Genome-wide DNA methylation changes in skeletal muscle between young and middle-aged pigs
- Long Jin†1,
- Zhi Jiang†2,
- Yudong Xia†3,
- Ping’er Lou1,
- Lei Chen4,
- Hongmei Wang1,
- Lu Bai1,
- Yanmei Xie1,
- Yihui Liu1,
- Wei Li1,
- Bangsheng Zhong1,
- Junfang Shen5,
- An’an Jiang1,
- Li Zhu1,
- Jinyong Wang4,
- Xuewei Li1Email author and
- Mingzhou Li1Email author
© Jin et al.; licensee BioMed Central Ltd. 2014
Received: 1 May 2014
Accepted: 31 July 2014
Published: 5 August 2014
Age-related physiological, biochemical and functional changes in mammalian skeletal muscle have been shown to begin at the mid-point of the lifespan. However, the underlying changes in DNA methylation that occur during this turning point of the muscle aging process have not been clarified. To explore age-related genomic methylation changes in skeletal muscle, we employed young (0.5 years old) and middle-aged (7 years old) pigs as models to survey genome-wide DNA methylation in the longissimus dorsi muscle using a methylated DNA immunoprecipitation sequencing approach.
We observed a tendency toward a global loss of DNA methylation in the gene-body region of the skeletal muscle of the middle-aged pigs compared with the young group. We determined the genome-wide gene expression pattern in the longissimus dorsi muscle using microarray analysis and performed a correlation analysis using DMR (differentially methylated region)-mRNA pairs, and we found a significant negative correlation between the changes in methylation levels within gene bodies and gene expression. Furthermore, we identified numerous genes that show age-related methylation changes that are potentially involved in the aging process. The methylation status of these genes was confirmed using bisulfite sequencing PCR. The genes that exhibited a hypomethylated gene body in middle-aged pigs were over-represented in various proteolysis and protein catabolic processes, suggesting an important role for these genes in age-related muscle atrophy. In addition, genes associated with tumorigenesis exhibited aged-related differences in methylation and expression levels, suggesting an increased risk of disease associated with increased age.
This study provides a comprehensive analysis of genome-wide DNA methylation patterns in aging pig skeletal muscle. Our findings will serve as a valuable resource in aging studies, promoting the pig as a model organism for human aging research and accelerating the development of comparative animal models in aging research.
KeywordsDNA methylation Skeletal muscle Pig Aging MeDIP-seq DNMTs
Aging is a nearly universal, chronic process that is shared by all organisms. The most prominent feature of the aging process in mammals is a gradual loss of function at the cellular, tissue and organismal levels. ‘Aging epigenetics’ is an emerging field that has generated exciting revelations. A global loss of DNA methylation has been identified as an age-related epigenetic alteration . Many studies have revealed that DNA methylation plays an important role in aging and in the development of various diseases [2, 3]. Therefore, a survey of epigenetic signatures that change with age might be useful to identify biomarkers of aging and age-associated diseases, which could potentially be used to make clinical diagnoses and prolong the lifespan.
The aging process and its underlying mechanisms have been studied extensively in rodent models [4, 5]. The sequencing and analysis of the pig genome  will greatly accelerate the development of the pig as a biomedical model for many diseases in humans, such as obesity and diabetes [7–9]. However, few aging studies have been carried out using pigs as models. Pigs age at a rate of approximately 5 years to every 1 year of human life, resulting in an average life expectancy of 15–20 years. Therefore, pigs could serve as an appealing model for studying aging because of their relatively longer lifespan than rodents and similar metabolic features, cardiovascular systems and proportional organ sizes relative to those of humans [10–12].
Notably, an increase in the incidence of age-related pathologies mostly begins at approximately the mid-point of a species’ life span [13–15]. It is well documented that the remarkable structural and functional changes that occur in skeletal muscle during aging, including a reduction of muscle mass and increased apoptosis [14, 16–19], are initiated at the mid-point of the lifespan . Consequently, studies focusing on DNA methylation changes in skeletal muscle during the mid-life period compared with the young stage are long overdue.
