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Expression profile and bioinformatics analyses of circular RNAs in keloid and normal dermal fibroblasts
Journal Pre-proof Expression profile and bioinformatics analyses of circular RNAs in keloid and normal dermal fibroblasts Zhibin Zhang, Kaihui Yu, Ougen Liu, Yifeng Xiong, Xinyue Yang, Shuhua Wang, Shulan Zhang, Yueying Feng, Yating Peng PII:
S0014-4827(19)30687-1
DOI:
https://doi.org/10.1016/j.yexcr.2019.111799
Reference:
YEXCR 111799
To appear in:
Experimental Cell Research
Received Date: 15 July 2019 Revised Date:
19 November 2019
Accepted Date: 20 December 2019
Please cite this article as: Z. Zhang, K. Yu, O. Liu, Y. Xiong, X. Yang, S. Wang, S. Zhang, Y. Feng, Y. Peng, Expression profile and bioinformatics analyses of circular RNAs in keloid and normal dermal fibroblasts, Experimental Cell Research (2020), doi: https://doi.org/10.1016/j.yexcr.2019.111799. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Graphical Abstract Establishment of circRNA- miRNA-mRNA network. The ceRNA network was established with the three differentially expressed circRNAs. The 3 differentially expressed circRNAs (red triangles), and their top 10 targeted miRNAs (yellow squares node) and genes (green rounds) are predicted using prediction algorithms (Targetscan, MiRDB and mirTarbase).
Expression profile and bioinformatics analyses of circular RNAs in keloid and normal dermal fibroblasts Zhibin Zhang1#, Kaihui Yu2#, Ougen Liu1, Yifeng Xiong3, Xinyue Yang1, Shuhua Wang1, Shulan Zhang1, Yueying Feng4, Yating Peng1* 1
Department of Dermatology, The Second Affiliated Hospital of Nanchang University,
Nanchang, Jiangxi, 330006, China;
2
Medical Department of Graduate School,
Nanchang University, Nanchang, Jiangxi, 330006, China; 3Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, China; 4Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, China; #Zhibin Zhang and Kaihui Yu contributed equally to this work *Correspondence: Yating Peng, Department of Dermatology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, China; Email: [email protected] Abstract Increasing evidence indicates that circular RNAs (circRNAs) play a crucial regulatory role in the pathogenesis of multiple diseases. However, no study has examined the potential biological function and expression profile of circRNAs in keloid dermal fibroblasts (KDFs). Therefore ,the aim of this study to investigate the expression profile of circRNAs and analyze their role in KDFs. Bioinformatic analyses and high-throughput RNA sequencing technology were applied to explore the expression profile of circRNAs in 3 human KDFs and normal dermal fibroblasts (NDFs). The differentially expressed circRNAs were verified by reverse transcription PCR (RT-PCR), quantitative real-time-PCR (qRT-PCR) and Sanger sequencing. A circRNA-microRNA (miRNA)-mRNA interaction network was created using bioinformatics tools. Hsa_circ_0008259, was selected to confirm its function by qRT-PCR and western blot. Collectively, 411 circRNAs, of which 206 were Abbreviations: circRNA,circular RNA; KDFs, keloid dermal fibroblasts; NDFs, normal dermal fibroblasts; MREs, miRNA response elements; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix; miRNA, microRNAs; lncRNA, long noncoding RNA
upregulated and 205 decreased, were found to be differentially expressed in KDFs and could bind to 2532 miRNA response elements (MREs). GO and KEGG pathways enrichment analyses showed that differentially expressed circRNAs were mainly involved in apoptosis, focal adhesion, PI3K-Akt and metabolic pathway, and may regulate the pathogenesis and development of keloid. Two candidate circRNAs (hsa_circRNA_0008259, hsa_circRNA_0005480) were verified to be significantly reduced in KDFs, and one candidate circRNA (hsa_circRNA_0002198) was significantly elevated in accordance with RNA-Seq data analysis. Overexpression of hsa_circRNA_0008259 inhibited type I and collagen expression. Taken together, our study demonstrates for the first time that circRNAs exhibits differential expression in KDFs, and may be key players in the pathogenesis of keloid, or act as biomarkers of keloid. Key words: circular RNAs; keloid; dermal fibroblasts, expression profile; bioinformatics; microRNA 1. Introduction Keloid is a skin fibrotic disease recognized by its symptoms such as abnormal collagen deposition following skin injury (e.g., surgery, surgery or trauma) [1]. Keloid often occur in the chest, shoulders, ears, and back, characterized by hyperplasia of the keloid exceeding the initial demarcation of injury, which not only affects the appearance of the skin, but also triggers pain, itching, infection, and even lead to dysfunction [2]. Recurrence after surgical excision and conventional treatment is a common challenge [3], which cause emotional and physical harm to the patient. The unbalance between fibroblast proliferation and apoptosis is the cytological basis for the continued proliferation of keloid decreasing their ability to degrade [4]. Fibroblasts are the main cellular components in keloid skin tissue which modulates the synthesis and remodeling of extracellular matrix (ECM) [5]. Keloid fibroblasts alter the expression and abundance of ECM components, especially the level of collagen [6]. Such alterations may lead to dermal fibrosis but the exact pathogenesis are still unclear. microRNAs (miRNAs) and long noncoding RNA (lncRNA) were reported to be
key players in the coordination of cell function and gene transcription [7], as well as in the formation of keloid [8-10]. In recent years, it has been found that circRNAs play a key role in fine-tuning gene expression. Unlike linear RNAs e.g., miRNA and lncRNA, circRNAs are highly stable and have a special sponging effect on miRNA, therefore are high research value and significance [11]. Given that circRNAs modulate gene function regulation and disease intervention, it is important to explore their application [12]. To date, no study has examined the association between circRNAs and keloid dermal fibroblasts (KDFs), as well as the expression profiles of circRNAs in KDFs. In this study, a comparison of circRNA across KDFs and normal dermal fibroblasts (NDFs) and a comprehensive analysis of circRNAs were performed. The analysis identified 411 differentially expressed circRNAs (206 upregulated and 205 downregulated) in KDFs. Furthermore, 7 candidate circRNAs were verified in KDFs by RT-PCR, Sanger sequencing, and qRT-PCR. Finally, hsa_circRNA_0008259, hsa_circRNA_0005480 were found to be markedly reduced in KDFs, whereas hsa_circRNA_0002198 was significantly elevated, suggesting that these circRNAs may be novel players regulating the pathogenesis of keloid. 2. Material and Methods 2.1 Sample collection and cell culture Fifteen patients with keloid and 15 healthy volunteers without any disease were enrolled in this study at the Second Affiliated Hospital of Nanchang University from October 2018 to May 2019. All patients were diagnosed by routine pathological examination and had not been treated previously. The clinical and demographic data of the subjects are summarized in Table 1. All experiments were performed after approval by the Ethics Committee of the Second Affiliated Hospital of Nanchang University and in accordance with the Helsinki declaration. Each patient signed an informed consent. Isolation and culture of primary human dermal fibroblasts was performed as per the protocol established previously [13]. Using normal saline, skin tissues derived from keloid patients and healthy human were rinsed thrice and cut to about 0.5 x 0.5 x 0.5 cm at 4°C and incubated overnight in a solution containing
neutral protease II (Sigma-Aldrich, USA). The epidermis was separated, and the dermis was cut. The cut tissue pieces were placed in a 1.0 mg/ml type I collagenase (solarbio, China) solution for 2 hours at 37° C, filtered through a 100 molybdenum filter. The filtrate was collected and centrifuged in a centrifuge tube. The cells were resuspended in the culture medium, inoculated in a 25 cm2 culture flask at a density of 4×10 4cells. Cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibico, USA) comprising of 10% fetal bovine serum (Gibico, USA) in 5% CO2 at 37°C in humidified incubator. Cells were used when they were cultured to passages 3-5. 2.2 CircRNAs sequencing Total RNA from KDFs and NDFs was extracted and purified using a Trizol kit (Life Technologies, USA). The presence of RNA degradation and genomic DNA contamination were assessed by agarose gel electrophoresis. ND-1000 Nanodrop (Thermo Fisher, USA) was applied to test the purity of total RNA, assessed the presence of protein or phenolic contamination based on the OD260/280 ratio, and assessed the presence of salt or carbohydrate contamination based on OD260/230 ratio. Agilent 2200 Bioanalyzer (Agilent Technologies, USA) accurately detects the integrity of RNA and calculates the RNA integrity index (RIN value) based on the RNA peak map, requiring RIN≥7.0. The Epicentre Ribo-Zero rRNA Removal Kit (illumina, USA) was applied to obtain ribosomal RNA from 3µg of total RNA, which was then treated with RNase R (Epicentre, USA) followed by fragmentation to about 200bp. Thereafter, the treated RNA fragments were reverse transcribed into cDNA using the Tmseq Stranded Total RNA Sample Pretreatment Kit (illumina, USA) to construct a library. The library quality checks were performed using 2200 Tapestation system and a Qubit fluorometer (invitrogen, USA). The libraries were constructed using NEBNext® Ultra™RNA Library Prep Kit (NEB, USA) in line with the company’s guidelines and then sequenced on HiSeq 3000 with 2 X 150 bp mode at RiboBio Co. Ltd, Guangzhou, China. 2.3 CircRNAs sequencing data analysis Two algorithms, CIRI2 and CIRCexplorer2 were used to distinguish circRNAs.
