Analysis of Long Intergenic Non-Coding RNAs Transcriptomic Profiling in Skeletal Muscle Growth During Porcine Embryonic Development

Analysis of Long Intergenic Non-Coding RNAs Transcriptomic Profiling in Skeletal Muscle Growth During Porcine Embryonic Development | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Long Intergenic Non-Coding RNAs Transcriptomic Profiling in Skeletal Muscle Growth During Porcine Embryonic Development Wenjuan Zhao, Zijing Li, Quan Liu, Su Xie, Mengxun Li, Yuan Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-323967/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jul, 2021 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Skeletal muscle growth plays a critical role during porcine muscle development stages. Genome-wide transcriptome analysis reveals that thousands of long intergenic non-coding RNAs (lincRNAs) have been identified in various species and implicated as crucial regulator involving in epigenetic regulation. However, comprehensive analysis of lincRNAs in embryonic muscle development stages remain still elusive. Here, we investigated the transcriptome profiles of duroc embryonic muscle tissues from days 33, 65, and 90 of gestation using RNA-seq, there were 228 putative lincRNAs identified. Moreover, these lincRNAs exhibit the characteristics of shorter transcripts length, longer exons, less exon numbers and lower expression level compared with protein-coding transcripts. Differential expression analysis showed that a total of 91 lincRNAs and 2638 mRNAs were differentially expressed. In addition, we also performed quantitative trait locus (QTL) mapping analysis for DE lincRNAs, 113 of 120 DE lincRNAs were localized on 2200 QTLs, we observed many QTLs involved in growth and meat quality traits. Furthermore, we predicted potential target genes of DE lincRNAs in cis or trans regulation. Gene ontology and pathway analysis reveals that potential targets of DE lincRNAs mostly were enriched in the processes and pathways related to tissue development, MAPK signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and insulin signaling pathway, which involved in skeletal muscle physiological functions. Based on cluster analysis, a co-expression network analysis of DE lincRNAs and their potential target genes indicated that DE lincRNAs highly regulated protein-coding genes associated with skeletal muscle development. In this study, many of the DE lincRNAs identified may play essential roles in pig muscle growth and muscle mass. Our study provides crucial information for exploring further the molecular mechanisms of lincRNAs during skeletal muscle development. Molecular Biology Molecular Genetics lincRNAs RNA-seq Skeletal muscle development Embryo Duroc Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Skeletal muscle is an important component of the body in mammals, mainly involved in the growth and development of the body. Skeletal muscle abnormalities can lead to physical dysfunction such as Muscular dystrophy, idiopathic inflammatory myopathies and cardiomyopathy 1 – 4 . During the past decades of molecular biology study, great progress has been made on the molecular mechanism underlying the growth and development of porcine skeletal muscle 5 , for example, MyoD, MyF5 and MRF4 are involved in myogenesis and differentiation 6 – 8 . Additionally, studies have found that insulin-like growth factors (IGF1) can act as an activator of MAPK/ERK and PI3K/Akt signaling pathways to promote the proliferation and differentiation of muscle cells 9 , and IGF1 mediated pathway that the IGF1–Akt–mTOR pathway has been found to participate in positive regulation of muscle growth 10 , 11 . In recent years, the emergence of long intergenic non-coding RNA has become a new research hotspot in the molecular biological field, which provides a new way to advance the research on the mechanism of skeletal muscle development. Long intergenic non-coding RNAs, which are a new class of RNA molecules longer than 200 nucleotides with little or no protein-coding capacity 12 . Recent evidences have established that lincRNAs have a significant role in regulating gene expression at epigenetic, transcriptional and post transcriptional levels 13 , 14 , they can perform essential functions during basic biological processes, such as chromatin modification 15 , imprinting 16 , 17 , maintenance of pluripotency 18 . With the emergence and widespread application of high-throughput sequencing technology, thousands of lincRNAs have been identified in genome-wide analysis, more and more lincRNAs have been functionally validated. A recent study indicated that lincRNA-p21 is involved in regulating the proliferation and apoptosis of vascular smooth muscle cells by enhancing the activity of P53, providing a new target for the treatment of atherosclerosis 19 . Currently, studies on lincRNAs in porcine embryo development are less well understood, therefore, our analysis in the differences of lincRNAs at embryonic development stages will provide a good model for studying the mechanisms that regulate skeletal muscle development. In the present study, we applied RNA sequencing to characterize global gene expression patterns of muscle tissues from duroc on days 33, 65, and 90 and systematically analyzed the muscle expression profile during porcine skeletal muscle development 20 . We identified 228 putative lincRNAs and found that many lincRNAs differentially expressed. Moreover, we predicted the potential target genes of DE lincRNAs by cis or trans ways. Gene Ontology and pathways enrichment analysis showed that lincRNAs potentially regulated the process of protein-coding genes. An interactive network was performed to elucidate the interplay between DE lincRNAs and their potential target genes. This study of skeletal muscle of transcriptome profiles will provide a useful resource to further explore the understanding of mechanisms, besides, elucidating the underlying mechanisms of skeletal muscle growth and development will be helpful for the improvement of production benefits of pig. Table 1 Summary of data from RNA-seq Sample Accession number Raw reads Clean reads Mapped reads Mapping ratio Uniquely mapping ratio 33d_1 SRR9829616 74,174,368 72,887,888 54,060,532 95.43% 74.14% 33d_2 SRR9829617 65,334,814 64,482,010 45,701,718 94.90% 70.88% 33d_3 SRR9829614 77,428,960 76,451,044 58,773,616 95.96% 76.88% 65d_1 SRR9829615 73,747,044 71,946,704 52,038,372 95.09% 72.33% 65d_2 SRR9829612 71,704,676 70,420,214 50,268,952 94.95% 71.38% 65d_3 SRR9829613 69,401,348 68,207,232 48,032,548 94.76% 70.42% 90d_1 SRR9829610 65,996,802 65,243,880 47,417,538 95.18% 72.68% 90d_2 SRR9829611 71,715,594 70,405,728 50,090,100 95.00% 71.14% 90d_3 SRR9829609 78,275,962 76,941,162 54,391,950 94.87% 70.69% Materials And Methods Ethics Statement All the experiments were done in accordance with the relevant guidelines and regulations of animal care and use committee and the study was approved by The Scientific Ethic Committee of Huazhong Agricultural University, Hubei province. Data Sources RNA-seq sequencing data containing nine samples was obtained from the NCBI SRA database. The accession numbers and reads of the RNA-seq data were shown in Table 1 . In this study, total male samples were strictly collected from the embryonic muscle tissue of duroc, and were grouped into three developmental stages (days 33, 65 and 90, three replicates for each stage) 20 . We identified 228 putative lincRNAs and found that many lincRNAs differentially expressed. Moreover, we predicted the potential target genes of DE lincRNAs by cis or trans ways. Gene Ontology and pathways enrichment analysis showed that lincRNAs potentially regulated the process of protein-coding genes. An interactive network was performed to elucidate the interplay between DE lincRNAs and their potential target genes. This study of skeletal muscle of transcriptome profiles will provide a useful resource to further explore the understanding of mechanisms, besides, elucidating the underlying mechanisms of skeletal muscle growth and development will be helpful for the improvement of production benefits of pig. RNAseq Reads Mapping and Transcriptomic Assembly To ensure the reliability of RNA reads and suitability for the subsequent analysis. FastQC (version 0.11.9) tool ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) was run to quality control checks on raw sequences data and the sequences of poor quality were trimmed and filtered with Trimmomatic (version 0.36) software to obtain clean reads 43 . The high-quality filtered reads were aligned against the porcine reference genome (Sscrofa11.1 ) using HISAT2 (version 2.0.3) with default parameters 44 . The pig reference genome file was downloaded from Ensemble ( ftp://ftp.ensembl.org/pub/release-99/gtf/sus_scrofa/ ). Next, SAM format files which obtained by mapping were converted to BAM format files with SAMtools (version 0.1.19). After that, String Tie (version 1.3.4) was used to assemble transcripts into nine GTF files, then transcripts of all the samples were combined by the Merge parameter of String Tie into a non-redundant transcript set to produce a uniform transcript GTF 45 . As a result of assembly produced a large amount of novel transcripts, which were mapped to reference annotation file using the GffCompare to discovery novel transcripts information 44 . The pipeline LincRNAs identification and analysis To identify porcine lincRNAs, we performed the following screening of the transcripts obtained after GffCompare, transcripts which the class-code annotated as ‘U’, were more than 200 bp in length and contained at least 2 exons were retained 46 . Next, all remaining transcripts were scored with CPC to determine their coding potential, transcripts of CPC < 0 were considered unable to encode proteins 47 . Then, we translated transcripts sequences into possible protein domians with Transeq 2 and excluded transcripts that were matched in the Pfam database (E-value < 1e-5) 48 . Furthermore, transcripts that contained similar known proteins in non-redundant reference sequence (NR) database and UniRef90 database were discarded by BLASTX tool (E-value < 1e-5) 49 . Finally, we performed normalization on the transcript by calculating the ‘fragments per kilo-base of exon model per million mapped reads’ (FPKM) using String Tie with the parameter ‘-B’,and transcripts were retained while FPKM was greater than 0.5 in at least a sample 44 . Comparison of identified lincRNAs and protein coding transcripts At present, the Ensembl database contains comprehensive genetic information for many species. We downloaded the pig reference annotation file that contained 45788 protein-coding transcripts corresponding to 23,422 protein-coding genes in order to compare the characteristic differences between identified lincRNAs and protein-coding genes. LincRNAs