Abstract
Background
Skeletal muscle development plays a crucial role in yield and quality
of pork; however, this process is influenced by various factors. In
this study, we employed whole-genome bisulfite sequencing (WGBS) and
transcriptome sequencing to comprehensively investigate the longissimus
dorsi muscle (LDM), aiming to identify key genes that impact the growth
and development of Duroc pigs with different average daily gains
(ADGs).
Results
Eight pigs were selected and divided into two groups based on ADGs: H
(774.89 g) group and L (658.77 g) group. Each pair of the H and L
groups were half-siblings. The results of methylation sequencing
revealed 2631 differentially methylated genes (DMGs) involved in
metabolic processes, signalling, insulin secretion, and other
biological activities. Furthermore, a joint analysis was conducted on
these DMGs and the differentially expressed genes (DEGs) obtained from
transcriptome sequencing of the same individual. This analysis
identified 316 differentially methylated and differentially expressed
genes (DMEGs), including 18 DMEGs in promoter regions and 294 DMEGs in
gene body regions. Finally, LPAR1 and MEF2C were selected as candidate
genes associated with muscle development. Bisulfite sequencing PCR
(BSP) and quantitative real-time PCR (qRT–PCR) revealed that the
promoter region of LPAR1 exhibited significantly lower methylation
levels (P < 0.05) and greater expression levels (P < 0.05) in the H
group than in the L group. Additionally, hypermethylation was observed
in the gene body region of MEF2C, as was a low expression level, in the
H group (P < 0.05).
Conclusions
These results suggest that the differences in the ADGs of Duroc pigs
fed the same diet may be influenced by the methylation levels and
expression levels of genes related to skeletal muscle development.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12864-024-10404-0.
Keywords: Duroc pigs, WGBS, DNA methylome, Transcriptome, Skeletal
muscle
Introduction
The Duroc pig, which was first discovered in South America in the
1960s, has rapidly become popular and blue is one of the top lean pig
breeds worldwide [[37]1]. This species has outstanding qualities, such
as a rapid growth rate, high feed conversion efficiency, and excellent
meat yield [[38]2–[39]4], and is often used as a terminal sire when
fattening pigs are produced. However, the growth performance of Duroc
pigs gradually declines after they reach 110 kg body weight. Selecting
Duroc pigs that can maintain good growth and meat production
performance from 110 kg to 130 kg will substantially increase the
economic benefits of pig farming.
The largest tissue in mammals, skeletal muscle, which comprises 40% of
the body, is essential for locomotor activity, energy expenditure, and
meat production performance [[40]5, [41]6]. The amount and quality of
meat produced are strongly based on the development and physical
properties of the muscle since it is a crucial end product of livestock
production [[42]7]. Therefore, exploring the mechanisms underlying
skeletal muscle growth and development is essential for increasing
livestock production performance.
However, skeletal muscle development is influenced by genetic and
nutritional factors and a range of intricate epigenetic regulatory
mechanisms, such as DNA methylation. DNA methylation is an epigenetic
marker that is crucial for regulating genomic function and maintaining
normal mammalian development by controlling gene activity and other
regulatory factors [[43]8, [44]9]. As research on DNA methylation has
intensified, numerous studies have highlighted its impact on muscle
growth and development in livestock [[45]10–[46]12]. DNA methylation
levels vary throughout different stages of skeletal muscle development
in pigs and gradually decrease as pig embryos grow and develop
[[47]13]. Furthermore, DNA methylation differs across pig breeds,
revealing correlations with skeletal muscle growth and development
[[48]14, [49]15]. However, further DNA methylation studies in siblings
or half-siblings of pigs are needed.
This study aimed to achieve the following objectives: 1) To select the
LDM of sibling and half-sibling Duroc pigs and perform a comprehensive
analysis of the global DNA methylation levels in the H (high ADG) and L
(low ADG) groups using WGBS (whole-genome bisulfite sequencing). 2) To
identify DMRs (differentially methylated regions) and DMGs
(DMR-associated genes) in the two comparison groups. 3) To analyse the
DNA methylome and transcriptome of LDMs in Duroc pigs, identify genes
associated with growth and development, and determine their expression
and methylation levels.