Then, mapping of reads to human reference genome hg19/GRCh37 was carried out using Tophat or BWA-MEM, respectively. CIRI2 distinguishes the paired chiastic clipping signals from the mapping information of reads by local alignment with BWA-MEM and join forces with sequential filtering procedures to eliminate latent false positives. CIRCexplorer2 applies TopHat and TopHat-Fusion alignment output to distinguish circRNAs. If a circRNA is detected by both methods, it will be deemed to an identified cirRNA. Back-spliced junction reads identified in CIRI2 were merged and scaled to RPM (Reads Per Million mapped reads, bwamem mapping) to quantify all circRNAs. CircRNAs with a P-value (<0.05) and fold change (≥1) were considered to be significantly differentially expressed. Hierarchical Clustering analysis was used to determine the expression outlook of differentially transcribed circRNAs under different experimental conditions. CircRNAs with the same or similar expression pattern will aggregate into clusters. Different color regions represent different clustering grouping information. This concept was used to assess the clustering mode of different samples or different experimental conditions. GO and KEGG pathways enrichment assessments were carried out for parental genes of circRNAs with different expression levels. The significant threshold was P < 0.05, and the biological functions and pathways with statistical significance were identified. 2.4 Establishment of circRNA/miRNA interaction network The binding sites of miRNAs on circRNAs were predicted by microRNAanda, RNA hybrid, PITA and TargetScan, and the predicted results of each software were intersected. The final predicted results were obtained by three software (any three of the four mentioned above). The miRNA-circRNA network was then created on the basis of the predicted results. 2.5 Verification of candidate circRNAs Genomic DNA and total RNA were amplified reverse transcription PCR (RT-PCR), PCR by designed primers annealed to the distal end of the high frequency differentially expressed circRNAs. The amplicons were resolved on 2% (w/v) agarose gel and verified through Sanger sequencing. The designed primers are provided in Supplementary table1. The ABI Prism 7900 system (Applied Biosystems, USA) was
used for quantitative real-time-PCR (qRT-PCR) was to assess the level of seven circRNAs
(hsa_circ_0002198,
hsa_circ_0005320,
hsa_circ_0008259,
hsa_circ_0000994, hsa_circ_0005480, hsa_circ_0047270, hsa_circ_0000375 ), after treatment with the SYBR green reagent. The specificity of primers was evaluated by analyzing the melting curve. The 2-ΔΔCt method was used for determination of the relative level of each circRNA. GAPDH was used as an internal reference for normalization. 2.6 Establishment of circRNA-miRNA-mRNA network The above network was created on the basis of the ceRNA hypothesis using Cytoscape 3.6.1 to study their interaction. The downstream target genes of miRNAs were forecast by 3 prediction algorithms (mirTarbase, MiRDB, Targetscan). Finally, ceRNA networks were created using three differentially expressed circRNAs and predicted miRNAs/mRNAs. 2.7 Plasmids construction and stable transfection To isolate stable KDFs over-expressing hsa_circ_0008259, hsa_circ_0008259 cDNA was synthesized cloned into CMV vector and lentivirus (Genechem, Shanghai, China). According to the manufacturer’ s instructions, human KDFs were infected with lentivirus at a multiplicity of infection of 50. All cell were followed by selection with 1 µg/mL puromycin for 2 weeks. The constructed vector (pLVX-circ-0008259) was verified by direct sequencing (Fig. S1). 2.8 Verification of hsa_circ_0008259 functions The primers of hsa_circ_0008259, COL1A1, COL3A1 and GAPDH were designed (Table S1). qRT-PCR process as described previously. Total cell protein was extracted from cells. Protein concentrations were determined using the bicinchoninic acid method. After boiling for 10 min in water, 30 µg of protein samples were subjected to 19% SDS-PAGE, transmembrane blotting, blocking, and overnight incubation with anti-COL1A1, anti-COL3A1 and anti-GAPDH antibodies. After washing the following day, the blots were incubated with the corresponding horseradish peroxidase-conjugated secondary antibodies. Finally, expression of the corresponding protein was observed via chemiluminescence.
