) 193 (0.41) Ref.

A 157 (0.31) 173 (0.35)

Genotypes T 181 (0.36) 177 (0.36) p = 0.828 OR = 1.03 (0.79–1.33) T 317 (0.63) 279 (0.56) p = 0.018⁎ OR = 1.36 (1.05–1.75) C 211 (0.42) 158 (0.33) p = 0.002⁎ OR = 1.52 (1.16–1.96) T 344 (0.68) 283 (0.59) p = 0.004⁎ OR = 1.47 (1.13–1.91) G 347 (0.69) 315 (0.65) p = 0.151 OR = 1.21 (0.93–1.58)

CC 106 (0.43) 109 (0.44) Ref.

TT 25 (0.10) 42 (0.17) Ref.

GG 89 (0.36) 115 (0.47) Ref.

TT 26 (0.10) 40 (0.17) Ref.

AA 27 (0.11) 38 (0.15) Ref.

CT 105 (0.42) 101 (0.41) p = 0.732 OR = 1.07 (0.73–1.57) CT 133 (0.53) 135 (0.54) p = 0.144 OR = 1.75 (0.83–3.69) GC 109 (0.44) 96 (0.40) p = 0.054 OR = 1.47 (0.99–2.17) GT 108 (0.43) 113 (0.47) p = 0.177 OR = 1.47 (0.84–2.57) GA 103 (0.41) 97 (0.40) p = 0.143 OR = 1.81 (0.82–4.00)

TT 38 (0.15) 38 (0.15) p = 0.885 OR = 1.04 (0.61–1.76) CC 92 (0.37) 72 (0.29) p = 0.010⁎ OR = 2.15 (1.20–3.85) CC 51 (0.20) 31 (0.13) p = 0.005 OR = 2.13 (1.26–3.59) GG 118 (0.47) 85 (0.36) p = 0.009 OR = 2.14 (1.21–3.77) GG 122 (0.48) 109 (0.45) p = 0.110 OR = 1.58 (0.90–2.75)

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Table 4 Genetic models for various studied genetic variants in the population of North India. PLIN1 rs2304796 CNT

Cases vs controls p, OR (95% CI)

PLIN1 rs2304795 CNT


Dominant model (TT + TC vs CC) Recessive model (TT vs TC + CC) Co-dominant model (TC vs TT + CC)

p = 0.756 OR = 1.05 (0.74–1.52)

Dominant model (CC + CT vs TT) Recessive model (CC vs CT + TT) Co-dominant model (CT vs CC + TT)

p = 0.026⁎ Dominant OR = 1.87 model (1.08–3.24) (CC + CG vs GG) p = 0.061 Recessive OR = 1.43 model (0.98–2.08) (CC vs CG + GG) p = 0.819 Co-dominant OR = 1.04 model (0.74–1.47) (CG vs CC + GG)

p = 0.985 OR = 1.00 (0.61– 1.61) p = 0.744 OR = 0.943 (0.66–1.35)

CFD rs1683563 GNC


ADIPOQ rs1501299 GNT

p = 0.024⁎ Dominant OR = 1.54 model (1.06–2.24) (GG + GT vs TT) p = 0.088 Recessive OR = 1.55 model (0.94–2.56) (GG vs GT + TT) p = 0.356 Co-dominant OR = 0.85 model (0.59–1.20) (GT vs GG + TT)


ADIPOQ rs3774261 GNA

p = 0.096 Dominant OR = 1.60 model (0.92–2.77) (GG + GA vs AA) p = 0.018⁎ Recessive OR = 1.57 model (1.08–2.28) (GG vs GA + AA) p = 0.304 Co-dominant OR = 1.20 model (0.85–1.72) (GA vs GG + AA)

Cases vs controls p, OR (95% CI) p = 0.109 OR = 1.54 (0.91– 2.61) p = 0.404 OR = 1.16 (0.82– 1.65) p = 0.780 OR = 0.95 (0.66–1.37)

p-Value corrected for age, gender, BMI, WHR and WHtR. ⁎ p-Value b 0.05 considered statistically significant.

