ORIGINAL ARTICLE

Comparative transcriptomic analysis of skeletal muscle tissueduring prenatal stages in Tongcheng and Yorkshire pigusing RNA-seq

Huijing Liu1& Yu Xi1 & Guorong Liu1

& Yuqiang Zhao1& Ji Li1 & Minggang Lei1

Received: 25 April 2017 /Revised: 13 November 2017 /Accepted: 13 December 2017# Springer-Verlag GmbH Germany, part of Springer Nature 2018

AbstractMyogenesis is accompanied by a number of changes in gene expression in mammals, and the transcriptional events that underlie theseprocesses have not been yet fully elucidated. In this study, RNA-seq was used to comprehensively compare the transcription profiles ofskeletal muscle between Tongcheng (TC) and Yorkshire (YK) pigs at 40, 55, 63, 70, and 90 days of gestation. One thousand threehundred seventeen and 691 differentially expressed genes (DEGs) were detected in TC andYK, respectively, among which 321 DEGswere shown to be common in TC and YK. STEM (Time-series ExpressionMiner) analysis revealed different gene expression profilesbetween the two breeds. One thousand six hundred seventy-seven genes showed significant differential expression between TC andYKat the identical stages, while three geneswere found to be common in all comparisons. A total of 3185 new putative transcriptswerealso predicted. Several gene expression profiles were further validated by qRT-PCR. Fifty-five dpc (days post coitum) was suggested tobe the key stage to contribute developmental differences between TC and YK. PTEN, EP300, ENSSSCG00000004979 (Myosin 9A),CDK14, IRS1, PPP1CC, and some ribosomal proteins were suggested to be the key candidate genes for elucidating the developmentaldifferences between the two breeds. In conclusion, we constructed comprehensive high-resolution gene expression maps of these twopig breeds, which not only provides an in-depth understanding of the dynamics of transcriptional regulation during myogenesis in thisstudy, but also would facilitate the elucidation of molecular mechanisms underlying myogenesis in the future studies.

Keywords Tongcheng andYorkshire pigs . Skeletal muscle . Transcriptome . RNA-seq . qRT-PCR

Introduction

In livestock, skeletal muscle is of major economic importancefor meat production. Understanding the complex mechanismunderlying skeletal muscle development is critical to geneticimprovement for higher lean meat percentage and better meatquality (Xu et al. 2009). Western pig breeds and indigenousChinese pig breeds have distinctly different characteristics inmeat production (Tang et al. 2007; Zhao et al. 2011; Zhaoet al. 2015b). As a typical western breed, Yorkshire (YK)pig has high lean meat percentage, fast muscle growth, andhigh body weight; while as a typical indigenous Chinesebreed, Tongcheng (TC) pig has been proved to be superiorin terms of perceived meat quality, but not in growth rateand lean meat content (Fan et al. 2006). Hence, understandingthe differences in growth and development of porcine skeletalmuscle between the two breeds will be beneficial for porcinegenetic improvement, which also contributes to the under-standing of human muscle regeneration and muscular atrophy

Electronic supplementary material The online version of this article(https://doi.org/10.1007/s10142-017-0584-6) contains supplementarymaterial, which is available to authorized users.

* Minggang Leileimg@mail.hzau.edu.cn

Huijing Liusuemorley@126.com

Yu Xixiyu1988@163.com

Guorong Liuliuguorong_gy@126.com

Yuqiang Zhaoyuqiangzhao@hotmail.com

Ji Liwatterfall@126.com

1 Key Laboratory of Swine Genetics and Breeding of AgriculturalMinistry, and Key Laboratory of Agricultural Animal Genetics,Breeding and Reproduction of Ministry of Education, College ofAnimal Science and Technology, Huazhong Agricultural University,Wuhan, People’s Republic of China

Functional & Integrative Genomicshttps://doi.org/10.1007/s10142-017-0584-6

due to the anatomical, physiological, pathological, and geno-mic similarities between pig and human (Lunney 2007).

Muscle fibers are the major component of muscle, whosenumber and size largely determine the muscle mass in pig. Asthe total number of muscle fibers is fixed before birth (Tanget al. 2007; Wigmore and Stickland 1983), prenatal skeletalmuscle development, namely myogenesis, is an important de-terminant of both muscle growth and meat quality (Tang et al.2007). Besides, it is an ideal model to study cell determinationand differentiation and become an extensively studied processin model animals (Perry and Rudnick 2000). Myogenesis is acomplex process that takes place in two distinct waves: theprimary and secondary waves of muscle fiber formation inpig, which occur from day 30 to 60 and day 54 to 90 ofgestation, respectively (Wigmore and Stickland 1983). Tocomprehensively investigate the mRNA expression levels inthe two waves, we collected skeletal muscle at five develop-mental stages (40, 55, 63, 70, 90 days of gestation) and per-formed transcriptome analysis using RNA-seq.

Several studies at mRNA level have been performed infetal and adult pigs, with the aims to investigate the genes thatinfluence the growth and biochemical properties of muscleand to analyze the expression of the genes that determinespecific fiber types in different porcine breeds (Cagnazzoet al. 2006; Davoli et al. 2011; Sun et al. 2017; Tang et al.2015; Zhao et al. 2011). In our previous work, five develop-mental stages (40, 55, 63, 70, 90 days of gestation) wereinvestigated to be the key stages in myogenesis between TCandYK pig breeds using digital gene expression (DGE) (Zhaoet al. 2015a, b). Here, we presented a further comprehensivestudy of the transcriptome at the key stages in myogenesis byRNA-seq. Hundreds of DEGs were reported to be related tomyogenesis. Also, thousands of novel splicing events werediscovered both in annotated genes and some unannotatedgenes, which are potentially novel transcripts. Hence, our re-sults provide valuable information for understanding of thedynamics of transcriptional regulation during myogenesisand the molecular mechanisms underlying myogenesis inthe future studies.

