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Molecular Pain

Open Access

Identification of lncRNA expression profile in the spinal cord of mice following spinal nerve ligation-induced neuropathic pain

  • Bao-Chun Jiang1, 2,
  • Wen-Xing Sun3,
  • Li-Na He1, 2,
  • De-Li Cao1, 2,
  • Zhi-Jun Zhang1, 2 and
  • Yong-Jing Gao1, 2Email author
Contributed equally
Molecular Pain201511:43

https://doi.org/10.1186/s12990-015-0047-9

Received: 31 March 2015

Accepted: 7 July 2015

Published: 17 July 2015

Abstract

Background

Neuropathic pain that caused by lesion or dysfunction of the nervous system is associated with gene expression changes in the sensory pathway. Long noncoding RNAs (lncRNAs) have been reported to be able to regulate gene expression. Identifying lncRNA expression patterns in the spinal cord under normal and neuropathic pain conditions is essential for understanding the genetic mechanisms behind the pathogenesis of neuropathic pain.

Results

Spinal nerve ligation (SNL) induced rapid and persistent pain hypersensitivity, characterized by mechanical allodynia and heat hyperalgesia. Meanwhile, astrocytes and microglia were dramatically activated in the ipsilateral spinal cord dorsal horn at 10 days after SNL. Further lncRNA microarray and mRNA microarray analysis showed that the expression profiles of lncRNA and mRNA between SNL and sham-operated mice were greatly changed at 10 days. The 511 differentially expressed (>2 fold) lncRNAs (366 up-regulated, 145 down-regulated) and 493 mRNAs (363 up-regulated, 122 down-regulated) were finally identified. The expression patterns of several lncRNAs and mRNAs were further confirmed by qPCR. Functional analysis of differentially expressed (DE) mRNAs showed that the most significant enriched biological processes of up-regulated genes in SNL include immune response, defense response, and inflammation response, which are important pathogenic mechanisms underlying neuropathic pain. 35 DE lncRNAs have neighboring or overlapping DE mRNAs in genome, which is related to Toll-like receptor signaling, cytokine–cytokine receptor interaction, and peroxisome proliferator-activated receptor signaling pathway.

Conclusion

Our findings uncovered the expression pattern of lncRNAs and mRNAs in the mice spinal cord under neuropathic pain condition. These lncRNAs and mRNAs may represent new therapeutic targets for the treatment of neuropathic pain.

Keywords

LncRNASpinal cordSpinal nerve ligationNeuropathic pain

Background

Neuropathic pain is one of the most common chronic pain in humans and characterized by an increase in the responsiveness of nociceptive neurons in the peripheral and central nervous system (CNS) [1]. Peripheral and central sensitization represents the altered functional status of nociceptive neurons and results from changes of a vast amount of functional protein and signaling pathways in the neuron and glial cell [2, 3]. Recent pharmaceutical research and discovery activities focus on well-characterized molecular targets, such as ion channels, G-protein-coupled receptors, and kinases in neurons and glial cells localized along the nociceptive pathways, which are regarded as direct contributors to the sensitization of pain signaling systems [4, 5]. However, the transcriptional or translational regulatory mechanisms underlying the expression and functional changes of these molecules are poorly defined.

RNAs that do not code for a protein (noncoding RNAs, ncRNAs) consist of two major classes: the small ncRNAs, which include microRNAs (miRNAs) and other noncoding transcripts of less than 200 nucleotides, and long noncoding RNAs (lncRNAs), which are a novel class of non-protein coding transcripts longer than 200 nucleotides [6]. LncRNAs were initially considered as transcriptional by-products, but recent data suggest that lncRNAs can regulate gene expression via interfering with transcription, post-transcriptional processing, chromatin remodeling, miRNA sequestration, and generating small ncRNAs [7, 8]. Also, lncRNAs are involved in various aspects of cell biology and disease etiology, such as development [9], immune [10], cardiovascular disease [11], oncogenesis [12], and neurological disease [13]. LncRNAs are highly expressed in the CNS, and their expression profiles are associated with specific neuroanatomical regions, cell types, or subcellular compartments suggesting their potential functional roles in the nervous system [1416]. It was reported that sciatic nerve resection induced differential expression of lncRNAs in dorsal root ganglia (DRG) [17]. Moreover, Zhao et al. have recently identified a functional lncRNA Kcna2, which contributed to neuropathic pain by silencing Kcna2 in DRG neurons [18]. These findings indicate the involvement of lncRNAs in neuropathic pain.

The spinal cord is responsible for receiving input from nociceptors and projecting to the brain, and plays an important role in the integration and modulation of pain-related signals. To clarify the molecular mechanisms underlying neuropathic pain and explore novel approaches for analgesic strategies, herein, we investigated the genome-wide expression of lncRNAs in the spinal cord following L5 spinal nerve ligation (SNL)-induced neuropathic pain. We found a large number of differentially expressed (DE) lncRNAs and mRNAs in the spinal cord after SNL. Among them, 39 correlated lncRNA-mRNA pairs, consisting of DE lncRNAs and mRNAs with adjacent or overlapping position relationship, were screened out. Our findings will provide new insights into the roles of lncRNAs in the regulation of neuropathic pain-associated genes.

Results

Model identification of neuropathic pain

The SNL model has been widely used in the investigation of the mechanisms underlying neuropathic pain [19]. Here we also found that SNL induced rapid (1 d) and persistent (>21 d) mechanical allodynia (Figure 1a) and heat hyperalgesia (Figure 1b) in mice. We then harvested the spinal cord at 10 days (maintenance phase) after SNL and checked the expression of astrocytic marker GFAP and microglial marker IBA-1, which are known to be upregulated in the spinal cord under neuropathic pain condition [20, 21]. As shown in Figure 1c, d, GFAP expression and IBA-1 expression were both increased in the ipsilateral dorsal horn in SNL animals but not in sham-treated animals, indicating that glial activation was induced in the spinal cord by SNL.
Figure 1

SNL induces persistent neuropathic pain and glial activation in the spinal cord. SNL-induced rapid and persistent mechanical allodynia (a) and heat hyperalgesia (b). Data are expressed as mean ± SEM (n = 5 for each group). ***P < 0.001, two-way repeated measures ANOVA. c, d Representative images of GFAP and IBA-1 immunofluorescence in the L5 spinal cord from sham and SNL mice. GFAP and IBA-1 immunoreactive were very low in sham-treated mice, but significantly increased in the ipsilateral superficial dorsal horn at 10 days after SNL.