annotation information was downloaded from the ALDB database, we acquired about 12,103 known lincRNA transcripts corresponding to 7,381 lincRNA genes, identified lincRNAs and protein-coding genes were aligned to the corresponding reference annotation files to obtain their detailed information, respectively 50 . Differential expression analysis of lincRNAs We used the python package called ‘HTseq-count’ to calculate the numbers of reads from nine samples 51 , and the resulting count files were used to evaluate the differential expression levels between different groups by the DEseq2 package in R 52 . By screening, lincRNA transcripts with |log2 fold change | ≥ 1 and adjusted p -value ≤ 0.05 were identified differentially expressed, protein-coding transcripts with |log2 fold change | ≥ 2 and adjusted p -value ≤ 0.05 were identified differentially expressed. QTL location analysis of differentially expressed lincRNAs To further explore the function of differentially expressed lincRNAs (DE lincRNAs), a correlation analysis was performed between DE lincRNAs with quantitative trait locus (QTL). The pig QTL reference file was downloaded from https://www.animalgenome.org/cgi-bin/QTLdb/SS/index , and the parameter ‘intersectBed’ was used to acquire DE lincRNAs to capture the QTL traits associated with lincRNAs. Prediction of potential target genes We predictd the molecular functions of protein-coding genes regulated by RNA in cis and trans. Firstly, the neighboring protein-coding genes nearby DE lincRNAs (< 100kb) were identified based on cis-prediction principles using Bedtools. For trans regulation of DE lincRNAs, we calculated the Pearson correlation coefficient (r) between DE lincRNAs and protein coding genes, we selected protein-coding genes that Pearson correlation coefficient ≥ 0.96, p -value ≤ 0.05 as the potential target genes of DE lincRNAs. Functional enrichment analysis GO and KEGG The list of potential target genes was performed to predict biological process and potential signaling pathway based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) in DAVID website ( http://david.abcc.ncifcrf.gov/home.jsp ). The GO terms and KEGG pathways with p- value ≤ 0.05 were considered to be significantly enriched 53 , 54 . Because of the poor annotation of the pig reference genome, the protein-coding gene IDs were converted into human homologous gene IDs using BioMart from Ensembl. Network Correlation analysis of DEL genes and DEPTGs Network intergraphs can intuitively reflect the relationship between DE lincRNAs and their potential target genes. We select PTGs that Pearson correlation coefficient ≥ 0.96 and p -value ≤ 0.05 differentially expressed in groups were defined as differentially expressed PTGs (DEPTGs), the highly correlated relationship between DE lincRNAs and the underlying potential target genes were established and visualized by Cytoscape (version 3.4). Validation of differentially expressed lincRNAs To verify our analysis results, qRT-PCR was carried out to test the expression between five DE lincRNAs and seven potential target genes which were randomly selected. There were 16 samples from embryonic muscle tissue used for the experiments (each experiment contained three biological replicates). Total RNA was extracted using Trizol reagent according to the manufacturer’s protocols, and reverse transcribed to cDNA using PrimeScript RT reagent Kit with gDNA Eraser r (Takara, Dalian, China). Quantitative PCR was performed using SYBR Premix Ex Taq II on Bio-Rad CFX-96 system (Bio-Rad Laboratories, Hercules). The relative expression levels of all genes were calculated by the 2 −∆∆CT method. All primers were designed with Primer 5 program ( Table S6 ). Data Availability All the raw data involved in this study could be obtained from public database. This data can be found here: https://www.ncbi.nlm.nih.gov/sra/?term=SRP216286 . Results Summary of RNA-seq data mapping and transcripts assembly in Duroc In this study, we downloaded 9 RNA-seq libraries which contained 647779568 paired-end reads from the NCBI during three embryonic muscle developmental stages of duroc. Samples were named separately 33d_1, 33d_2, 33d_3, 65d-1, 65d_2, 65d_3, 90d-1, 90d_2, 90d_3. After trimming and filtering, a total 636985862 clean reads were mapped to the annotated Sscrofa11.1 genome using HISAT2, we founded that approximately 95% of the quality-filtered reads were mapped and over 70% of the reads could be uniquely mapped to the genome ( Table 1 ). Based on the result, String Tie was used to reconstructed the transcripts and merge into a file that obtained 70869 transcripts. The RNA-seq process for identifying lincRNAs was shown in Fig. 1 . Finally, 228 putative lincRNAs were identified, there were 191 lincRNAs that have been annotated in the pig reference genome database, and these known lincRNAs were distributed throughout all chromosomes. The remaining 37 lincRNAs have no overlap with the pig annotation database, they were separately distributed on chromosomes 3 to 8 and 11 to 18, and chromosome 17 was found to have the highest novel lincRNAs density. Characteristics analysis of identified lincRNAs Previous study showed that the difference of lncRNAs with protein-coding genes in pig 21 . However, sequence characteristic of lincRNAs during embryonic muscle development remain unclear. Based on the annotated information for the pig reference genome, we examined the characteristic of putative lincRNAs in transcript length, exon length, exon numbers and expression level compared with protein-coding genes. As a result, we observed that the average transcript length of known lincRNAs, novel lincRNAs and protein-coding genes were about 1377bp, 1203bp and 3296bp, respectively. It followed that novel lincRNAs were similar to known lincRNAs and shorter than protein-coding genes in transcript length (Fig. 2 A). In addition, the average exon length of known lincRNAs, novel lincRNAs and protein-coding genes were 515bp, 505bp and 284bp, respectively. Although the average transcript length of lincRNAs was shorter, the average exon length of lincRNAs was longer than that of protein-coding genes (Fig. 2 B). In exon numbers, our result showed that the exon numbers of lincRNAs were gathered at 2–5, while the average exon numbers of protein-coding genes was 11.6, we noticed that this result was consistent with the above two research (Fig. 2 C). In normalized read counts expression level (FPKM), the average value of known lincRNAs, novel lincRNAs and protein-coding genes were 1.2, 0.9 and 4.7, respectively. We concluded that lincRNAs had a lower expression level compared with protein-coding genes. In general, lincRNAs were shorter in transcript length, but longer in exon length, had fewer exon, and were expressed at lower level compared with protein-coding genes (Fig. 2 D). Which were highly consistent with previous reports 22 , 23 . Differential expression analysis of lincRNAs To evaluate the differences in gene expression patterns during three developmental stages, DEseq2 was used to identify differentially expressed lincRNAs and protein-coding genes between two paired samples (D33 vs. D65; D65 vs. D90; D33 vs. D90). when |fold change| ≥1 and adjusted p -value ≤ 0.05, there were 66 DE lincRNA genes including 50 upregulated and 16 downregulated identified between Day 33 and 65 (Fig. 3 A), 29 DE lincRNA genes including 12 upregulated and 17 downregulated identified between Day 65 and 90 (Fig. 3 B), 74 DE lincRNA genes including 48 upregulated and 26 downregulated identified between Day 33 and 90 (Fig. 3 C). All DE lincRNAs in three groups were distributed in Fig. 3 D. In addition, when |fold change| ≥2 and adjusted p -value ≤ 0.01, a total 2638 DE protein-coding genes were identified (Fig. 3 E). QTL localization and functional enrichment QTL is closely associated with many traits. To explore the relationship between differentially expressed lincRNAs and QTL traits, we performed a correlation analysis by mapping DE lincRNAs to the QTL regions related to pig traits, the pig QTL database contains 31455 QTLs, representing 695 different traits 24 . Our analysis result showed that 113 of 120 DE lincRNAs were located in 2200 QTL, which corresponded to 331 traits, 27 trait types, 4 trait classes. The greatest number of QTLs were associated with the trait “Meat and Carcass Traits”, accounting for about 59% of the total QTLs. The second highest number of QTL traits “Production Traits” accounted for 11% of the total QTLs (Fig. 4 A). We statistically analyzed localization in QTLs associated with muscle, obesity, and growth traits, and found that most DE lincRNAs were targeted at the three trait types. Notably, 100 of 113 DE lincRNAs were closely associated with growth and 86 DE lincRNAs were located in muscle related traits, from this we hypothesized that DE lincRNAs could have an important effect on muscle growth and development (Fig. 4 B). Furthermore, we examined the distribution of these QTLs on chromosomes, and found that QTLs were distributed on all chromosomes. Interestingly, the greatest number of QTLs for these three traits were located on chromosome 4 and chromosome 6 (Fig. 4 C). Prediction of potential target genes of DE lincRNAs Previous studies have shown that lincRNAs can regulate the expression of target genes by cis or trans via, and participate in the functional regulation of some organisms 25 , 26 . Firstly, we predicted potential target genes of DE lincRNAs in cis regulation to determine the possible function of DE lincRNAs by searching for protein-coding genes around 100kb upstream and downstream of DE lincRNAs. We found 303 protein-coding genes were close to DE lincRNAs. Pearson correlation analysis revealed that 37 potential target genes (PTGs) were highly correlated with 29 DE lincRNAs (r ≥ 0.8, p -value ≤ 0.01). Among them, 12 of 37 PTGs differentially expressed were defined as DEPTGs. Meanwhile, most DE lincRNAs were significantly positively correlated with their PTGs. MSTRG.6732 and MSTRG.2061 were significantly negatively correlated with ERGIC1 and HMGB1. Besides, MSTRG.4842 and MSTRG.14169 could regulate their PTGs in two ways: positive regulation and negative regulation. The potential target genes for DE lincRNAs regulation were shown in Table 2 Table 2 The correlation between DEL genes and their adjacent protein-coding genes DELs Adjacent protein-coding genes Pearson correlation coefficient DELs Adjacent protein-coding genes Pearson correlation coefficient MSTRG.98 SYNE1 0.932778133 MSTRG.2696 C17orf105 0.800640629 MSTRG.7542 ZC3HAV1L 0.909721177 MSTRG.243 VGLL2 0.938895472 KIAA1549 0.802049845 MSTRG.222 7SK 0.923173536 MSTRG.7420 SLC2A4RG 0.878741211 MSTRG.2061 HMGB1 -0.864645067 MSTRG.7020 PAX1 0.941796265 MSTRG.1905 PIP4K2A 0.820942703 ENSSSCG00000031878 0.935502944 MSTRG.17805 RAI2 0.992836498 MSTRG.6732 ERGIC1 -0.886959706 MSTRG.17803 