Materials and methods

Ethics statement

All experimental animal procedures were performed by theapproved protocols of Hubei Province, PR China, for theBiological Studies Animal Care and Use Committee.

Preparation of experimental animals and tissues

Fifteen YK and 15 TC purebred sows were mated with theboars from the same purebred breeds. For each breed in

prenatal periods, three sows were slaughtered at five timepoint, which were 40, 55, 63, 70, and 90 dpc (days postcoitum). For sows slaughtered at 40 dpc, longissimus dorsimuscle tissues were dissected from the embryos, while thesame tissues were dissected from female fetuses from sowsat 55, 63, 70, and 90 dpc. These 30 samples were snap-frozenin liquid nitrogen and stored until further use.

RNA extraction, library construction, and RNAsequencing

Total RNA was extracted from the 30 samples using TRIzolreagent (Invitrogen, CA, USA) according to the manufac-turer’s instructions. The RNA integrity and concentrationwere assessed using the Agilent 2100 Bioanalyzer (AgilentTechnologies, Palo Alto, CA, USA) to meet the experimentalrequirement of sequencing platform. For each time point,equal quantities of RNA isolated from three individual muscletissues were pooled. Libraries were prepared using RNA-seqsample preparation kit from Illumina and poly (A) mRNAwasenriched from total RNA using oligo (dT) beads. Finally, thelibraries were sequenced on Illumina HiSeq™ 2000 (BeijingGenomics Institute, Shenzhen) to produce 90 bp paired-endreads.

Mapping of the reads to reference genome

The Sus_Scrofa genome and reference genes weredownloaded respectively from (ftp://ftp.ensembl.org/pub/release-75/fasta/sus_scrofa). By discarding low-quality rawreads, trimming, sequencing adapters, and excluding the readswith more than 10% unknown nucleotides or low-quality se-quence (more than half of the base qualities less than 10),high-quality reads (or clean reads) were obtained. Then, theclean reads were aligned to the rRNA sequence by bowtie(Langmead et al. 2009) with no more than 2 bp mismatches.The reads aligned to rRNAwere depleted. The filtered readswere mapped to the Sus_Scrofa genome, and gene sequenceswere annotated using TopHat2 (Kim et al. 2013). Unmappedor multi-position matched reads were excluded from furtheranalyses. The proportions of high-quality reads mapped to thegenome and to genes provided an overall assessment of thesequencing quality.

Transcript assembly and gene expression analysis

Cufflinks software (version 1.30) (Trapnell et al. 2010) wasused to assemble the individual transcripts from RNA-seqreads which had been aligned to the genome of Sus_Scrofawith Tophat2. Gene expression level was calculated usingFPKM (fragments per kilobase of transcript per millionmapped fragments) in Cufflinks. Due to the low reliabilityof the obtained reads for assembly purposes, Cufflinks was

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used to filter low-abundance transcripts using the default pa-rameter. Cuffmerge, a component of Cufflinks, was used tomerge the transcripts of several samples. Cuffdiff, a packageof Cufflinks, was used to identify the differentially expressedgenes among samples (p value ≤ 0.05). Fold changes of geneexpression between two samples were calculated with log 2FPKM (log 2 ratio). We used Bq ≤ 0.05 and the absolute valueof log 2Ratio ≥1^ as the threshold to judge significance dif-ferences in gene expression.

Cluster 3.0 and TreeView software were used to analyzethe systematic clustering of 10 libraries.

Time-series analysis

To cluster and visualize possible profiles of DEGs over time,STEM clustering method was used (Ernst and Bar-Joseph2006). This method assumes the values of gene expressionrepresent log ratios relative to the expression at 40 dpc. TheDEGs with similar expression trends can be grouped togetherto form a cluster of profiles as determined by the correlationcoefficient. Then, the approach selects a set of predeterminedtemporal model profiles and determines the statistical signifi-cance of the number of genes assigned to each profile com-pared to the number of genes expected based on chance.Significance is determined by permutation test (Ghandhiet al. 2011; Zhan et al. 2017).

Gene Ontology and Pathway analysis

GO (Gene Ontology) and Pathway enrichment analysis withfeatures corresponding to DEGs in each significant expressionprofile was performed using KOBAS (KEGG OrthologyBased Annotation System) (http://kobas.cbi.pku.edu.cn/index.php), which was based on the Kyoto Encyclopedia ofGenes and Genomes (KEGG) database. GO terms andPathways with statistical significance values (corrected p < 0.05) were selected.

Identification of novel transcript units

In consideration of Sus scrofa genome may be incomplete,novel transcript units (TUs) might be predicted used cufflinkssoftware (Trapnell et al. 2010) with ours sequencing data. Inthis study, the TUs found in intergenic regions with sequencelength ≥ 200 bp and exon number ≥ 2 were considered asputative novel TUs.

Alternative splicing analysis

Alternative splicing (AS) events were identified byASprofile software (Florea et al. 2013). Twelve types ofalternative splicing events were identified by Asprofile(http://ccb.jhu.edu/software/ASprofile).

Integration of protein-protein interaction networkand module analysis

Search Tool for the Retrieval of Interacting Genes (STRING)database is an online tool designed to evaluate protein-proteininteraction (PPI) information (Nesteruk et al. 2014;Szklarczyk et al. 2011). STRING (version 10.0) (http://version10.string-db.org/) covers 9,643,763 proteins from2031 organisms. To evaluate the interactive relationshipsamong DEGs, the DEGs were uploaded into STRINGdatabase, and the following active prediction methods wereemployed: neighborhood, co-expression, gene fusion,experiments, co-occurrence, database, and text mining, witha medium confidence score (0.400). Then, PPI networks werevisualized by Cytoscape software, and the topologicalproperty of the networks were evaluated by it. Degree, asone important topological property, is defined as the numberof links to node gene. And it is used for defining the hub genesof network.

