Oncotarget

Research Papers:

Comparative analyses of long non-coding RNA in lean and obese pigs

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Oncotarget. 2017; 8:41440-41450. https://doi.org/10.18632/oncotarget.18269

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Lin Yu, Lina Tai, Lifang Zhang, Yi Chu, Yixing Li _ and Lei Zhou

Abstract

Lin Yu1, Lina Tai1, Lifang Zhang1, Yi Chu1, Yixing Li1 and Lei Zhou1

1State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, P.R. China

Correspondence to:

Yixing Li, email: liyixing39@163.com

Lei Zhou, email: zhoulei@gxu.edu.cn

Keywords: lncRNA, pig, obesity, QTL

Received: April 28, 2017     Accepted: May 15, 2017     Published: May 26, 2017

ABSTRACT

Objectives: Current studies have revealed that long non-coding RNA plays a crucial role in fat metabolism. However, the difference of lncRNA between lean (Duroc) and obese (Luchuan) pig remain undefined. Here, we investigated the expressional profile of lncRNA in these two pigs and discussed the relationship between lncRNA and fat deposition.

Materials and Methods: The Chinese Luchuan pig has a dramatic differences in backfat thickness as compared with Duroc pig. In this study, 4868 lncRNA transcripts (including 3235 novel transcripts) were identified. We determined that patterns of differently expressed lncRNAs and mRNAs are strongly tissue-specific. The differentially expressed lncRNAs in adipose tissue have 794 potential target genes, which are involved in adipocytokine signaling pathways, the PI3k-Akt signaling pathway, and calcium signaling pathways. In addition, differentially expressed lncRNAs were located to 13 adipose-related quantitative trait loci which include 65 QTL_ID. Subsequently, lncRNA and mRNA in the same QTL_ID were analyzed and their co-expression in two QTL_ID were confirmed by qPCR.

Conclusions: Our study provides an insight into mechanism behind the fat metabolic differences between the two breeds and lays an important groundwork for further research regarding the regulatory role of lncRNA in obesity development.


Comparative analyses of long non-coding RNA in lean and obese pigs | Yu | Oncotarget

INTRODUCTION

Obesity has become a major health concern around the world and is the main risk factor for non-alcoholic fatty liver disease (NAFLD), type 2 diabetes and cardiovascular diseases (CVDs) [1]. Therefore, the study of fat deposition and its mechanism is of great benefit for prevention and treatment of obesity and its related diseases. The Luchuan pig is a typical obese breed as it has higher intramuscular fat and backfat thickness compared with the Duroc breed. They are good models to investigate the regulatory mechsniam of fat metabolism.

Long non-coding RNAs (lncRNAs) are defined as non-coding RNAs of at least 200 nucleotides. In the past, lncRNAs were considered to be “evolutionary junk” or transcriptional “noise” along with other non-coding RNAs [2, 3]. However, in recent years, as the rapid development of technologies has facilitated analysis of the “transcriptome”, there is increasing evidence that lncRNAs play a crucial role in many biological processes [4, 5], such as telomere homeostasis and chromosome replication [68], control of nuclear architecture and translation [9], X-chromosome inactivation [10], regulation of epigenetic modifications [11], control of mRNA and protein stability [12, 13], and regulation of miRNA activity [14, 15]. As research into lncRNA increased, many databases were established and included lncRNA data for both domesticated animals and poultry. At the time of publication, a total of 12,103 pig lncRNAs, 8,923 chicken lncRNAs and 8,250 cow lncRNAs are included in the ALDB database [16]. While analyzing lncRNA expression in pig, Zhao et al. developed a systematic protocol for the identification and characterization of lncRNAs in fetal porcine skeletal muscle [17]. Moreover, an antisense lncRNA of the PU.1 gene was identified, which can form a sense-antisense RNA duplex to promote adipogenesis [18]. Wang et al. investigated the lncRNAs in porcine endometrial tissue samples using RNA-seq [19]. Currently, porcine fat deposition is less well understood. Toward that end, in order to compare the lncRNA expression differences between the lean and obese breeds, lncRNA sequences were obtained from three different types of tissues (liver, muscle and fat) of Luchuan and Duroc pigs.

