Regular Contribution
Identification and Functional Profiling of Differentially Expressed Long Non-Coding RNAs in Nasal Mucosa with Allergic Rhinitis
2017 Volume 242 Issue 2 Pages 143-150
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2017 Volume 242 Issue 2 Pages 143-150
Long non-coding RNAs (lncRNAs) have been proved to play important roles in a variety of human immune diseases. However, their pathological effects on the development of allergic rhinitis (AR) have not been clearly understood. The aim of this study was to determine the expression profile of lncRNAs in nasal mucosa of AR patients by lncRNA microarray and to predict potential roles of specific lncRNAs in the pathogenic mechanisms of AR by analysis of lncRNA-mRNA co-expression network, Gene Ontology (GO) and pathway. The lncRNA microarray analysis showed that a total of 2,259 lncRNAs (1,033 up-regulated and 1,226 down-regulated) and 704 mRNAs (157 up-regulated and 547 down-regulated) were significantly differentially expressed in the nasal mucosa samples from 4 AR patients as compared to those from 4 non-allergic subjects (fold change > 2; P < 0.05). In addition, the lncRNA-mRNA co-expression network contained 143 network nodes including 76 lncRNAs and 67 mRNAs, in which 117 significant correlation pairs presented as positive, and 108 pairs presented as negative. The results from GO and pathway analysis indicated that the lncRNAs–coexpressed mRNAs were enriched in several biological processes and cellular signaling pathways related to AR development, such as positive regulation of interleukin-13 secretion, Fc epsilon RI signaling pathway and NF-kappa B signaling pathway. To summary, our study provides important information on the molecular mechanisms and biological functions of these AR-related lncRNAs, which could be utilized for developing novel therapeutic strategies for AR.
The incidence of allergic rhinitis (AR) is remarkably increasing recent years in industrial countries, which seriously affects the quality of life of AR patients (Meltzer 2016). AR is characterized by chronic inflammation of the nasal mucosa with hypersensitivity, hypersecretion and remodeling, which results from seasonal or perennial responses to specific allergens, such as pollens, dust mites, pets, pests, and molds. The pathological changes of their nasal mucosa are closely related to the massive infiltration and aberrant activation of many immune cells and the abnormal production of various inflammatory mediators (Baumann et al. 2013; Scadding 2014; Kamekura et al. 2016). Although numerous efforts have been exerted to verify the potential mechanisms following these changes, the treatment of AR is still quite difficult. Thus, it is very urgent to identify the key molecules that are involved in the AR pathologies, which may eventually lead us to better understand the underlying molecular mechanisms of AR pathogenesis and to further discover novel targets for the treatment of AR.
Long non-coding RNAs (lncRNAs), a subset of non-protein-coding RNAs, are defined as the transcripts of more than 200 nucleotides in length. Emerging evidence has postulated that lncRNAs play important roles in transcriptional, post-transcriptional, or epigenetic regulation of gene expression and are involved in a variety of biological processes, such as cell differentiation, embryonic development and metabolism (Ponting et al. 2009; Kung et al. 2013; Pagani et al. 2013; Spurlock et al. 2016). Aberrantly expressed lncRNAs have been detected in the development and progression of many human and animal diseases, which as endogenous regulatory molecules may affect the mRNA expression levels of critical genes and modulate pathologic signaling pathways (Kazemzadeh et al. 2015). However, the expression and function of lncRNAs in the pathogenesis of AR remain unclear.
In this study, we examined the expression profile of lncRNA in nasal mucosa tissues from AR patients via lncRNA microarray analysis in order to identify differentially expressed lncRNAs. Subsequently, we also predicted and analyzed the potential functions of these lncRNAs by bioinformatics methods in order to find novel insights into AR pathogenesis and new clues of gene therapy for AR.
