BioMed Research International

BioMed Research International / 2015 / Article
Special Issue

Biomarkers of Brain Function and Injury: Biological and Clinical Significance

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Research Article | Open Access

Volume 2015 |Article ID 276049 | 9 pages | https://doi.org/10.1155/2015/276049

Expression Signatures of Long Noncoding RNAs in Adolescent Idiopathic Scoliosis

Academic Editor: Diego Gazzolo
Received04 Jun 2014
Accepted31 Oct 2014
Published01 Sep 2015

Abstract

Purpose. Adolescent idiopathic scoliosis (AIS), the most common pediatric spinal deformity, is considered a complex genetic disease. Causing genes and pathogenesis of AIS are still unclear. This study was designed to identify differentially expressed long noncoding RNAs (lncRNAs) involving the pathogenesis of AIS. Methods. We first performed comprehensive screening of lncRNA and mRNA in AIS patients and healthy children using Agilent human lncRNA + mRNA Array V3.0 microarray. LncRNAs expression in different AIS patients was further evaluated using quantitative PCR. Results. A total of 139 lncRNAs and 546 mRNAs were differentially expressed between AIS patients and healthy control. GO and Pathway analysis showed that these mRNAs might be involved in bone mineralization, neuromuscular junction, skeletal system morphogenesis, nucleotide and nucleic acid metabolism, and regulation of signal pathway. Four lncRNAs (ENST00000440778.1, ENST00000602322.1, ENST00000414894.1, and TCONS_00028768) were differentially expressed between different patients when grouped according to age, height, classification, severity of scoliosis, and Risser grade. Conclusions. This study demonstrates the abnormal expression of lncRNAs and mRNAs in AIS, and the expression of some lncRNAs was related to clinical features. This study is helpful for further understanding of lncRNAs in pathogenesis, treatment, and prognosis of AIS.

1. Introduction

Adolescent idiopathic scoliosis (AIS), accounting for 90% of all idiopathic scoliosis (IS) cases, affects more than 2% of the pediatric population and results in more than 600,000 physician visits annually [1]. Approximately, 1.5% patients ultimately need surgical correction for their curvature [2]. However, the causes involved in AIS remain obscure.

Genetic twin studies and observation of familial aggregation have revealed significant genetic contribution to IS [3, 4]. A genetic survey study reported that an overall risk of IS in first-degree relatives was up to 11%, as compared to 2.4 and 1.4% in second- and third-degree relatives, respectively. These studies indicate that IS is an inheritable complex disease. Molecular studies found that some critical regions on chromosomes were potentially important for the occurrence of scoliosis [5, 6]. Genome-wide association studies of AIS in case-control cohorts also located candidate susceptibility genes, and these findings indicate that single nucleotide polymorphisms are valuable for the AIS prognosis [7]. However, inconsistent outcomes were observed among studies concerning the candidate genes in AIS [811]. Totally, the inheritance mode and pathogenic gene of AIS is uncertain until today.

Recent technological advances reveal that a major portion of the genome is being transcribed and that protein-coding sequences only account for a minority of cellular transcriptional output. MicroRNAs are endogenously expressed noncoding transcripts, shorter than 20 nucleotides. MicroRNA silences gene expression by targeting specific mRNAs on the basis of sequence recognition [12]. Long noncoding RNAs (lncRNAs) are transcripts longer than 100 nucleotides. lncRNAs in most cases mirror the features of protein-coding genes except that the former does not contain a functional open reading frame. A handful of studies have implicated that lncRNAs are involved in a variety of disease states [13] and altered lncRNA levels can result in aberrant expression of gene products that may contribute to many biological processes and human diseases [1416]. Circulating levels of lncRNAs were reported to reflect local pathophysiological conditions [17, 18]. Expression level of RNA in peripheral blood has been used to evaluate local circumstance of spine [19, 20]. However, the expression of lncRNAs and their functions in AIS are still unknown.

In the present study, we measured lncRNAs expression in AIS patients using microarray and quantitative PCR (qPCR) analysis. Our results disclosed lncRNAs expression profiles and provide new information for further exploration of pathogenesis and prognosis of AIS.

2. Materials and Methods

2.1. Patients

Written informed consent was obtained from all participants. The study was approved by the Institutional Review Board of Chinese Academy of Medical Sciences and Peking Union Medical College Hospital. One hundred and twenty AIS patients (AIS) and twenty normal children (NC) were included in the study. Of these participants, four AIS patients and four children were used for microarray analysis. No significant difference was found between two groups in age, menarche, and Risser sign. Detailed information of the four pairs of participants is summarized in Table 1. The diagnosis of AIS was made only when other causes of scoliosis, including vertebral malformation, neuromuscular disorder, and syndromic disorders, were ruled out. Participants were screened with Adams’ forward bend test. Cobb angle measured with a scoliometer was at least 10° [21]. Peripheral blood from each subject was added 3 times volume of Trizol LS reagent (Amboin) and immediately stored at −80°C until use.