In this study, we used the pig as a model to perform a genome-wide survey of differences in DNA methylation and gene expression in a representative skeletal muscle (longissimus dorsi muscle, LDM) between two age stages in female pigs: young (0.5 years old) and middle-aged (MA, 7 years old) [10, 11]. We identified the patterns of methylation in the pig genome and the age-related differentially methylated regions (DMRs), then performed functional enrichment analysis for genes exhibiting DMRs. We found more genes showing a hypomethylated gene body in the middle-aged pigs than in the young pigs; these genes were potentially involved in aging processes, such as the development of muscular atrophy. We believe that this study will serve as a valuable resource for aging studies while also promoting the pig as a model organism for human aging research and accelerating the considerable development of comparative animal models for aging research.
Global DNA methylation analysis
DNA methyltransferases (DNMTs) are crucial for the establishment and maintenance of genomic methylation patterns . To determine whether the global loss of DNA methylation observed in the MA pigs was associated with alterations in DNMTs, we evaluated the mRNA transcript levels of DNMT1 (the major maintenance methyltransferase), DNMT3a and 3b (two de novo methylation methyltransferase) in these six tissues. For DNMT1 and DNMT3a, no significant differences in gene expression were observed in most of the tissues between the two age groups (Additional file 1), whereas DNMT3b showed increased mRNA levels in most of the tissues (except the ovary) in the MA pigs. Previous studies have revealed increased expression of both DNMT3a and DNMT3b in aging fibroblasts and in the aging human liver [21, 22]. However, the expression level of DNMT3a showed no difference between young and MA pigs. Our result suggests that DNMT3b, rather than DNMT3a, may regulate the genomic methylation pattern in a tissue-specific manner in pigs during aging.
Summary of methylated DNA immunoprecipitation sequencing (MeDIP-seq) data
Approximately 46 gigabases (Gb) of MeDIP-seq data were generated from six skeletal muscle samples (approximately 7.64 Gb per sample), among which 81% (approximately 35.31 Gb) of the clean reads were aligned to the porcine reference genome build 9.2. After removing ambiguous reads from the clean reads, 75% of the reads (approximately 36.56 Gb) were uniquely aligned across all of the aligned reads. The reads that showed the same mapping locations in each sample were regarded as potentially duplicated clones generated via PCR amplification and treated as the same read. Consequently, we acquired 24.46 Gb (approximately 92% of the unique mappable reads) of uniquely aligned non-duplicated reads (Additional file 2). CpG sites covered by a read depth of more than 10 were scored as high-confidence CpG sites. On average, 34.32% of the CpG sites met this threshold (Additional file 3).
To study methylation changes on a genome-wide scale, we measured methylation levels along the chromosomes in the samples using a 1 Mb sliding window to smooth the distribution (Additional file 4). Correlations between methylation levels and genomic features were assessed. We found that the methylation levels across the chromosomes were negatively correlated with the chromosomal length (Pearson’s r = 0.633, P = 0.0036) and positively correlated with the GC content (r = 0.787, P = 6.49 × 10−5), single-nucleotide polymorphism (SNP) density (r = 0.549, P = 0.0149) and gene density (r = 0.516, P = 0.0236). In addition, a strong positive correlation was observed with the ratio between the observed and expected numbers of CpG sites (CpGo/e) (r = 0.931, P = 7.41 × 10−9), which agreed with the results of a previous study of porcine DNA methylomes (Additional file 5) . However, methylation levels were not correlated with the density of repeat regions (r = 0.348, P = 0.145). Among these genomic features, the CpGo/e ratio showed the highest correlation with the methylation level. The gene density also exhibited a significant correlation with the methylation level, which may due to the relatively higher GC content in the gene regions being examined (Additional file 6), suggesting the potential role of methylation changes in the regulation of gene transcription . Previous studies have demonstrated a strong genetic component of the variation in DNA methylation profiles , and a potential role has been suggested for CpG-SNPs in genetic variation of the epigenome . Previous studies demonstrated that the level of methylation contributes to variation in the substitution rates at CpG sites [26, 27]. We observed a positive correlation between the SNP density and methylation level, suggesting that the genetic variation reflected by SNPs may have a substantial impact on local methylation patterns and gene expression.
