Open Access

Differential expression of microRNAs in porcine parvovirus infected porcine cell line

  • Xinqiong Li1,
  • Ling Zhu1, 2,
  • Xiao Liu1,
  • Xiangang Sun1,
  • Yuanchen Zhou1,
  • Qiaoli Lang1,
  • Ping Li1,
  • Yuhan Cai1,
  • Xiaogai Qiao1 and
  • Zhiwen Xu1, 2Email author
Contributed equally
Virology Journal201512:128

https://doi.org/10.1186/s12985-015-0359-4

Received: 12 February 2015

Accepted: 14 August 2015

Published: 20 August 2015

Abstract

Background

Porcine parvovirus (PPV), a member of the Parvoviridae family, causes great economic loss in the swine industry worldwide. MicroRNAs (miRNAs) are a class of non-protein–coding genes that play many diverse and complex roles in viral infections.

Finding

Aiming to determine the impact of PPV infections on the cellular miRNAome, we used high-throughput sequencing to sequence two miRNA libraries prepared from porcine kidney 15 (PK-15) cells under normal conditions and during PPV infection. There was differential miRNA expression between the uninfected and infected cells: 65 miRNAs were upregulated and 128 miRNAs were downregulated. We detected the expression of miR-10b, miR-20a, miR-19b, miR-181a, miR-146b, miR-18a, and other previously identified immune-related miRNAs. Gene Ontology analysis and KEGG function annotations of the host target genes suggested that the miRNAs are involved in complex cellular pathways, including cellular metabolic processes, immune system processes, and gene expression.

Conclusions

These data suggest that a large group of miRNAs is expressed in PK-15 cells and that some miRNAs were altered in PPV-infected PK-15 cells. A number of microRNAs play an important role in regulating immune-related gene expression. Our findings should help with the development of new control strategies to prevent or treat PPV infections in swine.

Background

Porcine parvovirus (PPV) is a major cause of reproductive failure in swine (Sus scrofa, ssc), where infection is characterized by early embryonic death, stillbirths, fetal death, and delayed return to estrus [1]. Additionally, PPV is associated with porcine postweaning multisystemic wasting syndrome (PMWS) and diarrhea, skin disease, and arthritis in swine [1, 2]. Even though inactivated and attenuated vaccines are widely used, the PPV-associated diseases nevertheless cause serious economic losses to the swine industry worldwide [3]. As virus replication is highly dependent on the host cell, cellular microRNA (miRNA) modification of the complex cellular regulatory networks can greatly influence viral reproduction and pathogenesis. Therefore, determining the consequences of PPV infections on cellular gene regulatory networks is urgent.

miRNAs are involved in post-transcriptional regulation of gene expression in animals, plants, and some DNA viruses. miRNAs act as regulators, inhibiting the expression of specific mRNAs by recognizing partial complementary sites in a targeted mRNA, typically within the 3’ untranslated region (3’UTR). miRNAs perform critical functions in diverse biological processes, including proliferation, apoptosis, and cell differentiation [4]. It has been well established that miRNAs play many complex roles during viral infection [5]. Therefore, an increasing number of researchers have focused on the relationship between viruses and miRNAs.

As far as we know, knowledge on the role of miRNAs in PPV infection is lacking. In this study, we detected the miRNAs expressed in porcine kidney 15 (PK-15) cells following PPV infection using high-throughput sequencing.

Methods

We used the PPV-SC-L strain, stored at the Key Laboratory of Animal Diseases and Human Health of Sichuan Province, China, in this study. PK-15 cell cultures that were 50 % confluent were infected with PPV at 10 plaque-forming units (PFU) per cell. PK-15 cells inoculated with DMEM were maintained as uninfected control cells. Cells were harvested at 24 h post-infection [6]. The cultures for each group were performed in triplicate. The infected and uninfected cells were mixed separately and used for RNA extraction. Cell viability is not affected during timecourse of infection.

Total RNA from infected PK-15 cells and normal PK-15 cells was extracted using TRIzol (Invitrogen) according to the manufacturer’s instructions. RNA quality was assessed by formaldehyde/agarose gel electrophoresis and was quantified using a ND-1000 NanoDrop Spectrophotometer (Thermo Scientific, Wilmington, MA, USA). Approximately 20 μg total RNA was subjected to Kangcheng Bio-tech inc (Shanghai, China) for Solexa sequencing of miRNAs. The same RNA was used for qRT-PCR.

