PPAR-a and PPARGC1A gene variants have strong effectson aerobic performance of Turkish elite endurance athletes

Ercan Tural • Nurten Kara • Seydi Ahmet Agaoglu •

Mehmet Elbistan • Mehmet Yalcin Tasmektepligil •

Osman Imamoglu

Received: 1 October 2013 / Accepted: 12 June 2014

� Springer Science+Business Media Dordrecht 2014

Abstract The aim of this study was to investigate the effect

of PPAR-a intron 7G[C and PPARGC1A gene Gly482Ser

polymorphisms on aerobic performance of elite level

endurance athletes. This study was carried out on 170 in-

viduals (60 elite level endurance athletes and 110 sedentary

controls). Aerobic performance of athletes and sedentary

control groups were defined by maximal oxygen uptake

capacity. DNA was isolated from peripheral blood using

GeneJet Genomic DNA Purification kit. Genotyping of the

PPAR-a intron 7G[C and PPARGC1A Gly482Ser poly-

morphisms was performed using PCR–RFLP methods, and

statistical evaluations were carried out using SPSS 15.0.

Mean age of athletes were 21.38 ± 2.83 (18–29) and control

mean age were 25.92 ± 4.88 (18–35). Mean maximal oxy-

gen consumption of athletes were 42.14 ± 7.6 ml/(kg min)

and controls were 34.33 ± 5.43 ml/(kg min). We found

statistically significant differences between the athlete and

control groups with respect to both PPAR-a and PPARGC1A

genotype distributions (p = 0.006,\0.001, respectively) and

allele frequencies (\0.001,\0.001, respectively). Addition-

ally, when we examined PPAR-a and PPARGC1A genotype

distributions according to the aerobic performance test

parameters, we found a statistically significant association

between velocity, time and maximal oxygen consumption and

PPAR-a and PPARGC1A genotypes (p \ 0.001). To our

knowledge, this is the first study in Turkey examined PPAR-aintron 7G[C and PPARGC1A Gly482Ser gene polymor-

phisms in elite level endurance athletes. Our results suggest

that PPAR-a and PPARGC1A genes have strong effect on

aerobic performance of elit level athletes.

Keywords Endurance athlete � Aerobic performance �Gene polymorphism � Elite athlete � PPARGC1A � PPAR-a

Introduction

Elite performance is influenced by environmental, indi-

vidual and genetic factors [1]. Significant data confirm the

effects of several genes on physical performance of human

and elite athletes status. The advanced researches on

genetics of physical performance began in the late 1990s,

when the datas from Human Genom Project acquired

currency [1]. The development of technology for DNA

sequencing and genotyping ensured the identification of

genetic variations that contribute to athletic performance.

Differences in the DNA sequences between individuals are

responsible for differentiations in sport and exercise-rela-

ted traits. Several genes contribute complex genetic profile

of elite endurance athlete status [2]. The heritability of

maximal oxygen uptake (VO2max) may be as high as 50 %

and the heritability of the trainability of VO2max has been

estimated to be 47 % [4]. Several functional polymor-

phisms have been demonstrated to affect sporting pheno-

types. The angiotensin converting enzyme (ACE) genotype

is one of the best known of these [5–7]. Others include the

functional allele (577r) of ACTN3 (coding for human a-

actinin-3 gene), which has been associated with elite

sprinter athletic status [8, 9].

E. Tural (&)

Havza Vocational School, Department of Physiotherapy

Programme, Ondokuz Mayis University, Samsun, Turkey

e-mail: ercant@omu.edu.tr

N. Kara � M. Elbistan

Faculty of Medicine Medical Biology Dept. Section of Medical

Genetic, Ondokuz Mayis University, Samsun, Turkey

S. A. Agaoglu � M. Y. Tasmektepligil � O. Imamoglu

Yasar Dogu Faculty of Sport Sciences, Ondokuz Mayis

University, Samsun, Turkey

123

Mol Biol Rep

DOI 10.1007/s11033-014-3453-6

There is emerging evidence that peroxisome proliferator

activated receptor-alpha (PPAR-a) and peroxisome prolif-

erator activated receptor gamma coactivator 1 alpha

(PPARGC1A) play an important role in muscle fibre type

conversion [10, 11]. The first peroxisome proliferator

activated receptors (PPARs) were discovered in 1990 in

liver cells [12]. PPARs are transcriptional factors which,

through stimulation, affect the metabolism of glucose and

fat acids [13], the inflammatory processes and immune

responses [14], the division and differentiation of some

types of cells, the production of adipocytes [15], weight

control, and energy homeostasis [3, 16, 17]. Based on these

findings, we aimed to examine the effect of variations of

PPAR-a and PPARGC1A genes on aerobic performance of

elite endurance athletes.