Overview of lncRNAs and mRNA expression profiles after SNL

We then detected the expression profiles of lncRNAs and mRNAs in the L5 spinal cord at 10 days after SNL by microarray. First, we obtained a graphically overview of the expression signatures of lncRNAs and mRNAs by using scatter plot and hierarchical clustering analyses. The scatter plots showed that a large number of lncRNAs and mRNAs were differentially expressed between SNL and sham-operated mice (Figure 2a, b). Hierarchical cluster analysis of all lncRNAs or mRNA showed that the 3 sham or 3 SNL samples were clustered together respectively, and signal intensity was consistent in sham or SNL group (Figure 2c, d). The heatmap of DE lncRNAs or mRNAs whose expression were up-regulated or down-regulated by twofold were magnified (Figure 2e, f), indicating the high level of concordance in either SNL or sham samples. These data suggest that neuropathic pain is associated with the changes of lncRNAs and mRNAs in the spinal cord.
Figure 2

SNL results in the expression profiling changes of lncRNA and mRNA. Scatter plot comparing global lncRNA (a) or mRNA (b) gene expression profiles in the spinal cord between the SNL and sham mice. Green lines indicate twofold differences in either direction in lncRNA and mRNA expression. Heat map showing hierarchical clustering of overall lncRNAs (c) or mRNA (d) expression pattern of reliably measured probe sets. Heat map showing hierarchical clustering of LncRNAs (e) or mRNA (f), whose expression changes were more than twofold. In clustering analysis, up- and down-regulated genes are colored in red and green, respectively.

Differentially expressed lncRNAs and mRNAs

We further analyzed differentially expressed (DE) lncRNAs using significance analysis of microarrays method, following the criteria q-value <0.05, and fold change >2. The results showed that 511 lncRNAs, containing 366 up-regulated and 145 down-regulated, were significantly changed in SNL group, comparing with the sham group. The most up-regulated lncRNAs were: uc009egw.1, Speer7-ps1, MM9LINCRNAEXON12113+, ENSMUST00000118074, and uc009nzx.1, of which uc009egw.1 showed the largest up-regulation (Log2 fold change = 7,332.4243). The most down-regulated lncRNAs were: AK045739, AK020832, AK047380, ENSMUST00000171761 and uc008dwx.1, of which AK045739 showed the largest down-regulation (Log2 fold change = −45.320816). Detailed information including the top 20 up-regulated and 20 down-regulated lncRNAs was listed in Table 1.
Table 1

The detail information of the top 20 up-regulated and 20 down-regulated lncRNAs

Up-regulated

lncRNAs

Log2 fold change (SNL/sham)

P-value

Down-regulated

lncRNAs

Log2 fold change (SNL/sham)

P-value

uc009egw.1

7,332.4243

7.07E−08

AK045739

−45.320816

4.4E−09

Speer7-ps1

44.854053

3.44E−10

AK020832

−17.557217

4.08E−09

MM9LINCRNAEXON12113+

28.60862

0.000138

AK047380

−13.752911

0.0000278

ENSMUST00000118074

27.38603

0.0000454

ENSMUST00000171761

−11.646089

7.18E−09

uc009nzx.1

26.05991

8.21E−06

uc008dwx.1

−10.924568

0.00000859

ENSMUST00000165428

25.460197

0.0000126

AK134918

−9.165875

0.02724757

CJ300890

23.606705

4.64E−06

ENSMUST00000160545

−8.490716

0.0000666

MM9LINCRNAEXON11661+

20.514269

7.95E−08

AK013492

−8.088422

0.000000162

CJ059670

19.443495

1.31E−07

MM9LINCRNAEXON10414

−7.6248446

0.000158

NR_003548

18.795507

7.68E−08

CA874578

−6.9701576

0.017664054

AK086225

17.855263

0.0000016

AK045554

−6.809595

0.000000323

ENSMUST00000122927

15.532128

0.00000174

uc007cua.1

−6.378551

0.0000552

ENSMUST00000150343

14.018134

0.00000125

NR_030776

−5.654923

0.00000175

ENSMUST00000120145

13.556047

0.000000275

MM9LINCRNAEXON12090+

−4.920551

0.000137

MM9LINCRNAEXON10692+

13.423789

0.00000243

MM9LINCRNAEXON10317+

−4.7705894

0.00000728

ENSMUST00000121611

13.380642

0.00000161

uc007kom.1

−4.603345

5.78E−09

humanlincRNA1606+

13.149129

0.0000756

ENSMUST00000134042

−4.242255

0.0000337

AK085402

12.824574

0.000378

ENSMUST00000040306

−4.188862

0.00000142

ENSMUST00000121062

12.615327

0.000000185

AK157618

−4.0846663

0.000106

AK044525

12.42756

0.00000111

MM9LINCRNAEXON12066

−4.066157

0.00000917

In the DE mRNAs, there are 493 genes whose mRNA change was more than twofold, and the number of up-regulated (363) mRNAs was larger than down-regulated (122) mRNAs in SNL. These DE mRNAs contain many known genes involving in pain processing, including Cacna1g (calcium channel, voltage-dependent, T type, alpha 1G subunit, 16.0978 fold increase) [22], Trpv1 (transient receptor potential cation channel, subfamily V, member 1, 9.31-fold increase) [23], Ccl5 (chemokine (C-C motif) ligand 5, 3.93-fold increase) [24], Cx3cr1 (chemokine (C-X3-C) receptor 1, 2.51-fold increase) [25], and Irf5 (interferon regulatory factor 5) [26]. Besides, a lot of other genes, whose roles in pain have not been identified, were dramatically changed. Further analysis showed that 39 genes whose expression were changed >tenfold, including 38 up-regulated genes and 1 down-regulated gene, such as Sprr1a (small proline-rich protein 1A, 148.7-fold), Anxa10 (annexin A10, 76.3-fold), and Kng1 (kininogen 1, 38.4-fold); 66 genes whose expression was changed between 5- and 10-fold, including 64 up-regulated and 2 down-regulated genes. Detailed information about the top 20 up-regulated and 20 down-regulated mRNAs was listed in Table 2.
Table 2