NumberGroupSexHeightAge (years)Illness history (years)MenarcheClassificationCobb angle (°)Risser sign

A1AISF15513.71NPUMCIIc2500
A2AISF16715.92YPUMCIIc2404
A3AISF15815.01YPUMCIb445
A4AISF15914.23YPUMCIIc1443
N1NCF16317.90Y05
N2NCF16313.20Y03
N3NCF15812.30N00
N4NCF17015.50Y04

2.2. RNA Extraction

Total RNA was extracted using Trizol LS reagent according to the instructions recommended by the manufacturer. The RNA purity and concentration were evaluated with NanoDrop ND-1000 spectrophotometer. RNA integrity was determined with 1% formaldehyde denaturing gel electrophoresis, which revealed a good quality (data not shown).

2.3. RNA Labeling and Hybridization

Double-stranded cDNAs (containing T7 RNA polymerase promoter sequence) were synthesized from 1 mg total RNA according to the manufacturer’s instructions (Capitalbio) and labeled with a fluorescent dye (Cy3-dCTP). Labeled cDNA was denatured at 95°C for 3 min in hybridization solution. Agilent human lncRNA + mRNA Array V3.0 was hybridized in an Agilent Hybridization Oven overnight at 42°C and washed with two consecutive solutions (0.2% SDS, 2x SSC for 5 min at 42°C, and 0.2x SSC for 5 min at room temperature).

2.4. Microarray Analysis

The data from lncRNA + mRNA Array were used to analyze data summarization, normalization, and quality control using the GeneSpring software V11.5 (Agilent). The differentially expressed genes were selected if the change of threshold values was ≥2 or ≤−2 folds and if Benjamini-Hochberg corrected values were <0.05. The data was normalized and hierarchically clustered with CLUSTER 3.0 software. The data were performed to be Tree Visualization with Java Treeview software (Stanford University School of Medicine, Stanford, CA, USA).

2.5. Construction of the Coding-Non-Coding Gene Coexpression (CNC) Network

The CNC network was constructed based on the correlation analysis between the differentially expressed lncRNAs and mRNAs. LncRNAs and mRNAs with Pearson correlation coefficients not less than 0.99 were selected to draw the network using open source bioinformatics software Cytoscape (Institute of Systems Biology in Seattle). In network analysis, red nodes represent the upregulated lncRNAs, dark blue nodes represent the downregulated lncRNAs, pink nodes represent the upregulated mRNAs and light blue nodes represent the downregulated mRNAs, circular nodes represent mRNAs, triangular nodes represent lncRNAs, dashed lines represent a positive correlation, and solid lines represent an inverse correlation.

2.6. qPCR

qPCR was performed using SYBR Premix Ex Taq on Thermal Cycler Dice TP800 instrument. Four lncRNAs (ENST00000440778.1, ENST00000602322.1, ENST00000414894.1, and TCONS_00028768), which revealed differentially expression, were evaluated in all participants. Total RNA (2 μg) was reversely transcribed into cDNA using a PrimeScript RT reagent kit containing a gDNA Eraser (TaKaRa) according to the manufacturer’s instructions. PCR was performed in 20 μL of reaction system, including 10 μL SYBR Premix Ex Taq (2x), 0.4 μL of PCR forward primer (10 μM), 0.4 μL of PCR reverse primer (10 μM), 1 μL of cDNA, and 8.2 μL of double-distilled water. Primers for lncRNAs and mRNA are listed in Table 2. All experiments were performed in triplicates. All samples were normalized to GAPDH. The median in each triplicate was used to calculate relative lncRNAs concentrations (ΔCt = Ct median lncRNAs-Ct median GAPDH). Folds change was calculated using methods. The differences of lncRNAs expression between patients and control were analyzed using Student’s -test within SPSS (version 16.0 SPSS Inc.). A value of was considered as statistically significant.


lncRNAsSense primer (5′-3′)Antisense primer (5′-3′)Product (bp)

ENST00000602322.1accttcccacctaccagtctctggggaaaccaccatagctt150
ENST00000440778.1acttggttgcttttccccacagctttgggcttggaaagatgg107
ENST00000414894.1agccgccctattcagttcactccaagttccgagttgtggg148
TCONS_00028768gcaaacctggaaatctcggcggccgcagacatcatcttct142
GAPDHctataaattgagcccgcagcc gcgcccaatacgaccaaatc154

3. Results

3.1. LncRNAs Profiles

Tens of thousands lncRNAs were examined in microarray. A total of 139 lncRNAs revealed significantly different expression in AIS group, compared to NC group (≥2-fold) (Table S1, see Supplementary Material available online at http://dx.doi.org/10.1155/2015/276049). Compared to NC group, a total of 118 lncRNAs were consistently upregulated or downregulated in all tested AIS samples. ENST00000440778.1 was the most significantly downregulated lncRNA (fold change = 9.780566), while NR_024075 (Log2 fold change = 3.8194413) was the most significantly upregulated one. The heat maps of the expression ratios of lncRNAs are shown in Figure 1(a).