Materials and methods

Subjects

This study was carried out on 170 inviduals (60 elite level

endurance athletes and 110 sedentary controls). 60 elit level

endurance athletes with a mean age of 21.38 ± 2.83 years

and 110 healthy sedentary individuals with a mean age of

25.92 ± 4.88 years were included in the study. Athletes

were included in the study sample only if they had partici-

pated in national/international track and field champion-

ships. The control group consisted of 110 non-athletic

healthy individuals who were selected from the Turkish

population. All the participiants gave informed consent. This

study was approved by the ethical committee of Ondokuz

Mayıs University.

Genotyping

DNA was isolated from peripheral blood using GeneJet

Genomic DNA Purification Kit (Lithuanian). Genotyping

of PPAR-a and PPARGC1A polymorphisms were deter-

mined by polymerase chain reaction (PCR) and restriction

fragment length polymorphism (RFLP) analysis. PCR

amplification was performed in thermal cycler (Techne

Gradient, Cambridge, UK) and digestion of the PCR

products was carried out with restriction enzymes. PPAR-

a and PPARGC1A gene polymorphism identification was

conducted according to the method of Eynon et al. [18]

and Ahmetov et al. [3] respectively. The primer sequen-

ces, restriction enzymes, and fragment lengths are given in

Table 1. Amplification of the 266 bp fragment encom-

passing the PPAR-a G[C polymorphic site was performed

in 25 ll, 19 PCR buffer containing 2–6 pmol of each

primer (GmbH Biotech, Deutschland), 2 mM MgCl2,

200 lM of each dNTP (MBI, Fermentas, Lithuania),

100–200 ng DNA, and 0.25–1 U Taq polymerase (Pro-

mega, Madison, WI, USA). After initial denaturation at

95 �C for 5 min, amplification was performed by 35

cycles of denaturation at 95 �C for 1 m, annealing at

60 �C for 1 m, and extension at 72 �C for 1 m. Final

extension was allowed to proceed at 72 �C for 10 min

[18]. Amplification of the 378 bp fragment encompassing

the PPARGC1A polymorphic site was performed in 25 ll,

19 PCR buffer containing 2–6 pmol of each primer,

2 mM MgCl2, 200 lM of each dNTP, 200 ng DNA, and

0.25–1.5 U Taq polymerase (Promega). Following initial

denaturation at 95 �C for 8 min, amplification was per-

formed by 30 cycles of denaturation at 94 �C for 30 s,

annealing at 64 �C for 30 s, and extension at 72 �C for

2 min. The reaction was terminated by final extension at

72 �C for 7 min. The PPAR-a and PPARGC1A gene PCR

products were digested with TaqI and BsI restriction

enzymes respectively at 37 �C for 16 h. PCR reaction

products were separated on a 2 % agarose gel and RFLP

digested products were separated on 3 % nu-micropore

agarose gel. In order to validate the accuracy of this

method, each PCR reaction included internal controls for

each genotype. Second PCR was performed to confirm

samples of which results are not clear. Also, to confirm

the accuracy of the genotyping, repeated analysis was

performed on randomly selected samples. No discrepan-

cies were found.

Aerobic capacity measurement

Aerobic capacity of athletes and control groups were

determined by maximal oxygen consumption capacity

(VO2max) with 20 M MultiStage test (NewTest PowerTi-

mer-Finland). The 20-m Multistage test measures the

subject’s aerobic capasity (VO2max), the subjects sprints

20-m distance at given speeds, waits there until an alarm

sound is given and then sprints back the 20-m distance.

This will be repeated until the subject cannot maintain the

speed given by controlled by the alarm sounds [19].

