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An lncRNA-miRNA-mRNA ceRNA network for adipocyte differentiation from human adipose-derived stem cells

Human adipose tissue-derived stromal stem cells (HASCs) represent a promising regenerative resource for breast reconstruction and augmentation. However, the mechanisms involved in inducing its adipogenic differentiation remain to be fully elucidated. The present study aimed to comprehensively investigate the expression changes in mRNAs, microRNAs (miRNAs) and long non-coding (lnc)RNAs during the adipogenic differentiation of HASCs, and screen crucial lncRNA-miRNA-mRNA interaction axes using microarray datasets GSE57593, GSE25715 and GSE61302 collected from the Gene Expression Omnibus database. Following pretreatment, differentially expressed genes (DEGs), miRNAs (DE-miRNAs) or lncRNAs (DE-lncRNAs) between undifferentiated and differentiated HASCs were identified using the Linear Models for Microarray data method. A protein-protein interaction (PPI) network was constructed for the DEGs based on protein databases, followed by module analysis. The ‘lncRNA-miRNA-mRNA’ competing endogenous RNA (ceRNA) network was constructed based on the interactions between miRNAs and mRNAs, lncRNAs and miRNAs predicted by the miRWalk and lnCeDB databases. The underlying functions of mRNAs were predicted using the clusterProfiler package. In the present study, 905 DEGs, 36 DE-miRNAs and 577 DE-lncRNAs were screened between undifferentiated HASCs and differentiated adipocyte cells. PPI network analysis demonstrated that LEP may be a hub gene, which was also enriched in significant module 5. LEP was predicted to be involved in the Janus kinase-signal transducer and activator of transcription signaling pathway, and the regulation of inflammatory response. The upregulation of LEP was regulated by downregulated hsa-miRNA (miR)-130b-5p and hsa-miR-23a-5p (or hsa-miR-302d-3p). These miRNAs also respectively interacted with RP11-552F3.9 (or RP11-15A1.7), ultimately forming the ceRNA axes. In conclusion, the present study revealed that the RP11-552F3.9 (RP11-15A1.7)-hsa-miR-130b-5p/hsa-miR-23a-5p (hsa-miR-302d-3p)-LEP interaction axes may be crucial for inducing the adipogenic differentiation of HASCs via involvement in inflammation.


Introduction
Breast reconstruction and augmentation are frequently performed surgical procedures worldwide due to the high prevalence of breast cancer (1) and cosmetic demand. Autologous fat transfer to the subcutaneous tissue is the most commonly used technique in these plastic and reconstructive surgical procedures as it appears to be relatively inexpensive, readily obtainable, safe and complication-free compared with artificial implants (2). However, the long-term replacement outcomes may not be satisfactory, which may be, in part, attributed to low graft survival and poor vascularization (3). Therefore, it is necessary to further improve the autologous fat grafting technique to overcome the above limitations.
Human adipose-derived stem cells (HASCs) are a population of pluripotent cells, which have a high proliferation capacity, possess preferential potential to differentiate into adipocytes and can secrete angiogenic growth factors. Therefore, the addition of HASCs to lipoaspirate may prevent graft volume loss and enhance blood vessel generation in the grafts. This hypothesis has been confirmed in previous clinical trials (4)(5)(6). However, the use of autologous HASCs has not been Food and Drug Administration-approved; this may be due to the fact that the reconstructive mechanism of HASCs remains to be fully elucidated. Therefore, it is essential to investigate the molecular mechanisms that induce the transition of HASCs towards adipocytes and attempt to develop a more effective combination to improve the efficacy of HASC therapy for breast reconstruction and augmentation (7).
