Screening for genes, transcription factors and miRNAs associated with the myogenic and osteogenic differentiation of human adipose tissue-derived stem cells
- Authors:
- Published online on: October 25, 2016 https://doi.org/10.3892/ijmm.2016.2788
- Pages: 1839-1849
Abstract
Introduction
Human adipose tissue-derived stem cells (hASCs) are an attractive cell type for tissue engineering which may be harvested by direct excision or liposuction from human adipose tissue. Physiologically, hASCs are capable of differentiating into various lineages, such as adipocytes, osteoblasts, myocytes and chondrocytes (1,2). The ability of hASCs to undergo multilineage differentiation has attracted increasing interest in their use clinically and in regenerative medicine (3). A number of studies have suggested that hASCs possess significant potential for tissue rescue in multiple animal models, including heart failure, myocardial infarction, bone formation and wound healing, by differentiating into a variety of lineages (4–6).
Many factors have been reported to be involved in the mechanisms of hASC differentiation. Nutritional and hormonal signaling affects hASC differentiation in a negative or a positive manner, and the molecules involved in cell-matrix or cell-cell interactions play key roles in regulating the differentiation process (7–9). It is well known that fibroblast growth factor 2 (FGF2) inhibits the osteogenic differentiation of hASCs whereas it promotes chondrogenesis (10,11). Moreover, microRNA (miRNA or miR)-26a has been shown to modulate the late stage of osteoblast differentiation by targeting the transcription factor (TF) SMAD family member 1 (SMAD1) (4). The upregulation of miRNA-22 has been proved to promote the osteogenic differentiation of human adipose tissue-derived mesenchymal stem cells by suppressing histone deacetylase 6 (HDAC6) expression (12). Furthermore, hASCs are capable of differentiating into skeletal myocytes and cardiomyocytes under specific conditions (incubation in myogenic medium) (13,14). In vitro, sphingosylphosphorylcholine and transforming growth factor-β (TGF-β) induced the expression of smooth muscle-associated markers including α-smooth muscle actin, calponin and SM22 in hASCs (15,16). Numerous studies have been performed to reveal the molecular mechanisms controlling the differentiation of hASCs (7–16). However, the mechanisms responsible for the regulation of myocyte and osteocyte differentiation remain largely unknown.
Increasing evidence has proved that the conversion of hASCs into differentiated myocytes and osteocytes involves changes in gene expression which are mainly regulated by miRNAs and TFs (17,18). For instance, Luzi et al (4) showed that miR-26a expression was increased during hASC differentiation, whereas the expression of SMAD1 was complementary to that of miR-26a. In addition, Kim et al (17) reported that miR-196a regulates the differentiation and proliferation of hASCs by modulating the levels of the HOXC8 transcription factor.
To gain further insight into the molecular mechanisms responsible for the differentiation of hASCs into myocytes and osteocytes, we re-analyzed the microarray data GSE37329 through the identification of differentially expressed genes (DEGs) in hASC-derived myocytes and osteocytes compared with hASCs, as well as through functional annotation and protein-protein interaction (PPI) network construction. Furthermore, TFs and miRNAs targeting the DEGs were predicted and functionally analyzed.
Materials and methods
Gene datasets
The gene expression profile of GSE37329 was retrieved from the Gene Expression Omnibus (GEO) database available at http://www.ncbi.nlm.nih.gov/geo/ (19). This dataset was deposited by Berdasco et al (19) on October 3, 2013 and was based on GPL11532 platform (Affymetrix Human Gene 1.1 ST array, Santa Clara, CA, USA). A total of 7 samples were available for further study, including three hASC cell lines from healthy donors, two osteogenic lineages and two myogenic lineages which were all obtained through the in vitro induction of hASCs.
Data preprocessing
The raw expression data (Affymetrix CEL files) were firstly preprocessed by the Robust Multiarray Average (RMA) normalization approach of Bioconductor affy package in R (20) (http://www.bioconductor.org), which returned the expression signals of each probe as log 2 scale. When different probes were mapped to the same gene, the mean value of the probes was considered as the gene value. Subsequently, the probe serial numbers in the matrix were transformed into gene names using the platform R/Bioconductor note package of the dataset chip. The matrix consisting of 20,253 genes was finally acquired.
Screening of DEGs
To screen out the DEGs in the in vitro-obtained osteogenic and myogenic lineages derived from hASCs compared with the freshly isolated hASCs obtained from healthy donors, respectively, Linear Models for Microarray Data (Limma) package of Bioconductor (21) was applied in the comparisons (osteogenic lineages vs. hASCs and myogenic lineages vs. hASCs). Unadjusted P-values were calculated using the Student's t-test. Genes with P<0.05 and log 2|FC (fold change)| ≥1 were considered to be differentially expressed. Hierarchical cluster analysis with the eligible DEGs was then performed in order to identify clusters of samples and genes.