The detail information of the top 20 up-regulated and 20 down-regulated mRNAs

Gene symbol

Description

Log2 fold change (SNL/sham)

P-value

Up-regulated genes

 Sprr1a

Small proline-rich protein 1A

148.7115

1.84E−10

 Anxa10

Annexin A10

76.262054

1.61E−06

 4933402N22Rik

RIKEN cDNA 4933402N22 gene

46.512726

1.62E−10

 Vmn2r101

Vomeronasal 2, receptor 101

44.090027

1.2E−08

 Kng1

Kininogen 1

38.42939

2.14E−08

 Olfr803

Olfactory receptor 803

31.403961

7.82E−08

 Gpr151

G protein-coupled receptor 151

27.673513

5.95E−11

 LOC100048884

Novel member of the major urinary protein (Mup) gene family

24.719683

9.12E−09

 Mup11

Major urinary protein 11

24.027332

8.26E−10

 Mup7

Major urinary protein 7

23.950233

2.18E−08

 Mup12

Major urinary protein 12

23.768707

2.79E−10

 Mup13

Major urinary protein 13

23.234575

9.99E−08

 Mup19

Major urinary proteins 11 and 8

23.019644

0.000000314

 Mup8

Major urinary protein 8

22.686306

0.000000241

 Mup17

Major urinary protein 17

21.82689

8.07E−10

 Atf3

Activating transcription factor 3

19.8067

0.00000165

 Rreb1

Ras responsive element binding protein 1

19.512457

0.0000258

 Olfr648

Olfactory receptor 648

19.249556

0.00000434

 Clps

Colipase, pancreatic

18.952599

0.000000801

 Vax2

Ventral anterior homeobox containing gene 2

17.30259

0.000187

Down-regulated genes

 Lefty1

Left right determination factor 1

−10.109003

0.000000123

 Olfr866

Olfactory receptor 866

−7.406356

0.011693356

 Kcna5

Potassium voltage-gated channel, shaker-related subfamily, member 5

−5.9395947

0.0000537

 Tnnt2

Troponin T2, cardiac

−4.8715253

0.000213

 Csprs

Component of Sp100-rs

−4.639864

0.000183

 Gm5458

Predicted gene 5458

−3.9395294

0.000162

 Ypel4

Yippee-like 4 (Drosophila)

−3.8847303

0.0000976

 Sell

Selectin, lymphocyte

−3.7625916

0.000967

 Mnx1

Motor neuron and pancreas homeobox 1

−3.702038

0.003540842

 Fnip1

Folliculin interacting protein 1

−3.4727607

0.000226

 Epm2a

Epilepsy, progressive myoclonic epilepsy, type 2 gene alpha

−3.363634

0.00031

 H2-Ea-ps

Histocompatibility 2, class II antigen E alpha, pseudogene

−3.2939498

0.000021

 Chodl

Chondrolectin

−3.2821681

0.00000249

 Wtap

Wilms’ tumour 1-associating protein

−3.1569881

0.0000001

 Pira4

Paired-Ig-like receptor A4

−3.1222947

0.03241746

 Eml4

Echinoderm microtubule associated protein like 4

−3.117333

0.020077666

 Tnnt2

Troponin T2, cardiac

−3.0204759

0.0001

 Retnlg

Resistin like gamma

−2.9266624

0.000000051

 Mmp8

Matrix metallopeptidase 8

−2.9234846

0.000255

Real-time quantitative PCR (qPCR) validation of lncRNA and mRNA expression

To validate the reliability of the microarray results and also analyze the temporal changes of lncRNA and mRNA expression after SNL, the up-regulated lncRNAs including Speer7-ps1 and uc007pbc.1, the down-regulated lncRNAs, including ENSMUST00000171761 and ENSMUST00000097503, the up-regulated mRNA Cyp2d9, and the down-regulated mRNA Mnx1 were randomly selected and analyzed by qPCR. The spinal cord tissues were collected from naïve animals, and SNL animals at 1, 3, 10, and 21 days. Speer7-ps1 and uc007pbc.1, which are intergenic lncRNAs, were both significantly increased at 10 days and peaked at 21 days (Figure 3a, b). ENSMUST00000171761 and ENSMUST00000097503 are antisense overlap and bidirectional lncRNA with matching gene Tagap (T-cell activation Rho GTPase-activating protein) and Zfp236 (zinc finger protein 236). They were significantly decreased at 10 days and persisted to 21 days (Figure 3c, d). Cyp2d9, a member of cytochrome P450, family 2, subfamily d, was increased more than 12-fold at 10 days (Figure 3e). Mnx1 is a sequence-specific DNA binding transcription factor. It decreased from 1 to 21 days (Figure 3f). In addition, the fold changes of these lncRNAs and mRNAs detected by qPCR at SNL 10 days were consistent with the results from microarray (Figure 3g), further supporting the reliability of the array data.
Figure 3

QPCR validations of four deregulated lncRNAs and two deregulated mRNA in the spinal cord from SNL mice. The expressions of lncRNA Speer7-ps1 (a), lncRNA Uc007pbc.1 (b), lncRNA ENSMUST00000171761 (c), and lncRNA ENSMUST00000097503 (d) were significantly deregulated at 10 and 21 days after SNL. e The expression of Cyp2d9 mRNA was markedly up-regulated at 10 days after SNL. f The expression of Mnx1 mRNA was significantly down-regulated at 1, 3, 10 and 21 days after SNL. One-way ANOVA followed by Tukey’s multiple comparison test. *P < 0.01, **P < 0.01, ***P < 0.001. g Log 10 value of signal intensity detected by microarray.