NHS 0.866101456 MSTRG.5732 STAM2 0.89876083 MSTRG.17252 IER5 0.810425662 MSTRG.5387 C10orf71 0.999482502 MSTRG.16842 ssc-mir-125b-1 0.871701204 MSTRG.5199 ACTA1 0.852421469 MSTRG.15750 DLK1 0.909106508 MSTRG.4842 RHOF 0.993619533 MSTRG.14579 CTPS1 0.951838287 TMEM120B 0.962101405 MSTRG.14169 TUBB6 0.885525513 WDR66 -0.898219733 MPPE1 -0.869796489 PSMD9 -0.905954104 MSTRG.13914 MYOM3 0.903239933 MSTRG.4603 COL18A1 0.804671084 MSTRG.12042 HOXC6 0.87413117 MSTRG.4602 COL18A1 0.817579473 MSTRG.11764 PPARA 0.935640688 MSTRG.27 AFDN 0.870481656 MSTRG.11756 ENSSSCG00000035352 0.986041555 MSTRG.2696 MPP2 0.926075239 ENSSSCG00000033576 0.917380642 MPP3 0.888523664 PPARA 0.84472984 PPY 0.886803079 Functional enrichment analysis of PTGs associated with DE LincRNAs Furthermore, we also predicted the potential target genes from DE lincRNAs in trans regulation, and acquired 4609 PTGs corresponding to 50 DELs (r ≥ 0.96, p -value ≤ 0.01). Among these genes, 548 PTGs were differentially expressed in groups as DEPTGs. Which suggested that most of lincRNAs regulated gene expression through trans regulation. GO enrichment analysis showed that 4609 PTGs were enriched in 547 biological processes and 548 DEPTGs were enriched in 287 biological processes. In cases of biological process. Some GO terms were significantly associated with muscle development and energy metabolism, such as skeletal muscle tissue development, muscle contraction, cell proliferation, protein catabolic process, insulin receptor signaling pathway and regulation of glucose transport (Fig. 5 A; Fig. 5 C). Besides, 4609 PTGs and 548 DEPTGs were enriched in 64 pathways and 28 pathways, respectively. KEGG pathways were involved in Wnt signaling pathway, ECM-receptor interaction, MAPK, calcium signaling, ErbB signaling pathway and TGF-beta signaling pathway ( Fig. 5 B; Fig. 5 D). The results indicated that DE lincRNAs had an important role in regulating their potential target genes regulated composition and growth and development of muscle cells by muscle cells proliferate and differentiate, substance metabolism energy transport and conversion. Co expression network analysis of DE lincRNAs and DE potential target genes To understand the relationship of expression between DE lincRNAs and their DEPTGs. The expression regulation relationship between 50 DE lincRNAs and 548 DEPTGs was analyzed, we calculated the interaction of DE lincRNAs and DEPTGs. Pearson correlation analysis results were presented that 860 pairs between DE lincRNAs and DEPTGs with positive correlation and 86 pairs with negative correlation were identified (Fig. 6 A). We selected DE lincRNAs and DEPTGs related to skeletal muscle growth and development pathways to construct co-expression networks, and 24 DE lincRNAs exhibited a high co-expression relationship with 48 DEPTGs. Noticeably, DE lincRNA MSTRG.388, MSTRG.4602 and MSTRG.7020 were involved in the regulation of several DEPTGs (Fig. 6 B). In order to further explore the function of DE lincRNAs, we investigated nine DEPTGs involving in muscle development related pathways corresponding to 13 DE lincRNAs, we found that SHH targeted by lincRNA MSTRG.27 and MSTRG.388 played an important role in myogensis (Fig. 6 C), and SHH had an essential inductive function in the early activation of the myogenic regulatory factors Myf-5 and MyoD 27 , 28 . Besides, lincRNA MSTRG.4602, MSTRG.98 and MSTRG.243 regulated MYOZ1 that encoded calsarcin-2 protein participated in the expression of PPAR-Y2 in skeletal muscle 29 . Validation of lincRNA expressions through qRT-PCR According to the previous RNA-seq results, we selected nine pairs of DE lincRNA genes and their potential target genes and analysis their expression levels by qRT-PCR (MSTRG.98 vs. CA4, MSTRG.98 vs. MYOZ1, MSTRG.243 vs. MYOZ1, MSTRG.4602 vs. MYOG, MSTRG.4602 vs. TGFB2, MSTRG.4602 vs. MAPK14, MSTRG.4602 vs. FOXO3, MSTRG.17803 vs. FAIM2, MSTRG.4034 vs. CA4) ( Fig. 7 ) . The experimental results showed that the correlation (r 2 ) bwteen DE lincRNAs and potential target genes were at above 0.86 and the p-values were less than 0.01. The experimental results of the qRT-PCR have a similar tend to the original Pearson correlation coefficient between DE lincRNAs and potential target genes. Discussion Skeletal muscle growth and development are a complex process, which directly determine the meat production and quality in the pig industry. Skeletal muscle is mainly composed of muscle fibers, basement membrane, muscle satellite cells and nerves. Study found that the numbers of muscle fiber have been fixed before the pigs were born, indicating that muscle fiber development is mainly determined during the embryonic period 30 , 31 . Muscle fiber development takes place in two waves in pig embryonic, the first wave of muscle fiber formation occurs from 30 to 60 days and the second wave occurs from 45 to 90 days 32 , 33 . In our study, we investigated lincRNAs expression profile in days 33, 65, and 90, which included the primary, second, and final waves of muscle fiber development 33 . Even though the previous studies have showed lincRNAs associated with muscle growth in pig, the dynamic process of expression profile of lincRNAs in embryonic muscle fibers is rare, and our study provides theoretical basis for new exploration in the future. Based on RNA-seq data published in NCBI, we compared whole gene expression profile in muscle tissue from druoc in differentially development periods. Through a series of transcriptome pipeline analysis, there were 229 putative lincRNAs identified using RNA-seq sequencing, we predicted 39 novel lincRNAs that were not annotated from the nine muscle libraries, which enrich the pig lincRNA annotation and the specific features need to be further investigated in the future. Moreover, we performed a characteristic analysis of putative lincRNAs, involving in transcript and exon length, exon numbers and FPKM, the results showed that the similar characteristic of shorter transcript length, longer exon length, fewer exons, and lower expression levels compared with previous reports 34 , 35 . Meanwhile, the reliability of the analysis is further improved. We identified 95 DE lincRNA genes and 2638 DE protein-coding genes based on a designed pipeline. Previous studies have shown that there were a large number of lncRNA located within known QTL regions 36 . To understand the relation between DE lincRNAs and QTL, we performed also QTL localization analysis for differentially expressed lincRNAs. Some QTLs are involved in large regions,so that multiple genes are located on the same QTL, or multiple QTLs have the same gene location. In among, the specific mechanism may need to be verified by subsequent experiments. To explore the potential function of DE lincRNAs, we investigated the regulation of lincRNAs on gene expression through cis and trans regulation 26 . For the cis-regulation of DE lincRNAs, we found these genes have been shown to be associated with muscle cell proliferation and fat deposition. For example, DLK1 was a critical factor in regulating skeletal muscle development and regeneration through Notch dependent 37 . Previous studies found that PPARA was involved in the regulation of fat deposition in porcine subcutaneous fat and longissimus dorsi muscle 38 , 39 . Besides, myofibrillar structural protein myomesin-3 (MYOM3) was not only associated with muscular dystrophy related proteins and muscle strength, which could be a potential biomarker for monitoring of muscular dystrophy, but was hymethylated in ischemic cardiomyopathy 40 , 41 . Among them, DEL-MSTRG. 31882 and its potential target gene with patatin like phospholipase domain 4 (PNPLA4) showed significant positive correlation at the expression level. Therefore, we inferred that DE lincRNAs RNA modulates differences at different developmental stages by regulating their potential target genes. In this study, we investigated Gene Ontology and KEGG pathways analysis of potential target genes of DE lincRNAs, and found that skeletal muscle organ and tissue development processes, muscle contraction, striated muscle cell development were some of significantly enriched GO terms. This results suggest that identified DE lincRNAs have important impact in the skeletal muscle. Regulation of glucose import, regulation of glucose transport, and insulin receptor signaling pathway also significantly enriched, which were important ways of obtaining and transporting energy, we deduce that DE lincRNAs could participate in the regulatory mechanism of skeletal muscle development through mediating cellular energy responses. Our KEGG pathway analysis showed that significantly enriched pathways including MAPK signaling pathway, TGF-beta signaling pathway, Wnt signaling pathway, ECM − receptor interaction, regulation of kinase activity. Previous studies have confirmed that TGF-beta signaling pathway contributes to muscle development in mice 42 . Moreover, the extracellular matrix (ECM) is a network of structures surrounding muscle fibers, providing a close connection with cell proliferation, differentiation and metabolism. Therefore, we infer that DE lincRNAs could contribute to the differences in skeletal muscle development. In addition, some cardiac diseases, such as viral myocarditis, dilated cardiomyopathy, and hypertrophic cardiomyopathy, were also significantly enriched, these results suggest that DE lincRNAs may have an important effect on myocardial development. Conclusion In the study, we identified 228 putative lincRNAs and analysised the characteristics of lincRNAs transcriptome compared with protein-coding genes in embryonic muscle tissue of duroc. We observed numerous differentially expressed lincRNAs and protein-coding genes during differential development stages. Functional enrichment analysis of potential target genes by DE lincRNAs revealed that many lincRNAs participated in muscle growth and development related processes and pathways. Co-expression networks indicated the functional relatedness between protein-coding genes and lincRNAs. In summary, our work provides a valuable resource for future research into the potential functions of pig growth and development and is expected to promote the progress of pig production. Declarations Acknowledgments The work was supported by National Natural Science Foundation of China (NSFC, 31872322), the Fundamental Research Funds for the Central Universities (2662017PY030). Author Contributions C.L. conceived and designed the experiments and explained the data; W.Z. performed the analysis of this data with the help of Z.L.; Q.L. and S.X. contributed to the collection of samples; M.L. and Y.W. carried out the experiment of qRT-PCR; W.Z. wrote and revised the manuscript. All authors have reviewed and approved the manuscript. Competing interests The authors declare that they have no competing interests. References Li, R. et al. Exploring the lncRNAs Related to Skeletal Muscle Fiber Types and Meat Quality Traits in Pigs. Genes. 