variants of ADIPOQ (rs1501299 and rs3774261) were in slight LD among both cases (D′ = 0.249, r2 = 0.06) and controls (D′ = 0.297, r2 = 0.075) (Fig. 1). Further, haplotype combinations revealed that the haplotype G/G conferred 1.36-fold increased risk while the haplotype A/T provided nearly 1.47-fold (1/0.68) reduced risk towards low bone mass in the North Indian population. No significant LD was detected for the PLIN1 variants viz. rs2304796 and rs2304795. 3.5. Gene-gene interactions To evaluate the synergy among the studied genetic variants in disease etiology, gene-gene interaction analysis was performed. The interaction dendrogram created by MDR for the five-locus best model with a CVC of 10/10 and TBA of nearly 0.5 illustrated redundancy between rs1501299 and rs3774261 as indicated by short blue lines shown in Fig. 2a. While the other relatively longer blue lines depicted lesser redundancy between rs2304795 and rs1683563. The long green lines showed correlation between rs2304796 and the cluster of rs2304795 and rs1683563. Overall, the long green lines between a cluster of ADIPOQ (rs1501299 and rs3774261) and combined cluster of PLIN1 (rs2304796 and rs2304795) and CFD (rs1683563) demonstrated correlation. This model showed that the combination of all five variants exhibited a statistically significant increased risk of 5.41-fold (p b 0.0001) (Table 6). Moreover, analysis revealed that the cluster of rs2304795, rs1683563 and rs1501299 that showed significant results with chi square test also displayed a fairly high CVC (8/10) and TBA (0.537). This combination provided 2.39-fold increased risk for low bone mass (Table 6) that is also shown by the dark-shaded cells corresponding to the high-risk genotypes in the graphical model (Fig. 2b).

4. Discussion Unambiguous evidences during the past decades have been generated from experimental and epidemiological data suggestively linking the adipose metabolism with bone health (King et al., 2010; Yerges-Armstrong et al., 2010). This inverse association embarks from the common origin of these two lineages and emanates to the variations in genes involved in their metabolism (Nuttall and Gimble, 2000; Tian and Yu, 2015). The connection is accentuated by reports of the lowering of trabecular bone volume upon elevated bone marrow adiposity (Nuttall et al., 1998). The adipose tissue is thus an important aspect of study for gaining insight into bone-related disorders, although, ethnicity associated variations are observed (Cauley, 2011). In concordance with this background knowledge, the present study is the first of its kind from India to signify the association of various adipogenic genes viz. PLIN1, CFD and ADIPOQ with bone health. Evaluating the role of the adipogenic pathway is of grave concern taking into consideration the higher obesity profile prevailing among this population group (Ravikiran et al., 2010; Sharma et al., 2013). This ensues from the higher calorie and fat intake accompanied by a sedentary lifestyle (Unnikrishnan et al., 2012; Matharoo et al., 2013). The present study also lies in concordance with these reports as demonstrated by the significantly elevated values of BMI and WHtR among cases, implying the significance of fat reduction for achieving greater bone health. Polymorphisms in PLIN have been associated with reduced PLIN content and enhanced lipolysis in human adipocytes making it a significant obesity locus (Mottagui-Tabar et al., 2003). Moreover, certain studies have also linked it to bone measures like BMD in Japanese and Caucasian cohorts (Yamada et al., 2006; Cusano et al., 2012). Although, the direct

Table 5 Frequencies of ADIPOQ haplotypes according to bone health status in the population of North India. Haplotype

Frequency

(rs1501299)G/G(rs3774261) (rs1501299)T/A(rs3774261) (rs1501299)T/G(rs3774261) (rs1501299)G/A(rs3774261)

LD measure

rs1501299 and rs3774261

Cases (n = 254)

Controls (n = 250)

0.524 0.153 0.165 0.159

0.446 0.210 0.192 0.153

Cases

OR (95% CI)

0.015⁎ 0.021⁎ 0.275 0.802

1.36 (1.06–1.76) 0.68 (0.49–0.94) 0.84 (0.60–1.16) 1.05 (0.74–1.49)

Controls 2

D′

r

0.249

0.06

Odds ratio (OR) at 95% confidence interval (CI). ⁎ p-Value b 0.05 considered statistically significant.

p-Value

LOD

D′

r2

LOD

3.55

0.297

0.075

4.35


Fig. 1. Pair-wise LD plots demonstrating the two ADIPOQ genetic variants viz. rs1501299 and rs3774261 among cases and controls from the population of North India.

