RESEARCH ARTICLE

HExDB: a database for epigenetic changes occurring after horseexercise

Jeong-An Gim • Sugi Lee • Dae-Soo Kim • Kwang-Seuk Jeong • Chang Pyo Hong •

Jin-Han Bae • Jae-Woo Moon • Yong-Seok Choi • Byung-Wook Cho •

Hwan-Gue Cho • Jong Bhak • Heui-Soo Kim

Received: 16 October 2014 / Accepted: 26 November 2014

� The Genetics Society of Korea and Springer-Science and Media 2014

Abstract DNA methylation is an essential biochemical

modification that regulates gene expression. Exercise

induces changes in gene expression that adapt as metabolic

changes in the blood. We provide a database for the epi-

genetic changes after horse exercise (http://www.primate.

or.kr/hexdb). Horse Exercise Epigenetic Database (HEx-

DB) explicates the change in genome-wide DNA methyl-

ation patterns after exercise. Exercise changes the genome-

wide epigenetic patterns, and understanding the regions

that change is important for confirming exercise physio-

logical mechanisms. For this purpose, our database pro-

vides information on differentially methylated regions after

exercise that pass a set threshold. A total of 784 genes

based on NCBI RefSeq were identified as differentially

methylated in equines after exercise. Our database provides

clues for the study of exercise-related epigenetic pathways

in the thoroughbred horse.

Keywords Thoroughbred horse � Exercise �Differentially methylated region � Transposable element �MeDIP-Seq

Introduction

Since the 17th century in England, humans have primarily

bred thoroughbred racing horses for their speed, stamina,

and agility. These horses have many specific genetic fea-

tures associated with racing ability, such as single nucle-

otide polymorphisms (SNPs), gene expression, and

transcript analysis (Hill et al. 2010; McGivney et al. 2012;

Webbon 2012). Thoroughbred horses are placed under

heavy demand for high athletic performance; therefore,

Jeong-An Gim, Sugi Lee, Dae-Soo Kim contributed equally to this

work.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s13258-014-0251-4) contains supplementarymaterial, which is available to authorized users.

J.-A. Gim � K.-S. Jeong � H.-S. Kim (&)

Department of Biological Sciences, College of Natural Sciences,

Pusan National University, Busan 609-735, Republic of Korea

e-mail: khs307@pusan.ac.kr

S. Lee � Y.-S. Choi

Department of Statistics, College of Natural Sciences, Pusan

National University, Busan 609-735, Republic of Korea

D.-S. Kim

Genome Resource Center, Korea Research Institute of

Bioscience and Biotechnology (KRIBB), 111 Gwahangno,

Yuseong-gu, Daejeon 305-806, Republic of Korea

K.-S. Jeong

Institute of Environmental Technology & Industry, Pusan

National University, Busan 609-735, Republic of Korea

C. P. Hong � J.-W. Moon � J. Bhak

TBI, Theragen BiO Institute, Theragen Etex, Suwon 443-270,

Republic of Korea

J.-H. Bae

Research Center, Dongnam Institute of Radiological and

Medical Sciences (DIRAMS), Busan, Republic of Korea

B.-W. Cho

Department of Animal Science, College of Life Sciences, Pusan

National University, Miryang 627-702, Republic of Korea

H.-G. Cho

School of Computer Science and Engineering, College of

Engineering, Pusan National University, Busan 609-735,

Republic of Korea

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Genes Genom

DOI 10.1007/s13258-014-0251-4

Fig. 1 The Horse Exercise

Epigenetic Database schema for

identifying DNA methylated

regions. The database provides

the differentially methylated

peaks between two horses pre-

and post-exercise, and the user

decides the degree of peak

differential methylation

Fig. 2 A screenshot of Horse

Exercise Epigenetic Database

(HExDB). a The main web

interface of the database. The

database serves to retrieve

equine methylation patterns for

the researcher. b The results

page. The output shows the

methylation peaks and their

differences between horses pre-

and post-exercise at the

specified genomic regions

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many studies have attempted to identify traits associated

with horse exercise (Eivers et al. 2010; Park et al. 2012).

Exercise stimulates exercise-related genes and induces

physiological changes to adapt to the depleted energy in

host cells. Appropriate changes in gene expression patterns

after exercise is important to adapt to the different meta-

bolic state (Barres et al. 2012). Many studies have con-

centrated on comparing gene expression profiles and

methylation changes of pre- and post-exercise samples in

various species (Jemiolo and Trappe 2004; McGivney et al.

2010). In particular, the advent of next-generation

sequencing (NGS) technology has transformed the analysis

of gene expression profiles and DNA methylation patterns

to a genome-wide tool for identifying exercise-induced

genes and elucidating their roles (Barres et al. 2012; Park

et al. 2012). Although genome-wide gene expression and

DNA methylation data is very useful for further studies, the

information generated in these previous studies was not

made accessible to the public in an easy-to-use database.