Validation of genes by qRT-PCR

Total RNA was extracted from frozen muscle tissues usingTRIzol reagent (Invitrogen) according to the manufacturer’sprotocols. Isolated RNAwas treated with RNase-free DNase Ifor 30 min at 37 °C to remove residual DNA. cDNA wasprepared from the Revert Aid™ First Strand cDNASynthesis Kit (Thermo Fisher Scientific Inc., USA) followingthe manufacturer’s protocol. qRT-PCR was used to identifythe expression patterns of selected genes at different stagesbetween the two pig breeds. To amplify specific fragmentscorresponding to the selected genes, specific primers weredesigned with NCBI Primer-BLAST. DRAP1 was used as aninternal control to normalize the expression level of the targetgenes (Wang et al. 2015). qRT-PCR was performed in thereaction volume of 10 μl containing 5 μl Real Master MixSYBR, 2 μl ddH2O, 1 μl of each forward and reverse primers,and 1 μl of cDNA. All samples were amplified in triplicateand the mean and standard error values were calculated.Relative expressions of all genes were calculated by ΔΔCTmethod.

Results

Overview of the Sus_Scrofa transcriptome

A total of 755,658,102 reads were obtained from the 10 librar-ies with an average of 75,565,810 reads in each sample (thenumbers of reads ranging from 72,867,312 to 78,200,734)(Table 1). Of the total clean reads, 80.29% (606,699,262reads) from all the 10 samples were aligned to the genomeof Sus scrofa (Sscrofa10.2.75), and 78.43% were perfectly

Funct Integr Genomics

mapped. The total mapped reads represented about 20-foldcoverage of the genome. Approximately 19.71% of the totalreads could not bemapped to the genome, indicating that theremight be gaps or sequencing errors in the current assembly, oralternative splicing in the reference genome. Furthermore, thedistribution of mapped reads showed that 64.27–76.92% ofthe annotated genes had 80–100% coverage (ESM Fig. 1).

To examine the correlation of gene expression patternsamong the 10 samples, systematic cluster analysis was usedto assess the similarities between the 10 libraries of TC andYK pigs. The heat map showed that the gene expression pat-terns could be classified into distinct groups (Fig. 1). Thiscorrelation analysis revealed that stages YK70 and TC63,YK63 and TC70 shared similar expression patterns, whichwere clustered with TC55 in the larger clade, while the ex-pression patterns of TC40 and YK40 were similar, which wereclustered together with YK55 in another clade.

Development-dependent DEGs

To evaluate the expression of development-dependent DEGsduringmyogenesis, comparisons of gene expression were per-formed within each breed between five time points sequen-tially (40 dpc vs 55 dpc, 55 dpc vs 63 dpc, 63 dpc vs 70 dpc,and 70 dpc vs 90 dpc). Overall, we identified 1317 DEGs inTC and 691 in YK during development in at least one of thecomparisons. Among these DEGs, 321 were common in TCand YK (Fig. 2a). DEGs in each cluster are shown in Fig. 2b,c. Specially, the gene expression level in TC55 changed acute-ly when compared to TC40 or TC63, much more genes hadlower expression in TC55. One thousand three hundred sev-enteen DEGs in TC and 691 in YK were respectively

clustered into 40 candidate profiles based on their gene ex-pression pattern. Profiles 0, 39, 20, and 21 were found to besignificant in both TC and YK. Profiles 10, 11, 13, and 25were only significant in TC, whereas profiles 14 and 15 wereonly significant in YK (Fig. 3).

GO and KEGG analysis were performed to further under-stand the biological functions of the genes within the signifi-cant gene expression profiles. The results showed that theDEGs related to muscle development were significantlyenriched in profile 39 of both TC and YK, such GO termsincluded striated muscle contraction, myofibril assembly, stri-ated muscle cell differentiation, muscle cell development, andcytoskeletal protein binding (ESMTable 1). Additionally, sev-eral fundamental biological processes were found to be nota-bly enriched in profile 0 of both TC and YK, such as cellcycle, cell cycle process, mitotic cell cycle, nuclear division,and organelle fission (ESM Table 2).

Profile 10, 11, and 13 were only significant in TC, and theexpressions of DEGs in these profiles at 55 dpc were lowerthan those at 40 and 63 dpc; thus, the three profiles wereregarded as a big cluster. The primary enriched pathway ofthese DEGs was oxidative phosphorylation, and the expres-sion of almost all the DEGs involved in this pathway hadhigher level in TC than that of in YK. (ESM Table 3).Profiles 14 and 15 were the distinct significant profiles ofYK, the DEGs in the two profiles were downregulated from55 dpc to 63 dpc and had the lowest expression at 70 dpc, sothe two profiles were clustered together as a big cluster. Axonguidance pathway was the first enriched pathway (ESMTable 3). There were five DEGs, MSTN (myostatin), GHR(growth hormone receptor), SNED1 (sushi, nidogen, andEGF-like domains 1), TRIL (TLR4 interactor with leucine

Table 1 Statistics for filtering and mapping reads

Sample Total readsa Totally mappedb Uniquely mappedc Multiplely mappedd Unmappede

TC40 76,970,390 61,452,015 (79.84%) 60,155,315 (78.15%) 1,296,700 (1.69%) 15,518,375 (20.16%)

TC55 72,867,312 58,654,359 (80.49%) 57,530,085 (78.95%) 1,124,274 (1.54%) 14,212,953 (19.51%)

TC63 78,200,734 61,986,402 (79.27%) 60,627,306 (77.53%) 1,359,096 (1.74%) 16,214,332 (20.73%)

TC70 74,975,128 58,861,774 (78.51%) 57,727,758 (77.00%) 1,134,016 (1.51%) 16,113,354 (21.49%)

TC90 77,523,858 61,184,839 (78.92%) 59,559,099 (76.83%) 1,625,740 (2.09%) 16,339,019 (21.08%)

YK40 73,985,926 60,511,252 (81.79%) 58,972,818 (79.71%) 1,538,434 (2.08%) 13,474,674 (18.21%)

