CLDN5 affects lncRNAs acting as ceRNA dynamics contributing to regulating blood‑brain barrier permeability in tumor brain metastasis
- Authors:
- Published online on: January 10, 2018 https://doi.org/10.3892/or.2018.6208
- Pages: 1441-1453
Abstract
Introduction
The blood-brain barrier (BBB) is located at the level of cerebral capillaries in the forefront of the defense line of the central nervous system (CNS) and restricts the flow of essential components into and out of the CNS (1,2). The most important cellular elements of the BBB consist of endothelial cell, astrocyte, pericyte, and the adjacent neurons (3). The endothelial cells are connected by junction complex, in which tight junctions (TJs) play a significant role. Key components of the TJs are the transmembrane protein, and the best characterized TJs in cerebral endothelial cells are occluding, claudins and junctional adhesion molecules (JAMs) (4).
Brain metastases constitute a significant part of intracranial tumors. The majority of brain metastases originate from lung cancer, breast cancer and malignant melanoma (5). Metastatic cells invading the CNS parenchyma, however, have to pass the BBB. Since brain metastases represent a great therapeutic challenge, it is important to understand the mechanisms of the interaction of tumor cells with the BBB to find targets of prevention of brain metastasis formation.
The attachment of tumor cells to brain endothelial cells and the transendothelial migration of tumor cells are the key step in brain metastasis, plenty of molecules and signal pathways participated in this complicated process. Claudins are small proteins (20–27 kDa) (6), and the principal claudin in brain endothelial cells is claudin-5 (CLDN5) (7). CLDN5 forms the backbone of the tight junction and was shown to be solely responsible for the paracellular barrier that exists between epithelial cell, and absence of CLDN5 leads to a selective opening of the BBB to molecules smaller than 800 Da (8). CLDN5 has also recently been shown to directly play a role in the interaction of metastatic tumor cells and brain endothelial cells (9), but its mechanism of action remains largely unknown.
Recent studies have described a complicated interplay among diverse RNA molecules, including coding and non-coding RNAs (10,11). Human genome generates plurality of regulatory RNAs that are either long non-coding RNAs (lncRNAs) or small non-coding RNA such as microRNAs (miRNAs). LncRNAs are defined as >200 nucleotides and unable to be translated into proteins. Accumulating data show that lncRNAs are major regulators of physiological and disease-related gene expression through various mechanisms. The dysregulated lncRNA expression has been documented in various disease states, and their tissue specificity makes them attractive candidates as diagnostic or prognostic biomarkers or therapeutic agents. miRNAs are endogenous ~22 nucleotide RNAs, which post-transcriptionally regulate the gene expression through interaction between their 5′ end and the 3′ untranslated region (UTR) of mRNA. The functions of miRNA have been elucidated extensively and it participates in regulating a variety of cellular events (12–14). Recent studies indicated that some specific lncRNAs can serve as competitive endogenous RNAs (ceRNA) to control miRNAs available for binding with targets, functionally sequester miRNAs and as miRNA sponges, thereby alleviating the inhibitory effect of miRNA on their respective mRNA targets (15). The function introduces an extra layer of complexity in the miRNA-target interaction network. The complexity and diversity of potential ceRNA interactions have been described with the identification of abundant lncRNAs. Understanding this novel RNA interaction will lead to significant insight into gene regulatory networks in a variety of cell process (16–19).
The involvement of ceRNA regulation as a factor of CLDN5 modulation in brain vascular endothelial cells has not been previously investigated. To gain further understanding of how CLDN5 mediates its activities in tumor brain metastasis, herein, in this study, we examined changes in globe mRNA, miRNA, lncRNA gene expression when CLDN5 was overexpressed in the human brain vascular endothelial cell line, hCMEC/D3. We have identified a number of ceRNAs whose expression levels were altered as a consequence of high CLDN5 expression. The identified sets of lncRNA, miRNA and mRNA specific to CLDN-5-overexpressing hCMEC/D3 cells were subsequently confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR).
Materials and methods
Cell culture and transduction
Human brain vascular endothelial hCMEC/D3 cells were obtained from Institut Cochin (Université René Descartes, Paris, France). Cells were cultured in the EBM-2 medium supplemented with EGMTM-2 Bullet kit (Lonza, Walkersville, NJ, USA) in a humidified 37°C incubator with an atmosphere of 5% CO2.