We defined 24 categories of functional genomic elements and further classified the promoters into three types based on their CpG cites. Each type of promoter was then classified according to its distance from the transcription start site (Additional file 7) . We also classified CpG islands and CGI shores into four categories according to their genomic locations, as described in previous studies [24, 28]. We found that intermediate CpG promoters (ICPs) exhibited a relatively higher methylation status than did high CpG promoters (HCPs) and low CpG promoters (LCPs) (two-way ANOVA, P = 2.87 × 10−37). The methylation levels within the distal (D), proximal (P) and intermediate (I) regions of promoters also showed significant differences (two-way ANOVA, P = 1.44 × 10−7) (Additional file 7). This result agreed with a previous finding that methylation occurs more frequently at ICPs . Our data also suggested that a relatively higher methylation level within gene bodies is a general phenomenon in mammals , and it has been correlated with gene expression levels . We observed that the methylation level of exons was higher than that of introns (P = 9.8 × 10−9) (Additional file 7), reflecting the higher GC content of exons compared with their surrounding introns and further indicating the possible different roles of exons and introns in the regulation of gene transcription . Recently, DNA methylation at CpG island (CGI) shores has been demonstrated to play a more important role in gene regulation than that of the CGIs themselves . We observed distinct methylation levels for CGI shores in various genomic locations (Additional file 7), which may suggest the distinctive roles of these CGI shores in regulating gene expression.
Differential DNA methylation in the subtelomeric regions of young and MA pigs
Differentially methylated regions (DMRs) associated with aging
Summary of differentially methylated regions (DMRs)
Number of DMRs
Percentage of genomic length*
Percentage of genomic CpGs†
Age-related DMRs (n = 6)
Gene-body DNA methylation and gene expression
Functional enrichment analysis for genes with DMRs
Genes involved in the aging process
To further highlight the potential roles of genes involved in aging, we considered the intersection of genes that presented DMRs in their promoters and gene bodies with the known age-related genes deposited in the Human Ageing Genomic Resources (HAGR) database . Among the 288 genes potentially involved in the human aging process according to the HAGR-GenAge database, we did not identify any genes with DMRs in their promoters, whereas there were 12 known age-related genes included in the list of genes with DMRs in their gene bodies (Fisher’s exact test, P = 0.024) (Figure 5C).
This study provides a comprehensive analysis of genome-wide DNA methylation patterns in the skeletal muscle of aging pigs. Similar to previous reports in humans and mice, a global loss of methylation induced by transcriptional changes in DNMT3b was observed in various tissues of the MA pigs, suggesting that this type of epigenetic alteration is common in aging mammals. Although global DNA hypomethylation and promoter CpG island hypermethylation have been observed to progressively accumulate during aging , the present study identified more DMRs in gene bodies than in promoters, and gene-body hypomethylation was observed in more genes in the MA pigs (Figure 5 and Additional file 9). It is therefore reasonable to assume that during the loss of global methylation during aging, there is a greater tendency toward hypomethylation in the gene body rather than the promoter. It should be noted that based on our current data, it is not feasible to identify methylation changes on the X chromosome because of Xi . Further research using SNP data could allow allele-specific analysis of DNA methylation to identify the specific methylation changes on chromosome X .
It is believed that increased gene-body methylation correlates with increased transcription [30, 31, 37]; although some researchers have proposed that intragenic methylation might reduce gene expression . Our data suggested that methylation in the gene body reduces gene expression (Figure 4). However, gene-body methylation is only one of the many factors that influence gene expression. Further studies focusing on the methylation of specific regions that exhibit distinct gene regulation contexts are needed to elucidate the complicated epigenetic mechanism underlying aging and its associations with disease.
Previous reports have indicated that increased protein catabolism occurs in aging skeletal muscles [55, 56]. Structural and functional changes associated with aging, such as reductions in the muscle mass and muscle fibers, have been observed across a wide range of species, from worms to mammals . This type of epigenetic alteration of skeletal muscle with aging (Figure 6A and 6B; Additional file 10), consistent with findings in other species, was observed in pigs for the first time in the present work. Interestingly, genes related to tumorigenesis, as well as insulin sensitivity, exhibited a relatively higher expression level in the skeletal muscle of the MA pigs compared with younger pigs (Figure 6C), suggesting a higher risk of developing diseases with increased age.