2.9 Statistical analysis All data were added to the GraphPad Prism version 7.0 and analyzed using Student's t-test. The results are presented as mean±SD. Correlations were determined by Pearson correlation analysis. P<0.05 represented statistical significance. *P<0.05, **P<0.01. 3. Results 3.1 CircRNA expression profiling Our analysis detected 8067 circRNAs from KDFs and 8618 circRNAs from NDFs in this study. Fig. 1A shows the pearson`s correlation coefficient (r) between KDFs and NDFs. Fig. 1B presents a volcano plot showing the differentially expressed circRNAs. Hierarchical clustering revealed distinct differences in circRNA expression profile between two groups (Fig. 2). Collectively, 411 circRNAs exhibited a differential expression in KDFs relative to NDFs, with 206 upregulated and 205 downregulated. GO analysis of target genes was performed in three aspects: biological processes, molecular functions and cellular components. The majority of target genes were enriched in binding, intracellular, and cellular processes (Figure. 3). The KEGG pathway analysis indicated that apoptosis, Focal adhesion, PI3K-Akt signaling pathway, and metabolic pathway were significantly enriched by the differentially expressed circRNAs (Figure. 4). 3.2 Designing of the circRNA/miRNA interaction network To reveal the roles of circRNA, miRanda, RNAhybrid, PITA and TargetScan were used to predict the miRNA binding sites on the circular RNA, and the software prediction results were intersected by three (four of the above). Any three of the software together predict results as a final prediction. Collectively, 2532 miRNA response elements (MREs) could bind to 411 differentially expressed circRNAs. Cytoscape3.6.1 was utilized to design a network covering the top 20 up-regulated (red triangles) and down-regulated circRNAs (blue triangles) and their target miRNAs (yellow rounds, only top 10 included) (Figure. 5) . 3.3 Verification of the differentially expressed circRNAs To
detect
whether
the
7
candidate
circRNAs
(hsa_circ_0002198,
hsa_circ_0005320,
hsa_circ_0008259,
hsa_circ_0000994,
hsa_circ_0005480,
hsa_circ_0047270, hsa_circ_0000375) with high frequency differentially expressed (highly abundant, fold-change > 2 and P-value < 0.01 (Supplementary table2) ) in high throughput sequencing are real circRNAs, RT-PCR was carried out with convergent (line) and divergent (circular) circRNA-specific primers. The results showed that the 7 high frequency differentially expressed circRNAs could be distinctly amplified from cDNA by the divergent primers (Figure. 6A). The sequence of head-tail junctions was distinguished directly by Sanger sequencing of PCR amplifiers in 7 candidate circRNAs (Figure. 6B). Subsequently, the expression levels of the 7 high frequency differentially expressed circRNAs were verified via qRT-PCR. We
found
that
hsa_circ_0008259,
hsa_circ_0005480
were
significantly
downregulated, and hsa_circ_0002198 was significantly upregulated (Figure. 7) in KDFs, in accordance with the findings of RNA-Seq data assessment. 3.4 Establishment of circRNA- miRNA-mRNA network The
above
network
of
three
differentially
expressed
circRNAs
(hsa_circ_0008259, hsa_circ_0005480, hsa_circ_0002198) in KDFs was created on the basis of the circRNA-miRNA-mRNA theory which states that circRNAs possess a common binding site of MREs for mRNAs through which they can regulate each other. The circRNA-miRNA-mRNA network contained 196 miRNAs and 1980 mRNAs. The top 10 predicted target miRNAs (yellow squares) and the mRNA (blue rounds) of differentially expressed circRNAs (red triangles) were identified using Cytoscape3.6.1 (Figure. 8). 3.5 Verification of hsa_circ_0008259 functions As the fold change of hsa_circ_0008259 was the most significant different in KDFs, we explore whether hsa_circ_0008259 have biological functions on type I and collagen production which is a main characteristic of keloid. Overexpression of hsa_circ_0008259 was performed by cloning of hsa_circ_0008259 using lentiviral expression vector and the circularization was confirmed (Fig. S1). Overexpression of hsa_circ_0008259 reduced keloid decrease of COL1A1 and COL3A1 mRNA and protein levels in KDFs (Fig. 9).
4. Discussion In recent years, increasing evidence indicates that non-coding RNA have a crucial regulatory role in the pathogenesis of keloid, including miRNAs and lncRNAs. For example, miR-203 was implicated in decreased ECM production, invasion, and proliferation of keloid fibroblasts by inhibiting FGF2 and EGR1 expression [14]. lncRNA, HNF1A-AS1 affects the growth and proliferation of keloid through its adjacent target gene HNF1A [15]. Recently, circRNAs, a novel class of endogenous non-coding RNA, have been shown to be important indicators for early diagnosis, severity, and prognosis of multiple diseases [16-18]. Thus, circRNAs have great research value and significance. Jeck et al. identified about 25,000 circRNAs in human dermal fibroblast cell lines using high throughput sequencing methods [19]. In addition, circRNAs were found to be key factors that coordinate collagen hyperplasia and fibroblast proliferatio0n [20-22]. Therefore, it is likely that they have crucial roles in dermal fibroproliferative disease. However, as far as we know, no study has examined the impact and expression profile of circRNAs in keloid. Here, we explored the expression profile of circRNAs in KDFs and NDFs by high-throughput RNA sequencing and attempted to investigate the abnormal expression of circRNAs in KDFs. From our analyses, 206 circRNAs were identified to be markedly upregulated and 205 were markedly downregulated. According to GO and KEGG analyses, the function of most related pathways and genes in KDFs were linked to apoptosis [23], focal adhesion [24], PI3K-Akt signaling pathway [25], metabolic pathway [26]. The findings suggest that the pathogenesis of keloid might be related to the disruption of these biological pathways. The above signaling pathways, particularly PI3K-Akt signaling pathway, are closely related to the pathogenesis of keloid [25]. Consequently, aberrant expression of circRNAs might be participate and modulate the development of keloid. Among the seven circRNAs subjected to validation analysis, two circRNAs (hsa_circRNA_0008259, hsa_circRNA_0005480) were markedly reduced, and one candidate circRNA (hsa_circRNA_0002198) was significantly elevated, this results is in line with that of RNA sequencing data, which imply that these circRNAs may play
a crucial role to pathogenesis of keloid. It has been confirmed that circRNAs are important regulatory endogenous non-coding RNAs, that harbors abundant microRNA binding sites. On this basis, they function as miRNAs sponges in cells, thereby attenuating the inhibition of miRNAs on their target genes, enhancing the transcription of target genes. In the aforementioned mechanism, circRNAs serve as competitive endogenous RNAs [27, 28]. They influence the progress of diseases through their association with disease-related miRNAs [29]. Our bioinformatics prediction analyses revealed that three
distinctly
differentially
expressed
circRNAs
(hsa_circRNA_0008259,
hsa_circRNA_0005480, hsa_circRNA_0002198) contained 196 miRNA binding sites. Studies have shown that miR-21-5p (a binding target of hsa_circRNA_0008259) is significantly upregulated in keloid, and the invasion, migration, sphere-forming, proliferation, and collagen deposition functions of keloid keratinocytes/KDFs were significantly increased or decreased by knockdown or upregulation of miR-21-5p [30-31]. Our research showed overexpression of hsa_circRNA_0008259 inhibited type
I
and
collagen
expression.
Therefore,
we
inferred
that
hsa_circ_0008259/miR-21-5p might regulate keratinocyte, invasion, migration, sphere-forming, proliferation, and collagen deposition abilities. Furthermore, Li et al [32]. found that miR-31-5p (a binding target of hsa_circRNA_0002198) was significantly upregulated in keloid specimen, and studies have shown that miR-31-5p knockdown significantly promotes cell invasion, inhibites collagen I and III expression during hypoxia and fibronectin proliferation of hypertrophic scar fibroblasts [33]. Thus, we hypothesized that the hsa_ circ_0002198/miR-31-5p axis may have important functions in the development of keloid. In conclusion ,we plan to screen for additional differentially expressed circRNAs and validate the present findings using a large sample in further studies. The hsa_circ_0008259,
hsa_circ_0005480,
hsa_circ_0002198
overexpression
or
knockdown tests will be applied. In addition, we will explore the detailed mechanism of circRNA-miRNA-RNA network in keloid to confirm our findings in a large sample. The data presented here adds to the discovery of the pathomechanisms of keloid.
