Long non-coding RNA expression profiling in the lesional tissue and derived fibroblasts of keloid

Introduction

Keloid is a type of benign tumor unique to human skin, and is formed during an abnormal wound healing process [1]. The quality of life of the patients associated with keloid is dramatically impaired [2]. It is characterized by pathological accumulation of extracellular matrix (ECM) such as collagen, fibronectin and -smooth muscle actin, angiogenesis, and inflammatory cell infiltration [3]. Until now, the pathogenesis of keloid has been unclear. In previous studies, Chen et al. [4] reported that 402 genes were expressed differentially between keloid and normal tissue, including 250 up-regulated and 152 down-regulated genes which involved in the cellular skeleton and movement, cellular cycle modulation, metabolism and signal transduction factor. Additionally, Russell et al. [1] identified that several fibrosis-associated genes were deregulated in keloid. Importantly, several groups discovered that mTOR signaling is closely correlated with keloid formation, and the mTOR inhibitors possess therapeutic efficacy in in vitro and ex vivo studies [5].
Long non-coding RNA (lncRNA) is a subgroup of non-coding RNA, defined as longer than 200 nucleotides with limited or without protein-coding capacity. Recently, some lncRNAs were indentified to involve developmental processes and be associated to various disorders, particularly in cancer [6]. Noticeably, Liang et al. [7] reported 1731 up-regulated lncRNAs and 782 down-regulated lncRNAs in keloid lesions compared with normal skin tissue. They indicate that lncRNA CACNA1G-AS1-regulated mRNA which encodes a subtype of T-type Ca2+ channels may be the important machinery for the keloid formation. In addition, Zhu et al. [8] reported that lncRNA-ATB regulated the secretion of TGF-2 in keloid fibroblast (KFB)s by partly down-regulating the ZNF217 expression level via miR-200c, and demonstrated that lncRNA-ATB/miR-200c/ZNF217/TGF-2 signaling axis involved the initiation and progression of keloid. lncRNA H19 was reported to participate in the proliferation of keloid fibroblasts [9]. However the role of lncRNA in keloid remains to be clarified.

Aim

To clarify preliminarily which lncRNAs were involved in keloid and their underlying role in pathology.

Material and methods

Source of samples

This study has been approved by the Ethics Committee Board of the Institute of Dermatology, Chinese Academy of Medical Science and Peking Union Medical College. The written consent was obtained from all the involved patients before the surgery. The patients recruited in this study had not been pretreated for keloid at least 3 months before this operation, from which the keloid and normal tissue samples (adjacent normal tissue of peri-keloid lesion resected together with the keloid lesion) were obtained. The keloid and normal tissue samples were detached and identified by the surgeons who performed the corresponding operation. All the samples of the keloid were confirmed by histopathologic analysis. The adipose tissues of all samples were removed, and then immediately frozen by liquid nitrogen after resection. All samples were stored at –80°C until the performance of the microarray.
We considered the following details in this study design: (i) samples were obtained from the same skin location (chest) of 5 patients; (ii) lesional and normal tissues from individual patients served as the tested samples and the own control respectively; (iii) only the adipose tissue was removed from both samples, and the epidermis was reserved in the final sample for microarray analysis. We removed the adipose tissue but reserved the epidermis, as the fibroblast-keratinocyte cross talk was verified as a crucial mechanism in keloid pathogenesis in previous studies, for example the studies of Funayama et al. [10], Lim et al. [11] and Canady et al. [12], etc.

Cell culture

Keloid fibroblasts were isolated from intralesional tissues of keloid, and normal-fibroblast (NFB)s after the healthy men’s circumcision. The passage 1 (p1) to passage 2 (p2) cells were used in this study. Freshly resected specimens were removed of adipose tissue and washed in PBS three times. Then, the reserved tissues were cut into 3 mm × 8 mm × 5 mm tissue blocks and incubated in 5 mg/ml dispase II (D4693, Sigma-Aldrich, St. Louis, MO, USA) for 4 h at 37°C to exclude epidermis. After washing tissues with PBS again, the blocks were cut into 2 mm × 2 mm × 2 mm and incubated in 3 mg/ml collagenase, type I (17100017, Thermo Fisher Scientific, Waltham, MA, USA) at 2 h at 37°C. Then the tissue blocks were blown, and suspension filtrated through cell strainer. Cells were cultured in a sterile flask with Dulbecco’s modified Eagle’s medium (DMEM; Gibco, Invitrogen Corp., Carlsbad, CA, USA) containing penicillin (100 U/ml), streptomycin (100 mg/ml) and 10% fetal bovine serum within the incubator at 37°C and 5% CO2. The medium was changed every 3 days. When fibroblasts grew to confluent, they were digested with 0.25% trypsin and sub-cultured in fresh medium at a 1 : 2 split radio.