Class distribution of changed LncRNAs

lncRNAs were shown to regulate the expression of adjacent or overlapping mRNAs in genome [18, 27, 28]. Thus, the associations of DE lncRNAs with coding genes were analyzed and classified according to the method described by Li et al. [29]. LncRNAs are classified into four groups: intergenic lncRNAs (lncRNAs are located and transcribed from intergenic regions, and do not overlap with known protein coding genes or other types of genes in genome. It is also called lincRNAs), antisense lncRNAs (LncRNA exon is transcribed from the antisense strand and overlaps with a coding transcript exon), sense lncRNAs (LncRNA exon overlaps with a coding transcript exon on the same genomic strand), and bidirectional lncRNAs (LncRNA is oriented head to head with a coding transcript within 1,000 bp). As shown in Figure 4, among the DE lncRNAs, intergenic lncRNAs were the largest category, with 236 up-regulated and 90 down-regulated lncRNAs. The other DE lncRNAs included 100 antisense lncRNAs (78 up-regulated and 22 down-regulated), 59 sense lncRNAs (37 up-regulated and 22 down-regulated), and 26 bidirectional lncRNAs (15 up-regulated and 11 down-regulated).
Figure 4

Distribution of various types of DE lncRNAs. Four classes (sense overlap lncRNAs, antisense overlap lncRNAs, bidirectional lncRNAs and intergenic lncRNAs) were analyzed.

Functional prediction of DE mRNAs in SNL

To explore the molecular mechanism in neuropathic pain, we further did GO and pathway analysis of deregulated genes in SNL versus sham. The GO results showed that the most significant enriched molecular function of up-regulated genes in SNL was chemokine activity, CCR chemokine receptor binding, chemokine receptor binding, and cysteine-type endopeptidase inhibitor activity (Figure 5a). The most significant enriched biological processes of up-regulated genes in SNL were immune response, immune system process, defense response, and regulation of immune system process (Figure 5b). The most noteworthy enriched cellular components of up-regulated genes in SNL were extracellular region, extracellular space, extracellular region part, and external side of plasma membrane (Figure 5c). The most significant enriched molecular function of down-regulated genes in SNL were binding, receptor binding, calcium ion binding, and tropomyosin binding (Figure 5d). The most significant enriched biological processes of down-regulated genes in SNL were regulation of ATPase activity, monovalent inorganic cation transport, glucosamine-containing compound catabolic process, and amino sugar catabolic process (Figure 5e). The most significant enriched cellular components of down-regulated genes in SNL were extracellular region, striated muscle thin filament, extracellular space, and cell part (Figure 5f).
Figure 5

Biological functions of differentially expressed mRNAs with fold changes >2. ac The significant molecular function, biological process and cellular component of up-regulated mRNAs. df The significant molecular function, biological process and cellular component of down-regulated mRNAs.

Similarly, different genes were analyzed in KEGG. The results showed that the up-regulated genes in SNL are involved in complement and coagulation cascades, Toll-like receptor signaling pathway, chemokine signaling pathway, cytosolic DNA-sensing pathway, and cytokine–cytokine receptor interaction, Changas disease, and NOD-like receptor signaling pathway (Figure 6a). Down-regulated genes in SNL are involved in amyotrophic lateral sclerosis (ALS), prostate cancer, citrate cycle, glutamatergic synapse, osteoclast differentiation and NOD-like receptor signaling pathway (Figure 6b).
Figure 6

Pathway analysis for 366 up-regulated and 127 down-regulated mRNAs with fold changes >2. a The significant pathways for up-regulated genes in SNL group. b The significant pathways for down-regulated genes in SNL group.

Comparison of our DE mRNAs with previously published microarrays

Previous studies have shown differential gene expression profile in the spinal cord in rats with neuropathic pain [30, 31]. In order to compare neuropathic pain-associated gene expression patterns in mice and rats, we did the overlap analysis between other’s microarray data from rat [30] and our current data from mice (Figure 7a). LaCroix-Fralish et al. reported that 88 genes were upregulated and 83 genes were downregulated in the spinal cord 7 days after L5 nerve root ligation in rats [30]. Surprisingly, compared to 361 up-regulated genes and 119 down-regulated genes in mouse, only 1 gene (Cd74) was upregulated and 2 genes (Nefm, Aco2) were downregulated in both rats and mice (Figure 7b). In addition, we compared our array data with 79 significantly regulated genes which were identified by meta-analysis from 20 independent microarray experiments from rats and mice after tissue inflammation or nerve injury [2]. We observed an overlap of 15 genes with the meta-analysis dataset (Figure 7c). These genes included 14 up-regulated genes (Ctss, C1qb, C1qc, Npy, Cd74, Gal, Aif1, Calca, Cxcl10, Atf3, Ccl2, Ctsh, Fcgr2b and Sprr1a) and 1 down-regulated gene (Nefm) (Figure 7d).
Figure 7

Gene overlap analysis between the present data and previously published microarrays in pain model. a Venn diagram showing the number of common up- and down-regulated genes in our present mice model (mice-up, mice-down) and previously published rat model (rat-up, rat-down) after SNL. Only three genes were shared with the same tendency between the two microarray experiments. b The detailed information of the overlap genes that were significantly regulated in both the mice and rat spinal cord. c Venn diagram showing the overlap between gene-sets of our present data and previously published microarrays (Up-P up-regulated genes of the previous studies, Down-P down-regulated genes of the previous studies, Up-A up-regulated genes of the author’s data, Down-A down-regulated genes of the author’s data). d The detailed information of 14 up-regulated and 1 down-regulated overlapped genes between our present data and previously published microarrays.

Relational analysis of lncRNAs and mRNAs

As some lncRNAs have been suggested to play key roles in regulating the expression of their neighboring or overlapping genes in genome wide, we further screened out DE mRNAs related to DE lncRNAs based on their location distributions on mouse chromosomes by UCSC Genome Browser. In the spinal cord, there are 39 DE lncRNA-mRNA pairs for 35 DE lncRNAs and 35 DE mRNAs. Among them, 32 pairs exhibited coordinated expression changes, and 7 pairs were non-coordinated, which may suggest a complex and various regulatory mechanisms across different lncRNAs and their target mRNAs. Intriguingly, all the seven non-coordinated lncRNA-mRNA pairs belong to intergenic lncRNA-mRNA pairs (Table 3). Further GO and pathway analysis showed that the high enriched molecular functions include pheromone binding, chemokine activity, high-density lipoprotein binding, and phosphatidylcholine-sterol O-acyltransferase activator activity (Figure 8a). Based on gene-pathway network graph analysis, we found that the DE mRNAs from lncRNA-mRNA pairs, such as Cxcl9 (chemokine (C-X-C motif) ligand 9), Cxcl10 (chemokine (C-X-C motif) ligand 10), Cxcl11 (chemokine (C-X-C motif) ligand 11), Trhr (thyrotropin releasing hormone receptor), and Apoa2 (apolipoprotein A-II), might involve in toll-like receptor signaling pathway, calcium signaling pathway, and PPAR signaling pathway (Figure 8b; Table 3), which have been proven to be involved in neuropathic pain pathogenesis [3234].
Table 3