3.2. mRNAs Profiles

The expression of coding transcripts (i.e., mRNAs) was examined with microarray containing 33,982 coding transcripts probes. Up to 546 mRNAs showed significant difference between AIS and NC groups (Table S2). A total of 512 mRNAs were upregulated in AIS, while 34 mRNAs were downregulated. The heat map of the expression ratios of mRNAs is shown in Figure 1(b). GO analysis showed that these mRNAs might be involved in bone mineralization, neuromuscular junction, skeletal system morphogenesis, and nucleotide and nucleic acid metabolism. Pathway analysis indicates that the dysregulated mRNAs are involved in cell adhesion molecules, Wnt signaling pathway, Toll-like receptor signaling pathway, MAPK signaling pathway, and so on. Coding RNAs related to major biological processes are listed in Table 3. These results support the viewpoint that AIS may be a genetic disease involving musculoskeletal system.


Biological processesGene symbolGene nameEntrez gene IDFold changes

Nervous system developmentLTA “Phosphoinositide-3-kinase, catalytic, delta polypeptide”52932.1513674
TP53Tumor protein p5371572.0308032
MSH2“mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli)”44362.0266845
BCL2BCL2-associated athanogene 395312.130905
TP53Tumor protein p5371572.0308032
NCLNuclear RNA export factor 1104822.00108
PAFAH1B1“Platelet-activating factor acetylhydrolase 1b, regulatory subunit 1 (45 kDa)”50482.1079702
OLIG1Oligodendrocyte transcription factor 11164482.129439
PKD1Polycystic kidney disease 1 (autosomal dominant)2.18849
BTG2“BTG family, member 2”78322.7636645

Skeletal morphogenesis and developmentBCL2B-Cell CLL/lymphoma 25962.252202
IL6ST“Interleukin 6 signal transducer (gp130, oncostatin M receptor)”35722.2222419
MYCv-myc myelocytomatosis viral oncogene homolog (avian)46092.768515
TPP1Tripeptidyl peptidase I12002.0187664
AESAminoterminal enhancer of split1662.2728803
ALOX15Arachidonate 15-lipoxygenase2462.1062436

Muscle development and functionBCL2B-Cell CLL/lymphoma 25962.252202
FLI1Friend leukemia virus integration 123132.2254617
UTRNUtrophin74022.1682687
UTS2Urotensin 2109112.469184
HIF1A“Hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor)”30912.028285
TGFBR2“Transforming growth factor, beta receptor II (70/80 kDa)”70482.1115046
TTNTitin72732.7300012
LEF1Lymphoid enhancer-binding factor 1511763.1550286
HIF1A“Hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor)”30912.028285
FAM65B“Family with sequence similarity 65, member B”97502.247876
UTRNUtrophin74022.1682687

Notch signaling pathwayADAM17ADAM metallopeptidase domain 1768682.0420005
NOTCH2Notch 248532.1405845
CTBP1C-Terminal binding protein 114872.050679

MAPK signaling pathwayTAB2TGF-beta activated kinase 1/MAP3K7 binding protein 2231182.1040483
SOS1Son of sevenless homolog 1 (Drosophila)66542.0901704
DUSP16Dual specificity phosphatase 16808242.1876025
MAP3K1Mitogen-activated protein kinase kinase kinase 142142.343032
RPS6KA3“Ribosomal protein S6 kinase, 90 kDa, polypeptide 3”61972.0745454
CD14CD14 molecule9292.3363564
DUSP6Dual specificity phosphatase 618482.2230558
ARRB1“Arrestin, beta 1”4082.004528
TGFBR2“Transforming growth factor, beta receptor II (70/80 kDa)”70482.1115046
AKT3“v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma)”100002.0013347
MYCv-myc myelocytomatosis viral oncogene homolog (avian)46092.768515
TP53Tumor protein p5371572.0308032
NFKB1Nuclear factor of kappa light polypeptide gene enhancer in B-cells 147902.0809784

Wnt signaling pathwayCAMK2GCalcium/calmodulin-dependent protein kinase II gamma8182.1046312
TCF7“Transcription factor 7 (T-cell specific, HMG-box)”69322.6018746
PPARDPeroxisome proliferator-activated receptor delta54672.1376982
TCF3“Transcription factor 7 (T-cell specific, HMG-box)”69322.8202255
CAMK2DCalcium/calmodulin-dependent protein kinase II delta8172.5258021
PLCB2“Phospholipase C, beta 2”53302.2920265
NFATC1“Nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1”47722.0340881
MYCv-myc myelocytomatosis viral oncogene homolog (avian)46092.768515
TP53Tumor protein p5371572.0308032
LEF1Lymphoid enhancer-binding factor 1511763.1550286
CTBP1C-Terminal binding protein 114872.050679
PIAS4“Protein inhibitor of activated STAT, 4”515882.0654333
AESAminoterminal enhancer of split1662.2728803
INVSInversin271302.2433844
LRRFIP2Leucine rich repeat (in FLII) interacting protein 292092.1049194