11 , https://doi.org/10.3390/genes11080883 (2020). 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Supplementary Files SupplementaryMaterials.rar Cite Share Download PDF Status: Published Journal Publication published 27 Jul, 2021 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 29 Apr, 2021 Reviews received at journal 18 Apr, 2021 Reviewers agreed at journal 05 Apr, 2021 Reviewers invited by journal 05 Apr, 2021 Editor assigned by journal 05 Apr, 2021 Editor invited by journal 26 Mar, 2021 Submission checks completed at journal 25 Mar, 2021 First submitted to journal 13 Mar, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-323967","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":18422017,"identity":"06991f53-294c-4676-8245-9c3d1ea371dc","order_by":0,"name":"Wenjuan Zhao","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjuan","middleName":"","lastName":"Zhao","suffix":""},{"id":18422018,"identity":"79ce78f1-6bdc-4d2c-8923-ee4b2070f12b","order_by":1,"name":"Zijing Li","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zijing","middleName":"","lastName":"Li","suffix":""},{"id":18422019,"identity":"2a36e24c-4d1c-4315-a2da-f2cac059230a","order_by":2,"name":"Quan Liu","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Liu","suffix":""},{"id":18422020,"identity":"9a6bd47a-16d2-4dec-90dd-d94316e858f6","order_by":3,"name":"Su Xie","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Su","middleName":"","lastName":"Xie","suffix":""},{"id":18422021,"identity":"86f0162c-e0f4-4a55-8fc7-3283daf00ced","order_by":4,"name":"Mengxun Li","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengxun","middleName":"","lastName":"Li","suffix":""},{"id":18422022,"identity":"c22845ae-859c-4c01-820d-3f4fb2d52d04","order_by":5,"name":"Yuan Wang","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Wang","suffix":""},{"id":18422023,"identity":"d7aae833-36db-4116-ac46-9faf17da435b","order_by":6,"name":"Changchun Li","email":"data:image/png;base64,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","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Changchun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2021-03-13 07:44:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-323967/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-323967/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-021-94014-w","type":"published","date":"2021-07-27T15:01:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":7399640,"identity":"e2530d55-1f63-4c16-ae0e-33fc973a913c","added_by":"auto","created_at":"2021-03-26 21:39:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":454945,"visible":true,"origin":"","legend":"Overview of the identification pipeline for lincRNAs. (A) The identification pipeline of putative lincRNAs; (B) Venn diagram of putative lincRNAs, novel lincRNAs and differentially expressed lincRNAs: (C) The column diagram of chromosome distribution of putative lincRNAs. ","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/6d439795eeefff438aa472b6.jpg"},{"id":7398964,"identity":"c0782e11-2435-4df2-b5c7-5bb6934290a6","added_by":"auto","created_at":"2021-03-26 21:36:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":446531,"visible":true,"origin":"","legend":"Characterization of lincRNAs compared with protein-coding genes; (A) Comparison of transcript length; (B) Comparison of exon length; (C) Comparison of numbers of exon; (D) Comparison of expression level. ","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/8a391dfa4c40770d4e7d6b42.jpg"},{"id":7399641,"identity":"3a1e9d7a-34a3-4c0f-8f15-1fbd418926f1","added_by":"auto","created_at":"2021-03-26 21:39:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":530117,"visible":true,"origin":"","legend":"Heat map of differential expression analysis of lincRNAs and protein-coding genes during three developmental stages. (A) Heat maps of differentially expressed lincRNAs in D33 vs. D65 group; (B) Heat maps of differentially expressed lincRNAs in D33 vs. D65 group; (C) Heat maps of differentially expressed lincRNAs in D33 vs. D65 group;; (D) Histogram of differentially expressed lincRNAs among the three groups; (E) Heat maps of differentially expressed protein-coding genes among the three groups. ","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/2d08ea618d3908d8af6d58a6.jpg"},{"id":7399639,"identity":"21b4acfc-12f0-4a07-98bd-e627ed2a1345","added_by":"auto","created_at":"2021-03-26 21:39:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":577738,"visible":true,"origin":"","legend":"Analysis of the quantitative trait loci of DE lincRNAs. (A) The classification and number distribution of QTLs; (B) The number distribution of QTLs associated with muscle, obesity, and growth; (C) The chromosomal histograms of QTLs associated with muscle, obesity, and growth.","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/8d766a3ab539a79e5115f878.jpg"},{"id":7400099,"identity":"7ff8eae7-0051-475a-b4cd-463f182ace33","added_by":"auto","created_at":"2021-03-26 21:42:17","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":562030,"visible":true,"origin":"","legend":"Gene ontology and pathway analysis of PTGs of DE lincRNAs. (A) Biological processes analysis associated with muscle growth of PTGs of DE lincRNAs; (B) Pathway analysis of associated with muscle growth of PTGs of DE lincRNAs; (C) Biological processes analysis associated with muscle growth of DEPTGs of DE lincRNAs; (D) Pathway analysis of associated with muscle growth of DEPTGs of DE lincRNAs.","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/f0089582879469bdafd888fd.jpg"},{"id":7400508,"identity":"80c1c9ec-86af-4a15-91be-827039b737c4","added_by":"auto","created_at":"2021-03-26 21:45:17","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2071227,"visible":true,"origin":"","legend":"Correlation expression regulation analysis of DEL genes and their potential target genes. (A) Co-expression network diagram between DEL genes and DEPTGs; (B) Co-expression network diagram between DE lincRNA genes and DEPTGs enriched in skeletal muscle development related pathways; (C) The interaction of major DE lincRNA genes and DEPTGs enriched in muscle-related pathways.","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/9aee3f47625ec3c66960e0c9.jpg"},{"id":7400097,"identity":"6e683ee6-7740-4cf3-85f2-f4f11dfcbb2f","added_by":"auto","created_at":"2021-03-26 21:42:17","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":500449,"visible":true,"origin":"","legend":"Linear regression of DELs and their DEPTGs expression. The r0 and p0 represent the Pearson correlation coefficient and p-value of each pair of differentially expressed lincRNA and its potential target gene, the r and p were calculated by qRT-PCR verification experiment. (A) MSTRG.98 vs. CA4. (B) MSTRG.98 vs. MYOZ1. (C) MSTRG.243 vs. MYOZ1. (D) MSTRG.4602 vs. MYOG. (E) MSTRG.4602 vs. TGFB2. (F) MSTRG.4602 vs. MAPK14. (G) MSTRG.4602 vs. FOXO3. (H) MSTRG.17803 vs. FAIM2. (I) MSTRG.4034 vs. CA4.","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/0aae978d3fd054f483e551ad.jpg"},{"id":13683097,"identity":"9b9631c5-79d8-455b-8a62-b3186c75a634","added_by":"auto","created_at":"2021-09-17 12:00:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/c2990585-c2e6-4d0c-b6db-1a73bb1db347.pdf"},{"id":7398969,"identity":"fabef05f-dfbe-476c-b535-1507e5f43ba8","added_by":"auto","created_at":"2021-03-26 21:36:17","extension":"rar","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":362004,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.rar","url":"https://assets-eu.researchsquare.com/files/rs-323967/v1/ff0436e8acd4a4171488ba28.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAnalysis of Long Intergenic Non-Coding RNAs Transcriptomic Profiling in Skeletal Muscle Growth During Porcine Embryonic Development\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eSkeletal muscle is an important component of the body in mammals, mainly involved in the growth and development of the body. Skeletal muscle abnormalities can lead to physical dysfunction such as Muscular dystrophy, idiopathic inflammatory myopathies and cardiomyopathy\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. During the past decades of molecular biology study, great progress has been made on the molecular mechanism underlying the growth and development of porcine skeletal muscle\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, for example, MyoD, MyF5 and MRF4 are involved in myogenesis and differentiation\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, studies have found that insulin-like growth factors (IGF1) can act as an activator of MAPK/ERK and PI3K/Akt signaling pathways to promote the proliferation and differentiation of muscle cells\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and IGF1 mediated pathway that the IGF1\u0026ndash;Akt\u0026ndash;mTOR pathway has been found to participate in positive regulation of muscle growth\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In recent years, the emergence of long intergenic non-coding RNA has become a new research hotspot in the molecular biological field, which provides a new way to advance the research on the mechanism of skeletal muscle development.\u003c/p\u003e \u003cp\u003eLong intergenic non-coding RNAs, which are a new class of RNA molecules longer than 200 nucleotides with little or no protein-coding capacity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Recent evidences have established that lincRNAs have a significant role in regulating gene expression at epigenetic, transcriptional and post transcriptional levels\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, they can perform essential functions during basic biological processes, such as chromatin modification\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, imprinting\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, maintenance of pluripotency\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. With the emergence and widespread application of high-throughput sequencing technology, thousands of lincRNAs have been identified in genome-wide analysis, more and more lincRNAs have been functionally validated. A recent study indicated that lincRNA-p21 is involved in regulating the proliferation and apoptosis of vascular smooth muscle cells by enhancing the activity of P53, providing a new target for the treatment of atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Currently, studies on lincRNAs in porcine embryo development are less well understood, therefore, our analysis in the differences of lincRNAs at embryonic development stages will provide a good model for studying the mechanisms that regulate skeletal muscle development.