RT was performed as previously described [6]. Real-time PCR was performed using SYBR Green Real-time qPCR Master Mix (Arraystar, Rockville, MD, USA) on a ViiA 7 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s instructions. The amplification conditions were as follows: 95 °C for 10 min, followed by 40 cycles of 95 °C for 10 s and 60 °C for 60 s. Table 1 lists the primers used. All samples were assayed in triplicate. The cycle threshold (Ct) values were analyzed using the 2-∆∆Ct method. The U6 gene was used as the internal control.
Table 1

RT-qPCR primers

Gene

RT primer

 U6

5’CGCTTCACGAATTTGCGTGTCAT3’

 miR-RT Primer

5’GTCGGTGTCGTGGAGTCGTTTGCAATTGCACTGGATTTTTTTTTTTTTTTTTTV3’

V = A, G, C

Gene

Forward primer (5’–3’)

Reversed primer (5’–3’)

 ssc-miR-10b

TACCCTGTAGAACCGAATTTGT

GTCGGTGTCGTGGAGTCG

 ssc-miR-30a-5p

TGTAAACATCCTCGACTGGAAG

GTCGGTGTCGTGGAGTCG

 ssc-miR-16

TAGCAGCACGTAAATATTGGC

GTCGGTGTCGTGGAGTCG

 ssc-miR-17-5p

CAAAGTGCTTACAGTGCAGGTAG

GTCGGTGTCGTGGAGTCG

 ssc-miR-192

CTGACCTATGAATTGACA

GTCGGTGTCGTGGAGTCG

 ssc-miR-21

TAGCTTATCAGACTGATGTTGA

GTCGGTGTCGTGGAGTCG

 ssc-miR-19b

TGTGCAAATCCATGCAAAAC

GTCGGTGTCGTGGAGTCG

 ssc-miR-18a

TAAGGTGCATCTAGTGCAGATA

GTCGGTGTCGTGGAGTCG

 ssc-miR-152

TCAGTGCATGACAGAACTTGG

GTCGGTGTCGTGGAGTCG

 ssc-miR-novel-chr13_10861

TTCAAGTAACCCAGGATAGGCT

GTCGGTGTCGTGGAGTCG

 U6

TCGCTTTGGCAGCACCTAT

AATATGGAACGCTTCGCAAA

MiRanda and TargetScan were used to predict the targets of the differentially expressed miRNAs. Predicted miRNA targets were functionally annotated through the cell component, biological process, and molecular function information supported by GO analysis. GO analysis and KEGG pathway analyses were performed using DAVID (http://david.abcc.ncifcrf.gov/) with default parameters [7].

Results

We obtained 3,575,737 and 617,535 high-quality reads from the normal and infected cell samples, respectively, remained for miRNA analysis. The length distribution of the high-quality reads ranged 16–30 nt. Most sequence reads ranged 21–24 nt, which belonged to the typical size range (Fig. 1). We identified 533 and 286 porcine miRNAs in normal PK-15 cells and infected PK-15 cells, respectively. This indicates that the normal cells contained more miRNAs than the infected cells. The change of expression of miRNAs between normal and infected PK-15 cells reflects that miRNAs can play key roles during the viral infection process, where virus can affect cellular miRNAs expression profile on their own benefit. ssc-miR-21 was the most abundantly expressed miRNA, followed by ssc-miR-30a-5p. miRNAs were considered differentially expressed when the fold change (FC) difference between groups was >2 or ≤0.5 and P ≤ 0.01, or when a miRNA was not expressed in either the infected or control group. There were 193 differentially expressed miRNAs; 128 were downregulated and 65 were upregulated. The most upregulated and downgulated miRNA were ssc-miR-10b (36-fold) and ssc-miR-18a (0.01-fold) (Table 2).
Fig. 1

Length distribution of miRNA reads from Solexa sequencing. a Adapter-trimmed reads in the infected library; b adapter-trimmed reads in the control library

Table 2

Top 50 miRNAs significantly up- or downregulated in PK-15 cells in order of fold change (FC)