In this study, we identified differentially expressed lncRNA molecules and predicted their target genes. Moreover, the correlation between the identified lncRNA molecules and QTL were investigated. These results will provide a useful resource to further explore the role of lncRNAs in fat deposition.

RESULTS

Overview of lncRNA sequencing data

After 180 days of identical feeding conditions, the average backfat thickness of Luchuan pigs was 35.33 ± 0.57 mm while that of the Duroc pigs was 12 ± 1.00 mm. Next, three tissue samples (liver, muscle and fat) were collected from three animals from each porcine breed: L-liver (Luchuan liver), D-liver (Duroc liver), L-muscle (Luchuan muscle), D-muscle (Duroc muscle) , L-fat (Luchuan fat) and D-fat (Duroc fat). Total RNA from each sample was sequenced by using the Illumina HiSeq 2500 platform. A total of 16.84G, 14.82G, 13.37G, 12.26G, 15.56G and 14.00G clean data was generated in the L-liver, D-liver, L-muscle, D-muscle, L-fat and D-fat samples, respectively. The GC content averaged between 51.84 and 56.77% while the Q30 ranged between 95.08 and 96.16%. These results show that the quality of our six libraries was good and suitable for subsequent analysis. Next, the clean reads were aligned to the reference genome (Susscrofa 10.2) using Tophat v2.1.0 (Table 1). More than 68.54% of clean reads were uniq mapped in each sample. An average of 40.68% of clean reads were mapped to sense strand and 39.43% of clean reads were mapped to antisense strand. A high quality library is a necessary for lncRNA sequencing; therefore, the data from each read’s relative position in gene (5′–3′) were analyzed to ensure the quality of these six samples. Obviously, the vast majority of reads were evenly distributed throughout the gene by random sampling, which indicates that the quality and homogeneity of the samples was good (Figure 1A). Moreover, the ratio of reads corresponding to exon, intron, and intergenic regions was different (Figure 1B), suggesting that the RNA expression profiles were tissue specific.

Table 1: Categorization of reads and basic characteristics of lncRNAs in Luchuan and Duroc pigs

Luchuan

Duroc

Sample ID

L-liver

L-muscle

L-fat

D-liver

D-muscle

D-fat

Average

Clean Data

16840128832

13368920108

15559684336

14822619834

12261255102

14001588618

14475699472

GC (%)

51.84

55.57

55.9

54.32

56.77

56.18

55.09

N (%)

0.01

0.01

0.01

0.01

0.01

0.01

0.01

Q30 (%)

95.79

95.71

95.46

96.16

95.68

95.08

95.65

Clean Reads

113662050

91521480

106758414

102156440

84311820

96436194

99141066

Mapped Reads

93369950 (82.15%)

70725019 (77.28%)

82473446 (77.25%)

86378716 (84.56%)

65642547 (77.86%)

78681690 (81.59%)

79545228 (80.12%)

Uniq Mapped Reads

71734450 (76.83%)

51258518 (72.48%)

58949568 (71.48%)

61811541 (71.56%)

44992237 (68.54%)

56815782 (72.21%)

57593683 (72.18%)

Multiple Mapped Reads

21635500 (23.17%)

19466501 (27.52%)

23523878 (28.52%)

24567175 (28.44%)

20650310 (31.46%)

21865908 (27.79%)

21951545 (27.82%)

Reads Map to ‘+’

47306223 (41.62%)

35819308 (39.14%)

42098432 (39.43%)

43643606 (42.72%)

33366249 (39.57%)

40109194 (41.59%)

40390502 (40.68%)

Reads Map to ‘−’

46063727 (40.53%)

34905711 (38.14%)

40375014 (37.82%)

42735110 (41.83%)

32276298 (38.28%)

38572496 (40.00%)

39154726 (39.43%)