Microarray profiling

Total RNAs were extracted from the samples using Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. NanoDrop ND-1000 (Thermo Fisher Scientific, Waltham, MA, USA) was used to measure the quality of RNAs. The integrity of RNAs was measured by the standard denaturing agarose gel electrophoresis. The Human LncRNA Expression Microarray version 3.0 (ArrayStar, Inc., Rockville, MD, USA) was used in this study according to the manufacturer’s instructions. The 30,586 lncRNAs and 26,109 coding genes were included in this microarray. The lncRNAs are constructed using the most highly respected public transcriptome databases (Refseq, UCSC knowngenes, Gencode, etc), as well as the landmark publications. Each transcript was represented by a specific splice junction probe or exon probe which can identify accurately unique transcript. Housekeeping genes served as the positive probes, and negative probes were also printed on the array to control the hybridization quality.

RNA labeling and array hybridization

The sample labeling and array hybridization were taken following the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technologies, Santa Clara, CA, USA) with minor modifications. Briefly, the purification of the mRNA from total RNA was performed by removing rRNA (mRNA-ONLY™ Eukaryotic mRNA Isolation Kit, Epicentre Biotechnologies, Madison, WI, USA). Then, all samples were amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 3’ bias utilizing a random priming method (Agilent p/n 5190-0442). The labeled cRNA was purified by RNeasy Mini Kit (Qiagen p/n 74104, Valencia, CA, USA). NanoDrop ND-1000 was used to assess the concentration and specific activity of the labeled cRNA (pmol Cy3/ugc RNA). Agilent Gene Expression Hybridization Kit (Agilent p/n 5188-5242) was taken for the hybridization. Firstly, 5 l 10×blocking agent and 1 l 25× fragmentation buffer were added to 1 g of each labeled cRNA. Secondly, the mixtures were heated at 60°C for 30 min. Lastly, 25 l 2 × GE Hybridization buffer was added to dilute the labeled cRNA. The 50 l of hybridization solution was dispensed into the gasket slides and assembled to the lncRNA microarray slides. The slides were incubated in an Agilent Hybridization Oven (Agilent p/nG2545A) for 17 h at 65°C. After hybridization, washing and fixation, the processed slides were scanned using the Agilent DNA Microarray Scanner G2505C.

Data analysis

The images were processed with Agilent Feature Extraction software (v11.0.1.1). The raw data’s quantile normalization and further data analysis were performed with the GeneSpring GX (v12.1) software package (Agilent Technologies). The quantile normalization of the raw data is to make the distribution of probe intensities identical to each array. The probe quality was marked with ‘present’, ‘marginal’ and ‘absent’ according to their levels of intensity from strong to poor. After the quantile normalization, lncRNAs and mRNAs that have been identified by ‘present’ or ‘marginal’, at least 5 out of 10 samples were chosen for subsequent data processing. The selection criterion of differentially expressed lncRNAs and mRNAs was p-value < 0.05 and fold change  2. The differentially expressed lncRNAs and mRNAs between keloid and normal tissues were shown through volcano plot filtering and hierarchical clustering analysis (MeV 4.8) was performed to demonstrate the lncRNAs and mRNAs expression patterns among samples. The microarray analysis was assisted by Kangcheng Biology Engineering Co., Ltd., (Shanghai, China).
Currently, lncRNAs were found to exert their function via regulating associated or nearby mRNA. According to genomic location and context, lncRNAs are classified into different subgroups including intergenic, bi-directional, sense and antisense lncRNAs. Long intergenic non-coding RNAs (lincRNAs) and antisense lncRNAs have attracted a lot of attention because of recruiting chromatin modification complexes [13]. In this study, we defined sense lncRNAs, antisense lncRNAs and bidirectional lncRNAs as the genic lncRNAs for analysis of the interplay between lncRNAs and mRNAs. Additionally, lincRNAs and enhancer-like lncRNAs, a class of lncRNAs with an enhancer-like function in activation of gene expression [14], were also screened to detect the interplay between them and their associated mRNAs.