Currently, several genes have been identified to be associated with adipogenesis for HASCs. Cytokine interleukin-1α (IL-1α) is demonstrated to evidently inhibit the proliferation and adipogenic differentiation of HASCs through the activation of nuclear factor (NF)-κB and extracellular signal-regulated kinase 1/2 pathways; and subsequent upregulation of pro-inflammatory cytokines, including interleukin (IL)-8, IL-6, C-C motif chemokine ligand 2 and IL-1β, in adipose-derived stem cells (8). A study by Strong et al (9) analyzed the overall cytokine profile of HASCs undergoing adipogenic differentiation and also found a decrease in the expression of IL-1, but reported increases in IL-12, IL-17 and intercellular adhesion molecule-1. By transcriptome profile analysis, Satish et al (10) identified several novel genes and signaling pathways involved in regulating adipogenesis, including periostin, protein phosphatase 1 regulatory inhibitor subunit 1A and fibroblast growth factor 11. MicroRNAs (miRNAs) are a class of small RNAs that are important for the regulation of cellular processes by downregulating gene expression via binding to the 3'-untranslated region. There is also evidence to indicate the roles of miRNAs in adipogenic differentiation. The levels of miRNA (miR)-27a and miR-27b have been found to be downregulated following the adipogenic induction of HASCs. The overexpression of miR-27a or miR-27b inhibits adipocyte differentiation by downregulating the expression of prohibitin; and the target association between miR-27a/b and prohibitin was confirmed using a luciferase reporter assay (11). miR-17-5p and miR-106a were shown to promote the adipogenic lineage commitment of HASCs by directly targeting bone morphogenetic protein 2 and subsequently increasing adipogenic CCAAT enhancer binding protein α (C/EBPα) and peroxisome proliferator activated receptor (PPAR)γ (12). In addition to miRNAs, long non-coding RNAs (lncRNAs) have emerged as important factors contributing to adipocyte differentiation in HASCs. Nuermaimaiti et al (13) demonstrated that the knockdown of HOXA11-AS1 inhibited adipocyte differentiation, leading to the suppression of adipogenic-related gene transcription in addition to decreased lipid accumulation in HASCs. The knockdown of MIR31HG also inhibited adipocyte differentiation, whereas the overexpression of MIR31HG promoted adipogenesis in vitro and in vivo (14). However, the adipogenic differentiation-related genes, miRNAs and lncRNAs of HASCs have received limited investigation.
Several scholars have put forward the competing endogenous RNAs (ceRNAs) hypothesis as an lncRNA-miRNA-mRNA link: LncRNAs may serve as molecular sponges for miRNAs and functionally liberate mRNA-targeted regulated by the aforementioned active miRNAs. Certain adipocyte differentiation-related lncRNA-miRNA-mRNA interaction axes have previously been obtained in bone marrow mesenchymal stem cells (BMSCs) (15,16), but not in HASCs.
The aim of the present study was to screen crucial miRNAs, lncRNAs and mRNAs associated with the adipocyte differentiation of HASCs by constructing the miRNA-lncRNA-mRNA ceRNA regulatory network using microarray data collected from a public database. The results of the present study may improve current understanding of the molecular mechanisms that induce the transition of HASCs towards adipocytes and provide targets for inducing adipogenic differentiation.

Materials and methods
Gene Expression Omnibus (GEO) dataset. The lncRNA, miRNA and mRNA expression profiles of HASCs prior to and following adipocyte differentiation were retrieved from the public GEO database (http://www.ncbi.nlm.nih.gov/geo/) under accession nos. GSE57593, GSE25715 and GSE61302 (10), respectively. The GSE57593 microarray dataset (platform: GPL18109, Agilent-038314 CBC Homo sapiens lncRNA + mRNA microarray V2.0) included samples from four undifferentiated HASCs and six differentiated adipocyte cells, which were induced following adipogenic medium culture for 3 and 6 days, with three replicates of each. The GSE25715 non-coding RNA sequencing dataset (platform: GPL9442, AB SOLiD System 3.0, Homo sapiens), included samples from four undifferentiated HASCs [two with adapter set A (from the 5' to the 3' end) and two with adapter set B (from the 3' to the 5' end)] and eight adipocyte differentiated cells that were induced using adipogenic medium for 3 and 8 days, with two replicates of each and using adapter sets A and B. The GSE61302 microarray dataset (platform: GPL570, Affymetrix Human Genome U133 Plus 2.0 Array) included samples from five undifferentiated HASCs and 10 differentiated adipocyte cells which were induced with adipogenic medium for 7 days (four replicates) and 21 days (six replicates). Data preprocessing and differential expression analysis. For the microarray data, the raw data were preprocessed using the Robust Multichip Average algorithm (17) as implemented in the Bioconductor R package (version 3.4.1; http://www.bioconductor.org/packages/release/bioc/html/affy.html), including background correction, quantile normalization and median summarization. For the sequencing data, low expression value data (=0, 70%) were filtered.
In consideration of the different differentiated time, the present study only focused on the differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs) between the undifferentiated and differentiated cells. The DEGs, DELs and DEMs were identified using the Linear Models for Microarray data method (18) in the Bioconductor R package (version 3.4.1; http://www.bioconductor.org/packages/release/bioc/html/limma.html). The empirical Bayes t-test was used to calculate the p-value, which was subsequently adjusted by the Benjamini-Hochberg (BH) procedure (19). Genes were considered differentially expressed if they met the following conditions: P-value (adjusted) P<0.05 and |logFC(fold change)| >1 (that is, FC>2). A hierarchical cluster heatmap was created using the R package pheatmap (version: 1.0.8; https://cran.r-project.org/web/packages/pheatmap) based on the Euclidean distance to observe the ability of the DEGs, DELs and DEMs to distinguish the differentiated from the undifferentiated samples.