Acknowledgements We are grateful to all the study participants. Zhibin Zhang and Kaihui Yu analyzed the data, and wrote the manuscript. Yating Peng was designed the manuscript and instructed the experiments. Zhibin Zhang, Kaihui Yu, Ougen Liu, Xinyue Yang, Yifeng Xiong, Shuhua Wang, Shulan Zhang, Yueying Feng and Yating peng performed the experiments. Funding This study was granted by Natonal Natural Science Foundaton of China (project no: 81860548) and Education Department of Jiangxi Province of China (project no: GJJ170146). Conflict of interest none References [1] Y. He, Z. Deng, M. Alghamdi, L. Lu, M.W. Fear, L. He, From genetics to epigenetics: new insights into keloid scarring, Cell Prolif 50 (2017). [2] W. Mari, S.G. Alsabri, N. Tabal, S. Younes, A. Sherif, R. Simman, Novel Insights on Understanding of Keloid Scar: Article Review, J Am Coll Clin Wound Spec 7 (2015) 1-7. [3] M.C. van Leeuwen, S.C. Stokmans, A.E. Bulstra, O.W. Meijer, M.W. Heymans, J.C. Ket, M.J. Ritt, P.A. van Leeuwen, F.B. Niessen, Surgical Excision with Adjuvant Irradiation for Treatment of Keloid Scars: A Systematic Review, Plast Reconstr Surg Glob Open 3 (2015) e440. [4] S. Luo, M. Benathan, W. Raffoul, R.G. Panizzon, D.V. Egloff, Abnormal balance between proliferation and apoptotic cell death in fibroblasts derived from keloid lesions, Plast Reconstr Surg 107 (2001) 87-96. [5] W.J. Lee, J.H. Park, J.U. Shin, H. Noh, D.H. Lew, W.I. Yang, C.O. Yun, K.H. Lee, J.H. Lee, Endothelial-to-mesenchymal transition induced by Wnt 3a in keloid pathogenesis, Wound Repair Regen 23 (2015) 435-442. [6] R.F. Diegelmann, I.K. Cohen, B.J. McCoy, Growth kinetics and collagen synthesis of normal skin, normal scar and keloid fibroblasts in vitro, J Cell
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RNA (lncRNA) Expression Profiles in
Keloid, Med Sci Monit 24 (2018) 9032-9044. [16] J. Viereck, T. Thum, Circulating Noncoding RNAs as Biomarkers of Cardiovascular Disease and Injury, Circ Res 120 (2017) 381-399. [17] F. Wang, A.J. Nazarali, S. Ji, Circular RNAs as potential biomarkers for cancer diagnosis and therapy, Am J Cancer Res 6 (2016) 1167-1176.
[18] J. Chen, Y. Li, Q. Zheng, C. Bao, J. He, B. Chen, D. Lyu, B. Zheng, Y. Xu, Z. Long, Y. Zhou, H. Zhu, Y. Wang, X. He, Y. Shi, S. Huang, Circular RNA profile identifies circPVT1 as a proliferative factor and prognostic marker in gastric cancer, Cancer Lett 388 (2017) 208-219. [19] W.R. Jeck, J.A. Sorrentino, K. Wang, M.K. Slevin, C.E. Burd, J. Liu, W.F. Marzluff, N.E. Sharpless, Circular RNAs are abundant, conserved, and associated with ALU repeats, Rna 19 (2013) 141-157. [20] Y. Peng, X. Song, Y. Zheng, H. Cheng, W. Lai, circCOL3A1-859267 regulates type I collagen expression by sponging miR-29c in human dermal fibroblasts, Eur J Dermatol 28 (2018) 613-620. [21] Y. Peng, X. Song, Y. Zheng, X. Wang, W. Lai, Circular RNA profiling reveals that circCOL3A1-859267 regulate type I collagen expression in photoaged human dermal fibroblasts, Biochem Biophys Res Commun 486 (2017) 277-284. [22] Z.G. Yang, F.M. Awan, Du WW, Y. Zeng, J. Lyu, Wu, S. Gupta, W. Yang, B.B. Yang, The Circular RNA Interacts with STAT3, Increasing Its Nuclear Translocation and Wound Repair by Modulating Dnmt3a and miR-17 Function, Mol Ther 25 (2017) 2062-2074. [23] X. Jian, L. Qu, Y. Wang, Q. Zou, Q. Zhao, S. Chen, X. Gao, H. Chen, C. He, Trichostatin Ainduced miR-30a-5p regulates apoptosis and proliferation of keloid fibroblasts via targeting BCL2, Mol Med Rep 19 (2019) 5251-5262. [24] Z. Wang, K.D. Fong, T.T. Phan, I.J. Lim, M.T. Longaker, G.P. Yang, Increased transcriptional response to mechanical strain in keloid fibroblasts due to increased focal adhesion complex formation, J Cell Physiol 206 (2006) 510-517. [25] G. An, S. Liang, C. Sheng, Y. Liu, W. Yao, Upregulation of microRNA-205 suppresses vascular endothelial growth factor expression-mediated PI3K/Akt signaling transduction in human keloid fibroblasts, Exp Biol Med (Maywood) 242 (2017) 275-285. [26] Q. Li, Z. Qin, F. Nie, H. Bi, R. Zhao, B. Pan, J. Ma, X. Xie, Metabolic reprogramming in keloid fibroblasts: Aerobic glycolysis and a novel therapeutic strategy, Biochem Biophys Res Commun 496 (2018) 641-647.
[27] T.B. Hansen, T.I. Jensen, B.H. Clausen, J.B. Bramsen, B. Finsen, C.K. Damgaard, J. Kjems, Natural RNA circles function as efficient microRNA sponges, Nature 495 (2013) 384-388. [28] S. Qu, X. Yang, X. Li, J. Wang, Y. Gao, R. Shang, W. Sun, K. Dou, H. Li, Circular RNA: A new star of noncoding RNAs, Cancer Lett 365 (2015) 141-148. [29] S. Haque, L.W. Harries, Circular RNAs (circRNAs) in Health and Disease, Genes (Basel) 8 (2017). [30] L. Yan, R. Cao, Y. Liu, L. Wang, B. Pan, X. Lv, H. Jiao, Q. Zhuang, X. Sun, R. Xiao, MiR-21-5p Links Epithelial-Mesenchymal Transition Phenotype with Stem-Like Cell Signatures via AKT Signaling in Keloid Keratinocytes, Sci Rep 6 (2016) 28281. [31] J. Wu, L. Fang, Y. Cen, Y. Qing, J. Chen and Z. Li, MiR-21 Regulates Keloid Formation by Downregulating Smad7 via the TGF-beta/Smad Signaling Pathway, J Burn Care Res 40 (2019) 809-817. [32] C. Li, Y. Bai, H. Liu, X. Zuo, H. Yao, Y. Xu, M. Cao, Comparative study of microRNA profiling in keloid fibroblast and annotation of differential expressed microRNAs, Acta Biochim Biophys Sin (Shanghai) 45 (2013) 692-699. [33] X. Wang, Y. Zhang, B.H. Jiang, Q. Zhang, R.P. Zhou, L. Zhang, C. Wang, Study on the role of Hsa-miR-31-5p in hypertrophic scar formation and the mechanism, Exp Cell Res 361 (2017) 201-209.
Table 1 Clinical information of included subjects Characteristics
Keloid
Normal skin
Total number
15
15
Gender(Female/Male)
6/9
7/8
Age (y), mean ± SD
29.00±5.34
29.60±5.63
circRNA microarray
3
3
Age (y), mean ± SD
30.00±5.57
31.33±4.73
qRT-PCR
12
12
Age (y), mean ± SD
28.75±5.51
29.17±5.94
411 circRNAs including 206 upregulated and 205 downregulated circRNAs were validated to be differentially expressed in KDFs, binding with 2532 MREs.
Hsa_circRNA_0008259, hsa_circRNA_0005480 significantly reduced in KDFs, and hsa_circRNA_0002198 significantly elevated.
Differentially expressed circRNAs may be related to the pathogenesis and development of keloid.
Zhibin Zhang and Kaihui Yu analyzed the data, and wrote the manuscript. Yating Peng was designed the manuscript and instructed the experiments. Zhibin Zhang, Kaihui Yu, Ougen Liu, Xinyue Yang, Yifeng Xiong, Shuhua Wang, Shulan Zhang, Yueying Feng and Yating peng performed the experiments.