Statistical analysis

Analysis of the data was performed using the computer

software SPSS 15.0 (SPSS, Chicago, IL, USA) and

OpenEpi Info software package program [20]. Continuous

data were given as mean ± SD (standard deviation) and

median (min–max), categorical data were given as fre-

quency (percent). The frequencies of the alleles and

genotypes in athletes and controls were compared with ki-

square (X2) analysis. Odds ratio (OR) and 95 % confi-

dence intervals (CIs) were calculated. The comparisons of

two groups were performed by t test and Mann–Whitney

test. P value smaller than 0.05 (two-tailed) was regarded as

Mol Biol Rep

123

statistically significant. Power analysis was made by using

Minitab 15.0 package program.

Results

Physical Characteristics of Athletes and Control Groups are

presented in Table 2. Mean age of athletes were 21.38 ±

2.83 (range of age 18–29) and control mean age were

25.92 ± 4.88 (range of age18–35). Mean maximal oxygen

consumption of athletes were 42.14 ± 7.6 ml/(kg min) and

controls were 34.33 ± 5.43 ml/(kg min) (Table 2). Demo-

graphic variables and baseline characteristics of athletes

were given in Table 3. Sport Performance of Athletes and

Controls were given in Table 4. Table 5 and 6 presents the

distribution the genotype and allele frequencies of PPAR-aand PPARGC1A genes for elite endurance athletes and

control groups. There was statistically significant differences

between the athlete and control groups with respect to both

PPAR-a and PPARGC1A genotype distributions (p = 0.006,

\0.001, respectively) and allele frequencies (\0.001,

\0.001, respectively). Additionally, when we examined

PPAR-a and PPARGC1A genotype distributions according

to the aerobic performance test parameters, we observed

statistically significant association between velocity, time

and maximal oxygen consumption and PPAR-a and

PPARGC1A genotypes (p \ 0.001). Combined genotype

analysis results were presented in Table 7.

Discussion

Genes are responsible for about 50 % of the variability in

physical performance. It is also known that genes effect the

response to training [21]. Genetic studies in the field of

sport science is constantly increasing. Molecular genetic

tests based on DNA technologies are actively used in sports

genetics to assess the human predisposition to different

physical features. For example, some genes have been

found to be associated with speed and power characteristics

and endurance performance [22–24].

Table 1 Single nucleotide polymorphisms (SNPs) investigated in this study and PCR primers for the genotyping of PPAR-a and PPARGC1A

Gene and

Polymorphism

Region rs number Primer pairs Method Genotype

Wild

ref

Heterozygote Variant

PPAR-a Intron 7 rs4253778 (F)* 50-ACAATCACTCCTTAAATATGGTGG -30 Taq I based GG GC CC

G/C (R)* 50-AAGTAGGGACAGACAGGACCAGTA -30 PCR–RFLP

PPARGC1A Exon 8 rs8192678 (F)* 50-TAAAGATGTCTCCTCTGATT -30 BsI based GlyGly GlySer SerSer

Gly482Ser (R)* 50-GGAGACACATTGAACAATGAATAGG

ATTG -30PCR–RFLP

Table 2 Physical characteristics of athletes and control groups

Study group n Median ± SD Minimum–

Maximum

Age (year) Athletes 60 21.38 ± 2.83 18–29

Controls 110 25.92 ± 4.88 18–35

Height (cm) Athletes 60 173.65 ± 5.50 163–188

Controls 110 171.45 ± 6.159 158–189

Weight (kg) Athletes 60 72.27 ± 10.04 56–105

Controls 110 74.83 ± 10.82 44–115

VKI (kg/m2) Athletes 60 23.91 ± 2.82 20–32

Controls 110 25.42 ± 3.03 18–33

Total 170

Table 3 Demographic variables and baseline characteristics of

athletes

Athletes n (%) Controls n (%)

Gender

Male 57 (95) 100 (90.9)

Female 3 (5) 10 (9.1)

Smoking

Yes 11 (18,3) 30 (27.3)

No 49 (81,7) 80 (72.7)

Alcohol

Yes 5 (8.3) 13 (11.8)

No 55 (91.7) 97 (88.2)

Sport history (year) 9.23 ± 2.46 0

Total 60 (100) 110 (100)