Protein-protein interaction (PPI) network. To screen crucial genes, the DEGs were imported into PPI data that were collected from the Search Tool for the Retrieval of Interacting Genes (version 10.0; http://string db.org/) database (20). The PPIs with combined scores ≥0.4 (medium confidence) were selected to construct the PPI network, which was visualized using Cytoscape software (version 3.4; www.cytoscape.org/) (21). The network topological features, including the degree (number of interactions per node or protein), betweenness (number of shortest paths that pass through each node), and closeness centrality (average length of the shortest paths to access all other proteins in the network) were determined using the CytoNCA plugin in Cytoscape software (http://apps.cytoscape.org/apps/cytonca) (22) to rank the nodes in the PPI network and screen hub genes. Modules were identified to be significant with an Molecular Complex Detection (MCODE) score ≥4 and ≥6 nodes.
Furthermore, the MCODE (version:1.4.2, http://apps. cytoscape.org/apps/mcode) plugin of Cytoscape software was also used to identify functionally related and highly interconnected modules from the PPI network with a degree cut-off of 2, node score cut-off of 0.2, k-core of 2 and maximum depth of 100 (23).
ceRNA regulatory network construction. The DEM-related target genes were predicted using the miRWalk database (version 2.0; http://www.zmf.umm.uni-heidelberg. de/apps/zmf/mirwalk2) (24), which provides the largest collection of predicted and experimentally verified miR-target interactions with various miRNA databases, including miRWalk, miRanda, miRDB, miRMap, RNA22 and TargetScan. The miRNA-target gene interaction pairs were selected if they were predicted in at least five databases. The target genes were then overlapped with the DEGs to screen the DEM (upregulated)-DEG (downregulated) or DEM (downregulated)-DEG (upregulated) interaction pairs.

Function enrichment analysis. Gene Ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler tool (version 3.2.11; http://www.bioconductor. org/packages/release/bioc/html/clusterProfiler.html) to reveal the function of the DEGs in the PPI and the target genes of miRNAs. Adjusted P<0.05 using the BH method was set as the cut-off value (19).

Results
Differential expression analysis. Based on the given threshold (adjusted P<0.05 and |logFC| >1), a total of 925 DEGs were identified from 20,514 mRNAs between the undifferentiated HASCs and differentiated adipocyte cells, including 302 upregulated and 623 downregulated DEGs; 577 DELs were screened from 7,882 lncRNAs between the undifferentiated HASCs and differentiated adipocyte cells, including 323 upregulated and 254 downregulated DELs. A    PPI network analysis of DEGs to screen hub genes. A PPI network was constructed using the screened DEGs, which included 360 nodes (162 upregulated and 198 downregulated) and 1,381 interaction pairs (Fig. 2). According to the rank of three topological features, JUN, cyclin B1 (CCNB1), C-X-C motif chemokine ligand 10 (CXCL10), enolase 2 (ENO2), enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase (EHHADH), protein tyrosine phosphatase, receptor type C (PTPRC), Rac family small GTPase 2 (RAC2), leptin (LEP) and kinase insert domain receptor (KDR) were considered as hub genes in the PPI network (Table II). Six significant functionally related and highly interconnected modules were extracted from the whole PPI network ( Fig. 3; Table III). Hub gene CCNB1 was enriched in module 1, which was associated with cell cycle (Fig. 3A). Hub gene CXCL10 was enriched in module 2 ( Fig. 3B), which was associated with several inflammation pathways, including the chemokine signaling pathway, cytokine-cytokine receptor interaction, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Hub gene ENO2 and PTPRC were respectively enriched into module 3 (  (Table IV; Fig. 4A). As hub gene PTPRC and ENO2 were respectively enriched into module 4 and 3, but they were not included in the pathway-related genes, GO enrichment analysis was also performed. As a result, PTPRC was predicted to be involved in positive regulation of cytosolic calcium ion concentration (Table V; Fig. 4B). The function of ENO2 was not predicted.