Triplicate tissue samples were collected from three pigs of each species. GC (%) is the percentage of G and C bases / total nucleotides; N (%) is the percentage of unrecognized bases / total nucleotides; Q30 (%) is the percentage of bases’ mass greater than or equal Q30 in the clean data; Uniq Mapped Reads is the number and percentage of reads that mapped to a unique position in the reference genome in the clean reads; Multiple Mapped Reads is the number and percentage of reads that mapped to multiple positions in the reference genome in the clean reads; Reads Map to ‘+’ is the number of clean reads mapped to the sense strand; Reads Map to ‘−’ is the number of clean reads mapped to antisense strand.

Overview of lncRNA sequencing data.

Figure 1: Overview of lncRNA sequencing data. (A) The distribution of mapped reads on mRNA (5′–3′). Data in reflects the percentage of mapped reads assigned to all regions of mRNA. The location of the normalized mRNA is on the horizontal (x) axis; the percentage of reads as compared to total mapped reads for the position is on the vertical (y) axis. As the reference mRNA is different in length, each mRNA is divided into 100 intervals by length. (B) Reads mapped to different regions of the genome. (C) Venn diagrams show the result of four computational approaches. 4868 candidate lncRNAs were identified from a intersection results from the CNCI (coding-non-coding index), CPC (coding potential calculator), Pfam (protein folding domain database), and CPAT (coding potential assessing tool). (D) The type and number of predicted long non-coding RNAs (lncRNAs). Intronic lncRNA: lncRNA transcript from the intron region of gene; Antisense lncRNA: lncRNA has opposite transcriptional direction compared to adjacent mRNA; Sense lncRNA: lncRNA has the same transcriptional direction as the adjacent mRNA; Intergenic lncRNA: lncRNA transcribed from a position between two genes.

Identification of lncRNAs expressed in liver, muscle, and fat tissue of Luchuan and Duroc pig breeds

All mapped reads of the six libraries were assembled using Cufflinks [20]; then, the assembled transcripts were filtered using a rigorous method. Transcripts which were >200 bp in length, contained two exons, had at least three reads coverage, and had a 0.1 FPKM value were retained. As lncRNA do not encode proteins, the protein coding potential of the remaining transcripts was determined using four separate protocols: CPC, CNCI, CPAT and pafm. Finally, 4868 lncRNA transcripts were identified (Figure 1C), including 2403 lincRNAs (49.36%), 252 anti-sense lncRNAs (5.18%), 216 intronic lncRNAs (4.44%), and 1997 sense lncRNAs (41.02%) (Figure 1D). Moreover, 3235 novel lncRNA transcripts were revealed by blasting their sequences in NONCODE and lncRNAdb.

These 4868 lncRNA transcripts were distributed throughout all chromosomes found in pig, although chromosome 1 contained the greatest number of lncRNAs (Figure 2A). On the whole, there were fewer alternatively spliced isoforms per lncRNA molecule as compared with mRNA molecules (Figure 2B). Lengths between 600~1200 bp and ≥ 3000 bp from both lncRNA and mRNA molecules were most common (Figure 2C2D). All expression information of lncRNAs and genes are shown in Supplementary Tables 1–2. We noticed that all the lncRNAs tended to be expressed at a lower level than the protein-coding genes (Figure 3A). Then, all of differently expressed transcripts were filtered to include only those transcripts with a false discovery rate (FDR) < 0.05 and fold change ≥ 2 or ≤ 0.5; the FDR is obtained by from adjusting the p-value, and the fold change was obtain from gene expression of Luchuan / gene expression of Duroc pig. In adipose tissue, 503 lncRNA and 2173 mRNA molecules were detected (Figure 3B). All of the differentially expressed lncRNAs and mRNAs are shown in Supplementary Tables 3–4. Two Venn diagrams depict the differentially expressed lncRNA and mRNA molecules in each of the three tissue types (Figure 3C3D). A total of 386, 349 and 336 differentially expressed lncRNAs appear to be specific for the liver, muscle and fat, respectively. Tissue specific lncRNAs were a major proportion of the differentially expressed lncRNAs. Similarly, 1123, 800 and 1513 differently expressed mRNAs appear to be specific for the liver, muscle and fat, respectively.