Annotation

Normalized read counts

length

type

FC

Number of target genes

infected

control

ssc-miR-10b

42,588

1162

22

Up

36.35

738

ssc-miR-192

3769

102

21

Up

33.74

718

ssc-miR-20a

2432

116

22

Up

19.38

1490

ssc-miR-296-3p

195

3

21

Up

15.77

1863

ssc-miR-novel-chr17-18987

195

3

19

Up

15.77

1864

ssc-miR-92b-3p

2215

133

22

Up

15.56

1757

ssc-miR-30a-5p

98,034

6320

22

Up

15.49

1147

ssc-miR-novel-chr12-7961

1886

191

22

Up

9.43

1357

ssc-miR-novel-chr14-13888

582

58

23

Up

8.71

1368

ssc-miR-34a

358

37

22

Up

7.83

1663

ssc-miR-novel-chr16-17559

55

0

22

Up

6.5

1610

ssc-miR-novel-JH11865-1-42

55

0

23

Up

6.5

1727

ssc-miR-17-5p

2868

438

23

Up

6.42

1443

ssc-miR-16

11,873

1891

22

Up

6.25

1763

ssc-miR-22-3p

2267

365

22

Up

6.07

1487

ssc-miR-146b

75

3

21

Up

6.07

1139

ssc-miR-155-5p

426

62

22

Up

6.06

1146

ssc-miR-novel-chr2-20965

52

1

23

Up

5.64

1147

ssc-miR-novel-chrx-40705

758

147

22

Up

4.89

811

ssc-miR-221-3p

758

147

22

Up

4.89

811

ssc-miR-301

114

17

23

Up

4.59

1509

ssc-miR-191

741

156

23

Up

4.52

695

ssc-miR-novel-chr6-31692

46

3

22

Up

4.30

2019

ssc-miR-181a

637

147

24

Up

4.12

1221

ssc-miR-18a

88

9541

22

Down

0.0102

995

ssc-miR-novel-chr9-37990

20

1752

23

Down

0.0170

1512

ssc-miR-novel-chr9-39041

20

1752

23

Down

0.0170

1512

ssc-miR-novel-chr6-30729

13

1317

22

Down

0.0173

1083

ssc-miR-424-5p

33

2182

22

Down

0.0196

1817

ssc-miR-31

55

3118

22

Down

0.0208

1149

ssc-miR-novel-chrX-41190

0

431

21

Down

0.0227

335

ssc-miR-novel-chr11-6750

7

547

18

Down

0.0305

1406

ssc-miR-152

332

9880

21

Down

0.0346

1161

ssc-miR-542-5p

0

277

21

Down

0.0348

732

ssc-miR-499-5p

7

472

21

Down

0.0353

974

ssc-miR-142-3p

0

238

22

Down

0.0403

887

ssc-miR-135

0

235

23

Down

0.0408

1602

ssc-miR-194a

13

541

21

Down

0.0417

842

ssc-miR-361-5p

20

704

22

Down

0.0420

867

ssc-miR-185

59

1621

22

Down

0.0423

2285

ssc-miR-193a-5p

0

201

22

Down

0.0474

1142

ssc-miR-novel-chr5-29676

0

199

23

Down

0.0478

1066

ssc-miR-183

156

3132

23

Down

0.0528

1087

ssc-miR-29c

16

366

22

Down

0.0691

1120

ssc-miR-novel-chr5-29857

42

736

19

Down

0.0697

1711

ssc-miR-29a

267

3939

23

Down

0.0701

1079

ssc-miR-19a

498

6339

23

Down

0.0800

1436

ssc-miR-19b

1161

14,587

23

Down

0.0802

1299

ssc-miR--novel-chr13_10861

169

1483

22

Down

0.1199

857

ssc-miR-21

52,611

382,830

22

Down

0.1374

789

We selected 10 miRNAs to confirm the deep sequencing data. The expression levels of ssc-miR-10b, ssc-miR-30a-5p, ssc-miR-16, ssc-miR-17-5p, and ssc-miR-192 in the PPV-infected cells were higher than in the uninfected cells, whereas ssc-miR-21, ssc-miR-19b, ssc-miR-18a, ssc-miR-152, and ssc-miR-novel-chr13_10861 were downregulated compared to the uninfected cells (Fig. 2). The results were consistent with that of the deep sequencing analysis. In addition, reverse transcription–quantitative PCR (RT-qPCR) indicated the reliability of the deep sequencing data.
Fig. 2

RT-qPCR validation and expression analysis of differentially expressed miRNAs. The relative expression levels are presented as the mean and standard deviation (SD). **P < 0.01, *P < 0.05