DE lncRNAs and their neighboring or overlapping DE mRNAs

LncRNAs

Relationship

mRNAs

Function prediction of DE lncRNAs with related mRNAs

Sequence name

Fold change

Regulation

GeneSymbol

Fold change

Regulation

Molecular Function

Pathway

ENSMUST00000160110

3.9130898

Down

Antisense overlap

Phtf1

2.1720073

Down

GO:0003677 DNA binding

 

AK136749

2.089502

Up

Antisense overlap

Asap2

8.8652115

Up

  

ENSMUST00000121460

11.624642

Up

Antisense overlap

Mup2

16.324926

Up

GO:0005215 transporter activity

GO:0005550 pheromone binding

 

mouselincRNA1303+

2.959626

Up

Intergenic

Vmn1r54

2.5373068

Up

  

MM9LINCRNAEXON12110+

9.611986

Up

Intergenic

Apoa2

5.3063893

Up

GO:0005319 lipid transporter activity

GO:0008035 high-density lipoprotein binding

GO:0017127 cholesterol transporter activity

GO:0042803 protein homodimerization activity

GO:0046982 protein heterodimerization activity

GO:0055102 lipase inhibitor activity

GO:0060228 phosphatidylcholine-sterol O-acyltransferase activator activity

PPAR signaling pathway

MM9LINCRNAEXON11813−

2.2022471

Up

Intergenic

Ngfr

2.3073637

Up

GO:0005030 neurotrophin receptor activity

GO:0048406 nerve growth factor binding

Neurodegenerative disorders

Cytokine–cytokine receptor interaction

C75950

2.3177905

Up

Intergenic

Gm5136

2.2296717

Down

  

mouselincRNA1231−

2.3548565

Up

Intergenic

Hvcn1

2.0545347

Up

GO:0005244 voltage-gated ion channel activity

GO:0030171 voltage-gated proton channel activity

 

ENSMUST00000133243

2.2177694

Up

Intergenic

Uspl1

2.747246

Up

GO:0004221 ubiquitin thiolesterase activity

 

MM9LINCRNAEXON11661+

20.514269

Up

Intergenic

Asap2

8.8652115

Up

  

humanlincRNA1070+

6.5686955

Up

Intergenic

Vax2

17.30259

Up

GO:0003700 transcription factor activity

 

humanlincRNA2255−

6.4199057

Up

Intergenic

Trhr

2.1457152

Down

  

mouselincRNA1631+

2.131738

Up

Intergenic

Klhl15

2.0060081

Up

GO:0005515 protein binding

 

humanlincRNA1443−

4.366208

Up

Intergenic

Igsf10

7.495438

Up

GO:0005021 vascular endothelial growth factor receptor activity

GO:0005515 protein binding

GO:0005524 ATP binding

 

MM9LINCRNAEXON12110+

9.611986

Up

Intergenic

Dedd

2.2202826

Down

GO:0003677 DNA binding

GO:0005515 protein binding

 

MM9LINCRNAEXON10576−

5.209898

Up

Intergenic

Cxcl9

5.6018896

Up

GO:0008009 chemokine activity

Cytokine–cytokine receptor interaction

Toll-like receptor signaling pathway

MM9LINCRNAEXON11308+

3.7596319

Up

Intergenic

Zfp654

2.1100945

Down

GO:0003677 DNA binding

GO:0008270 zinc ion binding

 

BM248967

6.0079184

Up

Intergenic

Dgkk

3.3316648

Up

GO:0004143 diacylglycerol kinase activity

 

MM9LINCRNAEXON11616+

2.5639145

Up

Intergenic

Hexb

2.0003252

Up

GO:0004553 hydrolase activity, hydrolyzing O-glycosyl compounds

GO:0004563 beta-N-acetylhexosaminidase activity

GO:0042803 protein homodimerization activity

GO:0043169 cation binding

GO:0046982 protein heterodimerization activity

N-Glycan degradation

Aminosugars metabolism

Glycosaminoglycan degradation

Glycosphingolipid biosynthesis—globoseries

Glycosphingolipid biosynthesis—ganglioseries

Glycan structures—degradation

uc008iab.1

2.1543121

Down

Intergenic

Fam160b1

2.5731633

Up

  

MM9LINCRNAEXON12066−

4.066157

Down

Intergenic

Tnnt2

3.0204759

Down

GO:0005200 structural constituent of cytoskeleton

 

MM9LINCRNAEXON10576−

5.209898

Up

Intergenic

Cxcl11

2.7319772

Up

GO:0008009 chemokine activity

Cytokine–cytokine receptor interaction

Toll-like receptor signaling pathway

MM9LINCRNAEXON10576−

5.209898

Up

Intergenic

Cxcl10

6.9877048

Up

GO:0008009 chemokine activity

Cytokine–cytokine receptor interaction

Toll-like receptor signaling pathway

AK054438

2.3012707

Up

Intergenic

Ifi202b

9.431554

Up

GO:0005515 protein binding

 

MM9LINCRNAEXON10268−

6.8239675

Up

Intergenic

Irf8

2.335659

Up

GO:0003700 transcription factor activity

 

MM9LINCRNAEXON11735+

2.4868224

Down

Intergenic

Ppp2r5c

2.1656942

Down

GO:0008601 protein phosphatase type 2A regulator activity

 

DV650983

2.0293975

Down

Intergenic

Olfr1416

2.2257524

Up

GO:0004984 olfactory receptor activity

Olfactory transduction

MM9LINCRNAEXON11795+

2.5727692

Down

Intergenic

Cd68

2.6843183

Up

  

MM9LINCRNAEXON11793+

2.671865

Up

Intergenic

Cd68

2.6843183

Up

  