Features of lncRNAs and mRNAs in the genome.

Figure 2: Features of lncRNAs and mRNAs in the genome. (A) LncRNAs distribution by chromosome. (B) Alternatively spliced isoforms per lncRNA and mRNA molecule. (C&D) Distribution of lncRNA and mRNA molecules by length.

Differential expression of lncRNAs and mRNAs by tissue.

Figure 3: Differential expression of lncRNAs and mRNAs by tissue. (A) The number of differentially expressed lncRNAs and mRNAs by tissue. Red bar represents up-regulated transcripts and the green bar represent down-regulate transcripts. (B) Expression profiles of lncRNA and mRNA in each tissue category. We used log10(FPKM) as the final data to indicate expression level. (C, D) Tissue-specific expression of lncRNAs and mRNA.

Next, 1286 and 4271 unique and differentially expressed lncRNAs and mRNA were used to perform a tissue-specific clustering analysis. As shown in the heat map, the up-regulated lncRNAs were divided into six clusters. The expression pattern of Duroc lncRNAs (D-liver , D-muscle and D-fat ) was distinct from the expression of Luchuan lncRNAs (L-liver, L-muscle and L-fat ) (Figure 4A). In contrast, according to the heat map of differentially expressed mRNA transcripts, the cluster of up-regulated mRNA molecules was similar between the Luchuan liver and Duroc liver samples as well as between the Luchuan muscle and Duroc muscle samples. However, the Luchuan fat and Duroc fat samples expressed distinct mRNA molecules (Figure 4B). In order to further understand the potential function of lncRNA in fat deposition, the target genes of the lncRNAs were predicted. A portion of some lncRNA molecules and their target gene are presented in Table 2. All of the target genes are listed in Supplementary Table 5.

Heat-map of differently expression lncRNAs and mRNAs.

Figure 4: Heat-map of differently expression lncRNAs and mRNAs. (A) Cluster heat-map of differentially expressed lncRNAs from each sample. (B) Cluster heat-map of differentially expressed mRNAs from each sample.

Table 2: Differentially expressed lncRNAs and their target mRNA in each tissue

Tissue

lncRNA_ID

Fold change

Regulated

Gene_ID

Gene name

Fold change

Regulated

liver

TCONS_00109066

222.8439

up

ENSSSCG00000014368

25.75714

up

liver

TCONS_00156770

175.7342

up

ENSSSCG00000003967

ZMYND12

0.235277

down

liver

TCONS_00176571

79.80171

up

ENSSSCG00000002627

GSTA4

2.524484

up

liver

TCONS_00130158

0.007197

down

ENSSSCG00000006155

ZBTB10

0.277276

down

liver

TCONS_00062308

0.005966

down

ENSSSCG00000024537

5.06822

up

liver

TCONS_00143452

0.00467

down

ENSSSCG00000000878

DEPDC4

0.321732

down

muscle

TCONS_00013468

123.2883

up

ENSSSCG00000005087

SIX1

0.428436

down

muscle

TCONS_00108745

93.70243

up

ENSSSCG00000014083

ANKDD1B

2.762261

up

muscle

TCONS_00136478

81.86808

up

ENSSSCG00000006506

SYT11

0.325533

down

muscle

TCONS_00024736

0.006558

down

ENSSSCG00000023031

TNNT2

7.496831

up

muscle

TCONS_00018030

0.004239

down

ENSSSCG00000005087

SIX1

0.428436

down

muscle

TCONS_00008591

0.002903

down

ENSSSCG00000004602

TEX9

0.160484

down

fat

TCONS_00027769

167.275

up

ENSSSCG00000010837

FAM177B

63.01933

up

fat

TCONS_00181629

149.6883

up

ENSSSCG00000027340

112.1

up

fat

TCONS_00109934

73.74533

up

ENSSSCG00000013148

GLYATL2

59.994

up

fat

TCONS_00066375

0.01899

down

ENSSSCG00000010069

DERL3

18.56326

up

fat

TCONS_00194244

0.017954

down

ENSSSCG00000014861

MOGAT2

0.156778

down

fat

TCONS_00174306

0.007341

down

ENSSSCG00000001826

CCDC37

13.31454

up

The top three up regulated and down regulated lncRNAs with its target mRNA in each tissue.