relationship of PLIN with bone phenotype requires further investigation, its ability to inhibit basal lipolysis and promote triacylglycerol (TAG) storage resulting in deregulation of the bone marrow niche (Marcinkiewicz et al., 2006) provides an indirect connection explained by the fine balance between adipocytes and osteocytes. In the present study, a lack of association was observed for the exonic genetic variant rs2304796 with bone health. This was in contradiction to the report by Yamada et al. (2006), which demonstrated association of the C allele with reduced bone mass in the community-dwelling Japanese males. They reported the combined CC and CT genotypes as having less BMD for the total hip, lumbar spine, femoral neck and trochanter than the TT genotypes. No association was observed in the Japanese females, regardless of estrogen status. This disparity could be attributed to population diversity, ethnic heterogeneity and the fact that combined males and females were included in the present study. The CC + CT genotype of another variant rs2304795 present in the vicinity of rs2304796 was found to be significantly associated with BMD at the total hip in Caucasians (Cusano et al., 2012). The present study was in line with this documentation as the same genotypic model rendered 1.54-fold increased risk among North Indians. This effect could be ascribed to the genetic

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similarity of the North Indian population with the Caucasian and Indoscynthian race as the Northern and Central India is represented by Indo-European population (Bhasin et al., 1992). Additionally, the individuals included in this study were from a wide age group (29– 94 years) and comprised of both genders, which was in concordance with the present study. Although, dual-energy X-ray absorptiometry (DXA) was used for the diagnostic purpose, but a correlation between DXA and QUS has earlier been reported (Shenoy et al., 2014). Nevertheless, to exactly pin point the underlying mechanism, further studies are required. Contrasting effects exhibited by the two genetic variants of PLIN1 might also owe to the present haplotype results demonstrating a lack of LD between them, despite of physical proximity. One possibility can be that these variants are in strong LD with other functional mutations. Moreover, alterations at the synonymous polymorphisms rs2304796 and rs2304795 corresponding to val373 and pro371, respectively, could affect the RNA secondary structure further influencing protein expression. The single-stranded RNA molecule folds into the structure having MFE based on hydrogen bonding and base stacking interactions (Ding and Lawrence, 2003; Hofacker, 2003; Zmuda et al., 2009; Ritz et al., 2012). This further determines the stability of the structure. In the present study, the variant T allele of rs2304796 and wild type C allele of rs2304795 were found to be thermodynamically less favorable than their alternate alleles, suggesting an effect on the relative expression of the protein. Moreover, thermodynamically less stable alleles have shown association with the disease phenotype. The implication of these observations can be explained by a previous report of de Smit and van Duin (1990) where a ΔG of − 1.4 kcal/mol resulted in a 10fold decrease in the translation efficiency around a hairpin structure. Nevertheless, further verification using functional analysis is needed to substantiate this connection. The serine protease CFD is secreted by the adipose tissue and its plasma levels serve as a predictor of changes in abdominal subcutaneous fat during the times of elevated food intake (Cook et al., 1985; White et al., 1992; Ukkola et al., 2003). Furthermore, evidence states the involvement of various complement proteins in endochondral bone formation during the course of bone development (Andrades et al., 1996; Ignatius et al., 2011). Receptors for various complement regulators are also localized on different types of bone cells (Schoengraf et

Fig. 2. Best predicted models from multifactor dimensionality reduction (MDR) analyses. (a) Five-locus dendrogram depicting interaction among various genetic variants of PLIN1, CFD and ADIPOQ. The key given on extreme right side represents the color-coding scheme for the dendrogram model. (b) Three-locus best model showing distribution of genotype combinations among cases (left bars) and controls (right bars). Dark-shaded cells correspond to ‘high-risk’ genotypes, while light-shaded cells depict ‘low-risk genotypes’.