ENSMUST00000120184

2.5531633

Down

Sense overlap

Amy2b

2.4439986

Down

  

uc007vpp.1

2.1796808

Down

Sense overlap

Trhr

2.1457152

Down

GO:0004872 receptor activity

GO:0004997 thyrotropin-releasing hormone receptor activity

Calcium signaling pathway

Neuroactive ligand–receptor interaction

uc009pmr.1

3.20246

Down

Sense overlap

Elmod1

2.2530112

Down

  

uc007cua.1

6.378551

Down

Sense overlap

Tnnt2

3.0204759

Down

  

ENSMUST00000040306

4.188862

Down

Sense overlap

H2-Ea-ps

3.2939498

Down

  

uc008uzw.1

2.2820547

Up

Sense overlap

Laptm5

2.1142242

Up

  

ENSMUST00000117412

2.5116289

Up

Sense overlap

Gm10147

2.2881203

Up

  

ENSMUST00000119882

3.1487308

Up

Sense overlap

Gm10486

2.4736855

Up

  

ENSMUST00000119882

3.1487308

Up

Sense overlap

Gm14819

3.018787

Up

  

uc008tbm.1

10.098583

Up

Sense overlap

Mup17

21.82689

Up

  
Figure 8

Function prediction of DE lncRNAs with related mRNAs. a Molecular function enrichment analysis of DE lncRNAs-related mRNAs. The enrichment scores (−log10 (P-value)) of the GO molecular function were shown in the histogram. b Gene-pathway network graph of DE lncRNAs-related mRNAs from Table 3. The DE lncRNAs-related genes and the corresponding pathways were shown in the circles and boxes, respectively. The color of pathway terms is defined by the enrichment P value.

Discussion

Chronic neuropathic pain is a somatosensory disorder caused by nerve injury or disease that affects the nervous system [35]. Evidence suggested that the particular patterns of gene expression at different levels of the nociceptive system play important roles in the development and maintenance of neuropathic pain [2, 36]. Over the past decades, the molecular mechanisms underlying neuropathic pain have been extensively studied; however, the pathophysiological process of pain is still vague. LncRNAs were recently shown to regulate gene expression [37] and traffic cellular protein complexes, genes, and chromosomes to appropriate locations [8]. Their function in regulating gene expression switching in the maintenance phase of neuropathic pain is poorly understood. In this study, we for the first time identified the global expression changes in lncRNAs and analyzed their characteristics and possible relation with coding genes in the spinal cord under neuropathic pain condition. The 24,833 lncRNAs were detected in the spinal cord of mice. Among them, 366 lncRNAs were up-regulated and 145 lncRNAs were down-regulated at 10 days after SNL. These DE lncRNAs are consistently altered in a high percentage of analyzed spinal cords from SNL and sham mice, suggesting that lncRNAs may be involved in neuropathic pain processing. So far, most DE lncRNAs have not been functionally characterized. Although it was still too early to translate this knowledge into the development of novel analgesic agents for better pain relief, these findings may likely provide novel insight into the molecular basis of pain.

In this study, the expression profiles of mouse genome-wide mRNAs were also detected using lncRNA Microarray Chip at the same time. Among DE mRNAs, the up-regulated mRNAs are far more numerous than the down-regulated in SNL samples, which reflects the emergence of new biology processes and pathways in pathological conditions. A number of reported pain-related genes, including Cacna1g, Trpv1, Ccl5, Cx3cr1 and Irf5 were dramatically increased after SNL. Moreover, a lot of other mRNAs, such as Sprr1a, Anxa10, Kng1, and Gpr151 (G-protein-coupled receptor 151), whose functions are unclear in the spinal cord were also screened out. As the expression changes for some genes may be related to nerve damage and homeostatic responses to denervation, further studies are needed to identify whether they are involved in neuropathic pain processing.

Based on the GO term enrichment analyses of DE mRNA, we found that significantly enriched molecular functions and biological processes of up-regulated gene in SNL vs sham were mainly involved in chemokine activity, inflammation, and immunity. These findings are consistent with previous studies showing that neuroinflammation, manifested as infiltration of immune cells [38], activation of glial cells [39] and production of inflammatory mediators [40] in the peripheral and CNS, plays an important role in the induction and maintenance of chronic pain [41]. Additionally, our immunostaining of GFAP and IBA-1 showed dramatic glial activation in the spinal cord at 10 days after SNL. From significant pathway analyses of DE gene, the third most significant enriched pathway of the up-regulated genes in SNL vs sham is the toll-like receptor signaling pathway. Indeed, Tlr2 [42], Tlr4 [43], and Tlr7 [44] have been implicated as potential therapeutic targets in neuropathic and other pain models. The data collectively indicate that anti-neuroinflammation may be an effective strategy for the treatment of neuropathic pain.

Previous studies utilizing cDNA microarrays to analyze gene expression profiles primarily focus on pain models in rats, rarely in mice [2]. The overlap analysis showed little overlap between rat and mice spinal cord gene expression patterns under neuropathic pain states, suggesting the species difference in gene expression. However, we found that there were 15 overlap genes between our current data and meta-analysis results reported by LaCroix-Fralish et al. [2]. These overlap genes including Atf3, Sprr1al and Nefm can be induced by nerve damage, which contribute to chronic pain [4547]. In addition, gene ontology-based functional annotation clustering analyses of the previous gene chip study revealed strong evidence for regulation of immune-related genes in pain states, which was consistent with our data.