Gene ontology (GO) and KEGG pathway enrichment analysis

Considering the adipose tissue had the greatest number (2173) of differentially expressed mRNAs and is a major organ for fat deposition, a gene ontology (GO) analysis was performed for the adipose tissues. The most enriched GO terms are shown in Figure 5A; complete information is listed in Supplementary Table 6. We primarily focused on genes involved in electron carrier activity and antioxidant activity as these functions may be involved in the regulation of fat deposition. Meanwhile, these target genes also underwent a KEGG analysis to determine the potential biological function of the identified lncRNAs. The data showed that some pathways related to fat metabolism and energy metabolism were significantly enriched, such as signaling pathways associated with adipocytokines, calcium signaling, MAPK, FOXO and PI3k/Akt (Figure 5B and Supplementary Table 7). To our surprise, there were 16 target genes that function as part of the PI3k-Akt signaling pathway, which is closely related to insulin signaling pathway [21].

GO and KEGG analysis of target genes in adipose tissue.

Figure 5: GO and KEGG analysis of target genes in adipose tissue. (A) Gene Ontology analysis of target genes of differentially expressed lncRNAs from adipose tissue (L-fat vs D-fat). DEG Unigene: differentially expressed genes number in all annotation Biological Process GO term. All Unigene: Unigene number in all annotation Biological Process GO term. (B) KEGG pathway enrichment analysis of target genes of differentially expressed lncRNAs from adipose tissue (L-fat vs D-fat).

QTL location analysis of DE-lncRNAs and quantitative validation

Due to the tight connection between QTL and traits, and to further explore the role of differentially expressed lncRNA molecules on fat deposition, a correlation analysis was performed between lncRNA and fat-associated QTL by mapping differentially expressed lncRNAs onto pig QTL regions. This analysis indicated that 275 differentially expressed lncRNAs are located in 13 fat-associated QTL (Supplementary Table 8). At the same time, differentially expressed mRNAs were mapped onto these 13 QTL and 498 mRNAs were found to localize to the 13 QTL as well. Genes associated with the trait “abdominal fat weight” had the greatest number of associated differentially expressed lncRNAs (138) and mRNAs (513). The trait, “average backfat thickness”, had the second highest number of associated differentially expressed lncRNAs (89) and mRNAs (306) (Table 3).

Table 3: Differentially expressed lncRNA and mRNA molecules (L-fat VS D-fat) assigned into QTL trait regions

Trait

Number of QTL_ID

All DE-lncRNAs

Up-lncRNA

Down-lncRNA

All DE-mRNAs

Up-mRNA

Down-mRNA

Abdominal fat percentage

2

4

3

1

8

5

3

Abdominal fat weight

17

138

79

59

513

308

205

Adipocyte diameter

6

23

13

10

97

53

44

Arachidic acid content

2

3

1

2

3

2

1

Arachidonic acid content

1

3

2

1

1

1

0

Average backfat thickness

28

89

60

29

306

167

139

backfat above muscle dorsi

2

5

2

3

82

53

29

backfat at last rib

1

2

0

2

17

5

12

Backfat at rump

1

1

0

1

8

4

4

Backfat between 6th and 7th ribs

1

2

0

2

8

5

3

Backfat weight

2

3

2

1

15

5

10

Loin fat percentage

1

1

1

0

0

0

0

Percentage of backfat and leaf fat in carcass

1

1

1

0

7

3

4

Up-lncRNA: up regulated lncRNA number; Down-lncRNA: down regulated lncRNA number; Up-mRNA: up regulated mRNA number; Down-mRNA: down regulated mRNA number.