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Table 6 Interaction analysis among the various genetic variants in the population of North India. Genetic variant combination

TBA

CVC

p-Value

OR

rs2304795, rs1683563, rs1501299 rs2304796, rs2304795, rs1683563, rs1501299, rs3774261

0.537 0.480

8/10 10/10

0.0001⁎ b0.0001⁎

2.39 (1.65–3.46) 5.41 (3.64–8.02)

Odds ratio (OR) at 95% confidence interval (CI). ⁎ p value b 0.05 was considered statistically significant.

al., 2013), thus anticipating the association of CFD with bone-related phenotypes. Further substantiating this connection, the present results strongly reflect that presence of C allele in both homozygous and heterozygous form instead of the G allele at the rs1683563 locus might increase the susceptibility towards low bone mass. This might hold true especially in the North Indian population which has a high prevalence of obesity and other metabolic and immune disorders (Sood et al., 2001; Vikram et al., 2003; Sidhu et al., 2005; Sidhu and Kumari, 2006). Furthermore, a report by Leunissen (2009) suggests that weight gain during childhood is related to higher serum levels of acylation stimulating protein (ASP) and lower BMD in early adulthood. ASP is generated by the interaction of CFD with other complement proteins, thus providing a connection between CFD and the bone mass (Cianflone et al., 1999). Studies have also reported that the ASP-stimulated adipogenesis in pre-adipocytic cell lines is reflected by increases in CEBP, PPARγ, DGAT-1, adipsin (CFD) and triglyceride accumulation (Jones et al., 2004; MacLaren et al., 2010). Enhanced differentiation of bone marrow mesenchymal stem cells (MSCs) to the adipogenic lineage by promoting the release of adipokines such as ADN, ADIPOQ, ASP, leptin, chimerin, resistin, omentin-1, visfatin and suppressing intracellular function of osteogenic factors like runt-related transcription factor 2 (RUNX-2), β-catenin and osterix affects the bone remodeling process (Rajala and Scherer, 2003; Drevon, 2005; Gove and Fantuzzi, 2010; Muruganandan and Sinal, 2014). This relationship with both arms of bone remodeling i.e. osteocytes and adipocytes provided a rationale for this study. Moreover, rs1683563 has previously been shown to be in high LD with rs3826945 (D′ = 1, r2 = 0.314) (Stanton et al., 2011), implying that combination with other nearby functional variants might yield more fruitful information with respect to its genetic association with skeletal health. A deeper introspection into the effects of this polymorphism may prove it to be an informative marker for assessment of genetic risk for reduced BMD. ADIPOQ, is a potential contributor in bone metabolism through its receptors ADIPOR1 and ADIPOR2 which are localized on osteoblasts

(Lee et al., 2006; Kim et al., 2012). Furthermore, its mRNA expression is elevated in osteoblasts making it the metabolic link between bone and adipose tissue (Yamauchi et al., 2003). In support of this, the present study demonstrates that the GG genotype poses an increased susceptibility to bone loss as compared to the GT + TT genotype. The role of ADIPOQ variant rs1501299 has been positively demonstrated in the study by Kim et al. (2015). They reported that the BMD of Korean postmenopausal females carrying the GG genotype was not affected by calcium intake; however, BMD in the high calcium group was much higher than in the lower calcium intake group having GT + TT genotypes. After adjusting for multiple variables, the prevalence of multiple fractures was observed to be highest in GG subjects. Moreover, Huang et al. (2007) observed that Japanese subjects with the GT + TT genotype exhibited a greater response to exercise training, implying the risk associated with the alternate G allele. The association of the minor allele with BMD in these studies replicates the influence of ethnical heterogeneity. This represents the heterogeneity in the association of ADIPOQ rs1501299 variant with different parameters of bone health. In additional support, many studies demonstrate a correlation of ADIPOQ polymorphisms and plasma/serum concentrations with bone parameters like BMD (K et al., 2009; King et al., 2010), while, several others claim a lack of association (Zhang et al., 2007; Kim et al., 2015). The reason for such an incongruity can be ascribed to population diversity, age or health status of the study participants. Moreover, in vivo studies conducted in the mice model reveal an increase in trabecular bone mass upon transient over expression of ADIPOQ suggesting its importance in the bone remodeling process (Oshima et al., 2005). However, the precise mechanism still needs to be unravelled but a probable explanation could be that ADIPOQ enhances the expression of cyclooxygenase-2 (COX-2) which is associated with osteoblast differentiation and increases secretion of prostaglandins E2 (PGE2) from bone marrow MSCs by suppressing the adipogenic differentiation (Yokota et al., 2002; Yokota et al., 2003). No significant association of the ultrasound properties of bone was observed with another tag SNP, rs3774261, although it