Discussion
The present study aimed to identify crucial mRNAs, miRNAs and lncRNAs for the adipocyte differentiation of HASCs based on a series of bioinformatics analyses, including PPI network construction, module analysis, miRNA-mRNA regulatory pair prediction, ceRNA network generation and function enrichment. In these analyses, the LEP gene was enriched and was regulated by RP11-552F3.9 (or RP11-15A1.7)-hsa-miR-23a-5p/hsa-miR-302d-3p   .89  19  161  HELLS, EZH2, HJURP, CDT1, CENPF, MCM2, POLE2, FAM64A, SPAG5, KIF2C,  BLM, PLK4, CDCA8, KIF14, AURKA, KIF11, KIF15, CCNB1, CCNB2  2  13.00  13  78  CXCL10, GAL, CXCL5, CXCL11, C5, P2RY12, CCL28, CNR1, S1PR1, CXCL2   (or hsa-miR-130b-5p), and involved in the inflammatory response, indicating that the LEP-related ceRNA axis may be important for the differentiation of adipose tissue-derived stem cells into adipocytes. There is evidence demonstrating that LEP is important in adipocyte differentiation (26). Lee et al (27) observed that leptin treatment can promote lipid droplet formation and adipocyte differentiation, which was evaluated by the activity of glycerol-3-phosphate dehydrogenase activity, of HASCs. Additionally, the effect of leptin on adipocyte differentiation was found to be higher for HASCs than BMSCs (27). Another study indicated that, in BMSCs, leptin may accelerate osteogenic differentiation but inhibit adipocyte differentiation (28). Similarly, leptin was shown to have a suppressive effect on adipogenesis in dental pulp stem cells and periodontal ligament stem cells (29). These findings suggest that leptin may be a specific factor for regeneration of the subcutaneous fat layer using HASCs for tissue engineering. As expected, the LEP gene was also significantly upregulated in differentiated adipocyte samples compared with undifferentiated HASCs in the present study. It was predicted that the downstream of LEP may be involved in the JAK-STAT signaling pathway to mediate the inflammatory response via interaction with certain anti-inflammatory-related factors (IL4R, downregulated; Table IV; Fig. 3E). This prediction appears to have been indirectly verified by previous studies; it has been reported that leptin may have a promoting effect on the astroglial differentiation of stem cells through activation of the JAK-STAT pathway, with JAK-STAT inhibitors decreasing the expression of astrocyte marker leptin (30). STAT6 is reported to inhibit human IL-4 promoter activity in T cells and downregulate the gene expression of IL-4 (31). IL-4/IL4R can inhibit adipocyte differentiation by two mechanisms: Inhibiting adipogenesis via downregulating the expression of PPARγ and C/EBPα; and promoting lipolysis in mature adipocytes via enhancing the activity and translocation of hormone-sensitive lipase to decrease lipid deposits (32). However, the LEP-JAK-STAT-IL-4/IL4R signal pathways in the adipocyte differentiation of HASCs requires further experimental validation. In addition to the downstream pathways, the present  study also analyzed the upstream non-coding RNAs of LEP, including miRNAs and lncRNAs, which were previously considered to be crucial for the adipogenic differentiation of HASCs (13,14,(33)(34)(35)(36). The results identified the RP11-552F3.9 (or RP11-15A1.7)-hsa-miR-23a-5p/hsa-miR-302d-3p (or hsa-miR-130b-5p)-LEP ceRNA axes. miR-130 has been shown to affect adipocyte differentiation from preadipocytes, with overexpressing miR-130 impairing adipogenesis and reducing miR-130-enhanced adipogenesis, and its potential target may be adipogenesis-related gene PPARγ (37). In addition, the inhibition of miR-23a has been reported to increase the adipogenic differentiation of BMSCs (38). In line with these findings, the present study found that hsa-miR-130b-5p and has-miR-23a-5p were downregulated in adipocyte differentiated HASCs. There have been no reports on the roles of miR-302d-3p and the above lncRNAs (RP11-552F3.9 and RP11-15A1.7) for adipocyte differentiation, indicating they may be novel targets identified by the present study. RAC2 was identified as another hub gene that may be involved in the adipocyte differentiation of HASCs by miRNA-mRNA regulatory pair prediction and ceRNA network analysis. RAC2 was related to cell-substrate adhesion. It is well accepted that cell-substrate adhesion can control the fate of stem cells (39). A previous study showed that HASCs differentiated into adipocytes when the substrate stiffness decreased (40). Focal adhesion kinase (FAK) is a central protein involved in cell-substrate adhesion by Cas-Rac-lamellipodin signaling (41). The stimulation of Rac and increase in the activity of FAK enhanced cell tension by maintaining cell shape and matrix adhesion (42), whereas reduced cell stiffness and reduced adhesion strength have been observed in FAK-deficient cells (43). The inhibition of FAK has also been reported to lead to the elevation of adipogenic marker gene LEP and lipid accumulation in HASCs (43). These findings  implicate the underlying anti-adipogenic activity of FAK and RAC. In line with these findings, the present study found that RAC2 was downregulated in adipocyte differentiated HASCs.