Conflict of interest statement The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Sincerely, Yating Peng Department of Dermatology The Second Affiliated Hospital of Nanchang University No.1 Minde Road,Nanchang 330006,Jiangxi, China Phone: +86 18970064077 E-mail: address: [email protected]
S0014-4827(19)30687-1
DOI:
https://doi.org/10.1016/j.yexcr.2019.111799
Reference:
YEXCR 111799
To appear in:
Experimental Cell Research
Received Date: 15 July 2019 Revised Date:
19 November 2019
Accepted Date: 20 December 2019
Please cite this article as: Z. Zhang, K. Yu, O. Liu, Y. Xiong, X. Yang, S. Wang, S. Zhang, Y. Feng, Y. Peng, Expression profile and bioinformatics analyses of circular RNAs in keloid and normal dermal fibroblasts, Experimental Cell Research (2020), doi: https://doi.org/10.1016/j.yexcr.2019.111799. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Graphical Abstract Establishment of circRNA- miRNA-mRNA network. The ceRNA network was established with the three differentially expressed circRNAs. The 3 differentially expressed circRNAs (red triangles), and their top 10 targeted miRNAs (yellow squares node) and genes (green rounds) are predicted using prediction algorithms (Targetscan, MiRDB and mirTarbase).
Expression profile and bioinformatics analyses of circular RNAs in keloid and normal dermal fibroblasts Zhibin Zhang1#, Kaihui Yu2#, Ougen Liu1, Yifeng Xiong3, Xinyue Yang1, Shuhua Wang1, Shulan Zhang1, Yueying Feng4, Yating Peng1* 1
Department of Dermatology, The Second Affiliated Hospital of Nanchang University,
Nanchang, Jiangxi, 330006, China;
2
Medical Department of Graduate School,
Nanchang University, Nanchang, Jiangxi, 330006, China; 3Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, China; 4Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, China; #Zhibin Zhang and Kaihui Yu contributed equally to this work *Correspondence: Yating Peng, Department of Dermatology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, China; Email: [email protected] Abstract Increasing evidence indicates that circular RNAs (circRNAs) play a crucial regulatory role in the pathogenesis of multiple diseases. However, no study has examined the potential biological function and expression profile of circRNAs in keloid dermal fibroblasts (KDFs). Therefore ,the aim of this study to investigate the expression profile of circRNAs and analyze their role in KDFs. Bioinformatic analyses and high-throughput RNA sequencing technology were applied to explore the expression profile of circRNAs in 3 human KDFs and normal dermal fibroblasts (NDFs). The differentially expressed circRNAs were verified by reverse transcription PCR (RT-PCR), quantitative real-time-PCR (qRT-PCR) and Sanger sequencing. A circRNA-microRNA (miRNA)-mRNA interaction network was created using bioinformatics tools. Hsa_circ_0008259, was selected to confirm its function by qRT-PCR and western blot. Collectively, 411 circRNAs, of which 206 were Abbreviations: circRNA,circular RNA; KDFs, keloid dermal fibroblasts; NDFs, normal dermal fibroblasts; MREs, miRNA response elements; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix; miRNA, microRNAs; lncRNA, long noncoding RNA
upregulated and 205 decreased, were found to be differentially expressed in KDFs and could bind to 2532 miRNA response elements (MREs). GO and KEGG pathways enrichment analyses showed that differentially expressed circRNAs were mainly involved in apoptosis, focal adhesion, PI3K-Akt and metabolic pathway, and may regulate the pathogenesis and development of keloid. Two candidate circRNAs (hsa_circRNA_0008259, hsa_circRNA_0005480) were verified to be significantly reduced in KDFs, and one candidate circRNA (hsa_circRNA_0002198) was significantly elevated in accordance with RNA-Seq data analysis. Overexpression of hsa_circRNA_0008259 inhibited type I and collagen expression. Taken together, our study demonstrates for the first time that circRNAs exhibits differential expression in KDFs, and may be key players in the pathogenesis of keloid, or act as biomarkers of keloid. Key words: circular RNAs; keloid; dermal fibroblasts, expression profile; bioinformatics; microRNA 1. Introduction Keloid is a skin fibrotic disease recognized by its symptoms such as abnormal collagen deposition following skin injury (e.g., surgery, surgery or trauma) [1]. Keloid often occur in the chest, shoulders, ears, and back, characterized by hyperplasia of the keloid exceeding the initial demarcation of injury, which not only affects the appearance of the skin, but also triggers pain, itching, infection, and even lead to dysfunction [2]. Recurrence after surgical excision and conventional treatment is a common challenge [3], which cause emotional and physical harm to the patient. The unbalance between fibroblast proliferation and apoptosis is the cytological basis for the continued proliferation of keloid decreasing their ability to degrade [4]. Fibroblasts are the main cellular components in keloid skin tissue which modulates the synthesis and remodeling of extracellular matrix (ECM) [5]. Keloid fibroblasts alter the expression and abundance of ECM components, especially the level of collagen [6]. Such alterations may lead to dermal fibrosis but the exact pathogenesis are still unclear. microRNAs (miRNAs) and long noncoding RNA (lncRNA) were reported to be
key players in the coordination of cell function and gene transcription [7], as well as in the formation of keloid [8-10]. In recent years, it has been found that circRNAs play a key role in fine-tuning gene expression. Unlike linear RNAs e.g., miRNA and lncRNA, circRNAs are highly stable and have a special sponging effect on miRNA, therefore are high research value and significance [11]. Given that circRNAs modulate gene function regulation and disease intervention, it is important to explore their application [12]. To date, no study has examined the association between circRNAs and keloid dermal fibroblasts (KDFs), as well as the expression profiles of circRNAs in KDFs. In this study, a comparison of circRNA across KDFs and normal dermal fibroblasts (NDFs) and a comprehensive analysis of circRNAs were performed. The analysis identified 411 differentially expressed circRNAs (206 upregulated and 205 downregulated) in KDFs. Furthermore, 7 candidate circRNAs were verified in KDFs by RT-PCR, Sanger sequencing, and qRT-PCR. Finally, hsa_circRNA_0008259, hsa_circRNA_0005480 were found to be markedly reduced in KDFs, whereas hsa_circRNA_0002198 was significantly elevated, suggesting that these circRNAs may be novel players regulating the pathogenesis of keloid. 2. Material and Methods 2.1 Sample collection and cell culture Fifteen patients with keloid and 15 healthy volunteers without any disease were enrolled in this study at the Second Affiliated Hospital of Nanchang University from October 2018 to May 2019. All patients were diagnosed by routine pathological examination and had not been treated previously. The clinical and demographic data of the subjects are summarized in Table 1. All experiments were performed after approval by the Ethics Committee of the Second Affiliated Hospital of Nanchang University and in accordance with the Helsinki declaration. Each patient signed an informed consent. Isolation and culture of primary human dermal fibroblasts was performed as per the protocol established previously [13]. Using normal saline, skin tissues derived from keloid patients and healthy human were rinsed thrice and cut to about 0.5 x 0.5 x 0.5 cm at 4°C and incubated overnight in a solution containing
neutral protease II (Sigma-Aldrich, USA). The epidermis was separated, and the dermis was cut. The cut tissue pieces were placed in a 1.0 mg/ml type I collagenase (solarbio, China) solution for 2 hours at 37° C, filtered through a 100 molybdenum filter. The filtrate was collected and centrifuged in a centrifuge tube. The cells were resuspended in the culture medium, inoculated in a 25 cm2 culture flask at a density of 4×10 4cells. Cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibico, USA) comprising of 10% fetal bovine serum (Gibico, USA) in 5% CO2 at 37°C in humidified incubator. Cells were used when they were cultured to passages 3-5. 2.2 CircRNAs sequencing Total RNA from KDFs and NDFs was extracted and purified using a Trizol kit (Life Technologies, USA). The presence of RNA degradation and genomic DNA contamination were assessed by agarose gel electrophoresis. ND-1000 Nanodrop (Thermo Fisher, USA) was applied to test the purity of total RNA, assessed the presence of protein or phenolic contamination based on the OD260/280 ratio, and assessed the presence of salt or carbohydrate contamination based on OD260/230 ratio. Agilent 2200 Bioanalyzer (Agilent Technologies, USA) accurately detects the integrity of RNA and calculates the RNA integrity index (RIN value) based on the RNA peak map, requiring RIN≥7.0. The Epicentre Ribo-Zero rRNA Removal Kit (illumina, USA) was applied to obtain ribosomal RNA from 3µg of total RNA, which was then treated with RNase R (Epicentre, USA) followed by fragmentation to about 200bp. Thereafter, the treated RNA fragments were reverse transcribed into cDNA using the Tmseq Stranded Total RNA Sample Pretreatment Kit (illumina, USA) to construct a library. The library quality checks were performed using 2200 Tapestation system and a Qubit fluorometer (invitrogen, USA). The libraries were constructed using NEBNext® Ultra™RNA Library Prep Kit (NEB, USA) in line with the company’s guidelines and then sequenced on HiSeq 3000 with 2 X 150 bp mode at RiboBio Co. Ltd, Guangzhou, China. 2.3 CircRNAs sequencing data analysis Two algorithms, CIRI2 and CIRCexplorer2 were used to distinguish circRNAs.