Fig. 3. RNA secondary structures based on thermodynamic minimum free energy (MFE) of PLIN1 genetic variants rs2304796 and rs2304795. Red arrows denote the position of the two alleles; black arrows indicate the structural differences caused by the variant allele in comparison to the wild type allele. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)


is known to be positively correlated with serum ADIPOQ levels (Park et al., 2015). This is in consonance with a previous report documented on the Guangxi Zhuang female population (Wang et al., 2012). Both above mentioned genetic variants were found to lie within the intronic region of the ADIPOQ gene and it is evident that these introns present in the protein-coding gene transcripts may affect the stability and processing of the mRNA by alternative splicing or might affect gene expression via repression of translation or cleavage of RNA transcripts. Besides rs3774261 being an intronic enhancer it can enhance the transcriptional process by altering protein levels (Ying and Lin, 2004; Qiao et al., 2005). The two tag SNPs of ADIPOQ (rs1501299 and rs3774261) were found to be in slight LD of ~30% in the present North Indian population. However, similar LD was also observed among South Indians against T2D (Ramya et al., 2013), which also involves dysregulation of the adipogenic pathway (Tenenbaum et al., 2003). Contrastingly, Beckers et al. (2009) demonstrated an LD of 65% towards obesity in Belgian Caucasians. This disparity owes to the differences in disease etiology. Similar differences of disease specificity also justify the novel risk-giving G/G haplotype identified in the present study. Additionally, the occurrence of lower LD values could be attributed to the fact that comparatively higher values are observed between these independent variants with other neighboring variants (rs1501299 with rs2241766; rs3774261 with rs6773956) (Lee et al., 2006; Ling et al., 2009). The contrasting results of the haplotypes have still not been clear and further study with more number of SNPs, especially tagging SNPs in a larger cohort is necessitated. The need for carrying out an interactive analysis among all the genetic variants of the three studied genes and the high-risk associated with the 5-loci model arises from their common physiological function in adipose metabolism where PLIN1 is present in the periphery of lipid droplets (Blanchette-Mackie et al., 1995), and both CFD and ADIPOQ are secreted from mature functional adipocytes and steroidogenic cells (Kershaw and Flier, 2004). The correlation between rs1501299 and rs3774261 ensue from their common originating gene as well as the fact that they are in LD albeit, slight. While, the correlation between the cluster of rs2304795 and rs1683563 with rs2304796 might unfold the possibility of a common pathway between PLIN1 and CFD, and open up new avenues for a better understanding of the homeostasis between bone and adipose metabolisms. The lack of interaction between rs2304796 and rs2304795 is demonstrated by the absence of LD among them. To the best of our knowledge, this is the first study from India to peruse the possible influence of genetic variants and haplotypes in the PLIN1, CFD and ADIPOQ genes and their interactive effect on ultrasound T-score for deciphering the risk of bone deterioration. Having similar sensitivity as the DXA measurements, the ultrasound method carries an edge over it due to easy accessibility, absence of radiations and ability to measure sites having a high metabolic turnover rate (Frost et al., 2001; Herrmann et al., 2014). Although, PLIN1 and ADIPOQ have previously been studied with regard to various bone parameters like BMD, CFD serves as a novel determinant of bone loss. If analyzed in greater depth among various ethnic groups and in combination with other markers it might yield promising results. Prediction of RNA secondary structure and evaluation of intergenic interactions are other peculiar features. However, the study was also confronted with certain limitations like a lack of functional aspect and unaccountability of gene-environment interactions. The circulating ADIPOQ levels were also not quantified, but this is somewhat justified as several previous studies have documented its lack of relation with BMD in a clinical setting (Huang et al., 2004; Chanprasertyothin et al., 2006). Limited power due to the relatively small sample size prohibited the detection of minor effects for which extended cohorts will be needed in future to vindicate the genetic role of these variants. Although, the role of many adipogenic markers such as PPARgamma and leptin has been associated with bone measures like BMD in many independent studies (Ogawa et al., 1999; Morberg et al., 2003), but the definite influence of the