In the overexpression experiment, the CLDN5 expression vector was constructed by inserting a human CLDN5 cDNA into pLL3.7-GFP. CLDN5-pLL3.7 or control pLL3.7 vector were transfected into 293T cells using Lipofectamine 3000 (Thermo Fisher Scientific, Inc., Waltham, MA, USA). Supernatant containing retroviruses was collected and concentrated 48 and 72 h later and was used for the transduction. The hCMEC/D3 cells were transducted with the virus-containing medium plus 8 µg/ml polybrene. Forty-eight hours after beginning the transduction, GFP+ cells were sorted and the expression of CLDN5 was confirmed by qRT-PCR.
RNA extraction
Total RNA of CLDN5-overexpressing and control hCMEC/D3 cells were extracted using TRIzol Reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) according to the manufacturer's protocol. RNA quantity was evaluated by the 28S/18S ratio and RNA Integrity Number (RIN) was analyzed on the Agilent 2100 bioanalyzer using the Eukaryote Total RNA Nano assay (Agilent Technologies, Waldbronn, Germany). RNA purity was evaluated by the A260/A280 ratio using an RNA 6000 Nano Spectrophotometer (Agilent).
miRNA, mRNA, lncRNA microarray and computational analysis
For mRNA and lncRNA expression profiling, Affymetrix Human Transcriptome Array 2.0 (HTA 2.0) (Thermo Fisher Scientific, Inc.) was used. For miRNA expression profiling, Affymetrix GeneChip miRNA 4.0 Array (Thermo Fisher Scientific, Inc.) was used. After hybridization and washing, the arrays were scanned by an Affymetrix Microarray Scanner (Applied Biosystems, Grand Island, NY, USA). Raw data of HTA 2.0 were extracted and normalized by Affymetrix® Transcriptome Analysis Console (TAC) Software (Thermo Fisher Scientific, Inc.). miRNA QC Tool software (Thermo Fisher Scientific, Inc.) was used for miRNA 4.0 array data summarization, normalization, and quality control.
Difference analysis
Two-class differential was used to determine the differentially expressed miRNA, lncRNA and mRNA between the pLL3.7-CLDN5-transfected group (n=3) and pLL3.7 control group (n=3). The random variance model (RVM) t-test was applied to filter the differentially expressed genes for it can effectively raise the degrees of freedom in cases of small samples. The false discovery rate (FDR) was calculated to correct the P-value. P-values <0.05 and FDR <0.05 were considered as significant differences.
The differentially expressed probe sets were imported into Cluster and Tree View to perform hierarchical cluster analysis (HCA).
Construction of lncRNA-mRNA co-expression network
We utilized the expression profile of different lncRNAs and difference mRNAs to construct the lncRNA-mRNA network. This network distinctly revealed the relation between the lncRNAs and mRNAs, found the key regulation and ‘interaction venation’ thoroughly. This network assimilated the scale-free property of the huge data, to simulate the scale-free relation by the interaction between lncRNAs and mRNAs and interaction among themselves, and that is the correlation of pairwise expression. In order to get reliable and accurate relation of gene interaction and lncRNA regulation function, we recommend the number of no less than 30 and the number of genes no less than 200.
For each pair of mRNA-lncRNA, mRNA-mRNA, or lncRNA-lncRNA, we calculated the Pearson correlation and chose the significant correlation pairs to construct the network (20). The clustering coefficient represents the density of each gene with the adjacent gene, and the larger the clustering coefficient, the greater importance the gene has in regulating the network.
Gene ontology and pathway analysis
A gene ontology (GO) analysis was applied to analyze the main functions of the mRNAs of miRNA-lncRNA-mRNA internets. Specifically, a two-side Fisher's exact test and a χ2 test were used to classify the GO category. We computed P-values of the GO for each differential gene. Enrichment provides a measure for the significant function: As the enrichment increases, the corresponding function is more specific. Within the significant category, the enrichment Re was given as follows: Re=(nf/n)/(Nf/N), where nf is the number of flagged genes within the particular category, n is the total number of genes within the same category, Nf is the number of flagged genes in the entire microarray, and N is the total number of genes in the microarray.