Gene Ontology and Pathway analysis was performed for differentially expressed mRNAs. mRNAs involving musculoskeletal development are listed in their gene groups with associated Entrez Gene ID and fold changes per gene.
3.3. CNC Network

In the CNC network, there were 76 lncRNAs and 370 mRNAs. A total of 446 network nodes were gained and this was associated with 1121 pairs of coexpression of lncRNAs and mRNAs. The CNC network indicates that one mRNA is correlated with one to tens of lncRNAs and vice versa. The whole CNC network might implicate an interregulation between lncRNAs and coding RNAs in AIS. In particular, lncRNA ENST00000602322.1 was correlated with lncRNA ENST00000422231.2 and nine coding RNAs (Figure 2).

More and more evidences indicate that lncRNAs play important roles in gene expression. Thus, we explored the correlation between lncRNAs and mRNA. More than 1000 pairs of lncRNAs and mRNAs reached an absolute correlation coefficient greater than 0.99 and a False Discovery Rate less than 0.01. Target prediction indicated that seven lncRNAs (uc002ddj.1, uc021tnw, uc021zdc.1, HIT000067310, ENST00000577528.1, ENST00000602322.1, and TCONS_00001429) may influence the expression of related mRNAs. ENST00000602322.1 and HIT000067310 may regulate the mRNA expression of PCF11 and RCAN3, respectively. The two mRNAs are expressed in muscle system and encode proteins which regulate mRNA splicing and metabolism. These lncRNAs and mRNAs may play a role in the pathogenesis of AIS. Detailed information of lncRNAs and predicted mRNAs is listed in Table 4.


SourcealncRNAmRNAGene symbolCorrelation valuecis-Regulationb

p33784uc002ddj.1A_21_P0011418PKD10.9967847Sense
p33788uc021tnw.1A_24_P282108ZZEF10.9976724Sense
p26337uc021zdc.1A_23_P122615PNISR0.9907335Sense
p26595HIT000067310A_23_P391275RCAN30.9967367Intergenic
p112ENST00000577528.1A_23_P391275RCAN30.9965706Intergenic
p2444ENST00000602322.1A_33_P3210139PCF110.9932224Antisense
p26595HIT000067310A_23_P35205RCAN30.9900216Sense
p29552TCONS_00001429A_23_P35205RCAN30.9956033Antisense

Probe name of lncRNA. sense: the lncRNAs is a coding transcript exon on the same genomic strand; antisense: the lncRNA is transcribed from the antisense strand; intergenic: there are no overlapping or bidirectional coding transcripts nearby the lncRNA within 10 kbp.
3.4. LncRNA Classification and Subgroup Analysis

Recently, some special classes or 14 clusters of lncRNAs have been identified with specific function in human cells. Numerous lncRNAs were found to be transcribed from the human homeobox transcription factors (HOX) clusters [22]. These lncRNAs are expressed in temporal and site-specific fashions and may be associated with distinct and diverse biological processes [23, 24], such as cell proliferation, RNA binding complexes, and muscle development. In the present study, our microarray probes targeted 407 discrete transcribed regions of four human HOX loci. None of them was differentially expressed between AIS and NC.

Harrow and his colleagues defined a set of lncRNAs with enhancer-like function in human cell lines [25]. Depletion of these lncRNAs led to decreased expression of their neighboring protein-coding genes. In the present study, 1896 lncRNAs with enhancer-like function were detected while 4 of them were differentially expressed in AIS. The differentially expressed enhancer-like lncRNAs and their nearby coding genes (distance, 300 kb) are shown in Table S3.

3.5. qPCR Validation

Data from microarray were validated by qPCR in twenty pairs of samples. qPCR results revealed that ENST00000602322.1, ENST00000414894.1, and TCONS_00028768 were upregulated and ENST00000440778.1 was downregulated in AIS samples (), as compared to the control, which was consistent with those from microarray analysis (Figure 3).

3.6. Clinical Features of Validated lncRNAs

The correlation of lncRNA expression with clinical features was further analyzed. All 120 AIS patients were grouped according to age, height, menarche, classification, severity of scoliosis (measured by Cobb angle), and Risser grade (Table 5). No significant difference of lncRNAs expression was observed when patients were grouped according to menarche. The expression of ENST00000440778.1 and TCONS_00028768 was significantly different when grouped according to height (). The expression of ENST00000602322.1 was higher in younger patients (). Significant difference was also observed in ENST00000602322.1 expression between patients with single curve and double curves (). Lower expression of ENST00000414894.1 was observed in patients with Cobb angle greater than 40° (). The expression of ENST00000440778.1 was higher in patients with Risser grade ≤3 ().