Gene ontology and pathway analysis of differential expression genes

Gene ontology (GO) analysis and pathway analysis were performed to investigate the potential functions of the differentially expressed mRNAs in GO terms or biological pathways. The GO project can provide a controlled vocabulary to describe gene and gene products in any organism. Gene ontology analysis includes biological process (BP), cellular component [15] and molecular function (MF). The Fisher’s exact test is applied for avoiding overlap between the differentially expressed gene list and the GO annotation list. The significance of GO terms enrichment in the differentially expressed gene list is indicated by p-value. Pathway analysis, a functional analysis which maps genes to KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways was used to identify the active pathways based on differentially expressed genes. The significance of the pathway associated with the conditions is shown by enrichment scores calculated through p-value (Fisher p-value). Gene ontology and pathway analyses were conducted by online software DAVID (Database for Annotation, Visualization and Integrated Discovery).

Quantitative reverse transcription PCR

The extraction and concentration detection for total RNA were carried out according to the manufacturer’s protocol. Then, the RNA was reverse-transcribed to complementary DNA (cDNA) using the SuperScript TM III Reverse Transcriptase kit (Invitrogen; Thermo Fisher Scientific, Inc., CA, USA). Additionally, quantitative reverse transcription PCR (qRT-PCR) reactions were performed on ViiA 7 Real-time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.). The reaction conditions were 95°C for 10 min, followed by 40 cycles of 95°C for 10 s and 60°C for 1 min. The expression levels of uc003jox.1, ENST00000439703 and -actin were detected at least in triplicate. The primers were designed using Primer Premier 5.0 software (Premier Biosoft International, Palo Alto, CA, USA) and synthesized by Kangcheng Biology Engineering Co., Ltd. The expression levels of both lncRNAs were calculated through 2–CT formula; CT is the difference between the target lncRNA and -actin.

The primers sequences were as follows:
uc003jox.1:
forward 5’-TGGGACACTATCAGCAACTACG-3’
reverse 5’-GGTGAGGTTGGCGATTGTT-3’
ENST00000439703:
forward 5’-AAAGCAGAAGACACTGGTTGAGC-3’
reverse 5’-GCAGGTTTTGTTGCGAAGTG-3’
-actin:
forward 5’- GTGGCCGAGGACTTTGATTG-3’
reverse 5’- CCTGTAACAACGCATCTCATATT-3’.

Statistical analysis

Each experiment was repeated at least three times, and quantitative data were presented as mean and standard deviation (mean ± SD). Paired t-test was used to analyze the lncRNA expression differences between the keloid tissues and normal tissues; Student’s t-test was used to analyze the lncRNA expression differences between the keloid-derived fibroblasts and normal fibroblasts; Student’s t-test was also used to analyze the expression differences of uc003jox.1, ENST00000439703 and -actin between keloid tissues and normal tissues in qRT-PCR assay, and p-value < 0.05 was considered as a significant difference. All the analyses were performed using SPSS 16.0 software.