Although lncRNAs play important roles in the regulation of gene expression [48], there is a large gap between the number of existing lncRNAs and their known association with a particular molecular or cellular function [49]. Regulatory mechanisms and major functional principles of lncRNAs are complex and quite obscure. Unlike microRNA, there are no common languages that can be used to predict lncRNAs’ target genes and function by their sequence information or secondary structure. Accumulating evidence suggests that a number of lncRNAs function locally to activate or repress their neighboring or overlapping genes’ expression [18, 27, 50]. In this study, we found that intergenic lncRNAs (lincRNAs) were the largest category in all DE lncRNAs after SNL. In reality, lincRNAs are found to be conserved across multiple vertebrate species [51] and perform important functions in many cellular processes, from cell proliferation to cancer progression [52]. Furthermore, lincRNAs can function through different types of mechanisms, including cis or trans transcriptional regulation, translational control, splicing regulation, and other post-transcriptional regulation [33]. We examined whether their neighboring or overlapping protein-coding genes in the genome are simultaneously DE in the spinal cord after SNL, and found that there are 39 DE lncRNA-mRNA pairs. Our further analysis showed that an up-regulated lincRNA, MM9LINCRNAEXON10576− in the spinal cord after SNL was found to be located near Cxcl10, Cxcl9 and Cxcl11 gene cluster in mice chromosome 5. All the four RNAs have the same expression trends and increased more than twofold after SNL. Recently, studies using animal models have shown that upregulation of chemokines in the spinal cord play a vital role in the development and maintenance of chronic pain [41, 53, 54]. Indeed, recent research found that Cxcl10 and its receptor Cxcr3 were involved in inflammatory pain and cancer pain [5557]. Therefore, lncRNA MM9LINCRNAEXON10576− may contribute to neuropathic pain through regulation of chemokines Cxcl10, Cxcl9 and Cxcl11.

In our microarray results, 12 DE mRNA have their corresponding DE sense-overlap lncRNAs, and the change patterns of these lncRNA were same as that of their accompanying protein-coding genes. Di et al. found that a sense-overlap lncRNA arising from the CCAAT/enhancer-binding protein alpha (Cebpa) gene locus can bind to DNA methyltransferase 1 (DNMT1) and prevent Cebpa gene locus methylation, then to increase the expression of Cebpa gene. Their deep sequencing of transcripts associated with DNMT1 combined with genome-scale methylation and expression profiling extend the generality of this finding to numerous gene loci. [27]. Given that the 12 DE mRNA and their DE sense-overlap lncRNAs were both increased after SNL, it’s possible that the DE sense-overlap lncRNAs regulate the expression of their sense-overlapping mRNAs via demethylation after SNL.

Conclusion

Our results demonstrated that lncRNA transcripts were highly enriched and hundreds of lncRNAs were differentially expressed in the spinal cord after SNL. Dozens of DE lncRNAs were observed to have neighboring or overlapping DE mRNAs in genome. These lncRNAs may locally regulate their related protein-genes expression and play key roles in the pathogenesis of neuropathic pain. Further studies are required to clarify the molecular and cellular functions of DE lncRNAs and determine whether they can serve as novel analgesic targets in neuropathic pain.

Methods

Animals and surgery

Adult male ICR mice (male, 8 weeks) were maintained on a 12:12 light–dark cycle at a room temperature of 22 ± 1°C with free access to food and water. The experimental procedures were approved by the Animal Care and Use Committee of Nantong University and performed in accordance with the guidelines of the International Association for the Study of Pain. To produce a SNL, animals were anesthetized with isoflurane and the L6 transverse process was removed to expose the L4 and L5 spinal nerves. The L5 spinal nerve was then isolated and tightly ligated with 6-0 silk threads [58]. For sham operations, the L5 spinal nerve was exposed but not ligated.

Behavioral test

Animals were habituated to the testing environment daily for at least 2 days before baseline testing. The room temperature remained stable for all experiments. For testing mechanical sensitivity, animals were put in boxes on an elevated metal mesh floor and allowed 30 min for habituation before examination. The plantar surface of each hindpaw was stimulated with a series of von Frey hairs with logarithmically incrementing stiffness (0.02–2.56 g, Stoelting, Wood Dale, IL, USA), presented perpendicular to the plantar surface (2–3 s for each hair). The 50% paw withdrawal threshold was determined using Dixon’s up-down method [59]. For testing heat sensitivity, animals were put in plastic boxes and allowed 30 min for habituation. Heat sensitivity was tested by radiant heat using Hargreaves apparatus (IITC Life Science Inc., Woodland Hills, CA, USA) and expressed as paw withdrawal latency (PWL). The radiant heat intensity was adjusted so that basal PWL is between 10 and 14 s, with a cutoff of 18 s to prevent tissue damage.

Immunohistochemistry

At 10 days after SNL or sham-operation, animals were deeply anesthetized with isoflurane and perfused through the ascending aorta with PBS followed by 4% paraformaldehyde with 1.5% picric acid in 0.16 M PB. After the perfusion, the L4–L5 spinal cord segments were removed and postfixed in the same fixative overnight. Spinal cord sections (30 μm, free-floating) were cut in a cryostat. The sections were first blocked with 5% goat serum for 2 h at room temperature. The sections were then incubated overnight at 4°C with the following primary antibodies: GFAP antibody (mouse, 1:6,000; Millipore, Billerica, MA, USA), IBA-1 antibody (Mouse, 1:3,000, Serotec, Kidlington, UK). The sections were then incubated for 2 h at room temperature with FITC-conjugated secondary antibodies (1:1,000, Jackson ImmunoResearch). The stained sections were examined with a Leica fluorescence microscope, and images were captured with a CCD Spot camera.

Tissue collection and RNA isolation

We prepared nine mice for SNL and nine mice for sham-operation. At 10 days after operation, the animals were deeply anesthetized with isoflurane and perfused through the ascending aorta with saline. After the perfusion, the L4–L5 spinal cord segments were collected. Total RNA was extracted from the spinal cord dorsal horn tissue using Trizol reagent (Invitrogen, Carlsbad) according to the manufacturer’s protocol. The RNA concentration and purity were assayed by the absorbance values at 260 and 280 nm using the NanoDrop 1000 Spectrophotometer (Thermo). RNA integrity was checked by electrophoresis on 2% (m/v) agarose gels. After these testing, equal mRNA from three mice under the same treatment was mixed as one sample. Therefore, six samples (3 for SNL and 3 for sham) were sent for microarray analysis.