FeaturesENST00000602322.1ENST00000440778.1ENST00000414894.1TCONS_00028768

Menarche age (y)≤117.29 ± 0.824.74 ± 0.883.04 ± 0.525.51 ± 1.08
>117.75 ± 1.205.09 ± 1.252.95 ± 0.755.62 ± 1.44

Height (cm)≤1607.49 ± 1.034.40 ± 0.712.98 ± 0.595.05 ± 0.99
>1607.73 ± 1.175.34 ± 1.452.99 ± 0.785.95 ± 1.63

Onset of AIS (y)≤126.73 ± 0.60∗∗4.92 ± 1.093.09 ± 1.525.54 ± 1.19
>127.86 ± 1.29∗∗5.01 ± 1.172.87 ± 1.175.63 ± 1.43

Numbers of curvesa17.09 ± 0.744.95 ± 1.402.79 ± 0.665.81 ± 1.24
27.98 ± 1.194.98 ± 0.953.10 ± 0.665.46 ± 1.34

Cobb angle (°)≤407.60 ± 1.365.11 ± 1.702.47 ± 0.635.59 ± 1.60
>407.58 ± 1.954.90 ± 0.723.38 ± 0.655.59 ± 1.15

Risser signb≤37.51 ± 1.124.33 ± 0.812.93 ± 0.605.37 ± 0.97
>37.65 ± 1.075.21 ± 1.253.02 ± 0.725.76 ± 1.51

Relative lncRNAs levels were normalized to GAPDH (Ct = CtlncRNA − CtGAPDH). Results are presented as mean ± standard deviation. < 0.05, < 0.01. aCurves were counted and classified into single, double, and triple curves according to the apex number. bRisser sign refers to the amount of calcification of the human pelvis as a measure of maturity. On a scale of 5, it gives a measurement of ossification progression; the grade of 5 means that skeletal maturity is reached.

4. Discussion

Although many clinical, epidemiological, and basic science researches were performed, the etiopathogenesis of AIS is still unclear [26]. Over the past decades, significant achievements have been made in profiling the molecular signatures in diseases using gene expression microarray [27]. Microarray has been recognized as a feasible and useful approach to explore pathogenesis and genetics of diseases and seek specific biomarkers [28]. Recently, hundreds of lncRNAs have been discovered, and altered lncRNAs expression has been considered to be correlated with cancer pathogenesis and muscles differentiation [29, 30]. However, the potential role of lncRNA expression in etiology and pathogenesis of AIS has not been systematically investigated.

To uncover the expression pattern of lncRNAs in AIS, we investigated the lncRNA expression signatures using microarray and found hundreds of differentially expressed lncRNAs in AIS patients. These lncRNAs may be involved in the development and progression of AIS. Based on the previous work and computer analysis, four lncRNAs (ENST00000440778.1, ENST00000602322.1, ENST00000414894.1, and TCONS_00028768) were selected to validate the consistency using qPCR. The expression of ENST00000440778.1 was downregulated in microarray and qPCR. But the expression gap between AIS and normal control was narrower in qPCR than in microarray. The difference may result from selection bias. Samples used in microarray analysis may lack representativeness, and ENST00000440778.1 expression in microarray analysis was not completely coincident between participants. Besides, it is necessary to investigate the specific lncRNAs in bigger size of samples.

In the present study, the differentially expressed mRNAs in AIS patients involve musculoskeletal development processes, including bone mineralization, neuromuscular junction, skeletal system morphogenesis, and nucleotide and nucleic acid metabolism. Pathway analysis indicates that the dysregulated mRNAs are related to cell adhesion molecules, Wnt signaling pathway, Toll-like receptor signaling pathway, MAPK signaling pathway, and so on. Precious studies have revealed the relationship between synapse formation, bone mineralization, and Wnt signaling pathway [3133]. MAPK signaling pathway was also reported to be related to osteoblast differentiation and intervertebral disc cells degeneration [34, 35]. These biological processes and signaling pathway may play a significant role in the musculoskeletal system and pathogenesis of AIS.

More and more evidences reveal that lncRNAs act in both cis and trans [36, 37]. LncRNA ENST00000602322.1 locates on chromosome 11q and is adjacent to Pcf11 (Protein 1/Cleavage Factor 1). Pcf11 participates in transcription by coupling pre-mRNA [38]. Pcf11 is also found to play a role in transcription initiation, elongation, and mRNA export from nucleus to cytoplasm [3941]. Given the key role of Pcf11 in transcription, it may not be surprising that ENST00000602322.1 may relate to the pathogenesis of AIS. The more detailed mechanisms of ENST00000602322.1 in transcription and translation need further investigation.

ENST00000440778.1 was upregulated 9.78-fold in AIS. Its expression was even higher in each AIS patient than that in healthy participants. Little is known about the function of ENST00000440778.1. However, its potential role in AIS pathogenesis was implied not only by its expression change but also by the clinical data from different height and Risser sign.

Our findings demonstrate differential lncRNA expression patterns in AIS when grouped according to different clinical features. This suggests potential significance in treatment and prognosis evaluation. Clinically, classification and curvature angle are important in evaluating surgical treatment and making operation plan in AIS [42, 43]. Onset time and Risser grade also play important roles in evaluating the progression of scoliosis [44]. It has been reported that lncRNAs are more specific than protein-coding mRNAs [45] and are easier to be detected in the blood samples of neoplastic and nonneoplastic patients using conventional PCR method [17, 18]. The differential expression of lncRNAs is potentially valuable in development of specific PCR markers and in providing more support on treatment and prognosis.