Results

In this study, we firstly explore the differential expression of lncRNAs and mRNAs between keloid tissue (KT) and normal tissue (NT) on the chest from each of 5 patients (Table 1) using high-throughput lncRNA array. The profiling of differentially expressed (DE)-lncRNAs and DE-mRNAs is shown in Figures 1 A–D. From the microarray data, 3680 of DE-lncRNAs and 5448 DE-mRNAs (Supplementary tables) were identified in the KTs compared to NTs (fold change ≥ 2 and p < 0.05). Among the DE-lncRNAs, 2238 were up-regulated, and 1442 were down-regulated.
Furthermore, 2526 up-regulated and 2922 down-regulated DE-mRNAs were confirmed in the KT compared to NT (Figure 1 D). First, GO analysis was performed to identify the potential collective genes which contribute to keloid formation. The results of GO analysis in the biological process are shown in Figures 2 A, B. The most significantly up-regulated transcripts were involved in the extracellular structure organization and extracellular matrix organization. Meanwhile, the most significantly down-regulated transcripts were involved in the lipid metabolic process, small molecule metabolic process, oxidation-reduction process and carboxylic acid metabolic process. Secondly, the pathway analysis was performed to survey the systematic protein coding gene function. Our data revealed that 53 and 60 pathways contained up-regulated and down-regulated DE-mRNAs, respectively. The top 10 active pathways in up-regulated and down-regulated pathways are listed in Figures 2 C and D, respectively. The most enriched pathways include ECM-receptor interaction, and valine, leucine and isoleucine degradation respectively. Data on GO analysis and pathway analysis are approximately consistent with the previous study [7].
In this study, we defined sense lncRNAs, antisense lncRNAs and bidirectional lncRNAs as the genic lncRNAs. In the DE-lncRNAs database, 1628 genic lncRNAs have their coding genes, and we discovered that 426 of these coding genes remarkably altered expression levels in keloid. The 72.5% genic lncRNAs were regulated synergistically with their associated mRNAs, and the rest was in the opposite direction. Meanwhile, our data showed the synergetic differential expression of 393 enhancer-like lncRNAs and 864 long intergenic non-coding RNA (lincRNA)s with their nearby genes (Figure 3 A). Additionally, we screened the expression change folds of the mRNAs and their relative lncRNAs in the top 1 of active pathways in up-regulation and down-regulation. In the former group, COL5A2, COL6A2, ITGA1, ITGB1 and THBS3 mRNAs were reported to have relative lncRNA, including ENST00000419029, uc010gqe.2, uc003jox.1, ENST00000414157 and ENST00000447623, respectively, whereas, in the latter group, only ACAT2 has been identified to have its own one relative lncRNA, NR_037166. Intriguingly, we found that the mRNAs and their lncRNAs were up-regulated or down-regulated simultaneously (Figure 3 B).
Our findings showed that 8 kinds of DE-mRNAs in the Wnt pathway were changed along with the differential expression of their relative genic-lncRNAs, including NR_024047, ENST00000454723, etc. In the PI3K-Akt pathway, 9 kinds of DE-mRNAs have the change of their associated genic-lncRNAs such as ENST00000414157, uc001kfc.1, etc. (Figures 3 C, D). The relationships of DE-mRNAs and relative differentially expressed enhancer-like lncRNAs and lincRNAs in the Wnt pathway and the PI3K-Akt pathway are shown in Figures 3 E 1–3, suggesting that lncRNAs might contribute to the deregulation of the Wnt and PI3K-Akt-mTOR pathways in the keloid formation.
In addition, we detected differentially expressed lncRNAs in KFBs and NFBs through lncRNA microarray (Figure 4 A). Three KFBs and three NFBs were included during passages 1–2. We identified 738 up-regulated DE-lncRNAs and 493 down-regulated DE-lncRNAs in KFBs compared to NFBs (fold change  2 and p < 0.05) (Figure 4 B and Supplementary table). In comparison with the results of tissue microarray, 71 overlapped DE-lncRNAs were identified in the accordant regulation, suggesting that the deregulations of these lncRNAs were the stable events in the formation and development of keloid due to their permanent detection in both tissue and cell level. Among them, 59 were up-regulated; 12 were down-regulated (Table 2). Interestingly, some of overlapped DE-lncRNAs, such as uc003jox.1, ENST00000414157 and ENST00000439703, which are tightly associated with the PI3K-Akt pathway, were in accordance with the results of tissue assay. Furthermore, the up-regulation of uc003jox.1 and ENST00000439703 was validated by qRT-PCR in other samples of 79 KTs and 21 NTs (Figure 5 A, B).