Therefore, the focus of this study was to create a database

cataloguing the post-exercise differentially methylated

regions (DMRs) identified in two horses. Our database

provides the methylation peak data before and after

exercise.

Fig. 2 continued

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The Horse Exercise Epigenetic Database (HExDB) pro-

vides whole-genome methylation patterns in two pre- and

post-exercise individuals. One horse is a high-performance

male and the other a low-performance female. This database is

useful for functional studies in exercise, as well as in expli-

cating male/female-specific or high/low-performance-spe-

cific exercise traits. Specifically, at the ‘‘Gene Index’’ section,

users can compare exercise-related genes between before and

after exercise in two horses. The database also provides links

to the UCSC Genome Browser and Ensembl Database for

each genomic region and helps to visualize the differentially

methylated regions. This database will benefit those who wish

to perform functional studies of methylation in exercise and

uncover hidden roles of methylation in exercise.

Materials and Methods

Data sources and sample information

For methylated DNA immunoprecipitation followed by high

throughput sequencing (MeDIP-Seq) analysis, two retired

racing horses were selected: a high-performance male and a

low-performance female (Table S1). The performance of

each horses is defined by their racing records (W¼149,645,000

versus W¼0). All animal experiments were approved by the

Institutional Animal Care and Use Committee of Pusan

National University (approval number PNU-2013-0417).

The before exercise samples were taken at pre-exercise rest

state in two horses. After trotted for 30 min, each two

samples were immediately taken. Blood sampling was per-

formed in the presence of a veterinarian and care was taken

to minimize suffering. After extracting genomic DNA, raw

sequence data were first processed to filter out adapters and

low quality reads. Then, by using the SOAPaligner software

(version 2.21), sequences were aligned to the horse reference

genome (equCab2) (Li et al. 2009). In order to identify

genomic regions that were enriched in a pool of specifically

immunoprecipitated methylated DNA fragments, genome-

wide peak scanning was carried out by Model-based Ana-

lysis of ChIP-Seq data (MACS, version 1.4.2) with a P value

threshold of 1 9 10-4 (Zhang et al. 2008). The methylation

peak data were saved as *.wig files and converted to *.csv

files via Microsoft Excel.

User interface

The genomic location data and corresponding methylation

peak data were stored using the MySQL data management

system. The resolution of the methylation peak is 50 bp.

Users select the differences-of-interest: this database can

provide the differences of methylation peak between pre-

and post-exercise within two individual horses.

Results and Discussion

The HExDB contains methylation peaks pre- and post-

exercise in two horses and provides the raw data for ana-

lyzing the change in methylation patterns after exercise

according to gender or horse quality. Each dataset was

accessed via a PHP-based interface in an Apache web server

system and data were converted to a MySQL-queryable

database system. This database provides an optimal interface

to compare methylation state between horses before and after

exercise (Fig. 1). Each genomic region found in this database

can be easily accessed at the UCSC Genome Browser and

Ensembl database. This database will be upgraded when

additional genomic information (e.g., new genome assembly

or newly identified genes) becomes available (Fig. 2).

DNA methylation in the gene promoter region regulates

gene expression. Physical exercise changes the patterns of

DNA methylation in exercise-related genes, then changes

the level of gene expression (Barres et al. 2012; Ronn et al.

2013). This database provides whole genome methylation

patterns as well as the gene index. Therefore, users can

search DNA methylation patterns in promoter regions and

apply these results to further studies.

In mammals, transposable elements (TEs) account for

anywhere between 30 and 50 % of the total genome. Because

TE expression negatively affects genome stability, DNA

methylation is maintained in TE regions in order to inhibit

their expression (Carnell and Goodman 2003; Girardot et al.

2006). This database also contains information on TE

Fig. 3 A flowchart depicts the total process for identifying the

methylation status between pre- and post-exercise of two equine

individuals

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distribution, which can be used to screen the TE methylation

state.

In addition, the database can be used to identify equine

DMRs before and after exercise. One horse (GEUMBIT

SESANG) is male and high-performance; the other horse

(JIGUSANG SERYEOK) is female and low-performance.

This system provides a list of DMRs and their genomic

information between the two individuals. At the whole-

genome level, comparison of DNA methylation can pro-

vide important information related to exercise-related

pathways under epigenetic regulation. In order to begin the

comparison of corresponding two horses, users can select

Fig. 4 A screenshot of the

‘‘Gene index’’ section of Horse

Exercise Epigenetic Database

(HExDB). The table shows the

methylation state of specific

gene and its location (a). The

results of methylation state in

the AR gene, before (b) and

after (c) exercise in JIGUSANG

SERYEOK. This section

displays the results as a new tab

or window. Users can easily

compare methylation status

between pre- and post- exercise

in two horses

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the individuals and the methylation state as mandatory. The

differences and threshold (the minimum methylation

degree) can also be controlled by the user. Users are able to

download the genomic information for each DMR identi-

fied between the two exercise states (Fig. 3).