YK55 74,834,330 61,692,069 (82.44%) 60,013,347 (80.20%) 1,678,722 (2.24%) 13,142,261 (17.56%)

YK63 74,300,204 59,866,397 (80.57%) 58,335,941 (78.51%) 1,530,456 (2.06%) 14,433,807 (19.43%)

YK70 75,462,194 61,044,881 (80.89%) 59,644,439 (79.04%) 1,400,442 (1.85%) 14,417,313 (19.11%)

YK90 76,538,026 61,445,274 (80.28%) 60,058,708 (78.47%) 1,386,566 (1.81%) 15,092,752 (19.72%)

Total 755,658,102 606,699,262 (80.29%) 592,624,816 (78.43%) 14,074,446 (1.86%) 148,958,840 (19.71%)

a Total reads: total number of clean sequencing readsb Totally mapped: reads that completely aligned to the reference sequencec Uniquely mapped: reads aligned to only one positiondMultiple mapped: reads aligned to two or more positionse Reads not aligned to the reference sequence

Funct Integr Genomics

rich repeats), ENSSSCG00000024885, and XLOC_051137in both of these two big clusters.

Breed-specific DEGs

Even though myogenesis is similar among pig breeds, anumber of genes showed significantly different expres-sion between TC and YK. To evaluate the differential

gene expression between the two pig breeds duringmyogenesis, comparisons of gene expression at five timepoints between TC and YK were conducted. One thou-sand six hundred seventy-seven DEGs were detected be-tween TC and YK, most of which were found at 55 dpc.More notably, at 70 dpc, the lowest number of DEGs wasfound. YK-enriched genes were outnumbered by TC-enriched genes at all ages (Fig. 4a, b). These DEGs were

Fig. 1 Similarity of transcriptomeprofiles between 10 libraries.Systematic cluster analysis of 10samples based on the expressionlevels 17,683 genes by Cluster 3

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significantly enriched in pathways including focal adhe-sion, ECM-receptor interaction, Huntington’s disease,Alzheimer’s disease, non-alcoholic fatty liver disease(NAFLD), oxidative phosphorylation, Parkinson’s dis-ease, and Malaria. DEGs involved in oxidative phosphory-lation, particularly NADH dehydrogenases, were differential-ly expressed between the two breeds. The expression levels ofNDUFS7, NDUFA3, NDUFA4, NDUFAB1, NDUFB1,NDUFB2, and NDUFB11 were higher in TC, whereasNDUFB5 showed higher expression in YK (ESM Table 4).

We constructed PPI networks with the DEGs between TCand YK to explore the key candidate genes underlying thebreed differences on muscle development. Nine hundred tenDEGs with available PPI data from the STRING databasewere imported into Cytoscape software, then 64 core proteinsof degree ≥ 20 were used for constructing PPI networks. Thetop six hub nodes with higher degrees were screened, whichwere C-JUN (Jun proto-oncogene, AP-1 transcription factorsubunit), ACACB (acetyl-CoA carboxylase beta),ENSSSCG00000017694 (ACACA, acetyl-CoA carboxylase

Fig. 3 STEM clustering on DEGs during skeletal muscle developmentrespectively in TC and YK. Significant gene expression profiles resultingfrom c = 1 and m = 40 (c indicates maximum unit change in modelprofiles between time points, m indicates maximum number of model

profiles) are displayed as time course plots of log 2 gene expressionratios. The number of genes and p value are shown. a, b Eightsignificant gene expression profiles of DEGs of TC. c, d Six significantgene expression profiles of DEGs of YK

Fig. 2 Number of DEGs during skeletal muscle development. TC Tongcheng, YK Yorkshire. Stages: 40, 55, 63, 70, and 90 days post coitum (dpc). aVenndiagram showing the DEGs during TC and YK skeletal muscle development. The numbers of specific DEGs are 996 (TC) and 370 (YK). b TC, c YK

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alpha), PTEN (phosphatase and tensin homolog), MAPK9(mitogen-activated protein kinase 9), and EP300 (E1A bind-ing protein p300) (Fig. 5 and ESM Table 5).

We also constructed PPI networks with the stage-specific DEGs in 55 dpc between TC and YK. Three hun-dred forty-six DEGs with available PPI data from theSTRING database were imported into Cytoscape software,then 47 core proteins of degree ≥ 8 were used for construct-ing PPI networks. The top six hub genes were PTEN,EP300, ENSSSCG00000004979 (MYO9A, myosin IXA),CDK14 (cyclin-dependent kinase 14), IRS1 (insulin recep-tor substrate 1), and LOC733612 (PPP1CC, protein phos-phatase 1 catalytic subunit gamma). Fourteen DEGs thatupregulated in TC with available PPI data from theSTRING database were imported into Cytoscape software,then they were used to constructed PPI networks.

Ribosomal proteins ENSSSCG00000017065 (a memberof 39S ribosomal L22 mitochondrial precursor L22MT,MRP L22), ENSSSCG00000013051 (a member of 60Sribosomal L22, RPL22), ENSSSCG00000008170 (60S ri-bosomal protein L31, RPL31), RPL27, and RPS21 werethe predominant genes of the DEGs upregulated in TC(Fig. 6 and ESM Tables 6 and 7).

Identification of novel transcript units

Based on the alignment of the sequencing data to the pigreference genome, we obtained in total 3185 novel TUs with2–100 exons in the two pig breeds. The average length ofnovel TUs was 2915 bp, with a size range from 200 to47,278 bp, and the largest TU contained 2 exons (ESM

Fig. 5 The gene regulatoryinteraction networks with the 67core DEGs between TC and YK.Different nodes representeddifferent genes. The genes withhigher degrees had bigger size

Fig. 4 Number of DEGs between TC and YK. a The number of DEGs at each comparison in the identical period between TC and YK. b Three co-expressed DEGs among all the comparisons and the numbers of stage-specific DEGs were shown by Venn diagram

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Table 8). In addition, we foundmany repeated or incompletelyrepeated TUs between the two breeds.