Results
Genome-wide DNA methylation profiling and patterns
To investigate the DNA methylation patterns of LDMs in Duroc pigs, we
utilized single-base resolution WGBS [[50]16] to assess global DNA
methylation levels in both the H and L groups. After removing adapter
contaminants, low-quality reads, and reads containing Ns, we collected
490,435,608 to 669,644,232 clean reads from each of the eight DNA
library samples (Table [51]1). The mapping ratio of clean reads to the
Duroc pig genome ranged from 86.45% to 87.26% for all eight models. The
sequencing data were ready for further analysis, with sequencing depths
reaching 28.01 (H) and 31.36 (L). The bisulfite conversion efficiency
ranged from 99.00% to 99.04% per sample (Table [52]1).
Table 1.
Summary of genome-wide methylation sequencing data
Samples Clean reads Mapped reads Mapped ratio (%) Genome size Sequence
depth BS Conversion (%)
H1 595191668 519358211 87.26 2488630688 31.3 99.04%
H2 490435608 427408324 87.15 2488630688 25.76 99.02%
H3 514939188 446151742 86.64 2488630688 26.89 99.02%
H4 536252010 466334203 86.96 2488630688 28.11 99.00%
L1 525286770 454122700 86.45 2488630688 27.37 99.02%
L2 669644232 580918741 86.75 2488630688 35.01 99.03%
L3 600987034 522532244 86.95 2488630688 31.5 99.02%
L4 605375476 523792647 86.52 2488630688 31.57 99.04%
[53]Open in a new tab
Over 80% of the whole-genome loci had a coverage depth of 10x or more
(Fig. [54]1A), with most of the loci having a coverage depth of 10-30x,
resulting in a standard distribution (Fig. [55]1B). These findings
indicated high coverage of individual loci, a wide sequencing range,
and credible results.
Fig. 1.
[56]Fig. 1
[57]Open in a new tab
A Cumulative genomic distribution of the H (high ADG) and L (low ADG)
groups of LDMs of Duroc pigs at 110-130 kg. B Genome coverage
distribution of the H and L groups
Our investigation of the distribution of mCs in three sequence contexts
revealed that CG methylation was the most prevalent form of methylation
in all the samples, occurring less frequently in the CHH and CHG
sequences (Fig. [58]2A). The proportions of these three methylation
forms remained relatively stable across all eight samples. While CG
methylation levels were greater than 90% in most examples, CHG and CHH
methylation levels ranged between 1.62% and 6.80% (Fig. [59]2A).
Furthermore, to examine the methylation distribution in different
genetic regions, we evaluated the methylation levels of mCG, mCHG, and
mCHH in the gene-body, upstream, and downstream areas of the H and L
groups. On a genome-wide scale, we observed similar methylation
profiles for mCHG/CHG and mCHH/CHH in all regions (Fig. [60]2B).
However, from the upstream region to the gene body and downstream
areas, the methylation level of mCG decreased and then increased before
levelling off.
Fig. 2.
[61]Fig. 2
[62]Open in a new tab
DNA methylation patterns in Duroc pig LDMs. A The proportions of mCs
(mCG, mCHG, and mCHH) in the H and L groups. B The mCG, mCHG, and mCHH
methylation levels in different sequence regions in the H and L groups.
C Correlation analysis of methylation levels between samples from the
two groups. D Principal component analysis of all samples
Pearson correlation analysis and Principal Component Analysis (PCA)
were used to assess the similarity of the eight LDM samples. Pearson
correlation analysis of the CpG bases suggested that a high correlation
across all samples(r> 0.79) (Fig. [63]2C). Furthermore, PCA revealed no
significant distinction between the sample groups, as they did not form
separate clusters (Fig. [64]2D).
Identification of DMRs
The genomic regions with different DNA methylation levels in the H and
L groups were identified. In total, we identified 9450 DMRs: 6368
hyper-DMRs and 3082 hypo-DMRs (Fig. [65]3). Among the mCG methylation
types, 6365 DMRs were increased, and 3080 DMRs were decreased. For the
mCHG methylation type, only one DMR was decreased. Among the mCHH
methylation types, two DMRs were increased, and eight were decreased.