Pathway analysis was used to identify the significant pathway of the differential mRNAs according to KEGG, Biocarta, and Reatome. We used Fisher's exact test and the χ2 test to select the significant pathway, and the threshold of significance was defined by P-value and FDR. The enrichment Re was calculated as indicated in the equation above.
qRT-PCR analysis
Total RNA was reverse-transcribed with a PrimeScript™ RT reagent kit (Takara Biotechnology Co., Ltd., Dalian, China) for cDNA synthesis and genomic DNA removal. For miRNA detection, total RNA was reverse transcribed using miRNA specific primers. QPCR was performed according to the instructions of the SYBR premix Ex Taq™ II kit (Takara Biotechnology Co.) and carried out in the Takara real-time PCR system. GAPDH was used as lncRNA and mRNA control and U6 was used as a miRNA control. Gene-specific primers were designed using primer designing tools primer 5.0. The primer sequences are listed in Table I. The specificity of amplification was assessed by dissociation curve analysis, and the relative abundance of genes was determined with the 2−ΔΔCt method. All experiments were performed in triplicate.
Results
CLDN-5 induces significantly altered miRNA, lncRNA and mRNA expression patterns in hCMEC/D3 cells
To explore the regulatory mechanism of CLDN5 on permeability of BBB, we stably transfected the hCMEC/D3 cells with pLL3.7-CLDN5 or pLL3.7, and then analyzed the changes of miRNA, lncRNA, and mRNA levels in CLDN-5-overexpressing hCMEC/D3 cells.
In terms of the miRBaseV20 Database, 2578 human miRNAs were authenticated on the Affymetrix GeneChip microRNA 4.0 Array. Based on the RefSeq, UCSC, GENCODES, MGC, lincRNAs TUCPs, lincRNAdb and UCSC lincRNA, annotations of lncRNAs and mRNAs, the probe sets covered 22, 829 lncRNAs and 44, 699 mRNAs on the Affymetrix GeneChip HTA 2.0 Array.
The miRNA, lncRNA, and mRNA expression patterns were detected in pLL3.7-CLDN5-transfected group and pLL3.7 control group. We identified 41 miRNAs, 954 lncRNAs, and 222 mRNAs that had significant differential expression in the CLDN5-overexpressing group comparing with the control group (fold change ≥1.2 or ≤0.8, and P-value <0.05). The hierarchical clustering analysis showed that with the differentially expression of these miRNAs, lncRNAs and mRNAs, samples were non-random partitioned, they were divided into two groups (Fig. 1). Thus, the miRNA, lncRNA, and mRNA expression signatures identified here were likely to be representative.
Construction of miRNA-lncRNA-mRNA interaction of network regulated by reinforced CLDN5 and identification lncRNAs acting as ceRNAs
The miRNA-lncRNA-mRNA interaction network was constructed according to the work flow summarized in Fig. 2.
Firstly, the miRanda was applied to analyze the target mRNAs and lncRNAs of the 41 miRNAs, and then the interaction of these target mRNAs with distinct mRNAs and target lncRNAs with distinct lncRNAs, separately termed as target mRNAs (172 mRNAs, data not shown) and target lncRNAs (681 lncRNAs, data not shown). Of the target mRNAs and lncRNAs, the mRNAs and lncRNAs were selected which expression levels were negatively correlated with miRNA expression, and were termed the N&T mRNAs (152 mRNAs, data not shown) and N&T lncRNAs (618 lncRNAs, data not shown), and obtained the miRNA-mRNA, miRNA-lncRNA network.
Secondly, according to the correlation of gene expression of 222 mRNAs and 954 lncRNAs, we constructed the lncRNA-mRNA co-expression network (data not shown).
At last, based on the interaction network of miRNA-mRNA, miRNA-lncRNA, and lncRNA-mRNA, we obtained 1049 feed-forward loop networks and constructed the general miRNA-lncRNA-mRNA interaction network (data not shown). In this network, we got 148 miRNAs targeted lncRNAs, and these 148 lncRNAs were identified as ceRNAs and listed in Table II.