In our study, a total 3254 target genes were predicted for the 193 differentially expressed miRNAs. We successfully annotated about 2867 target genes through GO analysis. The upregulated biological process–related genes were involved in cellular process, metabolic process and biological regulation. The biological roles of the downregulated genes were cellular process, metabolic process, and biological regulation. GO enrichment analysis determined functional enrichment of upregulated and downregulated genes in cellular process and cell part and binding (Table 3). The target genes were classified according to Kyoto Encyclopedia of Genes and Genomes (KEGG) function annotations, and we identified pathways actively regulated by the miRNAs during PPV infection (Table 4). Some of the target genes were involved in immunity and virus infection.
Table 3

GO analysis of swine target genes. The table shows the GO annotation of the upregulated gene (A) and downregulated gene (B) in biological process, cellular component and molecular function. Ten GO terms for each process are listed

GO.ID

Term

Count

P-value

Biological process

 GO:0009987

cellular process

1782

1.0102E-05

 GO:0008152

metabolic process

1350

2.44953E-27

 GO:0065007

biological regulation

1260

0.000424577

 GO:0044238

primary metabolic process

1231

5.99319E-26

 GO:0044237

cellular metabolic process

1221

1.70495E-28

 GO:0050789

regulation of biological process

1192

0.002408788

 GO:0050794

regulation of cellular process

1147

0.000216533

 GO:0002376

immune system process

273

1.35305E-08

 GO:0006955

immune response

163

1.37682E-05

 GO:0000165

MAPK cascade

79

3.28195E-05

 Cellular Component

 GO:0044464

cell part

1772

1.04304E-42

 GO:0005623

cell

1772

1.25735E-42

 GO:0005622

intracellular

1589

1.48695E-38

 GO:0044424

intracellular part

1512

9.75601E-38

 GO:0043226

organelle

1258

3.15768E-22

 GO:0043229

intracellular organelle

1255

5.59497E-22

 GO:0005737

cytoplasm

1146

1.88382E-25

 GO:0043227

membrane-bounded organelle

1131

3.1329E-23

 GO:0043231

intracellular membrane-bounded organelle

1129

3.31685E-23

 GO:0044444

cytoplasmic part

886

1.59538E-15

 Molecular Function

 GO:0005488

binding

1781

7.2806E-35

 GO:0005515

protein binding

1406

1.01651E-30

 GO:0003824

catalytic activity

791

4.12422E-11

 GO:0043167

ion binding

425

2.86598E-07

 GO:0043169

cation binding

423

3.71961E-07

 GO:0046872

metal ion binding

416

4.07808E-07

 GO:0003676

nucleic acid binding

368

0.010086661

 GO:0036094

small molecule binding

366

1.33205E-09

 GO:0000166

nucleotide binding

341

4.24208E-09

 GO:0097159

organic cyclic compound binding

341

4.47117E-09

 B

 Biological Process

 GO:0009987

cellular process

1732

0.000468457

 GO:0008152

metabolic process

1280

0.000101041

 GO:0065007

biological regulation

1226

0.039474247

 GO:0044238

primary metabolic process

1179

0.011728824

 GO:0044237

cellular metabolic process

1153

0.011728824

 GO:0050789

regulation of biological process

1150

0.022891558

 GO:0050794

regulation of cellular process

1100

0.023393923

 GO:0002376

immune system process

265

3.49438E-08

 GO:0006955

immune response

161

7.5883E-06

 GO:0022402

cell cycle process

151

0.001985807

 Cellular Component

 GO:0044464

cell part

1699

1.61069E-32

 GO:0005623

cell

1699

1.89406E-32

 GO:0005622

intracellular

1506

1.8551E-26

 GO:0044424

intracellular part

1426

4.89384E-25

 GO:0043226

organelle

1212

8.24637E-20

 GO:0043229

intracellular organelle

1208

2.30659E-19

 GO:0043227

membrane-bounded organelle

1089

8.12018E-21

 GO:0043231

intracellular membrane-bounded organelle

1088

5.50768E-21

 GO:0005737

cytoplasm

1079

5.97213E-18

 GO:0044444

cytoplasmic part

836

2.48602E-11

 Molecular Function

 GO:0005488

binding

1748

3.63499E-36

 GO:0005515

protein binding

1408

1.06202E-38

 GO:0003824

catalytic activity

743

5.00132E-07

 GO:0043167

ion binding

410

2.18765E-06

 GO:0043169

cation binding

408

2.81762E-06

 GO:0046872

metal ion binding

400

4.31756E-06

 GO:0003676

nucleic acid binding

365

0.004482893

 GO:0036094

small molecule binding

335

6.20944E-06

 GO:0000166

nucleotide binding

309

2.81002E-05

 GO:0097159

organic cyclic compound binding

309

2.91504E-05

Table. 4

Target genes of 17 differentially expressed miRNAs involved in immune response pathways