This database also provides the confirmed methylation

state of a specific genomic region. This query option can

help to confirm the methylation degree of a specific

genomic region beyond threshold in the two horses and for

each exercise state. We provide the option to find the gene

name, and users can search for methylation state by

entering a gene name of interest. This information can help

users to identify highly methylated genomic regions

(Fig. 4).

In order to confirm the effect of methylation changes in

gene expression, it is important to compare DNA methyl-

ation patterns with gene expression data set. In the previous

study, differently expressed genes after exercise were

reveled included the corresponding horse blood samples

(Park et al. 2012), and total data sets were open to public

Fig. 4 continued

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(http://www.horsegenome.net/). Users can check methyla-

tion state against gene expression patterns, which provide

more comparative information between DNA methylation

and gene expression after exercise in horse.

Conclusion

We expect that HExDB will contribute to the study of epi-

genetics in exercise physiology. This database helps to

identify DMRs in horses before and after exercise. The gene

index section provides gene region methylation patterns and

genomic locations. Users can see and visually compare

methylation density, including gene information and TE

insertion status. HExDB is freely available at the URL

(http://www.primate.or.kr/hexdb). Any questions and advice

regarding the use of this database are always welcome.

Acknowledgments This study was supported by a grant from the

Next Generation BioGreen 21 Program (No. PJ0081062011), Rural

Development Administration, Republic of Korea.

Fig. 4 continued

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Conflict of interest The authors declare that they have no com-

peting interests.

References

Barres R, Yan J, Egan B, Treebak JT, Rasmussen M, Fritz T, Caidahl

K, Krook A, O’Gorman DJ, Zierath JR (2012) Acute exercise

remodels promoter methylation in human skeletal muscle. Cell

Metab 15:405–411

Carnell AN, Goodman JI (2003) The long (LINEs) and the short

(SINEs) of it: altered methylation as a precursor to toxicity.

Toxicol Sci 75:229–235

Eivers SS, McGivney BA, Fonseca RG, MacHugh DE, Menson K,

Park SD, Rivero JL, Taylor CT, Katz LM, Hill EW (2010)

Alterations in oxidative gene expression in equine skeletal

muscle following exercise and training. Physiol Genomics

40:83–93

Girardot M, Guibert S, Laforet MP, Gallard Y, Larroque H,

Oulmouden A (2006) The insertion of a full-length Bos taurus

LINE element is responsible for a transcriptional deregulation of

the Normande Agouti gene. Pigment Cell Res 19:346–355

Hill EW, McGivney BA, Gu J, Whiston R, Machugh DE (2010) A

genome-wide SNP-association study confirms a sequence variant

(g.66493737C [ T) in the equine myostatin (MSTN) gene as the

most powerful predictor of optimum racing distance for Thor-

oughbred racehorses. BMC Genom 11:552

Jemiolo B, Trappe S (2004) Single muscle fiber gene expression in

human skeletal muscle: validation of internal control with

exercise. Biochem Biophys Res Commun 320:1043–1050

Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J (2009)

SOAP2: an improved ultrafast tool for short read alignment.

Bioinformatics 25:1966–1967

McGivney BA, McGettigan PA, Browne JA, Evans AC, Fonseca RG,

Loftus BJ, Lohan A, MacHugh DE, Murphy BA, Katz LM et al

(2010) Characterization of the equine skeletal muscle transcrip-

tome identifies novel functional responses to exercise training.

BMC Genom 11:398

McGivney BA, Browne JA, Fonseca RG, Katz LM, Machugh DE,

Whiston R, Hill EW (2012) MSTN genotypes in Thoroughbred

horses influence skeletal muscle gene expression and racetrack

performance. Anim Genet 43:810–812

Park KD, Park J, Ko J, Kim BC, Kim HS, Ahn K, Do KT, Choi H,

Kim HM, Song S et al (2012) Whole transcriptome analyses of

six thoroughbred horses before and after exercise using RNA-

Seq. BMC Genom 13:473

Ronn T, Volkov P, Davegardh C, Dayeh T, Hall E, Olsson AH,

Nilsson E, Tornberg A, Dekker Nitert M, Eriksson KF et al

(2013) A six months exercise intervention influences the

genome-wide DNA methylation pattern in human adipose tissue.

PLoS Genet 9:e1003572

Webbon P (2012) Harnessing the genetic toolbox for the benefit of the

racing Thoroughbred. Equine Vet J 44:8–12

Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE,

Nusbaum C, Myers RM, Brown M, Li W et al (2008) Model-

based analysis of ChIP-Seq (MACS). Genome Biol 9:R137

Genes Genom

123