Functional annotation of the DEGs
Functional enrichment of the two sets of DEGs in the osteogenic and the myogenic lineages in vitro-induced from the hASCs was assessed based on the biological process (BP) category in Gene Ontology (GO) (22) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation terms (23). GO and KEGG signaling pathway analyses were performed using the GO Function package (version 1.14.0) in Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/GOFunction.html) (24), which conducted the standard hypergeometric test. A P-value <0.05 was considered to indicate a statistically significant difference.
PPI network construction
Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org/) is an online database which is comprised of more than 1,100 completely sequenced organisms and includes experimental as well as predicted interaction information (25). The up- and down-regulated genes in both sets of DEGs verified above were directly mapped to the STRING database in order to acquire significant PPI pairs which were previously verified by experiments, text mining and/or co-expressed analysis, respectively. Notable PPI pairs in which both of the genes were differentially expressed and the medium confidence was ≥0.4 were integrated to construct a PPI network. The network was visualized using CytoScape (26), available at http://www.cytoscape.org. Considering the complexity of PPI networks, we computed the degree of each node by measuring the numbers of links of the node in the network.
Computational identification of TFs
To determine the common mechanism responsible for the differentiation of hASCs into myocytes and osteocytes, DEGs shared in the osteogenic and the myogenic lineages were screened out. KEGG pathway enrichment analysis of the shared up- and downregulated genes was performed, respectively. P-values were calculated using hypergeometric distribution and a P-value <0.05 was considered to indicate a significant pathway.
To further explore the molecular mechanism, eukaryotic TFs for the shared and unshared DEGs in osteogenic and myogenic lineages were collected based on the the Encyclopedia of DNA Elements (ENCODE) data from the USCS Genome Browser (27) available at http://genome.ucsc.edu/. P-values were calculated using Fisher's exact test and adjusted using the Benjamini and Hochberg method to define the false discovery rate (FDR). Only the results with an FDR <5.5 E-06 were considered to be significant.
miRNAs-target gene interaction network construction
To better understand the function of miRNAs in regulating the differentiation of hASCs, miRNAs targeting the shared up- and downregulated DEGs screened above were predicted using the miRecords database (28) available at http://c1.accurascience.com/miRecords/ and the miRWalk database (29) available at http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/. The miRNA-target interactions that were presented in miRecords and/or miRWalk and verified by experiment were used for the construction of the miRNA-mRNA interaction network. The network was visualized using CytoScape and the degree of each miRNA node was also measured. Furthermore, the predicted miRNAs were annotated with BP terms in the GO database. P-values were calculated using hypergeometric distribution and GO terms with a P-value <0.05 were defined as significantly enriched.
Results
Screening of DEGs
Compared with the hASCs, 665 DEGs in myogenic lineages (370 up- and 295 downregulated genes) and 485 DEGs in osteogenic lineages (304 up- and 181 downregulated genes) were finally identified. The two sets of eligible DEGs were evaluated using unsupervised hierarchical clustering. As shown in Fig. 1, DEGs were found in different samples.
Annotating the biological functions of DEGs
To elucidate the functions of DEGs, the up- and downregulated genes in the in vitro-obtained myogenic and osteogenic lineages were mapped to BP terms in the GO database, and the top 10 GO terms are shown in Tables I and II, respectively. Briefly, the upregulated genes identified from the myogenic lineages were mainly involved in the regulation of multicellular organismal processes, inflammatory responses and cellular responses to chemical stimuli, whereas the downregulated genes were mainly involved in the regulation of multicellular organismal processes, single-multicellular organism processes, single-organism developmental processes and multicellular organismal development. On the other hand, the upregulated genes in the osteogenic lineages were mainly associated with responses to stimuli, regulation of multicellular organismal processes and regulation of localization, whereas the downregulated genes were mainly associated with anatomical structure development, system development and tissue development.
Table ITop 10 enriched GO terms in the BP category for both upregulated and downregulated differentially expressed genes in myocytes. |
Table IITop 10 enriched GO terms in the BP category for both upregulated and downregulated differentially expressed genes in osteocytes. |
KEGG pathway enrichment analysis was used to further understand the biological functions of the DEGs. Analysis of the myogenic lineages revealed that the upregulated genes mainly participated in neuroactive ligand-receptor interactions and drug metabolism-cytochrome P450 pathways (Table III), which was the same as the upregulated genes in the osteogenic lineages (Table IV). By contrast, the downregulated genes in the myogenic lineages were mainly enriched in pathways in cancer, ECM-receptor interactions and focal adhesion (Table III), while the downregulated genes in the osteogenic lineages were mainly involved in the TGF-β signaling pathway and pathways in cancer (Table IV).