This study was approved by the Ethics Committee of Hangzhou First People’s Hospital, Nanjing Medical University, Hangzhou, China, and informed consent was obtained at enrollment from each participating subject. Nasal mucosal tissue samples were obtained surgically from the inferior turbinates from 19 patients with perennial AR (AR group, 8 males and 11 females; mean age 31.5 years; range 22-58 years) and 14 non-allergic patients with nasal septum deviation (Control group; 6 males and 8 females; mean age 35.8 years; range 23-62 years), who were admitted to the Department of Otolaryngology, Hangzhou First People’s Hospital, Nanjing Medical University between 2013 and 2015. Each nasal cavity was locally treated with diluted epinephrine cotton tablets 10 min before sampling to reduce intraoperative nasal bleeding. All samples were immediately preserved in RNAlater Solution (Ambion, USA) after resection and then stored at −20℃ until use. All patients in the AR group had a positive skin-prick test (SPT) to dust mites, animal dander, cockroaches, and/or molds; and a positive screening of specific IgE. Each AR patient was diagnosed based on his/her medical history, nasal endoscopic examination, an allergen skin-prick test, and a serum specific IgE assay. None of the patients had received topical or systemic corticosteroid therapy for 2 weeks prior to study recruitment. The subjects with a history of smoking and/or other immune system disorders, such as rheumatoid arthritis, systemic lupus erythematosus and scleroderma, were not including in this study.
RNA extractionTotal RNA was isolated from nasal mucosal tissues using TRIzol Reagent (Life technologies, Carlsbad, CA, US) following the manufacturer’s instructions, and then quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA). The RNA integrity was inspected by an Agilent Bioanalyzer 2100 (Agilent technologies, Santa Clara, CA, US). Qualified RNA (RNA Integrity Number ≥ 7.0, 28S/18S ≥ 0.7) was further purified by RNeasy micro kit (QIAGEN, GmBH, Germany) and RNase-Free DNase Set (QIAGEN).
Microarray assayHuman lncRNA Microarray (4 × 180K; v6.0) was manufactured at Shanghai Biotechnology Corporation (Shanghai, China), which contains 95,956 capture probes for 77,103 lncRNAs and 18,853 RNAs based on the most authoritative databases such as GENCODE v21, Ensembl, LNCipedia v3.1, Lncrnadb, Noncode v4 and UCSC. Microarray assay was carried out according to the manufacturer’s protocols. Briefly, total RNA was amplified and labeled by Low Input Quick Amp WT Labeling Kit (Agilent technologies). Labeled cRNAs were purified using RNeasy mini kit (QIAGEN), and then they were hybridized with each slide using Gene Expression Hybridization Kit (Agilent technologies) in Hybridization Oven (Agilent technologies). The slides were washed by Gene Expression Wash Buffer Kit (Agilent technologies) after 17 hours hybridization. They were scanned by Agilent Microarray Scanner (Agilent technologies). Data were acquired with Feature Extraction software 10.7 (Agilent technologies). Raw data were normalized by Quantile algorithm, GeneSpring Software 11.0 (Agilent technologies). Differentially expressed lncRNAs and mRNAs with statistical significance between two groups were identified through volcano plot filtering (fold change ≥ 2.0 and P < 0.05). The Gene Cluster (v3.0) and Java TreeView software programs were used to perform the hierarchical cluster analysis of these differentially expressed lncRNAs and mRNAs.
Quantitative real-time reverse transcription PCR (qRT-PCR) validationTotal RNA was reversely transcribed into cDNA using PrimeScript RT Master Mix (TaKaRa, Dalian, China) following the manufacturer’s instructions. qRT-PCR was performed by using SYBR Premix Ex Taq II (TaKaRa) on the 7900 HT Sequence Detection System (ABI, USA). The primer sequences were listed in Table 1 and were synthesized by Invitrogen (Shanghai, China). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as an endogenous control and relative quantitation of lncRNA expression was determined using the comparative threshold cycle (2−ΔΔCt) method.
Primer sequences used for qRT-PCR.