\u003c/p\u003e \u003cp\u003eIn the present study, we applied RNA sequencing to characterize global gene expression patterns of muscle tissues from duroc on days 33, 65, and 90 and systematically analyzed the muscle expression profile during porcine skeletal muscle development\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. We identified 228 putative lincRNAs and found that many lincRNAs differentially expressed. Moreover, we predicted the potential target genes of DE lincRNAs by cis or trans ways. Gene Ontology and pathways enrichment analysis showed that lincRNAs potentially regulated the process of protein-coding genes. An interactive network was performed to elucidate the interplay between DE lincRNAs and their potential target genes. This study of skeletal muscle of transcriptome profiles will provide a useful resource to further explore the understanding of mechanisms, besides, elucidating the underlying mechanisms of skeletal muscle growth and development will be helpful for the improvement of production benefits of pig.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of data from RNA-seq\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccession\u003c/p\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClean reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMapped reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMapping ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUniquely\u003c/p\u003e \u003cp\u003emapping ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33d_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74,174,368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72,887,888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54,060,532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33d_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65,334,814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64,482,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45,701,718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33d_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77,428,960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76,451,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58,773,616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65d_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73,747,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71,946,704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52,038,372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65d_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71,704,676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70,420,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50,268,952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65d_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69,401,348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68,207,232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48,032,548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90d_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65,996,802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65,243,880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47,417,538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72.68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90d_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71,715,594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70,405,728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50,090,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90d_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRR9829609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78,275,962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76,941,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54,391,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eAll the experiments were done in accordance with the relevant guidelines and regulations of animal care and use committee and the study was approved by The Scientific Ethic Committee of Huazhong Agricultural University, Hubei province.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eRNA-seq sequencing data containing nine samples was obtained from the NCBI SRA database. The accession numbers and reads of the RNA-seq data were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In this study, total male samples were strictly collected from the embryonic muscle tissue of duroc, and were grouped into three developmental stages (days 33, 65 and 90, three replicates for each stage)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. We identified 228 putative lincRNAs and found that many lincRNAs differentially expressed. Moreover, we predicted the potential target genes of DE lincRNAs by cis or trans ways. Gene Ontology and pathways enrichment analysis showed that lincRNAs potentially regulated the process of protein-coding genes. An interactive network was performed to elucidate the interplay between DE lincRNAs and their potential target genes. This study of skeletal muscle of transcriptome profiles will provide a useful resource to further explore the understanding of mechanisms, besides, elucidating the underlying mechanisms of skeletal muscle growth and development will be helpful for the improvement of production benefits of pig.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eRNAseq Reads Mapping and Transcriptomic Assembly\u003c/h2\u003e \u003cp\u003eTo ensure the reliability of RNA reads and suitability for the subsequent analysis. FastQC (version 0.11.9) tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003c/span\u003e) was run to quality control checks on raw sequences data and the sequences of poor quality were trimmed and filtered with Trimmomatic (version 0.36) software to obtain clean reads\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The high-quality filtered reads were aligned against the porcine reference genome (Sscrofa11.1 ) using HISAT2 (version 2.0.3) with default parameters\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The pig reference genome file was downloaded from Ensemble (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ensembl.org/pub/release-99/gtf/sus_scrofa/\u003c/span\u003e\u003c/span\u003e). Next, SAM format files which obtained by mapping were converted to BAM format files with SAMtools (version 0.1.19). After that, String Tie (version 1.3.4) was used to assemble transcripts into nine GTF files, then transcripts of all the samples were combined by the Merge parameter of String Tie into a non-redundant transcript set to produce a uniform transcript GTF\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. As a result of assembly produced a large amount of novel transcripts, which were mapped to reference annotation file using the GffCompare to discovery novel transcripts information\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eThe pipeline LincRNAs identification and analysis\u003c/h2\u003e \u003cp\u003eTo identify porcine lincRNAs, we performed the following screening of the transcripts obtained after GffCompare, transcripts which the class-code annotated as \u0026lsquo;U\u0026rsquo;, were more than 200 bp in length and contained at least 2 exons were retained\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Next, all remaining transcripts were scored with CPC to determine their coding potential, transcripts of CPC\u0026thinsp;\u0026lt;\u0026thinsp;0 were considered unable to encode proteins\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Then, we translated transcripts sequences into possible protein domians with Transeq\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and excluded transcripts that were matched in the Pfam database (E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-5)\u003csup\u003e48\u003c/sup\u003e. Furthermore, transcripts that contained similar known proteins in non-redundant reference sequence (NR) database and UniRef90 database were discarded by BLASTX tool (E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-5)\u003csup\u003e49\u003c/sup\u003e. Finally, we performed normalization on the transcript by calculating the \u0026lsquo;fragments per kilo-base of exon model per million mapped reads\u0026rsquo; (FPKM) using String Tie with the parameter \u0026lsquo;-B\u0026rsquo;,and transcripts were retained while FPKM was greater than 0.5 in at least a sample\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComparison of identified lincRNAs and protein coding transcripts\u003c/h2\u003e \u003cp\u003eAt present, the Ensembl database contains comprehensive genetic information for many species. We downloaded the pig reference annotation file that contained 45788 protein-coding transcripts corresponding to 23,422 protein-coding genes in order to compare the characteristic differences between identified lincRNAs and protein-coding genes. LincRNAs annotation information was downloaded from the ALDB database, we acquired about 12,103 known lincRNA transcripts corresponding to 7,381 lincRNA genes, identified lincRNAs and protein-coding genes were aligned to the corresponding reference annotation files to obtain their detailed information, respectively\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eDifferential expression analysis of lincRNAs\u003c/h2\u003e \u003cp\u003eWe used the python package called \u0026lsquo;HTseq-count\u0026rsquo; to calculate the numbers of reads from nine samples\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and the resulting count files were used to evaluate the differential expression levels between different groups by the DEseq2 package in R\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. By screening, lincRNA transcripts with |log2 fold change | \u0026ge; 1 and adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 were identified differentially expressed, protein-coding transcripts with |log2 fold change | \u0026ge; 2 and adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 were identified differentially expressed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eQTL location analysis of differentially expressed lincRNAs\u003c/h2\u003e \u003cp\u003eTo further explore the function of differentially expressed lincRNAs (DE lincRNAs), a correlation analysis was performed between DE lincRNAs with quantitative trait locus (QTL). The pig QTL reference file was downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.animalgenome.org/cgi-bin/QTLdb/SS/index\u003c/span\u003e\u003c/span\u003e, and the parameter \u0026lsquo;intersectBed\u0026rsquo; was used to acquire DE lincRNAs to capture the QTL traits associated with lincRNAs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003ePrediction of potential target genes\u003c/h2\u003e \u003cp\u003eWe predictd the molecular functions of protein-coding genes regulated by RNA in cis and trans. Firstly, the neighboring protein-coding genes nearby DE lincRNAs (\u0026lt;\u0026thinsp;100kb) were identified based on cis-prediction principles using Bedtools. For trans regulation of DE lincRNAs, we calculated the Pearson correlation coefficient (r) between DE lincRNAs and protein coding genes, we selected protein-coding genes that Pearson correlation coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 as the potential target genes of DE lincRNAs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eFunctional enrichment analysis GO and KEGG\u003c/h2\u003e \u003cp\u003eThe list of potential target genes was performed to predict biological process and potential signaling pathway based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) in DAVID website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://david.abcc.ncifcrf.gov/home.jsp\u003c/span\u003e\u003c/span\u003e). The GO terms and KEGG pathways with \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered to be significantly enriched\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Because of the poor annotation of the pig reference genome, the protein-coding gene IDs were converted into human homologous gene IDs using BioMart from Ensembl.