KEGG pathways

Target genes

Differentially expressed microRNAs

FDR

T cell receptor signaling pathway

CTLA4, FYN, IKBKG, NFATC2, NCK1, CD8A, PIK3CG, CDC42, PTPN6, CD4, CD40LG, ICOS, PIK3R5, MAPK14, TNF, MAP3K7

miR-10b, miR-9, miR-30a-5p, miR-17-5p, miR-16, miR-18a, miR-19b, miR-20a, miR-19a, miR-122, miR-146b, miR-55-5p, miR-181a, miR-196b, let-7 g, let-7c

8.89308E-12

Toll-like receptor signaling pathway

CTSK, TLR7, MAP3K7, MAPK14, CXCL9, PIK3CG, NFKB1, CD40, STAT1, IL12B, CD86, IL6

miR-10b, miR-9, miR-30a-5p, miR-17-5p, miR-16, miR-18a, miR-19b, miR-20a, miR-21, miR-19a, miR-122, miR-146b, miR-155-5p, miR-181a, miR-196b, let-7 g, let-7c

1.04578E-07

NF-kappaB signaling pathway

MAP3K7, CXCL12, DDX58, LCK, XIAP, ATM, VCAM1, NFKB1, TNF, CD40LG

miR-10b, miR-9, miR-30a-5p, miR-17-5p, miR-16, miR-18a, miR-19b, miR-20a, miR-19a, miR-122, miR-146b, miR-155-5p, miR-181a, miR-196b

1.18108E-06

RIG-I-like receptor signaling pathway

MAP3K7, MAPK14, DHX58, DDX58, IKBKG, TANK, IKBKB, DDX3X, NFKB1, TNF, IL12B

miR-10b, miR-9, miR-30a-5p, miR-17-5p, miR-16, miR-18a, miR-19b, miR-21, miR-19a, miR-122, miR-146b, miR-155-5p, miR-181a, let-7c

1.70355E-05

Jak-STAT signaling pathway

JAK2, STAT4, STAT5B, JAK3, PIK3CG, PIM1, PTPN6, TYK2, MAPK14, STAT4, STAT1, IL7R, IL12B, IL6, PIK3R5, MYC

miR-9, miR-17-5p, miR-16, miR-18a, miR-19b, miR-20a, miR-21, miR-19a, miR-122, miR-146b, miR-155-5p, miR-181a, miR-196b, let-7 g, let-7c

0.000124339

NOD-like receptor signaling pathway

MAP3K7, MAPK14, IKBKG, IKBKB, NFKB1, TNF, IL6

miR-10b, miR-9, miR-17-5p, miR-16, miR-18a, miR-19b, miR-19a, miR-122, miR-146b, miR-155-5p, miR-181a, let-7 g, let-7c

0.001546381

Discussion and conclusion

Previous studies have shown that viruses have evolved a wide variety of means for resisting the host immune system [810]. Furthermore, miRNAs play important roles in controlling immune regulation, including cellular differentiation and immune response [1113]. Identifying and probing miRNAs in the immune system is important for understanding their physiological and pathological roles in PPV infection. In this study, we used high-throughput sequencing to identify miRNAs.

Recent studies have provided compelling evidence that cellular miRNAs play an important role in host defense against virus infection [14]. Many immune-related miRNAs have been identified in innate and adaptive immune systems, including the miR-17—92 cluster, miR-221, miR-10, miR-196b, miR-126, miR-155, miR-150; miR-181a, miR-326, miR-142-3p, miR-424, miR-21, miR-106a, miR-223, miR-146; the let-7 family, miR-9, and miR-34 [6]. We found many differentially expressed miRNAs in the normal and PPV-infected PK-15 cells. Among them, let-7 g, miR-17-5p, miR-17-3p, miR-20a, miR-181a, miR-16, miR-146b, miR-10b, and miR-155-5p were upregulated; let-7c, miR-122, miR-18a, miR-19a, miR-19b, miR-196b, miR-21, and miR-9 were downregulated. These data suggest that viral mechanisms can affect host miRNA expression. However, we did not detect differential expression of other previously identified miRNAs (miR-223, miR-150, miR-92a), although miR-10b, miR-20a, miR-30a-5p, miR-34a, miR-17—5p, miR-16, miR-146b, and miR-155-5p expression was significantly different. In contrast, expression of the downregulated immune-related miRNAs was not significantly different, except miR-18a, miR-19b, and miR-21. This suggests that miRNAs play an important role in the coordinated regulation of immune-related gene expression in PK-15 cells in response to PPV infection.