Our results will promote further development of the pig as a model organism for human aging research. Most of the studies carried out in pigs to date have been conducted in neonatal or very young animals (generally when the pigs reach peak commercial value at approximately 6 months of age), before they reach the age of 1 year [57, 58]. Limited studies have been carried out on relatively older pigs (aged 2 years or more). Here, younger (0.5 years old) and middle-aged (7 years old) pigs were examined to investigate DNA methylation changes during the aging process. Although the aging process differs across species, with human aging showing major differences from the aging of most other species because of the relatively longer lifespan of humans, many species, including humans, pigs and mice, exhibit similarities with respect to aging muscle, muscular protein catabolism and muscle atrophy . However, the time course of the muscle function changes occurring in pigs remains to be determined in further studies. In addition, pigs have a longer lifespan than that of rodents and present similar metabolic features, cardiovascular systems and proportional organ sizes to those humans . Consequently, pigs can serve as a good biomedical model for human studies on the chronic aging process and its associated diseases [6, 8, 9]. However, only two age groups: young and middle-aged pigs were used in our study, and examining pigs of additional consecutive ages is necessary to further elucidate the changes in epigenetic modifications associated with age, as well as the ultimately complicated mechanisms underlying the aging process.
In summary, the present study provides a comprehensive analysis of genome-wide DNA methylation patterns in the skeletal muscle of aging pigs. We identified remarkable DNA methylation changes, such as a tendency toward hypomethylation in gene bodies in the longissimus dorsi muscle of MA pigs. Furthermore, we identified numerous genes that exhibited age-related methylation changes and are potentially involved in the aging process. These genes are mainly related to protein catabolism, suggesting that predisposition to amyotrophy emerges during middle age. This study will serve as a valuable resource for aging studies, promoting the pig as a model organism for human aging research and accelerating the development of comparative animal models in aging research.
A total of six healthy female pigs (Chinese Jinhua breed) were used in this study from two age groups: 0.5 and 7 years old, representing young and middle-aged pigs, respectively. Each age group included three individuals, which were regarded as biological replicates. The animals were reared in the same environment and fed the same diet ad libitum during the experimental period. Food was withheld from the animals on the night before they were slaughtered. All experimental procedures and sample collection were approved by the Institutional Animal Care and Use Committee of the College of Animal Science and Technology of Sichuan Agricultural University, Sichuan, China, under permit No. DKY-B20121403.
Six types of tissues (brain, liver, ovary, spleen, heart and longissimus dorsi muscle) were rapidly sampled from each carcass and immediately frozen in liquid nitrogen. All tissue samples were stored at − 80°C until DNA and total RNA extraction.
Measurement of the global DNA methylation status
DNA from each collected tissue was extracted using the DNeasy Blood & Tissue Kit (Qiagen). Global DNA methylation was evaluated using the MethylFlash™ Methylated DNA Quantification Kit (Epigentek). The amount of input DNA for each assay was 100 ng to ensure optimal quantification. The experiments were performed according to the manufacturer’s instructions.
Quantitative PCR analysis of the DNMTgenes
Total RNA (10 μg) was extracted from the six muscle samples using TRIzol (Invitrogen). RNase-free DNase I (TaKaRa) was used to remove genomic DNA from the RNA samples. cDNA was synthesized using PrimeScript RT Master Mix (TaKaRa). Quantitative real-time PCR (q-PCR) was performed using SYBR Premix Ex Taq (TaKaRa) in the CFX96 Real-Time PCR Detection System (Bio-Rad). The primers used for q-PCR are listed in Additional file 11. All measurements were performed in parallel with a negative control (no cDNA template), and each RNA sample was analyzed in triplicate. Porcine ACTB, TBP and TOP2B were used as endogenous control genes [60, 61]. The gradient dilution PCR assays for these three reference genes showed stable, high amplification efficiencies (Additional file 12). Relative expression levels were then calculated using the ΔΔCt method .
Methylated DNA immunoprecipitation sequencing
DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen) and then eluted using 10 mM Tris•Cl, pH 7.5. The quality of the isolated DNA was then measured using a NanoDrop spectrophotometer. The ratio of the absorbance at 260 nm versus 280 nm (A260/A280) provides an estimate of the purity of the DNA. The A260/A280 value should be 1.8 to 2.0 for each DNA sample to guarantee quality. The initial volume of DNA for each sample should be at least 5 μg to ensure the success of subsequent MeDIP-seq experiments. The protocol for MeDIP-seq and detailed information on the construction of a MeDIP DNA library were provided in a previous report from our group . The MeDIP-seq data have been submitted to the GEO database under accession number GSE50716.