Alternative splicing analysis

AS can increase regulatory complexity and control the develop-mental programs in higher organisms. The software Asprofileenabled the identification of 12 putative types of AS events,including skipped exons (SKIP), approximate SKIP (XSKIP),multi-exon SKIP (MSKIP), approximate MSKIP (XMSKIP),alternative 5′ first exon (TSS), alternative 3′ last exon (TTS),alternative exon ends (5′, 3′, or both) (AE), approximate AE(5′, 3′, or both) (XAE), intron retention (IR), approximate IR(XIR), multi-IR (MIR), and approximateMIR (XMIR). In about50.0% (8830) of the expressed genes, 9877–11,205 AS eventswere detected (Table 2). Pathway enrichment analysis with fea-tures corresponding to genes which occurred AS events wasperformed (ESM Table 9). Four primary types of AS events,namely TTS, TSS, SKIP, and AE, were common in the twopig breeds. Other types of AS events including sporadic, andXMIR and MIR were rarely observed. Furthermore, most ofthe AS genes produced two or more isoforms (ESM Table 10).Several genes had been identified with more than ten alternativetranscripts. Seventeen different isoforms were observed in all

developmental stages of both breeds for MUC1, a membrane-bound protein. Over 95.0% of the AS events were TSS and TTSin all stages, and the rest AS events accounted for less than 2.0%individually (Table 2), suggesting that TSS and TTS are themostcommon AS events in TC and YK.

qRT-PCR of skeletal muscle

qRT-PCR was employed to assess the expression of eightDEGs randomly selected to represent different magnitudesfrom the RNA sequencing study. The genes were calponin 3(CNN3), collagen type I alpha 2 chain (COL1A2), myosinlight chain 1 (MYL1), myosin light chain 3 (MYL3), troponinI2, fast skeletal type (TNNI2), troponin T3, fast skeletal type(TNNT3), myozenin 1 (MYOZ1), and phosphatidylethanol-amine binding protein 4 (PEBP4). The correlations betweengene expression values obtained with RNA-seq and those ob-tained with qRT-PCR were calculated. Although the expres-sion obtained by RNA-seq and the relative expression by qRT-PCRwere different in some instances, the expression trends ofthese genes were similar. The analysis indicated that RNA-seqand qRT-PCR data had high consistency and the correlationvalues ranged from 0.697 (TNNI2) to 0.939 (PEBP4 andMYL3) between the two methods (Fig. 7).

ENSSSCG00000027597ENSSSCG00000013437

HIF3AFOXO3

SP1

EP300

ENSSSCG00000000766

CDK14

POLR2A

ENSSSCG00000004979

PDE5

ENSSSCG00000017129

MYH9

PDZD2

NME3

ENSSSCG00000021289

NR3C1

RHOBTB3

ENSSSCG00000016725

SETD7

ENSSSCG00000023949

CENPF

LOC733612

HSPG2

CLASP2

ITGA4

SLIT2

LDB3

ENSSSCG00000016781

ENSSSCG00000008478

PFAS

ENSSSCG00000029441

ENSSSCG00000028878

ENSSSCG00000003350

CBL

PTEN

GABARAP

IRS1

DVL1

NCK2

PGF-Rb

ENSSSCG00000023204

ENSSSCG00000016364

ENSSSCG00000025146

ENSSSCG00000015052

ENSSSCG00000015555

ENSSSCG00000016752

FDPS

ENSSSCG00000029066

ENSSSCG00000013743

LMO2

ENSSSCG00000013051

RPL27

RPS21

ENSSSCG00000017065

NDUFA4

ENSSSCG00000001696

HSPE1

ENSSSCG00000008170

ENSSSCG00000020790

ENSSSCG00000011055

A B

C D

Fig. 6 The gene regulatory interaction networks with the DEGs at 55 dpcbetween TC and YK. a The gene regulatory interaction networks with the47 core DEGs at 55 dpc between TC and YK. b The expression patternsof the hub genes in the above A interaction networks. c The gene

regulatory interaction networks with the 14 core DEGs at 55 dpcbetween TC and YK upregulated in TC. d The expression patterns ofthe hub genes in the above C interaction networks

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Table2

Statisticsof

alternativesplicingevents

Splicingevent

TC40

TC55

TC63

TC70

TC90

YK40

YK55

YK63

YK70

YK90

Alternative5′firstexon(TSS

)5300

a(5224b)

5383

(5304)

4741

(4664)

5100

(5020)

4957

(4888)

4899

(4824)

4779

(4701)

5135

(5063)

5179

(5105)

4859

(4791)

Alternative3′lastexon

(TTS)

5299

(5224)

5381

(5304)

4742

(4664)

5091

(5020)

4959

(4888)

4899

(4824)

4778

(4701)

5138

(5063)

5186

(5105)

4865

(4791)

Alternativeexon

ends

(5′,3′,orboth)(A

E)

46(21)

45(20)

36(15)

36(16)

48(21)

51(21)

38(17)

47(21)

46(21)

48(21)

ApproximateAE(5′,3′,orboth)(X

AE)

12(5)

22(8)

14(5)

20(7)

26(9)

26(10)

22(9)

24(9)

32(12)

14(6)

Skippedexons(SKIP)

67(58)

79(71)

71(56)

76(64)

68(58)

78(61)

83(61)

79(60)

75(59)

73(58)

67(58)

79(71)

71(56)

76(64)

68(58)

78(61)

83(61)

79(60)

75(59)

73(58)

ApproximateSK

IP(X

SKIP)

38(30)

46(38)

36(28)

33(28)

43(35)

34(30)

29(25)

38(34)

32(25)

39(35)

38(30)

46(38)

36(28)

33(28)

43(35)

34(30)

29(25)

38(34)

32(25)

39(35)

Multi-exon

SKIP

(MSK

IP)

25(21)

21(20)

27(22)

29(22)

22(18)

30(22)