Biological role prediction of lncRNAs function as ceRNAs in CLDN5-overexpressing hCMEC/D3 cells
GO and pathway analysis were applied to analyze the significant function and pathway of the mRNAs that contained in the 1049 feed-forward loop networks. Go analysis results showed that upregulated and downregulated mRNA respectively, were involved in 122 and 81 items significant functions (P-value <0.05, data not shown). The pathway analysis revealed that there were 29 and 6 significant pathways corresponding to the up and down regulated mRNAs separately (P-value <0.05, data not shown).
Further analysis indicated there are 10 mRNAs that contained both in the significant function and pathway (Table III), and they participated in 95 feed-forward loop networks. The miRNAs, lncRNAs, and mRNAs in 95 feed-forward loop are listed in Table IV.
 | Table IV.Functional prediction of the lncRNA ceRNAs based on pathway and GO analysis of mRNAs that locate together in the miRNA-lncRNA-mRNA feed-forward loop. |
The biological roles of 27 lncRNAs acting as ceRNAs were predicted through GO and pathway analysis of 10 mRNAs in the miRNA-lncRNA-mRNA interaction network. In addition, the results indicated that 10 mRNAs participated in 6 upregulated and 5 downregulated pathways involving in diverse biological processes, including tight junction, focal adhesion and cell adhesion molecules, and 10 upregulated and 5 downregulated functions that containing cell adhesion, heterophilic cell-cell adhesion, cell growth, cell apoptosis, etc (Fig. 3).
As a consequence, we predicted the important roles of the 27 ceRNAs in CLDN-5-overexpressing hCMEC/D3 cells.
Quantitative real-time RT-PCR (qRT-PCR) analysis of the distinctive expression of miRNAs, lncRNAs, and mRNAs in hCMEC/D3 cells with overexpressed CLDN5
To validate the results of microarray analysis, we selected 5 miRNAs, 5 mRNAs, and 5 lncRNAs with larger fold-change from the microarray results and analyzed their expression levels by qRT-PCR in pLL3.7-CLDN5 and pLL3.7 hCMEC/D3 cells. Our results confirmed the findings of the miRNAs, lncRNAs and mRNAs microarray dataset (Fig. 4A).
Based on the analysis of 95 miRNA-lncRNA-mRNA feed-forward loops in Table IV, we evaluated the expression levels of 3 miRNA, 3 lncRNA, and 3 mRNA that are respectively located in 3 feed-forward loops. The average expression level of miR-127-5p was significantly increased, while miR-4786-5p and miR-297 were reduced in CLDN5-overexpressing hCMEC/D3 cells compared with control hCMEC/D3 cells. Analysis showed relatively high expression of miR-127-5p and low expression of n338895 and PIK3CG, and low expression of miR-4786-5p and miR-297 and high expression of n339695, TCONS_00022673-XLOC_010971 and CLDN1, OCLN (Fig. 4B). The 3 feed-forward loops detection by qRT-PCR are represented in Fig. 4C.
Discussion
In recent years, studies have confirmed the dysregulation of lncRNAs by acting as ceRNAs have profound implications for tumor metastasis. Reports demonstrate that lncRNAs as ceRNAs play an important role in blood-tumor barrier (BTB), and their dysfunction leads to the change of permeability of BTB. Several models have been proposed to explain how ceRNAs regulate BTB. Examples include lncRNA TUG1, NEAT1, XIST, MALAT1, which function in ceRNA manner to regulate expression of transcript factor (TF), such as HSF2, SOX5, FOXC1/ZO-2 or nuclear factor (NF) NFYA through targeting different miR-144, miR-181d-5p, miR-137, and miR-140. These TFs and NF regulate the expression of claudin-5, occludin, and ZO-1, and then impact the permeability of BTB. Knockdown of lncRNAs TUG1, NEAT1, XIST, and MALAT1 resulted in increased the permeability of BTBs as well as decreased the expression of claudin-5, occludin, and ZO-1 (21–26). Although regulatory networks of ceRNA have now been shown to contribute to the permeability of BTB, the dysregulation of ceRNAs as a consequence of alterations in CLDN5 levels in BTB has not been previously investigated. In this study, we provide a comprehensive analysis of the roles of CLDN5 mediating the junctional and adhesion molecules, and signaling pathways through ceRNA interaction network in brain endothelial cells.