Microarray assay

The gene chip of the mouse lncRNA microarray V2.0 (8 × 60K, Arraystar), which includes 25,376 lncRNA probes and 31,423 coding gene probes, was used in the experiments. The total RNAs of sham and SNL groups were individually hybridized with gene chips. Briefly, RNA was purified from 1 μg total RNA after removing rRNA. The RNA sample was then transcribed into fluorescent cRNA along the entire length of the transcripts without 3′ bias utilizing random primers. The labeled cRNAs were hybridized to mouse lncRNA microarray. Finally, arrays were scanned by Agilent Scanner G2505B. The array images were analyzed by Agilent Feature Extraction software (version 10.7.3.1). The GeneSpring GX v11.5.1 software package (Agilent Technologies) was utilized to analyze quintile normalization and subsequent data processing. The microarray hybridization was carried out by Kangchen Bio-tech, Shanghai, China.

Bioinformatics analysis

Differentially expressed lncRNAs and mRNAs with statistical significance were identified through Volcano Plot filtering. The threshold used to screen up- or down-regulated RNAs was fold-change >2.0 (P < 0.05). Hierarchical clustering was carried out by Cluster 3.0, and the heat maps were generated in Java Treeview. The DE mRNAs which were adjacent to or overlap with the DE lncRNAs were recognized as DE lncRNAs related mRNAs using UCSC Genome Browser. The differentially expressed mRNAs or DE lncRNAs related mRNAs were analyzed by pathway annotation and gene ontology (GO) functional enrichment using CapitalBio® Molecule Annotation System V3.0 (MAS3.0). The −log10 (P-value) of the GO and pathway results were shown in the histogram.

Real-time reverse transcription-polymerase chain reaction (RT-PCR)

The microarray results were confirmed by RT-PCR. Total RNA was extracted from the spinal cord tissue as described above and total RNA was reverse transcribed using random hexamers primer (TaKaRa Bio Inc) according to the manufacturer’s description. The expression level of six genes was checked, including Speer7-ps1, uc007pbc.1, ENSMUST00000171761, ENSMUST00000097503, Cyp2d9, and Mnx1. The Gapdh was used as house-keeping gene. The sequences of all primers were shown in Table 4. RT-PCR was performed using the Fast Start Universal SYBR Green Master (TaKaRa Bio Inc) with 20-μl reaction system, according to the manufacturer’s protocol, in a Rotor-Gene 6000 instrument (Hamburg, Germany). The melting-curve analysis was performed in order to monitor the specificity of production. All experiments were replicated three times. The gene expression levels in the sham and SNL groups were analyzed with the 2−∆∆CT method.
Table 4

Primer sequences used in Real-Time PCR

Sequence name

Primer sequence

Amplicon size (bp)

Speer7-ps1

F: 5′-CATGCTCTCATGCTCACCGA-3′

70

R: 5′-TACGCTGTAGGACCAGAACAC-3′

uc007pbc.1

F: 5′-CATCTAGACCCGTAACGCCC-3′

340

R: 5′-TGGTAGGCAAGCATCCACAG-3′

ENSMUST00000171761

F: 5’-TCGGAGACTTCTCTTCCGGT -3’

108

R: 5′-AAGACAATGCAGATGGGGCA-3′

ENSMUST00000097503

F: 5′-AGGTCATCCCACTTTGGTACAC-3′

77

R: 5′-GAGTTTGGTTTGCGGGGTCT-3′

Cyp2d9

F: 5′-TGTCTACCCTGCGCAACTTT-3′

71

F: 5′-GTGATTGGCCTCCTTGGTCA-3′

Mnx1

F: 5′-GAACACCAGTTCAAGCTCAACA-3′

129

R: 5′-GCTGCGTTTCCATTTCATTCG-3′

Gapdh

F:5′-TGTTCCTACCCCCAATGTG-3′

129

R:5′-GTGTAGCCCAAGATGCCCT-3′

Statistical analysis

The behavioral data were analyzed by two-way analysis of variance. The RT-PCR results were reported as mean ± SEM and analyzed by the one-way analysis of variance followed by Tukey’s multiple comparison test. The criterion for statistical significance was P < 0.05.

Notes

Abbreviations

Anxa10

annexin A10

Apoa2

apolipoprotein A-II

Atf3

activating transcription factor 3

Cacna1g

calcium channel, voltage-dependent, T type, alpha 1G subunit

Ccl5

chemokine C-C motif ligand 5

Cebpa

CCAAT/enhancer-binding protein alpha

CNS: 

central nervous system

Cyp2d9

cytochrome P450, family 2, subfamily d, polypeptide 9

Cx3cr1

chemokine (C-X3-C) receptor 1

DE: 

differentially expressed

Dnmt1

DNA methyltransferase 1

GO: 

gene ontology

Gpr151

G-protein-coupled receptor 151

Irf5

interferon regulatory factor 5

Kng1

kininogen 1

miRNA: 

microRNA

lncRNA: 

long non-coding RNA

Mnx1

motor neuron and pancreas homeobox 1

Nefm

neurofilament, medium polypeptide

PPAR: 

peroxisome proliferator-activated receptor

SNL: 

spinal nerve ligation

Sprr1a

small proline-rich protein 1A

Tagap

T cell activation Rho GTPase-activating protein

Trhr

thyrotropin releasing hormone receptor

Trpv1

transient receptor potential cation channel, subfamily V, member 1

Zfp236

zinc finger protein 236

Declarations

Authors’ contributions

BCJ designed the microarray experiment, analyzed the data, and drafted the manuscript. WSX participated in the data analysis and prepared the figures. LNH did the real-time PCR analysis. DLC did the immunostaining. ZJZ prepared SNL model and did the behavioral test. YJG designed and supervised the overall experiment, revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank Dr. Fu-Lu Dong (Institutes of Biology and Medical Sciences, Soochow University) for aiding the technical assistance. This study was supported by the National Natural Science Foundation of China (NSFC 31371121, 31171062, and 81400915), the National Science Foundation for Young Scientists of Jiangsu Province (BK20140427), the Natural Science Foundation of the Jiangsu Higher Education Institutions (13KJB180016), the Natural Science Foundation of Nantong University (13040443), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Compliance with ethical guidelines

Competing interests The authors declare that they have no competing interests.

Open AccessThis 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)
Pain Research Laboratory, Institute of Nautical Medicine, Jiangsu Key Laboratory of Inflammation and Molecular Drug Target, Nantong University
(2)
Co-innovation Center of Neuroregeneration, Nantong University
(3)
Department of Nutrition and Food Hygiene, School of Public Health, Nantong University

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© Jiang et al. 2015

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