miR-21, which had high read numbers in both normal and PPV-infected cells, was downregulated; it is related to immune response and virus replication [15]. Moreover, it is a negative regulator of toll-like receptor 4 (TLR4) signaling by targeting programmed cell death 4 (PDCD4) [16]. miR-19b and miR-18a expression was downregulated in the infected cells, suggesting that they play a negative role in PPV replication. Although viruses may downregulate host miRNA by suppressing Dicer expression, the mechanism of downregulation remains unclear [17]. Therefore, future studies are necessary for investigating the mechanism of PPV downregulation of cellular miRNA.

miR-10 expression was upregulated in the infected cells. Mitogen-activated protein kinase kinase kinase 7 (MAP3K7), considered a target gene of miR-10, regulates the inhibitor of nuclear factor κB/nuclear factor κB (IκB/NF-κB) signaling pathway [18]. In addition, miR-10 controls brain-derived neurotrophic factor (BDNF) levels via the miRNA–mRNA regulatory network [19]. We surmise that a possible function of miR-10 in triggering an antiviral response is targeting the MAP3K7 and BDNF genes. The miR-30 family is involved in various biological and pathological processes. For example, miR-30a may be involved in B cell hyperactivity [20]. We detected miR-10 and miR-30 in this study, suggesting that they are related to the cellular immune response to PPV infection.

GO analysis showed that many of the identified miRNAs found in other studies were predicted to participate in immunity [21]. Many genes, including MAP3K7, IRAK1, TLR7, CD40, TGFBR1, RPS6KA3, IGF1R, CDC37, ITGA4, CBLB, ITGA5, IL7, ATM, DPP8, MAPK14, CD2, WNT2B, CAV1, and CD96, are involved in the immune-related programs. KEGG analysis showed that these targeted genes could participate in multiple signaling pathways, including that for retinoic acid–inducible gene-I (RIG-I)-like receptor, TLRs, Janus kinase–signal transducer and activator of transcription (JAK–STAT), and T-cell receptor. Interleukin 10 (IL10) plays an important role in virus infection by inhibiting several proinflammatory cytokines [22]. Let-7 g, let-7c, miR-19b, and miR-16 are involved in immune-related programs and may act through the target gene IL10. These results suggest that miRNAs participate in the regulation of piglet immunity. It has been established that miRNAs can target specific genes [23]; in the present study, let-7c, let-7 g, miR-18a, miR-196b, and miR-9 targeted MAP3K7, and miR-196b and miR-19b targeted dipeptidyl-peptidase 8 (DPP8), suggesting that cellular miRNAs play a key role in regulating gene expression in response to PPV infection. Genes targeted by miRNAs are involved in immune response–associated pathways in human parvovirus B19 infection [24]. We speculate that host miRNAs relate to common immune pathways in response to parvovirus infection.

To our knowledge, this is first study to survey the miRNA expression profiles in PPV-infected PK-15 cells through high-throughput sequencing. A number of miRNAs detected were previously described as immune system regulators. Target analysis confirmed that these miRNAs played an important role in PPV infection. These findings contribute to our understanding of the roles miRNAs play in host–pathogen interactions and help with the development of new control strategies to prevent or treat PPV infections in swine.

Notes

Declarations

Acknowledgments

This study was supported by the Program for New Century Excellent Talents in University of Ministry of Education of China (Project No: NCET-11-1059), and by the Excellent Doctoral Dissertation Fostering Foundation of Sichuan Agricultural University (04310734). miRNA sequencing services were provided by KangChen Bio-tech, Shanghai, China.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Animal Biotechnology Center, College of Veterinary Medicine, Sichuan Agricultural University
(2)
Key Laboratory of Animal Disease and Human Health, College of Veterinary Medicine, Sichuan Agricultural University

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Copyright

© Li et al. 2015

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