According to the normalized signal intensity of specific expression lncRNAs or mRNAs from our microarray assay, Pearson correlation coefficient (PCC) was calculated to evaluate the correlation between the differentially expressed lncRNAs and mRNAs. PCC ≥ 0.8 was considered as a statistically significant correlation pair. The co-expression network shown the significant pairs were constructed by using the Cytoscape software (The Cytoscape Consortium, San Diego, CA, USA).
Gene function analysisThe co-expression mRNAs were imported into the Database for Annotation, Visualization, and Integrated Discovery v6.8 (DAVID; http://david.abcc.ncifcrf.gov), which utilized Gene Ontology (GO) and pathway analysis to identify the enriched GO themes and cell signaling pathways of these mRNAs. The significant GO terms and pathways were retained in accordance with P < 0.05 and false discovery rate (FDR) < 0.05.
Statistical analysisStatistical analyses were performed using the SPSS software for Windows (version 16.0; SPSS, Inc., Chicago, IL, USA). The differences between the two groups were determined using a two-tailed Student’s t-test if the data are normally distributed. P < 0.05 was considered statistically significant.
To identify the differentially expressed lncRNAs associated with perennial AR, we examined the expression pattern of lncRNAs in the four nasal mucosa tissue samples from the AR group and the other four samples from the Control group using a microarray assay. To prepare for predicting target genes of lncRNAs, the expression levels of mRNAs in AR nasal mucosa were simultaneously analyzed using this microarray. Our data showed that a total of 2,259 lncRNAs (1,033 up-regulated and 1,226 down-regulated) and 704 mRNAs (157 up-regulated and 547 down-regulated) were significantly differentially expressed in the nasal mucosa tissue samples from the AR group relative to those from the Control group (fold change > 2; P < 0.05), as indicated by the volcano plots and heat maps (Fig. 1). The top 20 differentially expressed lncRNAs and mRNAs were listed in Tables 2 and 3. Among these lncRNAs, lnc-RAD9B-1:4 (log2 Fold change: 4.575409) and ENST00000505668 (log2 Fold change: −2.48488) were the most up-regulated and down-regulated lncRNAs, respectively. In addition, APOA2 (log2 Fold change: 4.681268) and PKDCC (log2 Fold change: −2.5797) were the most up-regulated and down-regulated mRNAs, respectively. Our data suggest that these 4 aberrantly expressed RNAs may play critical roles in the maintaining a normal function of the nasal mucosa.
Alterations in lncRNA and mRNA expression profiles between the AR and Control groups. Volcano plots show differentially expressed lncRNAs (A) and mRNAs (B) in the nasal mucosa tissues from the AR group relative to those from the Control group. The horizontal line represents a p value of 0.05 and the vertical lines correspond to 2.0-fold up and down. Heat maps indicate hierarchical clustering results of differentially expressed lncRNAs (C) and mRNAs (D). Each row represents a lncRNA or mRNA, and each column represents a sample. Red and green colors indicate up-regulated and down-regulated expression, respectively. C: Control group. AR: AR group.
Top 20 differentially expressed lncRNAs in AR nasal mucosa tissues compared with non-allergic nasal mucosa tissues.
Top 20 differentially expressed mRNAs in AR nasal mucosa tissues compared with non-AR nasal mucosa tissues.
To validate our microarray data, qRT-PCR was carried out to detect the expression levels of 7 lncRNAs and 2 mRNAs randomly selected from the 2,259 differentially expressed lncRNAs and 704 mRNAs in the 19 nasal mucosa tissue samples from the AR group and the 14 samples from the Control group, of which lnc-UBLCP1-4:2, lnc-RTL1-8:1 and NR_121637 were down-regulated, and lnc-GABPA-9:1, NR_103763, CCL21 and APOA2 were up-regulated in the nasal mucosa from the AR group. These results of qRT-PCR were consistent with those of the microarray assay, which indicated a concordance rate of 77.8% (7/9) (Fig. 2).