Table 4 Sport performance of athletes and controls

Performance

values

Study

group

n Mean ± SD Minimum–

Maximum

Time (s) Athletes 60 222.4 ± 77.2 125–590

Controls 110 149.3 ± 46.34 19.5–300

Speed (m/s) Athletes 60 11.36 ± 1.34 9.49–17.5

Controls 110 10.03 ± 1.01 8.49–12.5

Level Athletes 60 3.95 ± 1.28 2–10

Controls 110 2.76 ± 0.83 1–5

VO2Max (ml/kg/m) Athletes 60 42.14 ± 7.6 31.7–78.1

Controls 110 34.33 ± 5.43 21.6–49.1

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123

PPARGC1A and PPAR-a are expressed at high levels in

tissues that catabolize fatty acids, notably those in the liver,

skeletal muscle, and myocardium [25, 26]. In humans, the

PPARGC1A and the PPAR-a genes regulate the expression

of genes encoding several key enzymes involved in fatty

acid oxidation [3] and controlling oxidative phosphoryla-

tion [27].

PPAR-a, a ligand-dependent transcription factor, regu-

lates fatty acid metabolism in heart and skeletal muscle.

The intron 7G[C polymorphism (rs4253778) associated

Table 5 Distribution the

genotype and allele frequencies

of PPAR-a gene

The results that are statistically

significant are typed in bolda Fisher exact testb Yates correction

Genotype Athletes

(n = 60) (%)

Controls

(n = 110) (%)

v2 p value OR (95 % CI)

GG 38 (63.3) 42 (38.2) 10.282 0.006b

GC 19 (31.7) 54 (49.1)

CC 3 (5) 14 (12.7)

GG ? GC:CC 57:3 96:14 2.561 0.1736a 2.771 (0.7251–15.6)

GG:GC ? CC 38:22 42:68 8.874 0.002b 2.779 (1.453–5.3971)

Allele frequency

G 95 (79.2) 138 (62.7) 9.73 \0.001 2.253 (1.35–3.831)

C 25 (20.8) 82 (37.3)

Table 6 Distribution the

genotype and allele frequencies

of PPARGC1A

The results that are statistically

significant are typed in bolda Yates correction

Genotype Athletes

(n = 60) (%)

Controls

(n = 110) (%)

v2 p value OR (95 %CI)

GlyGly 13 (21.7) 32 (29.1) 29.895 \0.001a

GlySer 20 (33.3) 68 (61.8)

SerSer 27 (45) 10 (9.1)

GlyGly ? GlySer:SerSer 33:27 100:10 27.33 0.122a 0.12 (0.5354–0.2791)

GlyGly:GlySer ? SerSer 13:47 32:78 1099 0.296a 0.6742 (0.319–1.412)

Allele frequency

Gly482 46 (38.3) 132 (60) 14.61 \0.001 0.4155 (10.262–0.6548)

Ser482 74 (61.7) 88 (40)

Table 7 Comparison of

combined genotypes of PPAR-aand PPARGC1A

polymorphisms between

athletes and controls

The result that is statistically

significant typed in bolda Fisher exact testb Yates correctionc Mid-p exact test

SNP–SNP

interaction

SNP

genotypes

Athletes

n (%)

Controls

n (%)

v2 p OR (%95 CI)

PPAR-a/

PPARGC1A

PPAR-a(intron 7 G/C)

and

PPARGC1A

(Gly482Ser)

GGGlyGly 10 (16.7) 11 (10) 1.037 0.3104b 1.8 (0.7164–4.523)

GGGlySer 14 (23.3) 26 (26.63) 11.98 0.0005b 6.391 (2.174–18.79)

GGSerSer 14 (23.3) 5 (8.3) 0.365 0.5454 1,414

(0.6103–3.278)

GCGlyGly 2 (3.33) 15 (13.63) 0.04a (0.0235–0.999)

GCGlySer 6 (9.99) 35 (31.81) 8.941 \0.01b 0.2381

(0.935–0.605)

GCSerSer 11 (18.33) 4 (3.63) 0.003a (1.64–2.661)

CCGlyGly 1 (1.7) 6 (5.45) 0.443c (0.006–2.526)

CCGlySer 0 (0) 7 (6.36) 0.08c (0.001–0.095)

CCSerSer 2 (3.33) 1 (0.90) 0.569a (0.190–2.223)

Total 60 (100) 110 (100)

Mol Biol Rep

123

with athletic performance. The rare C-allele was predomi-

nant in power athletes, whereas the G-allele was more

frequent in endurance athletes [28]. It reported that endur-

ance training increases PPARGC1A mRNA levels [29–33],

and thus, may enhance skeletal muscle oxidative capacity

by PPAR-a regulation of gene expression [18, 34, 35].