Finally, for the mC methylation types, one DMR was increased, and two
DMRs were decreased. Detailed information on these DMRs is shown in
Supplementary Table 1.
Fig. 3.
[66]Fig. 3
[67]Open in a new tab
Number of DMRs in the H vs L group (P < 0.05)
To investigate the distribution of differential methylation across the
genome, we calculated the density of DMRs in 100-kb windows (Fig.
[68]4). Our analysis revealed the presence of DMRs on each chromosome.
Specifically, among the 9445 DMRs (mCG methylation type) (P < 0.05,
difference
[MATH: ≥15% :MATH]
) in the H vs L group, 6365 hyper-DMRs showed the greatest enrichment
on chromosomes 6 ([69]NC_010448.4), 1 ([70]NC_010443.5), and X
([71]NC_010461.5). In contrast, 3080 hypo-DMRs showed the greatest
enrichment on chromosomes 1 ([72]NC_010443.5), 6 ([73]NC_010448.4), and
X ([74]NC_010461.5).
Fig. 4.
[75]Fig. 4
[76]Open in a new tab
Distribution of DNA methylation on the Duroc pig chromosome. DMR
density was calculated in 100-kb windows across the genome. The height
of the column indicates the number of DMRs in each window. Hyper-DMRs
are shown in the red column. Hypo-DMRs are shown as green columns
GO and KEGG pathway enrichment analysis of DMR-related genes
We performed GO and KEGG pathway enrichment analyses to determine the
potential functions of the DMGs. Our results showed that the DMGs were
significantly enriched in several GO terms related to biological
processes, cellular components, and molecular functions (Fig. [77]5 and
Supplementary Table 2). Specifically, the top ten enriched GO terms
were cell (1762), cell part (1762), anatomical structure development
(726), single-organism developmental process (763), developmental
process (768), plasma membrane (567), cell periphery (581), binding
(1779), cellular process (1929), single-multicellular organism process
(762) and system development (593). In addition, we found that the DMGs
were significantly enriched in several GO terms associated with muscle
development, including actin cytoskeleton organization (112), actin
filament-based process (120), muscle structure development (82), actin
filament organization (69), and muscle tissue development (60).
Furthermore, KEGG pathway enrichment analysis was performed. A total of
45 KEGG terms were significantly enriched (Fig. [78]6, Supplementary
Table 3). The five most highly represented pathways were glutamatergic
synapses (37), axon guidance (49), focal adhesion (50), axon
regeneration (28), and morphine addiction (28).
Fig. 5.
[79]Fig. 5
[80]Open in a new tab
GO enrichment analysis of DMGs (those were annotated to different GO
terms in biological process, cellular component, and molecular
function) in the H vs L group
Fig. 6.
[81]Fig. 6
[82]Open in a new tab
KEGG enrichment analysis of DMGs (those were annotated to different
pathways) in the H vs L group
Integrated analysis of DMGs and DEGs
To further elucidate the relationship between DNA methylation and gene
expression during late growth and development in Duroc pigs, we
conducted a comprehensive analysis of the DNA methylome and
transcriptome of the LDM in our laboratory [[83]17]. A Venn diagram
analysis of the DEGs and DMGs in the H vs L comparison group revealed
316 overlapping genes (Fig. [84]7A, Supplementary Table 4). Among the
316 overlapping genes, the DMRs of 18, 294, and 22 genes were located
upstream (Fig. [85]7B), in the gene body (Fig. [86]7C) and downstream
(Fig. [87]7D), respectively. For example, LPAR1 in the H group showed
hypomethylation in upstream and gene-body regions compared with that in
the L group. MEF2C in demonstrated greater expression and
hypermethylation in the gene-body region in the H group than in the L
group. Additionally, SLIT3 expression was significantly downregulated,
and the methylation levels of DMRs located in the downstream region
were significantly increased.
Fig. 7.