Analysis of MeDIP-seq data
We filtered out reads that contained more than 5 ‘N’s and those in which over 50% of the sequence exhibited a low quality value (Phred score < 5). The sequencing reads were then aligned to the pig reference genome version 9.2 with up to four mismatches allowed, using SOAP2 software (Version 2.21) . We employed the Nov. 2009 (SGSC Sscrofa9.2/susScr2) assembly of the pig genome  (susScr2, SGSC Sscrofa9.2 (NCBI project 10718, GCA_000003025.2)), and the reference can be downloaded at http://hgdownload.cse.ucsc.edu/goldenPath/susScr2/chromosomes/. When multiple reads from one sequencing library were mapped to the same genomic location, these reads were regarded as potential clonal duplicates arising from PCR amplification biases and were identified to be a single read. The coverage depth for each CpG site was calculated from the number of DNA fragments covering that site, which were paired-end mapped reads, and further normalization among samples was performed based on the total DNA fragments for each sample. To avoid stochastic sampling drift and increase the confidence of the results, we filtered out CpG sites whose coverage showed a read depth of less than 10 when performing subsequent differential methylation analyses.
We also classified all of the genomic regions into 24 genomic elements while referring to annotation data for the pig reference genome to perform further detailed analyses according to previous studies . It should be noted that the TSS for many porcine genes are incomplete because of genome assembly and annotation issues related to the pig genome. We arbitrarily defined the region extending from − 2,200 to + 500 bp from the gene translation start site as the promoter. All promoters (−2,200 to + 500 bp) were classified into three groups based on their CpG content: high CpG promoters (HCPs) that contained a 500-bp area with a CpG ratio above 0.75 and a GC content above 55%; low CpG promoters (LCPs), with did not contain a 500-bp area with a CpG ratio above 0.48; and intermediate CpG promoters (ICPs), which were neither HCPs nor LCPs. Each promoter of 2,700 bp in length was then divided into three groups: distal (D), −1,000 to −2,200 bp; intermediate (I), −200 to −1,000 bp; and proximal (P), −200 to +500 bp. Consistent improvement of the assembly and annotation of the pig genome will further the search for epigenetic biomarkers of aging and promote development of the pig as a model organism for human biomedical research.
We classified CGIs (regions of at least 200 bp with a GC percentage greater than 55% and an observed-to-expected CpG ratio greater than 65%) and CGI shores (regions located within 2 kb of islands) into four classes: promoter, intragenic, 3’ transcript or intergenic locations, based on their distance from genes.
We also divided the gene structure into the first exon, first intron, internal exons, internal introns, and last exon, together with the TES (Transcription end site) 2 kb downstream and the intergenic region according to the pig genome (Sus scrofa 9.2).
Identification of DMRs
After filtering out low-quality reads (reads with a depth of less than 10), we identified DMRs across the whole genome using methods we have described previously  to identify the differential DNA methylation status between the two age groups. First, the normality and equal variance of the read depth at each CpG site across the sample groups were tested using Bartlett's test (passing if P > 0.05, failing if P < 0.05). Second, a parametric (when passing Bartlett's test) or non-parametric test (when failing Bartlett's test) was used to select highly variable CpGs (P < 0.01) as seed sites for candidate DMRs. Third, the 3’ downstream adjacent CpG sites were individually incorporated into the seed CpGs. To highlight the CpG-enriched regions, we allowed a distance of up to 200 bp between two adjacent CpGs. The average read depth of these two CpGs was then subjected to a new round of tests, which was continued repeatedly for the next CpG until a low-variance CpG (P > 0.01) was encountered, which was allowed to be up to 2 kb from the seed CpG. If five or more CpGs in a genomic region showed different read depths across samples that were statistically significant (P < 0.01), then the region was considered a DMR. The resulting P values for the DMRs were corrected using the Benjamini-Hochberg method (FDR < 0.01, 1,000 permutations).