Several limitations exist in this study. First, although the different expression patterns of identified lncRNA genes suggest potential function in AIS pathogenesis, direct supporting evidence is lacking. Second, only four pairs of samples were used in microarray analysis. This may lose some important information and decrease the accuracy of biomarker selection. Third, RNA expression in peripheral blood was tested in our study. Regarding the idiopathic scoliosis being a musculoskeletal disease, tissue sample from musculoskeletal system may be more ideal targets. To accurately and comprehensively elucidate the role of lncRNAs in AIS, more comprehensive studies and laboratory and clinical researches are needed.

In summary, to the best of our knowledge, this is the first study that describes the expression profiles of human lncRNAs in AIS using microarray. Altered lncRNAs may play a potential role in the pathogenesis and/or development of this musculoskeletal disease. More work will be needed to confirm whether these lncRNAs play an essential role in the pathogenesis, treatment, and prognosis of AIS.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Xiao-Yang Liu and Liang Wang contributed equally to this work.

Acknowledgment

The work was supported by the National Natural Science Foundation of China under Grant nos. 81171770, 81372007, and 81272054.

Supplementary Materials

Tens of thousands lncRNAs and mRNAs were examined in microarray, hundreds of which were differently expressed in AIS group compared to NC group. Detailed information of differently expressed lncRNAs and mRNAs was listed Table S1 and Table S2, respectively. Table S3 contains the differentially expressed enhancer-like lncRNAs and their nearby coding genes (distance < 300 kb).