27(21)

29(24)

25(20)

24(20)

25(21)

21(20)

27(22)

29(22)

22(18)

30(22)

27(21)

29(24)

25(20)

24(20)

ApproximateMSK

IP(X

MSK

IP)

12(12)

11(11)

12(12)

9(9)

9(9)

13(13)

8(8)

11(11)

9(9)

10(10)

12(12)

11(11)

12(12)

9(9)

9(9)

13(13)

8(8)

11(11)

9(9)

10(10)

Intron

retention(IR)

8(8)

7(7)

4(4)

5(5)

4(4)

6(6)

4(4)

5(5)

5(5)

5(5)

8(8)

7(7)

4(4)

5(5)

4(4)

6(6)

4(4)

5(5)

5(5)

5(5)

ApproximateIR

(XIR)

21(12)

20(11)

19(10)

19(10)

18(9)

19(10)

8(6)

18(9)

20(11)

8(6)

21(12)

20(11)

19(10)

19(10)

18(9)

19(10)

8(6)

18(9)

20(11)

8(6)

Multi-IR

(MIR)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

2(2)

ApproximateMIR

(XMIR)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

1(1)

Total

11,005

(5224)

11,205

(5304)

9877

(4664)

10,595

(5020)

10,324

(4888)

10,241

(4824)

9941

(4701)

10,710

(5063)

10,781

(5105)

10,110

(4791)

aRepresentsnumberof

alternativesplicingeventsin

each

stage

bRepresentsnumberof

genesthatundergoalternativesplicingeventsin

each

stagein

parentheses

Funct Integr Genomics

Discussion

Transcriptome analysis is important for elucidating the molec-ular constituents of cells and tissues and interpreting the func-tional elements of the genome (Qiu et al. 2013). Because of itshigh throughput, accuracy, repeatability, and low signal-to-noise ratio, the Illumina high-throughput sequencing platformis widely used for genome and transcriptome analysis (Wanget al. 2009). RNA-seq provides a more sensitive platform formeasuring DEGs than traditional microarray hybridization (Liand Schroeder 2012; Wang et al. 2014). Several studies haveidentified the molecular mechanisms in porcine prenatal mus-cles using microarrays (Cagnazzo et al. 2006; Davoli et al.2011), differential display RT-PCR (Murani et al. 2007),LongSAGE (Tang et al. 2007), and DGE (Zhao et al. 2011).The first genome-wide analysis of chimeric RNAs, single nu-clear polymorphisms (SNPs), and allele-specific expression(ASE) in prenatal skeletal muscle in pigs were performed byRNA-seq (Yang et al. 2016). However, to our knowledge,very few studies have addressed the transcriptomic expressionprofiles of porcine prenatal muscle using RNA-seq. Here, wepresented a preliminary analysis of comparative transcriptomebetween TC and YK pigs by RNA-seq. By sampling at fivedistinct stages, we acquired a comprehensive understanding ofthe dynamic changes in the transcriptome during myogenesis.This efficient deep sequencing not only allows the analysis oftranscriptome characteristics but also improves gene annota-tion at single-nucleotide resolution to provide valuable andcomplementary data for further studies.

Analysis summary of sequencing data

We obtained over 760 million reads in total with at least 73million reads for each stage, and the total mapped reads rep-resented about 20-fold coverage of the genome. Three

thousand one hundred eighty-five novel TUs with two ormore exons were obtained in the two pig breeds. qRT-PCRresults indicated that the Solexa sequencing was reliable. Inthis study, a majority of the annotated genes (> 64%) in the piggenome database were covered by more than 80% of the se-quencing reads, demonstrating the sensitivity of RNA-seq intranscript discovery even for lowly expressed genes. Theseresults are consistent with those observed in the other mam-malian species with RNA-seq (Yu et al. 2014; Zhan et al.2017).

Alternative splicing events in skeletal muscledevelopmental process

Alternative splicing is considered to be a key factor that in-creases cellular and functional complexity in eukaryotes(Majewska et al. 2017; Pan et al. 2008; Sebastian et al.2013). TSS and TTS accounted for most of the AS events inboth TC and YK, which was consistent with previous work(Hao et al. 2016). There were 7875 genes in total and 5020genes in average for each stage that underwent AS events inTC, while it was 7651 genes in total and 4897 genes in aver-age in YK. Some of these genes were involved in pathwaysrelated muscle growth and development, such as oxidativephosphorylation (Kim et al. 2008), calcium signaling pathway(Ponsuksili et al. 2009; Te et al. 2007), and cell adhesionmolecules (CAMs) (Ma et al. 2011). However, there are fewfunctional studies about the AS events of these genes in pig.The identification of novel transcripts and AS events will con-tribute to a better understanding of the regulatory mechanismsduring the porcine skeletal muscle myogenesis. Further re-search will be needed to determine the detailed regulation ineach breed, and theses observed differences will be highlyuseful in future studies. MEF2D as a number of MEF2 family,plays important role in late myogenesis (Sebastian et al.

Fig. 7 Validation of RNA-seq data with qRT-PCR. The left vertical axisindicates the gene expression by RNA-seq (red lines), and the right ver-tical axis indicates the relative expression by qRT-PCR (blue lines). The

horizontal axis indicates the samples of TC and YK. The c value showsPearson’s correlation between the two methods

Funct Integr Genomics

2013). Interestingly, the Mef2d protein isoforms producedfrom mutually exclusive splicing of the alpha exons have re-cently been shown to have opposite effect on murine skeletalmuscle differentiation (Sebastian et al. 2013). Another MEF2family number, MEF2C, whose alternative splicing of the αexon regulates myogenesis in C2C12 (Zhang et al. 2015).MSTN, a negative regulator of muscle growth and develop-ment, its alternative splicing isoforms could negatively regu-late pro-myostatin processing in muscle cells and preventMSTN-mediated inhibition of myogenesis in avian species(Shin et al. 2015). These AS events were not detected in ourdata, which might be due to their different species.