Next, we tried to identify which QTL play a crucial role in fat deposition. The number of differentially expressed lncRNAs in each QTL_ID was analyzed; 65 QTL_ID containing differentially expressed lncRNAs were identified. The score for each QTL_ID were sorted (score = the number of differentially expressed lncRNAs / the span length of each QTL_ID) (Supplementary Table 9). The top 20 QTL_ID are listed in Table 4. More than half of the top 20 QTL_ID are associated with the “average backfat thickness” trait. Next, the differentially expressed mRNAs were assigned to 65 QTL_ID. This analysis indicated that gene ELOVL6 is located in the same region of QTL_ID 21252 as lncRNA TCONS_00185144 and TCONS_00181156. Moreover, the gene STEAP4 as found in QTL_ID 21259 along with lncRNA TCONS_00199412 and TCONS_00197271. Subsequently, quantitative real-time PCR confirmed their co-expression relationship (Figure 6A6B). These results suggest that lncRNA may participate in regulations of genes in the same QTL_ID.

Table 4: QTL_ID ranked by number of differentially expressed lncRNA molecules

QTL_ID

DE-lncRNAs

Chrome

Trait

Name

Start

End

Score

31542

3

14

Arachidonic acid content

FA-C20:4

138365048

138414114

6.11E-05

658

1

10

Average backfat thickness

BFT

11227321

11306620

1.26E-05

12712

1

1

Abdominal fat weight

ABDF

294607712

294736101

7.79E-06

21436

1

16

Loin fat percentage

LOINFP

83843383

84125107

3.55E-06

735

1

1

Average backfat thickness

BFT

307398864

307784034

2.6E-06

17773

1

1

Average backfat thickness

BFT

140716292

141412550

1.44E-06

12713

1

1

Abdominal fat weight

ABDF

245011782

245777288

1.31E-06

22290

1

X

Average backfat thickness

BFT

113078996

113962590

1.13E-06

736

1

4

Average backfat thickness

BFT

140987596

142372300

7.22E-07

7293

3

4

Abdominal fat percentage

ABDFP

81983315

87016757

5.96E-07

22480

2

5

Arachidic acid content

FA-C20:0

56004411

59682626

5.44E-07

22509

1

10

Arachidic acid content

FA-C20:0

56004411

59682626

2.72E-07

5435

1

10

Average backfat thickness

BFT

28168636

32088890

2.55E-07

3000

2

10

Average backfat thickness

BFT

32088890

41334738

2.16E-07

7530

1

3

Average backfat thickness

BFT

122295139

126926633

2.16E-07

18001

1

9

Average backfat thickness

BFT

145703416

151394450

1.76E-07

17803

1

8

Average backfat thickness

BFT

811090

6651169

1.71E-07

23307

1

6

Backfat at rump

BFTR

152297333

158443390

1.63E-07

849

1

1

Abdominal fat weight

ABDF

226764071

233806417

1.42E-07

2923

1

13

Average backfat thickness

BFT

208227233

215641489

1.35E-07

Co-expression of transcripts validation via quantitative real-time PCR.

Figure 6: Co-expression of transcripts validation via quantitative real-time PCR. (A) IGV diagram indicates the location of co-expressed transcripts in the same QTL_ID. (B) Quantitative real-time PCR validation of lncRNAs and genes in the same QTL_ID region.

DISCUSSION

Fat deposition is a complex metabolic process involving many genes. Although many groups have studied genes related to backfat thickness [2224], until now, the relationship between fat deposition and lncRNAs is not very clear. In this study, the expression of lncRNA and mRNA molecules in the adipose tissue of Luchuan and Duroc pigs were investigated and the potential regulatory role of lncRNA was analyzed.