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Correlation analysis of DEL genes and DEPTGs\u003c/h2\u003e \u003cp\u003eNetwork intergraphs can intuitively reflect the relationship between DE lincRNAs and their potential target genes. We select PTGs that Pearson correlation coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.96 and \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 differentially expressed in groups were defined as differentially expressed PTGs (DEPTGs), the highly correlated relationship between DE lincRNAs and the underlying potential target genes were established and visualized by Cytoscape (version 3.4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eValidation of differentially expressed lincRNAs\u003c/h2\u003e \u003cp\u003eTo verify our analysis results, qRT-PCR was carried out to test the expression between five DE lincRNAs and seven potential target genes which were randomly selected. There were 16 samples from embryonic muscle tissue used for the experiments (each experiment contained three biological replicates). Total RNA was extracted using Trizol reagent according to the manufacturer\u0026rsquo;s protocols, and reverse transcribed to cDNA using PrimeScript RT reagent Kit with gDNA Eraser r (Takara, Dalian, China). Quantitative PCR was performed using SYBR Premix Ex Taq II on Bio-Rad CFX-96 system (Bio-Rad Laboratories, Hercules). The relative expression levels of all genes were calculated by the 2\u003csup\u003e\u0026minus;∆∆CT\u003c/sup\u003e method. All primers were designed with Primer 5 program (\u003cb\u003eTable S6\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eAll the raw data involved in this study could be obtained from public database. This data can be found here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra/?term=SRP216286\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSummary of RNA-seq data mapping and transcripts assembly in Duroc\u003c/h2\u003e \u003cp\u003eIn this study, we downloaded 9 RNA-seq libraries which contained 647779568 paired-end reads from the NCBI during three embryonic muscle developmental stages of duroc. Samples were named separately 33d_1, 33d_2, 33d_3, 65d-1, 65d_2, 65d_3, 90d-1, 90d_2, 90d_3. After trimming and filtering, a total 636985862 clean reads were mapped to the annotated Sscrofa11.1 genome using HISAT2, we founded that approximately 95% of the quality-filtered reads were mapped and over 70% of the reads could be uniquely mapped to the genome \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Based on the result, String Tie was used to reconstructed the transcripts and merge into a file that obtained 70869 transcripts. The RNA-seq process for identifying lincRNAs was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Finally, 228 putative lincRNAs were identified, there were 191 lincRNAs that have been annotated in the pig reference genome database, and these known lincRNAs were distributed throughout all chromosomes. The remaining 37 lincRNAs have no overlap with the pig annotation database, they were separately distributed on chromosomes 3 to 8 and 11 to 18, and chromosome 17 was found to have the highest novel lincRNAs density.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics analysis of identified lincRNAs\u003c/h2\u003e \u003cp\u003ePrevious study showed that the difference of lncRNAs with protein-coding genes in pig\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, sequence characteristic of lincRNAs during embryonic muscle development remain unclear. Based on the annotated information for the pig reference genome, we examined the characteristic of putative lincRNAs in transcript length, exon length, exon numbers and expression level compared with protein-coding genes. As a result, we observed that the average transcript length of known lincRNAs, novel lincRNAs and protein-coding genes were about 1377bp, 1203bp and 3296bp, respectively. It followed that novel lincRNAs were similar to known lincRNAs and shorter than protein-coding genes in transcript length (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In addition, the average exon length of known lincRNAs, novel lincRNAs and protein-coding genes were 515bp, 505bp and 284bp, respectively. Although the average transcript length of lincRNAs was shorter, the average exon length of lincRNAs was longer than that of protein-coding genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In exon numbers, our result showed that the exon numbers of lincRNAs were gathered at 2\u0026ndash;5, while the average exon numbers of protein-coding genes was 11.6, we noticed that this result was consistent with the above two research (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In normalized read counts expression level (FPKM), the average value of known lincRNAs, novel lincRNAs and protein-coding genes were 1.2, 0.9 and 4.7, respectively. We concluded that lincRNAs had a lower expression level compared with protein-coding genes. In general, lincRNAs were shorter in transcript length, but longer in exon length, had fewer exon, and were expressed at lower level compared with protein-coding genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Which were highly consistent with previous reports\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis of lincRNAs\u003c/h2\u003e \u003cp\u003eTo evaluate the differences in gene expression patterns during three developmental stages, DEseq2 was used to identify differentially expressed lincRNAs and protein-coding genes between two paired samples (D33 vs. D65; D65 vs. D90; D33 vs. D90). when |fold change| \u0026ge;1 and adjusted \u003cem\u003ep\u003c/em\u003e -value\u0026thinsp;\u0026le;\u0026thinsp;0.05, there were 66 DE lincRNA genes including 50 upregulated and 16 downregulated identified between Day 33 and 65 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), 29 DE lincRNA genes including 12 upregulated and 17 downregulated identified between Day 65 and 90 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), 74 DE lincRNA genes including 48 upregulated and 26 downregulated identified between Day 33 and 90 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). All DE lincRNAs in three groups were distributed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD. In addition, when |fold change| \u0026ge;2 and adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.01, a total 2638 DE protein-coding genes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQTL localization and functional enrichment\u003c/h2\u003e \u003cp\u003eQTL is closely associated with many traits. To explore the relationship between differentially expressed lincRNAs and QTL traits, we performed a correlation analysis by mapping DE lincRNAs to the QTL regions related to pig traits, the pig QTL database contains 31455 QTLs, representing 695 different traits\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Our analysis result showed that 113 of 120 DE lincRNAs were located in 2200 QTL, which corresponded to 331 traits, 27 trait types, 4 trait classes. The greatest number of QTLs were associated with the trait \u0026ldquo;Meat and Carcass Traits\u0026rdquo;, accounting for about 59% of the total QTLs. The second highest number of QTL traits \u0026ldquo;Production Traits\u0026rdquo; accounted for 11% of the total QTLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We statistically analyzed localization in QTLs associated with muscle, obesity, and growth traits, and found that most DE lincRNAs were targeted at the three trait types. Notably, 100 of 113 DE lincRNAs were closely associated with growth and 86 DE lincRNAs were located in muscle related traits, from this we hypothesized that DE lincRNAs could have an important effect on muscle growth and development (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, we examined the distribution of these QTLs on chromosomes, and found that QTLs were distributed on all chromosomes. Interestingly, the greatest number of QTLs for these three traits were located on chromosome 4 and chromosome 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of potential target genes of DE lincRNAs\u003c/h2\u003e \u003cp\u003ePrevious studies have shown that lincRNAs can regulate the expression of target genes by cis or trans via, and participate in the functional regulation of some organisms\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Firstly, we predicted potential target genes of DE lincRNAs in cis regulation to determine the possible function of DE lincRNAs by searching for protein-coding genes around 100kb upstream and downstream of DE lincRNAs. We found 303 protein-coding genes were close to DE lincRNAs. Pearson correlation analysis revealed that 37 potential target genes (PTGs) were highly correlated with 29 DE lincRNAs (r\u0026thinsp;\u0026ge;\u0026thinsp;0.8, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.01). Among them, 12 of 37 PTGs differentially expressed were defined as DEPTGs. Meanwhile, most DE lincRNAs were significantly positively correlated with their PTGs. MSTRG.6732 and MSTRG.2061 were significantly negatively correlated with ERGIC1 and HMGB1. Besides, MSTRG.4842 and MSTRG.14169 could regulate their PTGs in two ways: positive regulation and negative regulation. The potential target genes for DE lincRNAs regulation were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation between DEL genes and their adjacent protein-coding genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDELs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAdjacent protein-coding genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePearson correlation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDELs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eAdjacent