Measurement of telomere length using q-PCR
The high-quality DNA used in the MeDIP experiment was also used to measure telomere length in the young and MA pigs. The average telomere length was measured from a real-time PCR assay, following a previous description [36, 65]. Two separate PCR assays were performed, using a telomeric region primer (T) and a primer for a reference nuclear gene (pig GCG, single copy gene, S). The primer sequences were as follows: T forward, CGGTTTGTTTGGGTTTGGGTTTGGGTTTGGGTTTGGGTT; T reverse, GGCTTGCCTTACCCTTACCCTTACCCTTACCCTTACCCT; S forward, GAATCAACACCATCGGTCAAAT; and S reverse, CTCCACCCATAGAATGCCCAGT. The telomere (T) signal was normalized to the signal of the single-copy (S) gene to generate the T/S ratio, which reflected the relative telomere length, in all studied samples.
Functional enrichment analysis for genes with DMRs
The DAVID (Database for Annotation, Visualization and Integrated Discovery) web server (http://david.abcc.ncifcrf.gov/) was used to perform functional enrichment analysis of Gene Ontology (GO) and KEGG pathway categories . Genes with DMRs in their promoters and gene bodies were mapped to their respective human orthologs and then submitted to DAVID for enrichment analysis, which included GO biological processes (GO-BP), molecular function (GO-MF) terminologies and KEGG pathway categories. Only GO-BP, GO-MF or KEGG-pathway terms with a Benjamini-corrected P value less than 0.05 were considered to be significant and therefore included in the list.
Gene expression microarray
Total RNA (10 μg) was extracted from the six samples using TRIzol (Invitrogen) and further purified using an RNeasy column (Qiagen). The integrity of the total RNA was confirmed using a Bioanalyzer 2100 and the RNA 6000 Nano LabChip Kit (Agilent Technologies). Detailed information on the workflow of the microarray experiment is provided in a previous report from our group . First, we mapped 43,603 probes (60 mer in length) to the pig reference genome while allowing up to one mismatch, which resulted in 27,955 probes (64.11%) that were uniquely mapped. Among these uniquely mapped probes, 4,983 (11.43%) were uniquely mapped to exons in Ensembl genes (more than 60% sequence overlap). Multiple probes that mapped to the same or different exons of a specific gene were excluded. Therefore, only 3,074 probes (7.05%), which uniquely represented 3,074 genes and were considered to represent high-confidence gene expression data, were used in the subsequent analysis.
Differentially expressed genes were identified using the MultiExperiment Viewer (MeV) , and this software was also employed to perform subsequent hierarchical clustering of samples. The gene expression microarray data have been submitted to the GEO database under accession number GSE49791.
Bisulfite sequencing PCR
Methylation Primer Express Software V1.0 was used to design bisulfite sequencing PCR (BSP) primers, which are provided in Additional file 13. The bisulfite conversion of genomic DNA was performed using the EZ DNA Methylation-Gold™ Kit (Zymo Research, D5006). PCR was carried out using ZymoTaq™ PreMix (Zymo Research, E2004). The PCR product was then purified using the DNA Clean & Concentrator - 25™ Kit (Zymo Research, D4005), and the PCR product was cloned into the TA vector pCR2.1 (Invitrogen, K2000-01). Ten subclones were selected for each gene and subsequently sequenced using an ABI 3730 DNA sequencer (Applied Biosystems). All of the sequences were analyzed using BiQ Analyzer V2.0 software .
Longissimus dorsi muscle
Differentially methylated regions
Methylated DNA immunoprecipitation sequencing
High CpG promoter
Intermediate CpG promoter
Low CpG promoter
Human ageing genomic resources
Bisulfite sequencing PCR.
This work was supported by grants from the National Special Foundation for Transgenic Species of China (2014ZX0800950B), the National Natural Science Foundation of China (31301942, 31101701 and 31372284), the National High Technology Research and Development Program of China (863 Program) (2013AA102502), the Fund of Fok Ying-Tung Education Foundation (141117), the Fund for Distinguished Young Scientists of Sichuan Province (2013JQ0013), the Specialized Research Fund of Ministry of Agriculture of China (NYCYTX-009), the Program for Changjiang Scholars and Innovative Research Team in University (IRT13083), International Science & Technology Cooperation Program of China (2014DFA31260).
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