[88]Fig. 7
[89]Open in a new tab
Venn diagram analysis of DMGs and DEGs. A The number of overlapping
DEGs in the DNA methylome and transcriptome. B The number of
overlapping DEGs upstream. C The number of overlapping DEGs in the gene
body. D The number of overlapping DEGs downstream. All DEGs were
determined based on statistical significance at < 0.05
Validation of DNA methylomic and transcriptomic data
We conducted BSP (bisulfite sequencing PCR) and qRT–PCR (quantitative
real-time PCR) analyses of LPAR1 and MEF2C to validate the findings
from the DNA methylome and transcriptome analyses. The BSP and DNA
methylome analyses confirmed that the DNA methylation level of LPAR1 in
the gene promoter was significantly lower in the H group than in the L
group (Figs. [90]8A and [91]9A). Additionally, qRT–PCR analysis
indicated that the expression level of LPAR1 was significantly greater
in the H group than in the L group (P < 0.05) (Fig. [92]9B). DNA
methylome analysis revealed that the DNA methylation level of MEF2C in
the gene-body region was significantly greater in the H group than in
the L group (Fig. [93]8B). Additionally, the BSP results indicated that
the DNA methylation level of MEF2C in the gene-body region was slightly
greater in the H group than in the L group (Fig. [94]10A). Moreover,
qRT–PCR analysis demonstrated that the expression level of MEF2C was
significantly greater in the H group than in the L group (P <
0.05)(Fig. [95]10B).
Fig. 8.
[96]Fig. 8
[97]Open in a new tab
The methylation levels of LPAR1 and MEF2C in the gene region with 3 kb
flanking regions of the two groups. The heights of the bars represent
the methylation percentages for the H (red) and L (blue) groups. The
green and red boxes indicate significant hypomethylation and
hypermethylation, respectively (P < 0.05). The X-axes indicate the
position on the scaffolds (middle). The gene structures are shown on
the bottom, with the closed boxes representing exons
Fig. 9.
[98]Fig. 9
[99]Open in a new tab
A Upstream methylation pattern of LPAR1 in the LDMs of Duroc pigs. Open
and filled circles denote unmethylated or methylated positions,
respectively. B The relative expression level of LPAR1 in the LDMs of
Duroc pigs determined via qRT–PCR. The data are shown as the mean ±
S.E. (n = 4). *, P < 0.05
Fig. 10.
[100]Fig. 10
[101]Open in a new tab
A Gene-body methylation pattern of MEF2C in the LDMs of Duroc pigs.
Open and filled circles denote unmethylated or methylated positions,
respectively. B The relative expression level of MEF2C in the LDMs of
Duroc pigs determined by qRT–PCR. The data are shown as the mean ± S.E.
(n = 4). *, P < 0.05
Discussion
ADG is considered a growth trait of pigs and an essential indicator in
the process of pig production that directly affects the economic
efficiency of farmers. Our laboratory previously reported the
identification and functional prediction of circular RNAs, long
noncoding RNAs, and mRNAs related to growth traits and skeletal muscle
development in Duroc pigs with different ADGs [[102]18]. In this study,
we applied WGBS technology for methylation analysis of Duroc pigs at
various growth rates to identify significant differential DMR-related
genes.
The WGBS method was first proposed in 1992 [[103]19]. This method is
now widely used in DNA methylation studies in pigs because it is a high
throughput, high specificity, high sensitivity, high resolution, and
high coverage technique. Corbett RJ et al. used WGBS, RNA-seq, and
smRNA-seq methods to identify DMRs at the stage of porcine foetal
myogenesis and to validate their relationship with differentially
expressed mRNAs and miRNAs [[104]20]. In addition, a study mapped the
DNA methylome in the developing pig testis via WGBS [[105]21].
To investigate the influence of DMRs on the growth rate of Duroc pigs,
we conducted methylation profiling of the LDMs and identified 9,445
DMRs. Some of these DMRs were located within genes associated with
muscular development, such as MEF2C, DMD, and TGFB2 [[106]22–[107]24].