  1. Supplementary Material

References

  1. J. Ogilvie, “Adolescent idiopathic scoliosis and genetic testing,” Current Opinion in Pediatrics, vol. 22, no. 1, pp. 67–70, 2010. View at: Publisher Site | Google Scholar
  2. E. J. Rogala, D. S. Drummond, and J. Gurr, “Scoliosis: incidence and natural history. A prospective epidemiological study,” Journal of Bone and Joint Surgery—Series A, vol. 60, no. 2, pp. 173–176, 1978. View at: Google Scholar
  3. M. O. Andersen, K. Thomsen, and K. O. Kyvik, “Adolescent idiopathic scoliosis in twins: a population-based survey,” Spine, vol. 32, no. 8, pp. 927–930, 2007. View at: Publisher Site | Google Scholar
  4. C. A. Wise, X. Gao, S. Shoemaker, D. Gordon, and J. A. Herring, “Understanding genetic factors in idiopathic scoliosis, a complex disease of childhood,” Current Genomics, vol. 9, no. 1, pp. 51–59, 2008. View at: Publisher Site | Google Scholar
  5. N. H. Miller, “Genetics of familial idiopathic scoliosis,” Clinical Orthopaedics and Related Research, vol. 462, pp. 6–10, 2007. View at: Publisher Site | Google Scholar
  6. N. H. Miller, C. M. Justice, B. Marosy et al., “Identification of candidate regions for familial idiopathic scoliosis,” Spine, vol. 30, no. 10, pp. 1181–1187, 2005. View at: Publisher Site | Google Scholar
  7. S. Sharma, X. Gao, D. Londono et al., “Genome-wide association studies of adolescent idiopathic scoliosis suggest candidate susceptibility genes,” Human Molecular Genetics, vol. 20, no. 7, pp. 1456–1466, 2011. View at: Publisher Site | Google Scholar
  8. T. Esposito, R. Uccello, R. Caliendo et al., “Estrogen receptor polymorphism, estrogen content and idiopathic scoliosis in human: a possible genetic linkage,” Journal of Steroid Biochemistry and Molecular Biology, vol. 116, no. 1-2, pp. 56–60, 2009. View at: Publisher Site | Google Scholar
  9. J. Wu, Y. Qiu, L. Zhang, Q. Sun, X. Qiu, and Y. He, “Association of estrogen receptor gene polymorphisms with susceptibility to adolescent idiopathic scoliosis,” Spine, vol. 31, no. 10, pp. 1131–1136, 2006. View at: Publisher Site | Google Scholar
  10. Y. Takahashi, M. Matsumoto, T. Karasugi et al., “Lack of association between adolescent idiopathic scoliosis and previously reported single nucleotide polymorphisms in MATN1, MTNR1B, TPH1, and IGF1 in a Japanese population,” Journal of Orthopaedic Research, vol. 29, no. 7, pp. 1055–1058, 2011. View at: Publisher Site | Google Scholar
  11. N. L. S. Tang, H. Y. Yeung, K. M. Lee et al., “A relook into the association of the estrogen receptor α gene (PvuII, XbaI) and adolescent idiopathic scoliosis: a study of 540 Chinese cases,” Spine, vol. 31, no. 21, pp. 2463–2468, 2006. View at: Publisher Site | Google Scholar
  12. R. W. Carthew and E. J. Sontheimer, “Origins and mechanisms of miRNAs and siRNAs,” Cell, vol. 136, no. 4, pp. 642–655, 2009. View at: Publisher Site | Google Scholar
  13. I. I. G. M. van de Vondervoort, P. M. Gordebeke, N. Khoshab et al., “Long non-coding RNAs in neurodevelopmental disorders,” Frontiers in Molecular Neuroscience, vol. 6, article 53, 2013. View at: Publisher Site | Google Scholar
  14. C. A. Klattenhoff, J. C. Scheuermann, L. E. Surface et al., “Braveheart, a long noncoding RNA required for cardiovascular lineage commitment,” Cell, vol. 152, no. 3, pp. 570–583, 2013. View at: Publisher Site | Google Scholar
  15. R. J. Taft, K. C. Pang, T. R. Mercer, M. Dinger, and J. S. Mattick, “Non-coding RNAs: regulators of disease,” Journal of Pathology, vol. 220, no. 2, pp. 126–139, 2010. View at: Publisher Site | Google Scholar
  16. T. Gutschner, M. Hämmerle, M. Eißmann et al., “The noncoding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells,” Cancer Research, vol. 73, no. 3, pp. 1180–1189, 2013. View at: Publisher Site | Google Scholar
  17. D. G. Weber, G. Johnen, S. Casjens et al., “Evaluation of long noncoding RNA MALAT1 as a candidate blood-based biomarker for the diagnosis of non-small cell lung cancer,” BMC Research Notes, vol. 6, no. 1, article 518, 2013. View at: Publisher Site | Google Scholar
  18. D. Li, G. Chen, J. Yang et al., “Transcriptome analysis reveals distinct patterns of long noncoding RNAs in heart and plasma of mice with heart failure,” PLoS ONE, vol. 8, Article ID e77938, 2013. View at: Google Scholar
  19. M. Stuss, P. Rieske, A. Cegłowska et al., “Assessment of OPG/RANK/RANKL gene expression levels in peripheral blood mononuclear cells (PBMC) after treatment with strontium ranelate and ibandronate in patients with postmenopausal osteoporosis,” Journal of Clinical Endocrinology and Metabolism, vol. 98, no. 5, pp. E1007–E1011, 2013. View at: Publisher Site | Google Scholar
  20. R. Nowak, J. Szota, and U. Mazurek, “Vitamin D Receptor gene (VDR) transcripts in bone, cartilage, muscles and blood and microarray analysis of vitamin D responsive genes expression in paravertebral muscles of Juvenile and Adolescent Idiopathic Scoliosis patients,” BMC Musculoskeletal Disorders, vol. 13, article 259, 2012. View at: Publisher Site | Google Scholar
  21. S. L. Weinstein, L. A. Dolan, J. C. Cheng, A. Danielsson, and J. A. Morcuende, “Adolescent idiopathic scoliosis,” The Lancet, vol. 371, no. 9623, pp. 1527–1537, 2008. View at: Publisher Site | Google Scholar
  22. J. L. Rinn, M. Kertesz, J. K. Wang et al., “Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs,” Cell, vol. 129, no. 7, pp. 1311–1323, 2007. View at: Publisher Site | Google Scholar
  23. M. Guttman, I. Amit, M. Garber et al., “Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals,” Nature, vol. 458, no. 7235, pp. 223–227, 2009. View at: Publisher Site | Google Scholar