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adipogenic pathway in the susceptibility towards bone related diseases is still poorly understood. The present results suggest the likelihood of PLIN1, CFD and ADIPOQ as being important candidate genes conferring risk towards low bone mass. It also establishes the possibility of various genetic variants to affect the bone tissue through BMD-independent mechanisms such as bone geometry, bone quality, etc. The association of variants in these genes with bone metabolism further emphasize that the impairment in metabolic pathways that result in obesity and related phenotypes also exert their influence on bone health. Improving the understanding of the relationship between the adipose and bone metabolism along with identifying high-risk variants will help to develop early intervention therapies for patients at high risk of low bone mass and to combat the skeletal menace. Funding sources This work was supported by the Centre with Potential for Excellence in Particular Areas (CPEPA), UGC, India [F.8-2/2008(NS/PE)], University with Potential for Excellence (UPE), UGC, India [F.14-2/2008(NS/PE)] and Maulana Azad National Fellowship (MANF), UGC, India [F1-17.1/ 2010/MANF-SIK-CHA-6123]. Acknowledgement We gratefully acknowledge all the participants and staff of Dr. Hardas Singh Orthopaedic Hospital and Super Speciality Research Centre, Amritsar, India and Dr. Chhina Orthopaedics for helping us in sample collection. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.gene.2017.02.009. References Adeli, K., Ogbonna, G., 1990. Rapid purification of human DNA from whole blood for potential application in clinical chemistry laboratories. Clin. Chem. 36, 261–264. Andrades, J.A., Nimni, M.E., Becerra, J., et al., 1996. Complement proteins are present in developing endochondral bone and may mediate cartilage cell death and vascularization. Exp. Cell Res. 227:208–213. http://dx.doi.org/10.1006/excr.1996.0269. Arden, N.K., Baker, J., Hogg, C., et al., 1996. The heritability of bone mineral density, ultrasound of the calcaneus and hip axis length: a study of postmenopausal twins. J. Bone Miner. Res. Off. J. Am. Soc. Bone Miner. Res. 11:530–534. http://dx.doi.org/10.1002/ jbmr.5650110414. Bahceci, M., Gokalp, D., Bahceci, S., et al., 2007. The correlation between adiposity and adiponectin, tumor necrosis factor alpha, interleukin-6 and high sensitivity C-reactive protein levels. Is adipocyte size associated with inflammation in adults? J. Endocrinol. Investig. 30, 210–214. Beckers, S., Peeters, A.V., de Freitas, F., et al., 2009. Association study and mutation analysis of adiponectin shows association of variants in APM1 with complex obesity in women. Ann. Hum. Genet. 73:492–501. http://dx.doi.org/10.1111/j.1469-1809. 2009.00532.x. Bhasin, M.K., Walter, H., Danker-Hopfe, H., 1992. The Distribution of Genetical, Morphological, and Behavioural Traits among the Peoples of Indian Region: Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka. Kamla-Raj Enterprises. Blanchette-Mackie, E.J., Dwyer, N.K., Barber, T., et al., 1995. Perilipin is located on the surface layer of intracellular lipid droplets in adipocytes. J. Lipid Res. 36, 1211–1226. Brasaemle, D.L., Rubin, B., Harten, I.A., et al., 2000. Perilipin A increases triacylglycerol storage by decreasing the rate of triacylglycerol hydrolysis. J. Biol. Chem. 275: 38486–38493. http://dx.doi.org/10.1074/jbc.M007322200. Cauley, J.A., 2011. Defining ethnic and racial differences in osteoporosis and fragility fractures. Clin. Orthop. 469:1891–1899. http://dx.doi.org/10.1007/s11999-011-1863-5. Chanprasertyothin, S., Saetung, S., Payattikul, P., et al., 2006. Relationship of body composition and circulatory adiponectin to bone mineral density in young premenopausal women. J. Med. Assoc. Thail. Chotmaihet Thangphaet 89, 1579–1583. Cianflone, K., Maslowska, M., Sniderman, A.D., 1999. Acylation stimulating protein (ASP), an adipocyte autocrine: new directions. Semin. Cell Dev. Biol. 10:31–41. http://dx.doi. org/10.1006/scdb.1998.0272. Cohen, A.J., Roe, F.J., 2000. Review of risk factors for osteoporosis with particular reference to a possible aetiological role of dietary salt. Food Chem. Toxicol. Int. J. Publ. Br. Ind. Biol. Res. Assoc. 38, 237–253. Cook, K.S., Groves, D.L., Min, H.Y., Spiegelman, B.M., 1985. A developmentally regulated mRNA from 3T3 adipocytes encodes a novel serine protease homologue. Proc. Natl. Acad. Sci. U. S. A. 82, 6480–6484.

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