These findings are consistent with previous studies that have
investigated DNA methylation patterns in different pig breeds and at
various developmental stages. For example, Li XJ et al. compared DNA
methylation profiles between Wannanhua and Yorkshire pigs and
identified 58283 DMRs, including 1425 DMGs that may play a role in
muscle development [[108]15]. Similarly, Wang et al. discovered 722
DMRs and 466 DMGs, including ADCY1, AGBL4, EXOC2, FUBP3, PAPPA2,
PIK3R1, MGMT, and MYH8, which are associated with muscle growth in
muscle tissues of Chenghua pigs and Yorkshire pigs, from a total of
2,416,211 CpG sites [[109]13]. Furthermore, Yang et al. generated a
single-base resolution DNA methylome map of porcine skeletal muscle
across 27 developmental stages using WGBS and identified more than
40,000 developmentally differentially methylated CpGs associated with
muscle development genes [[110]25]. The results from our study and
those of previous studies all suggest that DNA methylation plays an
important role in muscle growth and development in pigs.
Furthermore, we identified pathways associated with muscle growth and
development, providing a valuable theoretical basis for further
research. For example, 53 DMGs were significantly enriched in the
calcium signalling pathway, 53 DMGs were increased dramatically in the
MAPK signalling pathway, and 62 DMGs were substantially enriched in the
PI3K-AKT signalling pathway. Interestingly, the MAPK and PI3K-AKT
signalling pathways were also identified in a recent study of global
DNA methylation in porcine skeletal muscle [[111]26]. The PI3K-AKT and
MAPK signalling pathways play important roles in muscle development,
influencing the proliferation and differentiation of muscle cells
[[112]27, [113]28]. Calcium is an essential intracellular transduction
signal involved in various biological functions, including myogenesis
[[114]29], muscle contraction [[115]30], and muscular dystrophy
development [[116]31]. Overall, DNA methylation has the potential to
influence the growth rate and muscle production of pigs through the
pathways mentioned above.
In the present study, the BSP and qRT–PCR results showed that the H
group had significantly decreased DNA methylation levels of LPAR1 in
the promoter region and upregulated expression of this gene. Messmer T
et al. reported that LPAR1, IGF1R, and TFRC receptor expression was
upregulated in the early stages of differentiation and that
supplementation with the appropriate ligands effectively induced
differentiation [[117]32]. Stimulation of the sphingosine kinase and
sphingosine 1-phosphate pathways by LPAR1 significantly increased the
migration of skeletal muscle cells [[118]33]. A study conducted by Ray
R et al. revealed that suppressing the LPAR1 gene in myogenic cells
significantly inhibited the cellular differentiation process [[119]34].
Interestingly, LPAR1 was also enriched in the PI3K-AKT signalling
pathway. These findings suggest that LPAR1 plays a crucial role in
regulating the growth and development of muscles in Duroc pigs. SLIT3
in the H group exhibited hypomethylation in the downstream region and
upregulated expression. Mice deficient in SLIT3 were reported to
exhibit reduced skeletal muscle mass, muscle strength, and physical
activity [[120]35]. Luan M et al. discovered through WGAS that SLIT3
may have an effect on loin muscle area [[121]36]. Upregulation of SLIT3
during the later growth stages of Duroc pigs may affect muscle
development. The hypermethylation of MEF2C in the gene-body region and
upregulated expression of this gene were found in the H group. MEF2C is
a member of the myocyte enhancer factor 2 family and plays a role in
myogenesis [[122]37–[123]39]. In recent years, an increasing number of
comprehensive and systematic analyses of the role of MEF2C in
myogenesis and muscle regeneration have been conducted. For example,
Piasecka A. et al. reported that MEF2C is an essential factor
regulating the quantity and quality of the microtranscriptome.
Specifically, deleting MEF2C led to the downregulation of specific
muscle-specific miRNAs during muscle cell differentiation [[124]40].
Loumaye A et al. reported that MEF2C was able to maintain the slow
expression and protein content of the myosin heavy chain beta(MyHC-
[MATH: β :MATH]
) subtype in differentiated myotubes [[125]41]. Moreover, Kim HB et al.