Molecular regulation underlying the differencesin myogenesis between YK and TC

In our laboratory’s previous work (Zhao et al. 2015a, b), dif-ferences in muscle morphological changes were observed be-tween the two pig breeds. Firstly, primary muscle fiber startedearlier but progressed more slowly in TC than in YK, and thesecondary muscle fiber appeared at 55 dpc in YK, which wasearlier than in TC. Secondly, the number of primary musclefibers in TC was larger than that in YK; Cagnazzo et al. at-tributed the prolonged proliferation of myoblasts in LR todelayed muscle fiber differentiation, which results in in-creased primary muscle fibers (Cagnazzo et al. 2006); whilethe number of secondary muscle fibers was larger in YK thanin TC, which is probably due to the higher proliferation andthe later muscle fiber differentiation in TC than in YK assuggested by Zhao et al. (2011); besides, the total number ofmuscle fibers per unit area was bigger in TC. In the presentstudy, the heat map brought out that stage 55 dpc of TC wasdistinct from all the other stages. The number of DEGs in TC(Fig. 2b) and the distinct significant profiles in TC (Fig. 3a, b)showed that the expression levels of most DEGs at 55 dpc inTC changed acutely and there were much more DEGs at 55dpc than all the other stages between TC and YK (Fig. 4). Allthese results demonstrated that the appearance of secondarymuscle fibers brought about gene expression dynamic chang-es and contributed to developmental difference between TCand YK. Moreover, the spatial and temporal-specific changesstarted from 55 dpc, so 55 dpc was suggested to be the keystage to contribute differences between TC and YK.

In mammals, the embryonic myoblasts, which are mostnumerous at the time of primary myogenesis, may be theprogenitors of primary fibers and slow-twitch oxidative fibersin adults, whereas fetal myoblasts contribute to secondaryfibers and satellite cells (Dunglison et al. 1999). These resultssuggest that TC might have more slow-twitch oxidative fibersthan YK in adults. Previous studies have shown that the meatfrom the animals of more oxidative muscle fibers is juicier andhas a more intense flavor than that from the animals of less

oxidative muscle fibers (Valin et al. 1982), which might ex-plain the better flavor of TC meat compared with YK meat.

To uncover the molecular regulation mechanism underly-ing the differences between YK and TC, we firstly analyzedthe gene expression patterns related to muscle fiber develop-ment. Profiles 10, 11, and 13 were only significant in TC; theexpression of all these DEGs at 55 dpc were lower either thanthose at 40 or 63 dpc, so they were regarded as a big cluster.Profiles 14 and 15 were the distinctly significant profiles inYK, and they were considered as one big cluster because oftheir similar gene expression patterns. Interestingly, these twobreed-specific big clusters had opposite gene expression pat-terns from 55 to 70 dpc. And the secondary waves of musclefiber formation in pig occur from day 54 to 90 of gestation(Wigmore and Stickland 1983); previous study reported thatmyogenesis was almost completed before 77 dpc (Zhao et al.2011). These suggested that the DEGs of the two big clustershad opposite expression patterns during secondary musclefiber formation in pig. What’s more, MSTN, GHR, SNED1,TRIL, ENSSSCG00000024885, and XLOC_051137 wereboth in these two big clusters. GHR had similar expressionpatterns with MSTN, which is consistent with one previouswork (Yang et al. 2015). MSTN is an inhibitor of skeletalmuscle growth, and the knockout of this gene would resultin a dramatic increase of muscle mass in mice (McPherronet al. 1997). Liu et al. demonstrated upregulation of MSTNinmyoblasts during GH receptor antagonism (Liu et al. 2003).Moreover, Vijayakumar et al. demonstrated lower MSTN ex-pression in the muscles of both lean and obese mice withinactivated GH receptors compared with in normal mice(Vijayakumar et al. 2012). These results suggest that theGH-MSTN interaction pathway might play a vital role duringmyogenesis in pigs. The expression patterns of the two geneshad upregulation in TC during secondary muscle fiber forma-tion, and that of YK had downregulation. That might be thereason why YK had more secondary muscle fibers than that ofTC. And SNED1, TRIL, ENSSSCG00000024885, andXLOC_051137 might also play roles during myogenesis.The DEGs of the big cluster in TC were obviously enrichedin oxidative phosphorylation pathway. Oxidative phosphory-lation is presumably required to synthesize the large pool ofATP needed to sustain the energy demands of developingmyofibers. Previous researches proved that there is a directconnection between energy metabolism and myogenesis(Cagnazzo et al. 2006). The DEGs in this pathway had a lowerexpression at 55 dpc, which was in accordance with that pri-mary myoblasts would almost cease to proliferate and second-ary myoblasts had not appeared. Several researches haveshown that the genes involved in oxidative phosphorylationhave higher expression levels in obese pig breeds than in leanpig breeds (Xu et al. 2009). The DEGs in this pathway all hadhigher expression levels in TC than in YK. The higher expres-sion of these genes in TC skeletal muscle indicates that TC

Funct Integr Genomics

possesses higher oxidative metabolic capacity than YK. It hasbeen shown that a higher IMF content confers more oxidativemetabolic capacity to the skeletal muscles (Katsumata 2011).The gene expression patterns of the two breed-specific bigclusters all had dynamic changes at 55 dpc. Which suggestedthat 55 dpc might be the important stage to explain the skeletalmuscle development difference. And there were the mostDEGs at 55 dpc between the two breeds. The DEGs betweenTC and YK were also significantly enriched in oxidativephosphorylation, andmost DEGs had higher expression levelsin TC than in YK. It was hypothesized that NADH dehydro-genase and ATPase as well as muscle fiber types may beassociated with meat quality, particularly muscle fat contentand meat color in pigs (Kim et al. 2008), which is also sup-ported by these results. However, the expression of NDUFB5was higher in YK, suggesting that NDUFB5 may have anopposite effect on meat quality. The effects of oxidative phos-phorylation pathway on skeletal muscle myogenesis and meatquality are deserved to be investigated in future studies.