Then, mapping of reads to human reference genome hg19/GRCh37 was carried out using Tophat or BWA-MEM, respectively. CIRI2 distinguishes the paired chiastic clipping signals from the mapping information of reads by local alignment with BWA-MEM and join forces with sequential filtering procedures to eliminate latent false positives. CIRCexplorer2 applies TopHat and TopHat-Fusion alignment output to distinguish circRNAs. If a circRNA is detected by both methods, it will be deemed to an identified cirRNA. Back-spliced junction reads identified in CIRI2 were merged and scaled to RPM (Reads Per Million mapped reads, bwamem mapping) to quantify all circRNAs. CircRNAs with a P-value (<0.05) and fold change (≥1) were considered to be significantly differentially expressed. Hierarchical Clustering analysis was used to determine the expression outlook of differentially transcribed circRNAs under different experimental conditions. CircRNAs with the same or similar expression pattern will aggregate into clusters. Different color regions represent different clustering grouping information. This concept was used to assess the clustering mode of different samples or different experimental conditions. GO and KEGG pathways enrichment assessments were carried out for parental genes of circRNAs with different expression levels. The significant threshold was P < 0.05, and the biological functions and pathways with statistical significance were identified. 2.4 Establishment of circRNA/miRNA interaction network The binding sites of miRNAs on circRNAs were predicted by microRNAanda, RNA hybrid, PITA and TargetScan, and the predicted results of each software were intersected. The final predicted results were obtained by three software (any three of the four mentioned above). The miRNA-circRNA network was then created on the basis of the predicted results. 2.5 Verification of candidate circRNAs Genomic DNA and total RNA were amplified reverse transcription PCR (RT-PCR), PCR by designed primers annealed to the distal end of the high frequency differentially expressed circRNAs. The amplicons were resolved on 2% (w/v) agarose gel and verified through Sanger sequencing. The designed primers are provided in Supplementary table1. The ABI Prism 7900 system (Applied Biosystems, USA) was
used for quantitative real-time-PCR (qRT-PCR) was to assess the level of seven circRNAs
(hsa_circ_0002198,
hsa_circ_0005320,
hsa_circ_0008259,
hsa_circ_0000994, hsa_circ_0005480, hsa_circ_0047270, hsa_circ_0000375 ), after treatment with the SYBR green reagent. The specificity of primers was evaluated by analyzing the melting curve. The 2-ΔΔCt method was used for determination of the relative level of each circRNA. GAPDH was used as an internal reference for normalization. 2.6 Establishment of circRNA-miRNA-mRNA network The above network was created on the basis of the ceRNA hypothesis using Cytoscape 3.6.1 to study their interaction. The downstream target genes of miRNAs were forecast by 3 prediction algorithms (mirTarbase, MiRDB, Targetscan). Finally, ceRNA networks were created using three differentially expressed circRNAs and predicted miRNAs/mRNAs. 2.7 Plasmids construction and stable transfection To isolate stable KDFs over-expressing hsa_circ_0008259, hsa_circ_0008259 cDNA was synthesized cloned into CMV vector and lentivirus (Genechem, Shanghai, China). According to the manufacturer’ s instructions, human KDFs were infected with lentivirus at a multiplicity of infection of 50. All cell were followed by selection with 1 µg/mL puromycin for 2 weeks. The constructed vector (pLVX-circ-0008259) was verified by direct sequencing (Fig. S1). 2.8 Verification of hsa_circ_0008259 functions The primers of hsa_circ_0008259, COL1A1, COL3A1 and GAPDH were designed (Table S1). qRT-PCR process as described previously. Total cell protein was extracted from cells. Protein concentrations were determined using the bicinchoninic acid method. After boiling for 10 min in water, 30 µg of protein samples were subjected to 19% SDS-PAGE, transmembrane blotting, blocking, and overnight incubation with anti-COL1A1, anti-COL3A1 and anti-GAPDH antibodies. After washing the following day, the blots were incubated with the corresponding horseradish peroxidase-conjugated secondary antibodies. Finally, expression of the corresponding protein was observed via chemiluminescence.