  24. A. M. Khalil, M. Guttman, M. Huarte et al., “Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 28, pp. 11667–11672, 2009. View at: Publisher Site | Google Scholar
  25. U. A. Ørom, T. Derrien, M. Beringer et al., “Long noncoding RNAs with enhancer-like function in human cells,” Cell, vol. 143, no. 1, pp. 46–58, 2010. View at: Publisher Site | Google Scholar
  26. R. G. Burwell, “Aetiology of idiopathic scoliosis: current concepts,” Pediatric Rehabilitation, vol. 6, no. 3-4, pp. 137–170, 2003. View at: Publisher Site | Google Scholar
  27. Z. Yin, D. Guan, Q. Fan et al., “LncRNA expression signatures in response to enterovirus 71 infection,” Biochemical and Biophysical Research Communications, vol. 430, no. 2, pp. 629–633, 2013. View at: Publisher Site | Google Scholar
  28. F. Yang, L. Zhang, X.-S. Huo et al., “Long noncoding RNA high expression in hepatocellular carcinoma facilitates tumor growth through enhancer of zeste homolog 2 in humans,” Hepatology, vol. 54, no. 5, pp. 1679–1689, 2011. View at: Publisher Site | Google Scholar
  29. F. Yang, L. Zhang, X.-S. Huo et al., “Long noncoding RNA high expression in hepatocellular carcinoma facilitates tumor growth through enhancer of zeste homolog 2 in humans,” Hepatology, vol. 54, no. 5, pp. 1679–1689, 2011. View at: Publisher Site | Google Scholar
  30. M. Cesana, D. Cacchiarelli, I. Legnini et al., “A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA,” Cell, vol. 147, pp. 358–369, 2011. View at: Publisher Site | Google Scholar
  31. R. Velázquez-Cruz, H. García-Ortiz, M. Castillejos-López et al., “WNT3A gene polymorphisms are associated with bone mineral density variation in postmenopausal mestizo women of an urban Mexican population: findings of a pathway-based high-density single nucleotide screening,” Age, vol. 36, article 9635, 2014. View at: Publisher Site | Google Scholar
  32. T. Nagaoka, R. Ohashi, A. Inutsuka et al., “The Wnt/planar cell polarity pathway component Vangl2 induces synapse formation through direct control of N-cadherin,” Cell Reports, vol. 6, pp. 916–927, 2014. View at: Publisher Site | Google Scholar
  33. J. N. Collins, B. J. Kirby, J. P. Woodrow et al., “Lactating Ctcgrp nulls lose twice the normal bone mineral content due to fewer osteoblasts and more osteoclasts, whereas bone mass is fully restored after weaning in association with up-regulation of Wnt signaling and other novel genes,” Endocrinology, vol. 154, no. 4, pp. 1400–1413, 2013. View at: Publisher Site | Google Scholar
  34. Y. Liu, W.-K. Zheng, W.-S. Gao, Y. Shen, and W.-Y. Ding, “Function of TGF-beta and p38 MAKP signaling pathway in osteoblast differentiation from rat adipose-derived stem cells,” European Review for Medical and Pharmacological Sciences, vol. 17, no. 12, pp. 1611–1619, 2013. View at: Google Scholar
  35. C.-C. Niu, S.-S. Lin, L.-J. Yuan et al., “Hyperbaric oxygen treatment suppresses MAPK signaling and mitochondrial apoptotic pathway in degenerated human intervertebral disc cells,” Journal of Orthopaedic Research, vol. 31, no. 2, pp. 204–209, 2013. View at: Publisher Site | Google Scholar
  36. K. Plath, S. Mlynarczyk-Evans, D. A. Nusinow, and B. Panning, “Xist RNA and the mechanism of X chromosome inactivation,” Annual Review of Genetics, vol. 36, pp. 233–278, 2002. View at: Publisher Site | Google Scholar
  37. M. V. Koerner, F. M. Pauler, R. Huang, and D. P. Barlow, “The function of non-coding RNAs in genomic imprinting,” Development, vol. 136, no. 11, pp. 1771–1783, 2009. View at: Publisher Site | Google Scholar
  38. Z. Zhang and D. S. Gilmour, “Pcf11 is a termination factor in Drosophila that dismantles the elongation complex by bridging the CTD of RNA polymerase II to the nascent transcript,” Molecular Cell, vol. 21, no. 1, pp. 65–74, 2006. View at: Publisher Site | Google Scholar
  39. T. J. Loya, T. W. O'Rourke, and D. Reines, “A genetic screen for terminator function in yeast identifies a role for a new functional domain in termination factor Nab3,” Nucleic Acids Research, vol. 40, no. 15, pp. 7476–7491, 2012. View at: Publisher Site | Google Scholar
  40. M. Rougemaille, G. Dieppois, E. Kisseleva-Romanova et al., “THO/Sub2p Functions to Coordinate 3′-End Processing with Gene-Nuclear Pore Association,” Cell, vol. 135, no. 2, pp. 308–321, 2008. View at: Publisher Site | Google Scholar
  41. Z. Zhang, A. Klatt, A. J. Henderson, and D. S. Gilmour, “Transcription termination factor Pcf11 limits the processivity of Pol II on an HIV provirus to repress gene expression,” Genes & Development, vol. 21, no. 13, pp. 1609–1614, 2007. View at: Publisher Site | Google Scholar
  42. J. S. Hoashi, P. J. Cahill, J. T. Bennett, and A. F. Samdani, “Adolescent scoliosis classification and treatment,” Neurosurgery Clinics of North America, vol. 24, no. 2, pp. 173–183, 2013. View at: Publisher Site | Google Scholar
  43. G. Qiu, J. Zhang, Y. Wang et al., “A new operative classification of idiopathic scoliosis: a Peking Union Medical College method,” Spine, vol. 30, no. 12, pp. 1419–1426, 2005. View at: Publisher Site | Google Scholar
  44. Y. Smorgick, Y. Mirovsky, K. C. Baker, Y. Gelfer, E. Avisar, and Y. Anekstein, “Predictors of back pain in adolescent idiopathic scoliosis surgical candidates,” Journal of Pediatric Orthopaedics, vol. 33, no. 3, pp. 289–292, 2013. View at: Publisher Site | Google Scholar
  45. J. R. Prensner, M. K. Iyer, O. A. Balbin et al., “Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression,” Nature Biotechnology, vol. 29, no. 8, pp. 742–749, 2011. View at: Publisher Site | Google Scholar

Copyright © 2015 Xiao-Yang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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