(2020) reported that O-GlcNAcylation of MEF2C was necessary for
regulation of myoblast differentiation [[126]42]. Interestingly, MEF2C
was also enriched in the MAPK signalling pathway. Overall, MEF2C might
also be a master regulator in Duroc pigs with different growth rates.
Thus, our study provides data support and new research ideas for
exploring genes related to skeletal muscle growth and development in
Duroc pigs.
Conclusion
In conclusion, Our study demonstrates that the methylation status
affects the growth rate of pigs and the expression level of genes
related to muscle growth and development.
Materials and methods
Animals
All animal care and treatment procedures were approved by Ethics
Committee of Shandong Agricultural University, China, and performed
according to the Committee’s guidelines and regulations (Approval No.:
2004006). Duroc pigs came from a core breeding farm (our study has
taken the informed consent of the animal owner), with the measurement
data in the pig herd to 30 to 110 kg body weight (individuals in the
top 30% of ADG), and the performance measurement was continued to about
130 kg body weight. According to the ADG (Supplementary Table 5), eight
pigs were selected and divided into two groups: the H (774.89 g) group
and the L (658.77 g) group, and each pair of high and low groups were
half-siblings. All pigs were humanely slaughtered by electronic
stunning followed by exsanguinations at the local abattoir. The LDM
tissues were sampled and snap-frozen in liquid nitrogen, and stored at
-80 ^∘C for later use.
DNA isolation, BS-seq library construction, and sequencing
High-quality genomic DNA was extracted from the LDM using DNeasy Blood
& Tissue Kits (QIAGEN, CA, USA). DNA concentration and integrity were c
Agarose Gel Electrophoresis, respectively. Then, the DNA libraries for
bisulfite sequencing were prepared. Briefly, genomic DNAs were
fragmented into 100-300 bp by Sonication (Covaris, Massachusetts, USA)
and purified with a MiniElute PCR Purification Kit (QIAGEN, MD, USA).
The fragmented DNAs were end-repaired, and a single “A” nucleotide was
added to the three ^′ ends of the blunt fragments. Then, the genomic
fragments were ligated to methylated sequencing adapters. Chips with
adapters were bisulfite converted using a Methylation-Gold kit (ZYMO,
CA, USA), and unmethylated cytosine was converted to uracil during
sodium bisulfite treatment. Finally, the altered DNA fragments were PCR
amplified and sequenced using Illumina HiSeq™ 2500 by Gene Denovo
Biotechnology Co. Ltd. (Guangzhou, China).
BS-seq reads mapping and methylation level analysis
To get high-quality clean reads, raw reads were filtered according to
the following rules: 1) removing reads containing more than 10% of
unknown nucleotides (N); 2) removing low-quality reads containing more
than 40% of low-quality (Q-value
[MATH: ≤20 :MATH]
) bases.
By default, the obtained clean reads were mapped to the species
reference genome Sus scrofa v. 11.1 using BSMAP software [[127]43]
(version: 2.90). Then a custom Perl script was used to call methylated
cytosines and the methylated cytosines were tested with the correction
algorithm described by [[128]44]. The methylation level was calculated
based on methylated cytosine percentage in the whole genome, in each
chromosome, and in different regions for each sequence context (CG,
CHG, and CHH). Additionally, the methylation profile at flanking 3-kb
regions and the gene-body (or transposable elements) was plotted
according to the average methylation levels of each 200-bp interval to
evaluate different methylation patterns in other genomic regions.