A total of 4,868 differentially expressed lncRNA transcripts and 8843 differentially expressed mRNA transcripts were obtained from three tissues (liver, muscle, and fat). A significantly greater number of lncRNAs were found on chromosome 1 as compared to other chromosomes. In addition, the number of alternatively spliced isoforms per lncRNA molecule was significantly less than the number per mRNA molecule. These observations are in agreement with Shen et al. [25]. In addition, lncRNA molecules were of lower abundance as compared with mRNA molecules (Figure 3A). Both lncRNA and mRNA expression were strongly tissue-specific (Figure 3C3D), which is also apparently indicated by the heat maps (Figure 4A4B). All of results indicated that the lncRNA identified in this study have strong tissue-specific expression. Adipose tissue was found to have the greatest number of up-regulated lncRNA molecules and differentially expressed mRNA molecules. Adipose tissue data were used to perform the KEGG analysis and enrichment. A total of 794 lncRNA target genes were assigned to 226 functional signaling pathways. We focused on those target genes associated with calcium signaling.

Calcium is a key intracellular signal responsible for regulating numerous cellular processes. As an extracellular Ca2+ sensor, CaSR activation in the visceral white adipose tissue is associated with increase of adipose progenitor cells proliferation and elevate of adipocyte differentiation [26]. Another study reported that Seipin promotes fat storage in adipose tissue by regulating intracellular calcium homeostasis [27]. Both our data and that of others suggest that fat deposition is regulated by calcium signaling. Further study in this field may provide new strategies to control fat deposition.

The common method of predicting lncRNA target genes is to search a 100 kb upstream or downstream region to identify nearby protein coding regions. In order to obtain more reliable target gene information for lncRNA molecules, target genes were predicted using QTL_ID regions. We found that lncRNA TCONS_00199412 and TCONS_00197271 were co-expressed with gene STEAP4 in the QTL_ID 21259 region. In addition, lncRNA TCONS_00185144 and TCONS_00181156 were co-expressed with ELOVL6 (elongation of long chain fatty acids family member 6) and all three transcripts were localized to the QTL_ID 21252 region. Subsequently, their co-expression was confirmed using quantitative real-time PCR (Figure 6B). ELOVL6 is believed to be involved in insulin resistance, lipogenesis, and obesity [28]. These results indicate the feasibility of using the QTL_ID region to predict the lncRNA target genes.

In conclusion, our data provide further basic knowledge of pig’s lncRNAs. These results lay important groundwork for the further investigation of the regulatory role of lncRNA in fat deposition.

MATERIALS AND METHODS

Animals and samples collection

Three Luchuan and three Duroc boars were used in this study. The animals were allowed access to feed and water ad libitum and were housed under identical conditions. All pigs were sacrificed at 180 days of age, they had been performed overnight fasting before sacrificed. Three types of tissue samples (liver, muscle, and fat) were collected from three animals of each porcine breed. All animal experiments were performed under approval of the Institutional Animal Care and Use Committee (IACUC) of Guangxi University.

RNA quantification and qualification

A total of 1.5 μg RNA per sample were obtained using TRIzol reagent (Invitrogen, USA); rRNA was removed using a Ribo-Zero rRNA Removal Kit (Epicentre, Madison, WI, USA). 1.5% agarose gels were used for monitoring RNA degradation and DNA contamination. RNA concentration and purity were analyzed using a NanoDrop 2000 Spectrophotometer (ThermoFisher Scientific, Wilmington, DE). RNA integrity was assessed using an RNA Nano 6000 Assay Kit and Agilent Bioanalyzer 2100 System (Agilent Technologies, CA, USA).