First of all, we used the Affymetrix GeneChip HTA 2.0 Array to analyze the distinct lncRNAs and mRNAs in CLDN5-overexpressing hCMEC/D3 cells. The sample matched miRNA expression profiling was determined by the Affymetrix GeneChip miRNA 4.0 Array. We identified a set of 41 miRNAs, 954 lncRNAs, and 222 mRNAs that differently expressed between pLL3.7-CLDN5-transfected and pLL3.7 control group. Such differentiation signified the potential roles of CLDN5 in cerebral vascular endothelial cells.
Furthermore, we discussed the effect of miRNA competition on the regulation of both lncRNAs and mRNAs, as well as the implications of lncRNA function as ceRNA through constructing the complex miRNA-lncRNA-mRNA interaction network. The possible biology functions of these regulatory ceRNAs mainly include tight junction, focal adhesion, cell-cell adhesion, cell growth and apoptosis. To our knowledge, this is the first study to show the lncRNA acting as ceRNAs were associated with clautin-5 function in brain vascular endothelial cells.
Our qRT-PCR expression analysis confirmed there are a series of miRNAs, lncRNAs, and mRNAs aberrantly expressed in the CLDN5-overexpressing hCMEC/D3 cells, which indicated that the differently expressed non-coding and coding RNAs might be characteristics regulated by CLDN5.
The expression of miR-127 was found to be upregulated, whereas miR-4786-5p and miR-297 were downregulated by CLDN5 overexpression. CLDN5 mediated interactions between metastasis tumor cells and brain endothelial cells. Recent studies showed miR-127 was aberrantly downregulated and acted as a functional tumor suppressor in several brain metastatic tumors, such as breast cancer (27), osteosarcoma (28), lung cancer (29), hepatocellular carcinoma (30), while miR-297 acts as an oncogene and is upregulated in lung adenocarcinoma and osteosarcoma (31,32). Based on the findings obtained above, increased miR-127 and decreased miR-297 expression levels in claudin-5-overexpressing brain vascular endothelial cells can make BBB impermeable and reduce brain metastasis from cancer.
The PIK3CG, CLDN1 and OCLN mRNA regulated by ceRNA play key roles in the BBB. PIK3CG participates in the PI3K-Akt signaling pathway involved in regulation of numerous important cell processes including cell growth, differentiation, and metabolism. PI3K also was found to be among a couple of genes that were specifically altered in brain metastases of various tumor entities (33,34), confirming previous reports that found specific alterations of the PI3K-Akt pathway in melanoma (35) and non-small lung cancer (36) brain metastases. The results of our study suggest that the PI3K-Akt pathway might be a promising target to prevent brain metastases by overexpression of claudin-5 through ceRNA. Occludin, claudin-1, and claudin-5 are all TJ proteins, and they interact with other junctional proteins and play an important role in several pathologies, including tumor brain metastasis (37,38). Our findings are particularly interesting in the dysregulated expression of CLDN1 and OCLN mRNA, in conjunction with the high CLDN5 levels.
Overall, our study identified and analyzed lncRNA function as ceRNA in CLDN5-overexpressing cerebral vascular endothelial cells, and in which ceRNA works as a downstream effector of the CLDN5 to strengthen several signaling pathways and mediate its role in maintaining BBB permeability. Understanding these ceRNA regulating networks would advance the development of prevention and therapy strategies for tumor brain metastasis.
Our findings indicated CLDN5 plays a key role in regulating BBB permeability through ceRNA network, so the activation of CLDN5 can inhibit tumor brain metastasis, which would be applied in medical treatment. Recent reports demonstrated that some proteases degrade tight junction protein, including CLDN5, and their inhibitor or antagonist prevents degration of tight junction protein and attenuates BBB disruption. Examples include telomerase (39,40), and matrix metalloproteinases (MMPs) (41). Several models have been proposed to explain how telomerase regulated tight junction protein. First, telomerase activity may affect CLDN5; moreover AZT as an inhibitor of telomerase could modulate CLDN5 expression; at last, catalytic component of the telomerase holoenzyme may affect wnt signaling pathways (40). Earlier studies have shown that MMPs and their inhibitors-TIMPs play an essential role in the permission of drugs to cross the BBB (42,43).
Acknowledgements
This study was supported by the National Natural Science Foundation of China (no. 81471229) and the Natural Science Foundation of Beijing City (nos. 7152098 and 7142054).
Competing interests
The authors declare that they have no competing interests.
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