Secondly, PPI network analysis of DEGs in 55 dpc betweenTC and YK showed some key candidate genes explaining thedifferences. PTEN, EP300, ENSSSCG00000004979, CDK14,IRS1, and PPP1CCwere identified as the hub genes. PTEN is alipid tyrosine phosphatase that negatively regulates the Akt/PKB signaling pathway and thereby reduces AKT activationto reduce signals for cell metabolism, proliferation, and survival(Chalhoub and Baker 2009; Vivanco and Sawyers 2002). Astudy shows that IGFBP-2 and PTEN have been implicatedas playing a role in murine skeletal muscle cell differentiation(Sharples et al. 2013). Embryonic development is very sensitiveto EP300 gene dosage, and the EP300 null cells are particularlydefective in retinoid acid (RA) signaling (Yao et al. 1998).Blunting of EP300 impairs myogenic expression and myoblastdifferentiation (Chen et al. 2015). ENSSSCG00000004979 is amember of unconventional myosin 9A protein family. Cyclin-dependent kinases (CDKs) are crucial regulators of the eukary-otic cell cycle whose activities are controlled by associatedcyclins. CDK14, also named PFTK1 acts as a CDK that spe-cifically interacts with cyclin D3, regulates cell cycle progres-sion and cell proliferation (Shu et al. 2007; Zhang et al. 2016).IRS1 is a critical mediator of cell apoptosis (Porter et al. 2013.Ramocki et al. 2008). Insulin-like signaling mediated by Irs1and Irs2 activates the Akt→mTOR signaling cascade and in-hibits Foxo pathways, which reciprocally regulate skeletal mus-cle growth/atrophy (Long et al. 2011). miR-628 promotes burn-induced skeletal muscle atrophy via targeting IRS1 (Yu et al.2016). S6 kinase 1 (S6K1) promotes protein synthesis as well ascell survival (Sabatini 2006;Wullschleger et al. 2006). URI andPP1γ (also named PPP1CC) as integral components of anS6K1-regulated mitochondrial pathway dedicated, in part, tooppose sustained S6K1 survival signaling (Djouder et al. 2007).

Ribosomal proteins were the key regulation genes of theDEGs upregulated at 55 dpc in TC. Previous research shows

that ribosomal proteins play central role in the regulation ofskeletal muscle mass (Djouder et al. 2007). And the skeletalmuscle mass is different between TC and YK. One studydemonstrates that RPL3L expression decreased myotube sizeas a result of decreased myoblast fusion (Chaillou et al. 2016).Ribosomal p70 S6 kinase (p70S6K), a mitogen- and aminoacid–sensitive serine-threonine kinase that ubiquitously regu-lates cell size, including C2C12 mouse skeletal myoblasts(Deng et al. 2010; Montagne et al. 1999). This suggested thatthe effect of ribosomal proteins on muscle fiber developmentrealize the effect on skeletal muscle mass, and ribosomal pro-teins should be important genes to explain the developmentaldifference between the two pig breeds. RPS27 binds toMDM2 through its N-terminal region, and overexpression ofRPS27 stabilizes TP53 by inhibiting MDM2-induced TP53ubiquitination (Xiong et al. 2011). And TP53 regulatesmyogenesis by triggering the differentiation activity of pRb(Porrello et al. 2000). RPL31, a protein that is part of the 60Slarge ribosomal subunit, was shown to modulate the expres-sion of the tumor suppressor p53 (Porrello et al. 2000) and cellcycle regulator p21 (Mirzayans et al. 2012). RpS21 can inter-act strongly with P40, a ribosomal peripheral protein encodedby the stubarista (sta) gene, and their under-expression impairsthe control of cell proliferation in both hematopoietic organsand imaginal discs (Torok et al. 1999). RPL22 may be in-volved in the proliferation of human pulmonary arterialsmooth muscle cells (HPASMCs) (Sun et al. 2012). RpL22-deficient mice leads to developmental arrest of T cells andablation of RPL22 impairs αβ T cell development(Anderson et al. 2007; Stadanlick et al. 2011). MRPL22mightbe one biomarker for ankylosing spondylitis (Zhao et al.2015a, b). The expression levels of all these ribosomal pro-teins were higher in TC than those in YK.

Those results could explain the developmental differencebetween the two pig breeds to a certain extent. And theseDEGs will be the focus of our further research to study theporcine prenatal skeletal muscle development and meat quality.

Conclusion

A genome-wide gene expression profiles and breed-specifictranscriptome dynamics during myogenesis in TC and YKwere obtained in this study. A large number of novel tran-scripts and AS events were detected across all the stages;TSS and TTS were found to be the main form of pig. Resultsshowed that myogenesis is more complicated in TC than inYK. Fifty-five dpc might be the key stage and oxidativephosphorylation could be the vital pathway for the develop-mental difference between TC and YK. The study also pro-vided a set of useful candidate genes for future investigationin to molecu la r mechan i sms under ly ing porc inemyogenesis. Overall, our results provide a valuable insight

Funct Integr Genomics

in understanding the developmental differences betweenTC and YK as well as the biological and molecular basisof myogenesis in Sus scrofa.

Acknowledgments We appreciate the Beijing Genomics Institute (BGI)and Gene Denovo for providing us with technical assistance in RNAsequencing and bioinformatics analysis; we also thank the help ofAnimal Husbandry Bureau of Tongcheng County.

Authors’ contributions ML conceived this study and supervised theexperiment. YZ and JL designed the breeding and sampling plan. HL,YX, GL, YZ, and JL participated in sampling. HL carried out the exper-iment and drafted the manuscript. HL analyzed the data. All authors readand approved the final version of the manuscript.

Funding information This search project was supported financially bythe National Porcine Industry Technology System (CARS-36).

Compliance with ethical standards

Competing interests The authors declare that they have no competinginterests.

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