Preparation of the lncRNA-Seq libraries

Six cDNA libraries were generated using NEBNextR UltraTM Directional RNA Library Prep Kit. First strand cDNA was synthesized using reverse transcriptase with random hexamer primers. Subsequently, DNA polymerase I and RNase H were used for second strand cDNA synthesis. The exonuclease/polymerase activities can convert overhangs into blunt ends. After adenylation was completed, ligation was performed with NEBNext Adaptor. The final 150–200 bp fragments were selected by filtration with AMPure XP Beads (Beckman Coulter, Beverly, USA). Then 3 μl USER Enzyme (NEB, USA) was used with size-selected and adaptor-ligated cDNA at 37°C for 15 min before PCR. At last, PCR products were generated using Phusion High-Fidelity DNA polymerase and purified using the AMPure XP system. An Agilent Bioanalyzer 2100 and qPCR were used to assess the quality of the libraries.

lncRNA identification and analysis

Cufflinks software [20] was used to assemble the transcriptome. The resulting sequence was based on the reads mapped to the reference genome (Susscrofa 10.2). Then, the assembled transcripts were annotated using the Cuffcompare program (a Cufflinks package). The unknown transcripts were retained and screened for putative lncRNAs. Four computational approaches (CPC/CNCI/Pfam/cpat) were used to screen the putative lncRNAs for protein coding ability. Transcripts >200 nt and > two exons were retained as lncRNA candidates and were further screened by CPC/CNCI/Pfam/cpat that to ensure every transcript is a long non-coding RNA. At last, Cuffcompare was used to categorize the lncRNA transcripts as lincRNA, intronic lncRNA, anti-sense lncRNA and sense lncRNA.

Quantification of expression levels and differential expression analysis

The FPKM (fragments per kilo-base of exon per million fragments mapped, calculated based on the length of the fragments and reads count mapped to the fragment) of both lncRNAs and coding genes were calculated using Cuffdiff (v2.1.1) [20] for each sample. Gene FPKMs were computed by summing the FPKMs of transcripts in each group. The P value was adjusted using Q value [29]. A threshold for significantly different expression was set as a Q value<0.01 and |log2(fold change)|>1.

GO enrichment analysis and KEGG pathway enrichment analysis

Gene Ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) and the target genes of differentially expressed lncRNAs was implemented using the topGO-R software packages. KOBAS [30] software was used to test the statistical enrichment of differentially expressed genes in KEGG pathways.

Quantitative real-time PCR

Quantitative real-time PCR (qPCR) was used to validate RNA-seq results. Three replicate tissue samples from each pig were obtained and three pigs were used from each breed. Total RNA from these samples was extracted using TRIzol reagent (Invitrogen, USA); then, total RNA was purified using RNase-free DNase I (GeneStar, Beijing, China). One μg total RNA was reverse transcribed using M-MLV RNAse H-negative reverse transcriptase (Takara, Dalian, China). Finally, quantitative real-time PCR was performed on a qTOWER 3.0 real-time PCR System (Analytik-jena) with 2 × RealStar Green Fast Mixture (GeneStar, Beijing, China). The quantitative real-time PCR primer pairs used in this study are listed in Supplementary Table 10. The reaction conditions were: denaturation for 30 s at 95°C followed by 40 cycles of 95°C 15 s and 60°C 1 min. Relative gene expression levels were calculated from the Ct value and analyzed using the 2-ΔΔCT method. Samples were analyzed in triplicate to ensure their statistical significance.

Abbreviations

lncRNA: long non-coding RNA; DE-lncRNAs: differentially expressed long non-coding RNAs; DE-mRNAs: differentially expressed mRNAs; QTL: quantitative trait loci; DEG: differentially expressed gene; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes

Authors’ contributions

Lin Yu, Yixing Li and Lei Zhou conceived the project and design the protocol; Lin Yu, Lina Tai, Lifang Zhang and Yi Chu performed the experiments; Lin Yu, Yixing Li and Lei Zhou wrote the manuscript. All authors read and approved the final manuscript.

ACKNOWLEDGMENTS

We are grateful to Biomarker Technologies Co. Ltd for transcriptome sequencing.

CONFLICTS OF INTEREST

The authors have declared that no competing interests exist.

FUNDING

This work was supported by the grants from National Natural Science Foundation of China (31660641, 31301947), the Fok Ying Tong Education Foundation (141025), Guangxi Natural Science Foundation (2014GXNSFDA118014), and the Scientific Research Foundation of Guangxi University (XTZ130719).

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