Validation of lncRNA microarray data by qRT-PCR. Up-regulated and down-regulated lncRNAs are indicated by bars above and below the horizontal axis in the chart, respectively. The heights of the bars represent the values of log2 fold changes.
To predict the target genes of lncRNAs and to investigate the potential interaction between the lncRNAs and mRNAs in nasal mucosa of AR patients, we analyzed the correlation between the top 300 differentially expressed lncRNAs and mRNAs from our microarray data by calculating PCC. Based on the value of PCC (PCC ≥ 0.8), the co-expression network were constructed and visualized by using the Cytoscape software. The network contained 143 network nodes including 76 lncRNAs and 67 mRNAs, in which 117 significant correlation pairs presented as positive, and 108 pairs presented as negative. The network also indicated that a single lncRNA could regulate the mRNA expression of multiple coding genes and some lncRNAs could co-regulate the expression of the same gene. For example, three coding genes (SRGAP2C, CBS and PKIB) were regulated by one lncRNA (NR_110169). NR_125386, together with ENST00000569912, regulated the mRNA expression of IL17RE (Fig. 3).
LncRNA-mRNA co-expression network. The rounded rectangle nodes represent lncRNAs, and the cycle nodes represent mRNAs. The red and green colors denote high and low expression, respectively. The lines with arrowheads or blunt ends represent the positive or negative regulation, respectively. The quantitative regulatory relationship between lncRNAs and mRNAs is indicated by the different areas of these nodes.
To explore the potential functions of the differentially expressed lncRNAs in the progression of AR, we analyzed the functional enrichment (GO and pathway annotations) of their coexpressed mRNAs. The GO analytical data showed that several significantly over-represented GO terms were included in biological process, molecular function and cellular component (P < 0.05; FDR < 0.05). Among these GO terms, some were associated with inflammation response and immune dysfunction, such as positive regulation of inflammatory response (GO: 0050729), positive regulation of interleukin-13 secretion (GO: 0050808), secretory granule (GO: 0030141) and neuropeptide receptor activity (GO: 0008188). Pathway analysis indicated that 33 pathways were significantly enriched among the lncRNAs–coexpressed mRNAs. Many of these pathways were linked to activation or repression of immune cells, such as Fc epsilon RI signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, T cell receptor signaling pathway and cytokine-cytokine receptor interaction (Fig. 4).
Enrichment analysis of lncRNA co-expressed mRNAs. GO functional enrichment analysis (A-C) and pathway annotation (D) are performed, respectively. The significantly GO terms are included in biological process (A), cellular component (B) and molecular function (C). Ordinate is the significant GO/pathway term and the abscissa is the p value of the negative logarithm (P < 0.05).
AR, an airway allergic disease, is caused by hypersensitivity to one or multiple antigens resulting in chronic allergic inflammation of the nasal mucosa. The pathogenesis of AR is rather complex. Although many studies have shown that a large number of genes are involved in the pathogenesis of AR, it is unclear how these genes are regulated. With the rapid development of genomics technology, thousands of non coding RNAs such as miRNAs and lncRNAs have been discovered, and their roles in regulating coding genes have been intensively studied. Abnormal expression of non coding RNAs may contribute to a number of human allergic disorders. Using a miRNA microarray assay, Yu et al. (2011) showed that the miRNA expression profile was altered in the nasal mucosa of AR patients. And also, miR-143 was found to inhibit interleukin-13-induced inflammatory cytokine and mucus production in nasal epithelial cells from AR patients by targeting IL13Rα1 (Teng et al. 2015). These studies have suggested that the non-coding RNAs are involved in the regulation of AR-related gene expression and the pathological process of AR.