2.9 Statistical analysis All data were added to the GraphPad Prism version 7.0 and analyzed using Student's t-test. The results are presented as mean±SD. Correlations were determined by Pearson correlation analysis. P<0.05 represented statistical significance. *P<0.05, **P<0.01. 3. Results 3.1 CircRNA expression profiling Our analysis detected 8067 circRNAs from KDFs and 8618 circRNAs from NDFs in this study. Fig. 1A shows the pearson`s correlation coefficient (r) between KDFs and NDFs. Fig. 1B presents a volcano plot showing the differentially expressed circRNAs. Hierarchical clustering revealed distinct differences in circRNA expression profile between two groups (Fig. 2). Collectively, 411 circRNAs exhibited a differential expression in KDFs relative to NDFs, with 206 upregulated and 205 downregulated. GO analysis of target genes was performed in three aspects: biological processes, molecular functions and cellular components. The majority of target genes were enriched in binding, intracellular, and cellular processes (Figure. 3). The KEGG pathway analysis indicated that apoptosis, Focal adhesion, PI3K-Akt signaling pathway, and metabolic pathway were significantly enriched by the differentially expressed circRNAs (Figure. 4). 3.2 Designing of the circRNA/miRNA interaction network To reveal the roles of circRNA, miRanda, RNAhybrid, PITA and TargetScan were used to predict the miRNA binding sites on the circular RNA, and the software prediction results were intersected by three (four of the above). Any three of the software together predict results as a final prediction. Collectively, 2532 miRNA response elements (MREs) could bind to 411 differentially expressed circRNAs. Cytoscape3.6.1 was utilized to design a network covering the top 20 up-regulated (red triangles) and down-regulated circRNAs (blue triangles) and their target miRNAs (yellow rounds, only top 10 included) (Figure. 5) . 3.3 Verification of the differentially expressed circRNAs To
detect
whether
the
7
candidate
circRNAs
(hsa_circ_0002198,
hsa_circ_0005320,
hsa_circ_0008259,
hsa_circ_0000994,
hsa_circ_0005480,
hsa_circ_0047270, hsa_circ_0000375) with high frequency differentially expressed (highly abundant, fold-change > 2 and P-value < 0.01 (Supplementary table2) ) in high throughput sequencing are real circRNAs, RT-PCR was carried out with convergent (line) and divergent (circular) circRNA-specific primers. The results showed that the 7 high frequency differentially expressed circRNAs could be distinctly amplified from cDNA by the divergent primers (Figure. 6A). The sequence of head-tail junctions was distinguished directly by Sanger sequencing of PCR amplifiers in 7 candidate circRNAs (Figure. 6B). Subsequently, the expression levels of the 7 high frequency differentially expressed circRNAs were verified via qRT-PCR. We
found
that
hsa_circ_0008259,
hsa_circ_0005480
were
significantly
downregulated, and hsa_circ_0002198 was significantly upregulated (Figure. 7) in KDFs, in accordance with the findings of RNA-Seq data assessment. 3.4 Establishment of circRNA- miRNA-mRNA network The
above
network
of
three
differentially
expressed
circRNAs
(hsa_circ_0008259, hsa_circ_0005480, hsa_circ_0002198) in KDFs was created on the basis of the circRNA-miRNA-mRNA theory which states that circRNAs possess a common binding site of MREs for mRNAs through which they can regulate each other. The circRNA-miRNA-mRNA network contained 196 miRNAs and 1980 mRNAs. The top 10 predicted target miRNAs (yellow squares) and the mRNA (blue rounds) of differentially expressed circRNAs (red triangles) were identified using Cytoscape3.6.1 (Figure. 8). 3.5 Verification of hsa_circ_0008259 functions As the fold change of hsa_circ_0008259 was the most significant different in KDFs, we explore whether hsa_circ_0008259 have biological functions on type I and collagen production which is a main characteristic of keloid. Overexpression of hsa_circ_0008259 was performed by cloning of hsa_circ_0008259 using lentiviral expression vector and the circularization was confirmed (Fig. S1). Overexpression of hsa_circ_0008259 reduced keloid decrease of COL1A1 and COL3A1 mRNA and protein levels in KDFs (Fig. 9).
4. Discussion In recent years, increasing evidence indicates that non-coding RNA have a crucial regulatory role in the pathogenesis of keloid, including miRNAs and lncRNAs. For example, miR-203 was implicated in decreased ECM production, invasion, and proliferation of keloid fibroblasts by inhibiting FGF2 and EGR1 expression [14]. lncRNA, HNF1A-AS1 affects the growth and proliferation of keloid through its adjacent target gene HNF1A [15]. Recently, circRNAs, a novel class of endogenous non-coding RNA, have been shown to be important indicators for early diagnosis, severity, and prognosis of multiple diseases [16-18]. Thus, circRNAs have great research value and significance. Jeck et al. identified about 25,000 circRNAs in human dermal fibroblast cell lines using high throughput sequencing methods [19]. In addition, circRNAs were found to be key factors that coordinate collagen hyperplasia and fibroblast proliferatio0n [20-22]. Therefore, it is likely that they have crucial roles in dermal fibroproliferative disease. However, as far as we know, no study has examined the impact and expression profile of circRNAs in keloid. Here, we explored the expression profile of circRNAs in KDFs and NDFs by high-throughput RNA sequencing and attempted to investigate the abnormal expression of circRNAs in KDFs. From our analyses, 206 circRNAs were identified to be markedly upregulated and 205 were markedly downregulated. According to GO and KEGG analyses, the function of most related pathways and genes in KDFs were linked to apoptosis [23], focal adhesion [24], PI3K-Akt signaling pathway [25], metabolic pathway [26]. The findings suggest that the pathogenesis of keloid might be related to the disruption of these biological pathways. The above signaling pathways, particularly PI3K-Akt signaling pathway, are closely related to the pathogenesis of keloid [25]. Consequently, aberrant expression of circRNAs might be participate and modulate the development of keloid. Among the seven circRNAs subjected to validation analysis, two circRNAs (hsa_circRNA_0008259, hsa_circRNA_0005480) were markedly reduced, and one candidate circRNA (hsa_circRNA_0002198) was significantly elevated, this results is in line with that of RNA sequencing data, which imply that these circRNAs may play
a crucial role to pathogenesis of keloid. It has been confirmed that circRNAs are important regulatory endogenous non-coding RNAs, that harbors abundant microRNA binding sites. On this basis, they function as miRNAs sponges in cells, thereby attenuating the inhibition of miRNAs on their target genes, enhancing the transcription of target genes. In the aforementioned mechanism, circRNAs serve as competitive endogenous RNAs [27, 28]. They influence the progress of diseases through their association with disease-related miRNAs [29]. Our bioinformatics prediction analyses revealed that three
distinctly
differentially
expressed
circRNAs
(hsa_circRNA_0008259,
hsa_circRNA_0005480, hsa_circRNA_0002198) contained 196 miRNA binding sites. Studies have shown that miR-21-5p (a binding target of hsa_circRNA_0008259) is significantly upregulated in keloid, and the invasion, migration, sphere-forming, proliferation, and collagen deposition functions of keloid keratinocytes/KDFs were significantly increased or decreased by knockdown or upregulation of miR-21-5p [30-31]. Our research showed overexpression of hsa_circRNA_0008259 inhibited type
I
and
collagen
expression.
Therefore,
we
inferred
that
hsa_circ_0008259/miR-21-5p might regulate keratinocyte, invasion, migration, sphere-forming, proliferation, and collagen deposition abilities. Furthermore, Li et al [32]. found that miR-31-5p (a binding target of hsa_circRNA_0002198) was significantly upregulated in keloid specimen, and studies have shown that miR-31-5p knockdown significantly promotes cell invasion, inhibites collagen I and III expression during hypoxia and fibronectin proliferation of hypertrophic scar fibroblasts [33]. Thus, we hypothesized that the hsa_ circ_0002198/miR-31-5p axis may have important functions in the development of keloid. In conclusion ,we plan to screen for additional differentially expressed circRNAs and validate the present findings using a large sample in further studies. The hsa_circ_0008259,
hsa_circ_0005480,
hsa_circ_0002198
overexpression
or
knockdown tests will be applied. In addition, we will explore the detailed mechanism of circRNA-miRNA-RNA network in keloid to confirm our findings in a large sample. The data presented here adds to the discovery of the pathomechanisms of keloid.