Discussion

In this study, we have shown a number of lncRNAs and mRNAs differentially expressed between keloid and control in either tissue microarray or cell microarray, and 71 overlapped DE-lncRNAs were identified in accordant regulation in both assays. Importantly, the 72.5% genic lncRNAs were regulated synergistically with their associated mRNAs, and the rest was in the opposite direction in keloid tissue. Finally, the up-regulation of PI3K-Akt pathway associated lncRNAs uc003jox.1 and ENST00000439703, which were screened out through tissue microarray and cell microarray, were validated by qRT-PCR in other samples of 79 KTs and 21 NTs.
Recently, quantities of lncRNAs have been reported to involve cancer formation and development [16]. For instance, Sun et al. [17] reported that lncRNA HOXA11-AS promoted proliferation of gastric cancer through modulating chromatin modification factors. The studies of Liang et al. [7], Zhu et al. [8] and Zhang et al. [9] already indicated that some lncRNAs play an important role in keloid. In addition, Li et al. [18] reported that lncRNA8975-1 was up-regulated fibroblast from hypertrophic scar and involved collagen expression. Based on our findings that a great deal of lncRNAs was deregulated in KT and KFBs, our study demonstrates that numerous lncRNAs are involved in the pathogenesis and development of the keloid. However, the actual functions of numerous lncRNAs are still elusive. Therefore, the mechanism of lncRNA-mRNA interaction in keloid is needed to be clarified, especially the interplay between lncRNAs and mRNAs in the pathways closely related with keloid.
Our data showed that the Wnt and PI3K-Akt signaling pathways were included in high-enrichment pathways of up-regulation; it coincides with the previous report of Smith et al. [19] that multiple genes of the Wnt pathway were misexpressed in fibroblast detached from fibroblast lesion. Sato also observed an increase in -catenin protein, the crucial modulator of Wnt signaling, in KTs [20]. Moreover, involvement of PI3K-Akt-mTOR axis was identified in keloid pathogenesis. For example, treatment of mTOR complex 1 and 2 inhibitors resulted in suppression of Akt and mTOR signaling and inhibition of the proliferation, migration and invasion of the keloid cell [5]. Additionally, Zhang et al. [21] also found that the green tea extract inhibited type I collagen production by interfering with the PI3K-Akt-mTOR pathway in KFBs.
In this preliminary study, there are the following limitations. We have not performed any functional study about specific lncRNA. In addition, the actual mechanism by which lncRNAs participated in the pathogenesis and development of keloid remains to be clarified.

Conclusions

Our study demonstrates that numerous lncRNAs are involved in the pathogenesis and development of the keloid, and most of them (e.g. uc003jox.1 and ENST00000439703) were deregulated in a synergism with their associated mRNAs. Our study provides a new sight to the role of lncRNA in pathogenesis of keloid.

Acknowledgments

This study was supported by CAMS Innovation Fund for Medical Sciences (2017-12M-1-017 and 2016-12M-1-005).
This work was supported by grants from the National Natural Science Foundation of China (No. 81371755, 81673083), the PhD Programs Foundation of Ministry of Education of China (20131106120046), the Jiangsu Provincial Special Program of Medical Science (BL2012003) and the Jiangsu Province Natural Science Foundation (No. BK20131064) to Heng Gu. Wenbo Bu, Xu Chen and Song Xu are supported by the PUMC Youth Fund and Fundamental Research Funds for the Central Universities (3332015116, 3332014008, 3332015026, 2016RC320005).

Conflict of interest

The authors declare no conflict of interest.

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Supplementary (web page, Internet)

Lists of differential expressed lncRNAs in tissue and cell

Differentially expressed lncRNAs in tissue and cell. P-value presented the expression differences of lncRNA normalized intensities between keloid tissues and normal tissues, keloid-derived fibroblasts and normal fibroblasts, respectively, and p-value < 0.05 referred to have significant differences. FDR was a modified p-value which was calculated from Benjamini Hochberg FDR. Fold change indicated the absolute ratio of lncRNA normalized intensities between keloid tissues and normal tissues, keloid-derived fibroblasts and normal fibroblasts, respectively. Regulation column: up represents up-regulation and vice versa. The seqname referred to the lncRNA name based on the most highly respected public transcriptome databases (Refseq, UCSC knowngenes, Gencode, etc.), as well as the landmark publications.