Furthermore, it was predicted that RAC2 may be regulated by GDNF-AS1-hsa-miR-378a-5p. Previous evidence has shown that miR-378 is an adipogenesis-related miRNA in human    (44). The expression of miR-378a is upregulated in the adipose tissues of high fat diet-induced obese mice, and during the differentiation of preadipocytes (45,46). Investigations of the mechanism have revealed that miR-378 may induce adipogenesis by targeting mitogen-activated protein kinase 1 (45), E2F transcription factor 2 and Ras-related nuclear-binding protein 10 (46). Accordingly, it was hypothesized that hsa-miR-378a-5p may be upregulated in adipocyte differentiated HASCs, which was demonstrated in the present study. However, further experiments are required to confirm the role of this miRNA in HASC differentiation and its targeted interactions with RAC2. There are no previous reports on the roles of GDNF-AS1 in HASC differentiation, indicating it may also be a novel target identified by the present study. Hub genes CXCL10 in module 2 and EHHADH in module 6 were shown to be respectively regulated by hsa-miR-411-3p and hsa-miR-487a, being involved in inflammatory and amino acid metabolism pathways for HASC differentiation. As reported for LEP above, inflammation promotes the adipocyte differentiation of HASCs, whereas CXCL10 is a well-known pro-inflammatory chemokine (47). Therefore, CXCL10 may be upregulated in adipocyte differentiated HASCs, which was confirmed in the present study. EHHADH has been reported as a downstream target upregulated by PPAR (48). PPAR is an important marker in stimulating adipogenesis (12). EHHADH may also be expressed at a high level in adipocyte differentiated HASCs, which was in consistent with the present study. These two miRNAs regulating CXCL10 and EHHADH have not been demonstrated to be responsible for HASC differentiation, which highlights potential directions in future investigations. CCNB1, JUN and PTPRC were suggested to be important for adipocyte differentiation from HASCs according to the PPI network analysis. With reference to previous studies, high expression levels of CCNB1 (49) and JUN (50) may be positively associated with the proliferation of stem cells. Generally, the differentiation process can be executed only following weakening of the proliferation ability of stem cells. In the adipocyte differentiation of HASCs, CCNB1 and JUN may be downregulated, which was verified in the present study. PTPRC is also known as CD45, a JAK phosphatase, which negatively regulates cytokine receptor signaling via inhibiting the activity of STAT3 (51,52). According to the findings of LEP above, PTPRC may be downregulated for the adipocyte differentiation of HASCs, which was in line with the results of the present study.
There were some limitations in the present study. First, adipocyte differentiated cells were induced following different culture durations in the GSE57593, GSE25715 and GSE61302 datasets, which may lead to differences in the expression levels of the identified mRNAs, miRNAs and lncRNAs if the same samples were used for their detection. Second, the sample size of each dataset (GSE57593: Four undifferentiated HASCs and six adipocyte differentiated cells; GSE25715: Four undifferentiated HASCs and eight adipocyte differentiated cells: GSE61302: Five undifferentiated HASCs and 10 adipocyte differentiated cells) was small. Additional high-throughput sequencing experiments with larger samples are required to confirm the conclusions. Third, the present study comprised preliminary screening; however, further wet experiments, including quantitative-polymerase chain reaction analysis, western blotting, dual luciferase reporter assays, and knockout or overexpression in vitro and in vivo, are indispensable to confirm the expression levels of the identified target genes and validate the regulatory associations between DEMs and DELs/DEGs. In conclusion, the present study preliminarily identified several crucial DEGs (LEP, RAC2, CXCL10, EHHADH CCNB1, JUN and PTPRC), DEMs (has-miR-130b-5p and has-miR-23a-5p, has-miR-302d-3p, has-miR-378a-5p, hsa-miR-411-3p and hsa-miR-487a) and DELs (RP11-552F3.9, RP11-15A1.7 and GDNF-AS1) for inducing the adipogenic differentiation of HASCs. Among these, the RP11-552F3.9 (or RP11-15A1.7)-hsa-miR-302d-3p-LEP ceRNA interaction axes may be particularly important and represents a novel mechanism for the adipogenic differentiation of HASCs. Further in vitro and in vivo investigations are required to confirm their roles in breast reconstruction and augmentation.