protein-coding genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ePearson correlation coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSYNE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.932778133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.2696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eC17orf105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.800640629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.7542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eZC3HAV1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.909721177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eVGLL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.938895472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eKIAA1549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.802049845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e7SK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.923173536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.7420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSLC2A4RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.878741211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.2061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eHMGB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-0.864645067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.7020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePAX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.941796265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.1905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ePIP4K2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.820942703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eENSSSCG00000031878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.935502944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.17805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eRAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.992836498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.6732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eERGIC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.886959706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.17803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eNHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.866101456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.5732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSTAM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.89876083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.17252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eIER5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.810425662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.5387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eC10orf71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.999482502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.16842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003essc-mir-125b-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.871701204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.5199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eACTA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.852421469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.15750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eDLK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.909106508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.4842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRHOF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.993619533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.14579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eCTPS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.951838287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTMEM120B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.962101405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.14169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eTUBB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.885525513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWDR66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.898219733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eMPPE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-0.869796489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePSMD9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.905954104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.13914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eMYOM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.903239933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.4603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCOL18A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.804671084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.12042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eHOXC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.87413117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.4602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCOL18A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.817579473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.11764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ePPARA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.935640688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAFDN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.870481656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMSTRG.11756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eENSSSCG00000035352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.986041555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMSTRG.2696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMPP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.926075239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eENSSSCG00000033576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.917380642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMPP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.888523664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ePPARA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.84472984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePPY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.886803079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of PTGs associated with DE LincRNAs\u003c/h2\u003e \u003cp\u003eFurthermore, we also predicted the potential target genes from DE lincRNAs in trans regulation, and acquired 4609 PTGs corresponding to 50 DELs (r\u0026thinsp;\u0026ge;\u0026thinsp;0.96,\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.01). Among these genes, 548 PTGs were differentially expressed in groups as DEPTGs. Which suggested that most of lincRNAs regulated gene expression through trans regulation. GO enrichment analysis showed that 4609 PTGs were enriched in 547 biological processes and 548 DEPTGs were enriched in 287 biological processes. In cases of biological process. Some GO terms were significantly associated with muscle development and energy metabolism, such as skeletal muscle tissue development, muscle contraction, cell proliferation, protein catabolic process, insulin receptor signaling pathway and regulation of glucose transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Besides, 4609 PTGs and 548 DEPTGs were enriched in 64 pathways and 28 pathways, respectively. KEGG pathways were involved in Wnt signaling pathway, ECM-receptor interaction, MAPK, calcium signaling, ErbB signaling pathway and TGF-beta signaling pathway \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The results indicated that DE lincRNAs had an important role in regulating their potential target genes regulated composition and growth and development of muscle cells by muscle cells proliferate and differentiate, substance metabolism energy transport and conversion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eCo expression network analysis of DE lincRNAs and DE potential target genes\u003c/h2\u003e \u003cp\u003eTo understand the relationship of expression between DE lincRNAs and their DEPTGs. The expression regulation relationship between 50 DE lincRNAs and 548 DEPTGs was analyzed, we calculated the interaction of DE lincRNAs and DEPTGs. Pearson correlation analysis results were presented that 860 pairs between DE lincRNAs and DEPTGs with positive correlation and 86 pairs with negative correlation were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). We selected DE lincRNAs and DEPTGs related to skeletal muscle growth and development pathways to construct co-expression networks, and 24 DE lincRNAs exhibited a high co-expression relationship with 48 DEPTGs. Noticeably, DE lincRNA MSTRG.388, MSTRG.4602 and MSTRG.7020 were involved in the regulation of several DEPTGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In order to further explore the function of DE lincRNAs, we investigated nine DEPTGs involving in muscle development related pathways corresponding to 13 DE lincRNAs, we found that SHH targeted by lincRNA MSTRG.27 and MSTRG.388 played an important role in myogensis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), and SHH had an essential inductive function in the early activation of the myogenic regulatory factors Myf-5 and MyoD\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Besides, lincRNA MSTRG.4602, MSTRG.98 and MSTRG.243 regulated MYOZ1 that encoded calsarcin-2 protein participated in the expression of PPAR-Y2 in skeletal muscle\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eValidation of lincRNA expressions through qRT-PCR\u003c/h2\u003e \u003cp\u003eAccording to the previous RNA-seq results, we selected nine pairs of DE lincRNA genes and their potential target genes and analysis their expression levels by qRT-PCR (MSTRG.98 vs. CA4, MSTRG.98 vs. MYOZ1, MSTRG.243 vs. MYOZ1, MSTRG.4602 vs. MYOG, MSTRG.4602 vs. TGFB2, MSTRG.4602 vs. MAPK14, MSTRG.4602 vs. FOXO3, MSTRG.17803 vs. FAIM2, MSTRG.4034 vs. CA4) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The experimental results showed that the correlation (r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) bwteen DE lincRNAs and potential target genes were at above 0.86 and the p-values were less than 0.01. The experimental results of the qRT-PCR have a similar tend to the original Pearson correlation coefficient between DE lincRNAs and potential target genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eSkeletal muscle growth and development are a complex process, which directly determine the meat production and quality in the pig industry. Skeletal muscle is mainly composed of muscle fibers, basement membrane, muscle satellite cells and nerves. Study found that the numbers of muscle fiber have been fixed before the pigs were born, indicating that muscle fiber development is mainly determined during the embryonic period\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Muscle fiber development takes place in two waves in pig embryonic, the first wave of muscle fiber formation occurs from 30 to 60 days and the second wave occurs from 45 to 90 days\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In our study, we investigated lincRNAs expression profile in days 33, 65, and 90, which included the primary, second, and final waves of muscle fiber development\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Even though the previous studies have showed lincRNAs associated with muscle growth in pig, the dynamic process of expression profile of lincRNAs in embryonic muscle fibers is rare, and our study provides theoretical basis for new exploration in the future.