Differentially methylated regions (DMRs) and functional enrichment
analysis
To identify differentially methylated regions (DMRs) between two
samples(Methyl Kit (V1.4.10)), the minimum read coverage to call a
methylation status for a base was set to 3 DMRs for each sequence
context (CG, CHG, and CHH) according to different criteria: 1) For CG,
numbers of CG in each window
[MATH: ≥5 :MATH]
, the absolute value of the difference in methylation ratio
[MATH: ≥0.15 :MATH]
, and P
[MATH: ≤0.05 :MATH]
; 2) For CHG, numbers in a window
[MATH: ≥5 :MATH]
, the absolute value of the difference in methylation ratio
[MATH: ≥0.25 :MATH]
, and Q
[MATH: ≤0.05 :MATH]
; 3) For CHH, numbers in a window
[MATH: ≥15 :MATH]
, absolute value of the difference in methylation ratio
[MATH: ≥0.15 :MATH]
, and Q
[MATH: ≤0.05 :MATH]
; 4) For all C, numbers in a window
[MATH: ≥20 :MATH]
, absolute value of the difference in methylation ratio
[MATH: ≥0.2 :MATH]
, and Q
[MATH: ≤0.05 :MATH]
. To analyze the functional enrichment of genes affected by DMRs, gene
ontology (GO) enrichment analysis ([129]http://www.geneontology.org/)
and KEGG pathway enrichment analysis ([130]http://www.kegg.jp/kegg/)
were conducted for DMGs by the hypergeometric test with a corrected
p-value
[MATH: ≤0.05 :MATH]
.
Bisulfite sequencing PCR
Methprimer-designed BSP primers, which are mentioned in Table [131]2.
Using a BisulFlashTM DNA Modification Kit (Epigentek, Farmingdale,
USA), the bisulfite conversion of isolated LDM genomic DNA was shown.
The targeted portion was amplified by PCR using TaKaRa EpiTaq™ HS
(TaKaRa, Japan). The PCR products were cloned into the pMD18-T vector
(TaKaRa, Japan) and transformed into Escherichia coli DH5
[MATH: α :MATH]
competent cells (TaKaRa, Japan). Thirty positive clones were sequenced
for each group. Site-specific methylation measurements were analyzed
using QUMA-Analyzer.
Table 2.
Primers used in the study
Gene Primer sequence Purpose Product size(bp)
Lpar1-BSP-F 5’-GAATATTTTTTGAGAAGTTTGAGAAGTTT-3’ Identification of 209
Lpar1-BSP-R 5’-CCATAACAACAATATAACAAAAAAATTAAC-3’ methylation level
Mef2c-BSP-F 5’-TATGTGTGTTTTATAGTATTATTTTTTGTTTT-3’ Identification of
358
Mef2c-BSP-R 5’-ATCTCCTAATAAACTTAAATTTTACAAAATTA-3’ methylation leveL
Lpar1-F 5’-GGAAAGTACCTTGCCACAGAA-3’ qRT-PCR 129
Lpar1-R 5’-GAAGCGGCGGTTGACATA-3’
Mef2c-F 5’-GAGCGTGCTGTGTGACTGTGAG-3’ qRT-PCR 82
Mef2c-R 5’-CATGTCCGTGCTGGCATACTGG-3’
Gapdh-F 5’-AAAGGCCATCACCATCTTCC-3’ qRT-PCR 135
Gapdh-R 5’-GCCCCACCCTTCAAGTGAGCC-3’
[132]Open in a new tab
RNA extraction and qRT-PCR
High quality RNA was extracted from the LDMs using a RNA extraction kit
(Tiangen, China). First-strand cDNA was synthesized using the
PrimeScript RT reagent Kit (TaKaRa, Japan). The primers for qPCR were
designed using Primer Premier 6.0 and were listed in Table [133]2. The
qRT-PCR assays were performed in a
[MATH: 20μ :MATH]
L reaction volume on a Roche LightCycler® 96 with TB Green as the
fluorescent dye according to the manufacturer’s instructions (TaKaRa,
Japan). After normalization with GAPDH,relative RNA levels in samples
were calculated by the comparative threshold cycle (Ct) method.
Integrative analysis of transcriptome and WGBS data
To investigate the relationship between DNA methylation and gene
expression (the same batch of Duroc pigs was used for RNA-Seq and
WGBS), all DMGs were divided into three groups based on DMR location
(upstream, gene body, and downstream), and a Venn diagram was used to
visually demonstrate the overlap of genes between DMGs and DEGs.
Statistical analysis
The qPCR data were analyzed by the one-way ANOVA model followed by
Tukey’s multiple range tests to separate the means using the SAS
computer program for Windows (version 9.2). Data were presented as
means ± SDs, and the statistical significance was set at P < 0.05.
Supplementary Information
[134]Supplementary Material 1.^ (851.5KB, zip)
Acknowledgements