LncRNAs, an emerging member of non-coding RNAs, were simply considered as transcriptional noise in the beginning. However, recent studies have postulated that lncRNAs are also involved in important biological processes and are tightly related to the development of human diseases through the complex molecular mechanisms (Thai et al. 2013; McKiernan et al. 2014). To explore the expression levels of lncRNAs in airway allergic diseases, Tsitsiou et al. (2012) found a change in expression of lncRNAs, in circulatin CD8+ T cells from patients with severe asthma. Another report showed that lncRNAs BCYRN1 was important to regulate the protein level of transient receptor potential 1 (TRPC1) by increasing the stability of TRPC1. As a result, BCYRN1 was able to promote the proliferation and migration of airway smooth muscle cells in asthma (Zhang et al. 2016). Taken together, these studies have provided evidence that lncRNAs may play a critical role in the development of asthma. In the present study, we are the first to assess lncRNA expression profile in AR and non-allergic nasal mucosa using high throughput lncRNA microarray. Our data showed a total of 2,259 lncRNAs that were significantly expressed in the nasal mucosa from AR patients as compared to the same tissues obtained from non-allergic patients, with fold changes of 2 or more, which will help us understand the AR-related global transcriptome in depth.
Although without protein coding capability, increasing evidence has shown that lncRNAs could function as an emerging class of modulators in the regulation of coding gene expression and cellular activity. The major mechanisms include chromatin remodeling, complex assembly scaffolding, cis or trans- gene expression regulating, demethylation promoting, transcriptional regulation of target genes, and mRNA processing control, etc (Sone et al. 2007; Wahlestedt 2013; Qiao et al. 2016). In order to dissect the functions of lncRNAs, we used a lncRNA microarray assay and lncRNA-mRNA co-expression analysis to predict the potential functions of the differentially expressed lncRNAs in the nasal mucosa of AR patients. Our data showed that these differentially expressed lncRNAs might contribute to the development and progression of AR by altering the mRNA expression levels of numerous target coding genes, within which we have specified and further confirmed 5 randomly selected lncRNAs.
To further understand the biological functions and molecular mechanisms of AR-related lncRNAs, we utilized GO and pathway analysis to identify the biological functions and signaling networks enriched among the lncRNAs-coexpressed mRNAs. Notably, these mRNAs were involved in multiple immune regulations or responses, such as promoting interleukin-13 secretion, facilitating leukocyte migration, and enhancing inflammatory response. Pathway analysis profiling also showed that several signaling pathways in immune cells have been regulated by lncRNAs–coexpressed mRNAs, including T cell receptor signaling pathway, Toll-like receptor signaling pathway, Fc epsilon RI signaling pathway, cytokine-cytokine receptor interaction, and NF-kappa B signaling pathway, etc. Interestingly, accumulated reports have showed that these biological processes, molecular functions, cellular components and signaling pathways are tightly associated to the pathogenesis of AR (Oliver et al. 2000; Wang and Zheng 2011; Couto Alves et al. 2013).
To validate the accuracy of high through put microarray assay, we further performed qRT-PCR to examine our microarray data, and found that the concordance rate was 77.8% (7/9), strongly suggesting the reliability of the microarray results. As we have completed the bioinformatics studies of lncRNAs in the development and progression of AR, more studies clarifying the exact molecular mechanisms of specific lncRNAs implicated in AR through the implementation of a series of functional experiments will be needed. These studies will provide us with a more complete understanding of lncRNA-mediated AR pathogenesis.
In conclusion, our study suggests that a portion of lncRNAs may participate in some specific biological processes and signaling pathways involved in the pathogenesis of AR through regulating target genes. Thus, our study will lay the foundation for future functional and mechanism studies of AR-related lncRNAs, even provide novel therapeutic targets for AR patients.
This work was supported by grants from the Science and Technology Development Project of Hangzhou (No. 20140733Q05), Medical and Health Technology Program of Zhejiang (No. 2013RCA041) and Zhejiang Provincial Natural Science Foundation of China (No. LY17H130001).
The authors declare no conflict of interest.