\u003c/p\u003e \u003cp\u003eBased on RNA-seq data published in NCBI, we compared whole gene expression profile in muscle tissue from druoc in differentially development periods. Through a series of transcriptome pipeline analysis, there were 229 putative lincRNAs identified using RNA-seq sequencing, we predicted 39 novel lincRNAs that were not annotated from the nine muscle libraries, which enrich the pig lincRNA annotation and the specific features need to be further investigated in the future. Moreover, we performed a characteristic analysis of putative lincRNAs, involving in transcript and exon length, exon numbers and FPKM, the results showed that the similar characteristic of shorter transcript length, longer exon length, fewer exons, and lower expression levels compared with previous reports\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the reliability of the analysis is further improved.\u003c/p\u003e \u003cp\u003eWe identified 95 DE lincRNA genes and 2638 DE protein-coding genes based on a designed pipeline. Previous studies have shown that there were a large number of lncRNA located within known QTL regions\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. To understand the relation between DE lincRNAs and QTL, we performed also QTL localization analysis for differentially expressed lincRNAs. Some QTLs are involved in large regions,so that multiple genes are located on the same QTL, or multiple QTLs have the same gene location. In among, the specific mechanism may need to be verified by subsequent experiments.\u003c/p\u003e \u003cp\u003eTo explore the potential function of DE lincRNAs, we investigated the regulation of lincRNAs on gene expression through cis and trans regulation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. For the cis-regulation of DE lincRNAs, we found these genes have been shown to be associated with muscle cell proliferation and fat deposition. For example, DLK1 was a critical factor in regulating skeletal muscle development and regeneration through Notch dependent\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Previous studies found that PPARA was involved in the regulation of fat deposition in porcine subcutaneous fat and longissimus dorsi muscle\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Besides, myofibrillar structural protein myomesin-3 (MYOM3) was not only associated with muscular dystrophy related proteins and muscle strength, which could be a potential biomarker for monitoring of muscular dystrophy, but was hymethylated in ischemic cardiomyopathy\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Among them, DEL-MSTRG. 31882 and its potential target gene with patatin like phospholipase domain 4 (PNPLA4) showed significant positive correlation at the expression level. Therefore, we inferred that DE lincRNAs RNA modulates differences at different developmental stages by regulating their potential target genes.\u003c/p\u003e \u003cp\u003eIn this study, we investigated Gene Ontology and KEGG pathways analysis of potential target genes of DE lincRNAs, and found that skeletal muscle organ and tissue development processes, muscle contraction, striated muscle cell development were some of significantly enriched GO terms. This results suggest that identified DE lincRNAs have important impact in the skeletal muscle. Regulation of glucose import, regulation of glucose transport, and insulin receptor signaling pathway also significantly enriched, which were important ways of obtaining and transporting energy, we deduce that DE lincRNAs could participate in the regulatory mechanism of skeletal muscle development through mediating cellular energy responses. Our KEGG pathway analysis showed that significantly enriched pathways including MAPK signaling pathway, TGF-beta signaling pathway, Wnt signaling pathway, ECM\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction, regulation of kinase activity. Previous studies have confirmed that TGF-beta signaling pathway contributes to muscle development in mice\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Moreover, the extracellular matrix (ECM) is a network of structures surrounding muscle fibers, providing a close connection with cell proliferation, differentiation and metabolism. Therefore, we infer that DE lincRNAs could contribute to the differences in skeletal muscle development. In addition, some cardiac diseases, such as viral myocarditis, dilated cardiomyopathy, and hypertrophic cardiomyopathy, were also significantly enriched, these results suggest that DE lincRNAs may have an important effect on myocardial development.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eIn the study, we identified 228 putative lincRNAs and analysised the characteristics of lincRNAs transcriptome compared with protein-coding genes in embryonic muscle tissue of duroc. We observed numerous differentially expressed lincRNAs and protein-coding genes during differential development stages. Functional enrichment analysis of potential target genes by DE lincRNAs revealed that many lincRNAs participated in muscle growth and development related processes and pathways. Co-expression networks indicated the functional relatedness between protein-coding genes and lincRNAs. In summary, our work provides a valuable resource for future research into the potential functions of pig growth and development and is expected to promote the progress of pig production.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by National Natural Science Foundation of China (NSFC, 31872322), the Fundamental Research Funds for the Central Universities (2662017PY030).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.L. conceived and designed the experiments and explained the data; W.Z. performed the analysis of this data with the help of Z.L.; Q.L. and S.X. contributed to the collection of samples; M.L. and Y.W. carried out the experiment of qRT-PCR; W.Z. wrote and revised the manuscript. All authors have reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi, R. \u003cem\u003eet al.\u003c/em\u003e Exploring the lncRNAs Related to Skeletal Muscle Fiber Types and Meat Quality Traits in Pigs. \u003cem\u003eGenes.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/genes11080883\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiciliot, S., Rossi, A. C., Dyar, K. A., Blaauw, B. \u0026amp; Schiaffino, S. 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Genome-wide transcriptome analysis reveals that thousands of long intergenic non-coding RNAs (lincRNAs) have been identified in various species and implicated as crucial regulator involving in epigenetic regulation. However, comprehensive analysis of lincRNAs in embryonic muscle development stages remain still elusive. Here, we investigated the transcriptome profiles of duroc embryonic muscle tissues from days 33, 65, and 90 of gestation using RNA-seq, there were 228 putative lincRNAs identified. Moreover, these lincRNAs exhibit the characteristics of shorter transcripts length, longer exons, less exon numbers and lower expression level compared with protein-coding transcripts. Differential expression analysis showed that a total of 91 lincRNAs and 2638 mRNAs were differentially expressed. In addition, we also performed quantitative trait locus (QTL) mapping analysis for DE lincRNAs, 113 of 120 DE lincRNAs were localized on 2200 QTLs, we observed many QTLs involved in growth and meat quality traits. Furthermore, we predicted potential target genes of DE lincRNAs in cis or trans regulation. Gene ontology and pathway analysis reveals that potential targets of DE lincRNAs mostly were enriched in the processes and pathways related to tissue development, MAPK signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and insulin signaling pathway, which involved in skeletal muscle physiological functions. Based on cluster analysis, a co-expression network analysis of DE lincRNAs and their potential target genes indicated that DE lincRNAs highly regulated protein-coding genes associated with skeletal muscle development. In this study, many of the DE lincRNAs identified may play essential roles in pig muscle growth and muscle mass. Our study provides\u0026nbsp;crucial information for exploring further the molecular mechanisms of lincRNAs during skeletal muscle development.\u003c/p\u003e","manuscriptTitle":"Analysis of Long Intergenic Non-Coding RNAs Transcriptomic Profiling in Skeletal Muscle Growth During Porcine Embryonic Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-26 21:36:14","doi":"10.21203/rs.3.rs-323967/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-04-29T07:43:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-04-19T03:13:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3dd15bb3-7211-4a00-a3f8-b8816c562270","date":"2021-04-06T02:18:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-05T17:55:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-04-05T17:40:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-03-26T17:28:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-03-25T12:33:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2021-03-13T07:38:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f84062e5-4d63-4eeb-bb94-b4e386835d6c","owner":[],"postedDate":"March 26th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3243467,"name":"Molecular Biology"},{"id":3243468,"name":"Molecular Genetics"}],"tags":[],"updatedAt":"2021-08-22T15:12:03+00:00","versionOfRecord":{"articleIdentity":"rs-323967","link":"https://doi.org/10.1038/s41598-021-94014-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2021-07-27 15:01:55","publishedOnDateReadable":"July 27th, 2021"},"versionCreatedAt":"2021-03-26 21:36:14","video":"","vorDoi":"10.1038/s41598-021-94014-w","vorDoiUrl":"https://doi.org/10.1038/s41598-021-94014-w","workflowStages":[]},"version":"v1","identity":"rs-323967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-323967","identity":"rs-323967","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}