Acknowledgements We are grateful to all the study participants. Zhibin Zhang and Kaihui Yu analyzed the data, and wrote the manuscript. Yating Peng was designed the manuscript and instructed the experiments. Zhibin Zhang, Kaihui Yu, Ougen Liu, Xinyue Yang, Yifeng Xiong, Shuhua Wang, Shulan Zhang, Yueying Feng and Yating peng performed the experiments. Funding This study was granted by Natonal Natural Science Foundaton of China (project no: 81860548) and Education Department of Jiangxi Province of China (project no: GJJ170146). Conflict of interest none References [1] Y. He, Z. Deng, M. Alghamdi, L. Lu, M.W. Fear, L. He, From genetics to epigenetics: new insights into keloid scarring, Cell Prolif 50 (2017). [2] W. Mari, S.G. Alsabri, N. Tabal, S. Younes, A. Sherif, R. Simman, Novel Insights on Understanding of Keloid Scar: Article Review, J Am Coll Clin Wound Spec 7 (2015) 1-7. [3] M.C. van Leeuwen, S.C. Stokmans, A.E. Bulstra, O.W. Meijer, M.W. Heymans, J.C. Ket, M.J. Ritt, P.A. van Leeuwen, F.B. Niessen, Surgical Excision with Adjuvant Irradiation for Treatment of Keloid Scars: A Systematic Review, Plast Reconstr Surg Glob Open 3 (2015) e440. [4] S. Luo, M. Benathan, W. Raffoul, R.G. Panizzon, D.V. Egloff, Abnormal balance between proliferation and apoptotic cell death in fibroblasts derived from keloid lesions, Plast Reconstr Surg 107 (2001) 87-96. [5] W.J. Lee, J.H. Park, J.U. Shin, H. Noh, D.H. Lew, W.I. Yang, C.O. Yun, K.H. Lee, J.H. Lee, Endothelial-to-mesenchymal transition induced by Wnt 3a in keloid pathogenesis, Wound Repair Regen 23 (2015) 435-442. [6] R.F. Diegelmann, I.K. Cohen, B.J. McCoy, Growth kinetics and collagen synthesis of normal skin, normal scar and keloid fibroblasts in vitro, J Cell
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RNA (lncRNA) Expression Profiles in
Keloid, Med Sci Monit 24 (2018) 9032-9044. [16] J. Viereck, T. Thum, Circulating Noncoding RNAs as Biomarkers of Cardiovascular Disease and Injury, Circ Res 120 (2017) 381-399. [17] F. Wang, A.J. Nazarali, S. Ji, Circular RNAs as potential biomarkers for cancer diagnosis and therapy, Am J Cancer Res 6 (2016) 1167-1176.
[18] J. Chen, Y. Li, Q. Zheng, C. Bao, J. He, B. Chen, D. Lyu, B. Zheng, Y. Xu, Z. Long, Y. Zhou, H. Zhu, Y. Wang, X. He, Y. Shi, S. Huang, Circular RNA profile identifies circPVT1 as a proliferative factor and prognostic marker in gastric cancer, Cancer Lett 388 (2017) 208-219. [19] W.R. Jeck, J.A. Sorrentino, K. Wang, M.K. Slevin, C.E. Burd, J. Liu, W.F. Marzluff, N.E. Sharpless, Circular RNAs are abundant, conserved, and associated with ALU repeats, Rna 19 (2013) 141-157. [20] Y. Peng, X. Song, Y. Zheng, H. Cheng, W. Lai, circCOL3A1-859267 regulates type I collagen expression by sponging miR-29c in human dermal fibroblasts, Eur J Dermatol 28 (2018) 613-620. [21] Y. Peng, X. Song, Y. Zheng, X. Wang, W. Lai, Circular RNA profiling reveals that circCOL3A1-859267 regulate type I collagen expression in photoaged human dermal fibroblasts, Biochem Biophys Res Commun 486 (2017) 277-284. [22] Z.G. Yang, F.M. Awan, Du WW, Y. Zeng, J. Lyu, Wu, S. Gupta, W. Yang, B.B. Yang, The Circular RNA Interacts with STAT3, Increasing Its Nuclear Translocation and Wound Repair by Modulating Dnmt3a and miR-17 Function, Mol Ther 25 (2017) 2062-2074. [23] X. Jian, L. Qu, Y. Wang, Q. Zou, Q. Zhao, S. Chen, X. Gao, H. Chen, C. He, Trichostatin Ainduced miR-30a-5p regulates apoptosis and proliferation of keloid fibroblasts via targeting BCL2, Mol Med Rep 19 (2019) 5251-5262. [24] Z. Wang, K.D. Fong, T.T. Phan, I.J. Lim, M.T. Longaker, G.P. Yang, Increased transcriptional response to mechanical strain in keloid fibroblasts due to increased focal adhesion complex formation, J Cell Physiol 206 (2006) 510-517. [25] G. An, S. Liang, C. Sheng, Y. Liu, W. Yao, Upregulation of microRNA-205 suppresses vascular endothelial growth factor expression-mediated PI3K/Akt signaling transduction in human keloid fibroblasts, Exp Biol Med (Maywood) 242 (2017) 275-285. [26] Q. Li, Z. Qin, F. Nie, H. Bi, R. Zhao, B. Pan, J. Ma, X. Xie, Metabolic reprogramming in keloid fibroblasts: Aerobic glycolysis and a novel therapeutic strategy, Biochem Biophys Res Commun 496 (2018) 641-647.
[27] T.B. Hansen, T.I. Jensen, B.H. Clausen, J.B. Bramsen, B. Finsen, C.K. Damgaard, J. Kjems, Natural RNA circles function as efficient microRNA sponges, Nature 495 (2013) 384-388. [28] S. Qu, X. Yang, X. Li, J. Wang, Y. Gao, R. Shang, W. Sun, K. Dou, H. Li, Circular RNA: A new star of noncoding RNAs, Cancer Lett 365 (2015) 141-148. [29] S. Haque, L.W. Harries, Circular RNAs (circRNAs) in Health and Disease, Genes (Basel) 8 (2017). [30] L. Yan, R. Cao, Y. Liu, L. Wang, B. Pan, X. Lv, H. Jiao, Q. Zhuang, X. Sun, R. Xiao, MiR-21-5p Links Epithelial-Mesenchymal Transition Phenotype with Stem-Like Cell Signatures via AKT Signaling in Keloid Keratinocytes, Sci Rep 6 (2016) 28281. [31] J. Wu, L. Fang, Y. Cen, Y. Qing, J. Chen and Z. Li, MiR-21 Regulates Keloid Formation by Downregulating Smad7 via the TGF-beta/Smad Signaling Pathway, J Burn Care Res 40 (2019) 809-817. [32] C. Li, Y. Bai, H. Liu, X. Zuo, H. Yao, Y. Xu, M. Cao, Comparative study of microRNA profiling in keloid fibroblast and annotation of differential expressed microRNAs, Acta Biochim Biophys Sin (Shanghai) 45 (2013) 692-699. [33] X. Wang, Y. Zhang, B.H. Jiang, Q. Zhang, R.P. Zhou, L. Zhang, C. Wang, Study on the role of Hsa-miR-31-5p in hypertrophic scar formation and the mechanism, Exp Cell Res 361 (2017) 201-209.
Table 1 Clinical information of included subjects Characteristics
Keloid
Normal skin
Total number
15
15
Gender(Female/Male)
6/9
7/8
Age (y), mean ± SD
29.00±5.34
29.60±5.63
circRNA microarray
3
3
Age (y), mean ± SD
30.00±5.57
31.33±4.73
qRT-PCR
12
12
Age (y), mean ± SD
28.75±5.51
29.17±5.94
411 circRNAs including 206 upregulated and 205 downregulated circRNAs were validated to be differentially expressed in KDFs, binding with 2532 MREs.
Hsa_circRNA_0008259, hsa_circRNA_0005480 significantly reduced in KDFs, and hsa_circRNA_0002198 significantly elevated.
Differentially expressed circRNAs may be related to the pathogenesis and development of keloid.
Zhibin Zhang and Kaihui Yu analyzed the data, and wrote the manuscript. Yating Peng was designed the manuscript and instructed the experiments. Zhibin Zhang, Kaihui Yu, Ougen Liu, Xinyue Yang, Yifeng Xiong, Shuhua Wang, Shulan Zhang, Yueying Feng and Yating peng performed the experiments.
Conflict of interest statement The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Sincerely, Yating Peng Department of Dermatology The Second Affiliated Hospital of Nanchang University No.1 Minde Road,Nanchang 330006,Jiangxi, China Phone: +86 18970064077 E-mail: address: [email protected]