In this study we compared the allele frequencies at the

PPAR-a intron 7G[C and Gly482Ser locus of the

PPARGC1A gene in elite-level endurance athletes and

sedentary controls and we found significant differences

between the athlete and control groups with respect to both

PPAR-a and PPARGC1A genotype distributions

(p = 0.006, \0.001, respectively) and allele frequencies

(\0.001, \0.001, respectively). Additionally, when we

examined PPAR-a and PPARGC1A genotype distributions

according to the aerobic performance test parameters, we

found a statistically significant association between veloc-

ity, time and maximal oxygen consumption and PPAR-aand PPARGC1A genotypes (p \ 0.001). There are limited

study investigated PPAR-a intron 7G[C polymorphism

and PPARGC1A Gly482Ser polymorphism in elit endur-

ance athletes. In a previous study in consistent with our

results, Cievzcvz et. al. [36] reported that, frequencies of

the PPAR-a GG genotype (73.33 vs. 54.70 %; p = 0.04)

and G allele (82.50 vs. 70.17 %; p = 0.01) were signifi-

cantly higher in the elite combat athletes compared with

sedentary controls. Their results confirm the significance of

the PPAR-a gene as a useful genetic marker in combat

athletes. Ahmetov et al. [3] tested this hypothesis in the

study of a mixed cohort of 786 Russian athletes in 13

different sporting disciplines prospectively stratified by

performance (endurance oriented athletes, power-oriented

athletes and athletes with mixed endurance/power activity)

and they reported that PPAR-a intron 7G [ C polymor-

phism was associated with physical performance in Rus-

sian athletes, and this may be explained, in part, by the

association between PPAR-a genotype and muscle fiber

type composition. In another study of Ahmetov et al.’s [2]

total of 1,423 Russian athletes and 1,132 controls were

genotyped for 15 gene polymorphisms and intron 7G[C

polymorphism and PPARGC1A Gly482Ser polymorphism

were associated with endurance athlete status, the propor-

tion of slow-twitch muscle fibers and maximal oxygen

consumption. Lucia et al. [27] investigated PPARGC1A

Gly482Ser polymorphism in world-class Spanish male

endurance athletes and they indicated a role for the-

Gly482Ser genotype in determining aerobic fitness. This

finding has relevance from the perspective of physical

performance. Eynon et al.’s [18] data also indicated that

alower frequency of the Ser482 allele and possibly a higher

frequency of the GG genotype are associated with

increased endurance performance ability. He et al. [37]

results do not support previous data on Caucasians showing

an association between the Gly482Ser variant and VO2max

but suggest the potential role of another polymorphism

(A2962G) to explain individual VO2max differences in

Chinese men. Maciejewska et al. [38] results suggest that

the PPARGC1A Gly482Ser polymorphism is associated

with elite endurance athletic status. These findings support

the hypothesis that the PPARGC1A 482Ser allele may

impair aerobic capacity: thus, the Gly482 allele may be

considered a beneficial factor for endurance performance.

Maciejewska [39] data confirmed that GG genotype was

more prevalent in the group of endurance athletes therefore

G allele may be considered as one of the endurance-related

allele. Gineviciene [40] study suggested that the ACE,

PPARGC1A and PPAR-a polymorphisms genotypes were

associated, separately and in combination, with Lithuanian

footballers’ performance.

The present study suggested that, PPAR-a intron 7G[C

polymorphism G allele and PPARGC1A Gly482Ser poly-

morphism Ser482 allele affect endurance athletic perfor-

mance and aerobic capacity in Turkish elite endurance

athletes in Turkey. To our knowledge, this is the first study

in Turkey examined PPAR-a intron 7G[C and

PPARGC1A Gly482Ser gene polymorphisms in elite level

endurance athletes. Our results suggested that, PPAR-a and

PPARGC1A genes have strong effect on aerobic perfor-

mance of elit level athletes. Larger sample sizes and

functional studies are necessary to further substantiate

these findings.

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