Table IIITop 10 enriched KEGG pathways of upregulated and downregulated differentially expressed genes in myocytes. |
Table IVTop 10 enriched KEGG pathways of upregulated and downregulated differentially expressed genes in osteocytes. |
PPI network construction
There were 363 nodes and 996 edges in the PPI network of DEGs in myogenic lineages (Fig. 2). Based on the number of links, the top 8 nodes were identified as vascular endothelial growth factor A (VEGFA; degree, 57), interleukin (IL)6 (degree, 49), FBJ murine osteosarcoma viral oncogene homolog (FOS; degree, 41), FGF2 (degree, 37), jun proto-oncogene (JUN; degree, 35), IL1B (degree, 34), phosphoinositide-3-kinase, regulatory subunit 1 (PIK3R1; degree, 28) and nerve growth factor (NGF; degree, 27). In addition, 246 nodes and 520 edges constructed the PPI network of DEGs in the osteogenic lineages (Fig. 3), and the top 8 nodes were VEGFA (degree, 40), endothelin 1 (EDN1; degree, 24), IL1B (degree, 24), FGF2 (degree, 22), insulin-like growth factor 1 (IGF1; degree, 21), leptin (LEP; degree, 19), NGF (degree, 18) and matrix Gla protein (MGP; degree, 14). Considering the higher degree of VEGFA, IL1B, FGF2 and NGF in both networks, we hypothesized that these four genes play similar roles in the differentiation of hASCs into the two cell types.
Enrichment analysis of TFs
To further explore the molecular mechanisms responsible for the differentiation of hASCs into myocytes and osteocytes, the shared and unshared DEGs in the in vitro-obtained osteogenic and myogenic lineages were analyzed, respectively (Fig. 4). The results of the KEGG enrichment analysis revealed that 205 shared upregulated genes were mainly involved in metabolism-related pathways, including drug metabolism and tyrosine metabolism, and 128 shared downregulated genes were significantly enriched in the TGF-β signaling pathway (Fig. 4 and Table V).
Table VEnriched KEGG pathways of shared genes between two groups (myocytes vs. hASCs and osteocytes vs. hASCs). |
The relationship between TFs and DEGs may aid in defining regulatory controls. Finally, a total of 27 TFs targeting the shared upregulated genes were predicted. In addition, 11 TFs, which are all involved in the targeting of the shared upregulated genes, were predicted to target the shared downregulated genes, including RAD21, zinc finger protein 263 (ZNF263), signal transducer and activator of transcription 3 (STAT3), RE1-silencing transcription factor (REST, also known as NRSF), tripartite motif containing 28 (TRIM28, also known as KAP1), GATA binding protein 2 (GATA2), CCCTC-binding factor (CTCF), E1A binding protein p300 (EP300), early growth response 1 (EGR1), CCAAT/enhancer binding protein (C/EBP), beta (CEBPB) and MYC-associated factor X (MAX). The expression of these 11 TFs in the three sample types is shown in Fig. 5. The results revealed that the expression of EGR1 was significantly higher in the hASCs than in the osteogenic and the myogenic lineages. Conversely, the expression of STAT3 was significantly lower in the hASCs than in the osteogenic and the myogenic lineages. Differential expression of the other 9 TFs among the three cell types was not found.
In addition, 26 and 21 TFs were predicted to regulate the unshared up- and downregulated genes in the myogenic lineages, respectively. In the osteogenic lineages, 11 TFs were predicted to target the upregulated genes whereas only RAD21 was found to regulate the downregulated genes. Moreover, RAD21 was also included among the TFs regulating unshared downregulated genes in the myogenic lineages, including VEGFA and SMAD family member 6 (SMAD6).
MiRNA-DEG interaction analysis
A total of 66 and 98 miRNA-mRNA pairs were finally screened out for the shared up- and downregulated genes in the osteogenic and the myogenic lineages to construct an miRNA-target gene interaction network, respectively (Fig. 6). In the network, hsa-miR-1, with the highest degree, regulated 20 common genes differentially expressed in the two cell types, including Forkhead box P1 (FOXP1), E2F transcription factor 7 (E2F7), chemokine (C-C motif) ligand 13 (CCL13), monocyte to macrophage differentiation-associated (MMD) and pyruvate dehydrogenase kinase, isozyme 4 (PDK4). Moreover, the shared upregulated genes FOXO1, TLR4 and downregulated gene IL1B were regulated by >9 miRNAs during the differentiation of hASCs, and shared downregulated GATA6 was regulated by four hsa-miR-181 family members namely miR-181a, miR-181b, miR-181c and miR-181d.
Further, functional annotation revealed that the shared upregulated genes targeted by the predicted miRNAs were mainly involved in immune response-related BPs, including detection of fungus, and host defense responses. By contrast, the shared downregulated genes were significantly enriched in response to ozone, smooth muscle adaptation and regulation of myosin light chain kinase activity (Table VI).
Discussion
In the present study, we aimed to extend our understanding of the molecular mechanisms responsible for the differentiation of hASCs into myocytes and osteocytes. We found that four proteins encoded by VEGFA, FGF2, NGF and IL1B were differentially expressed in the myogenic and the osteogenic lineages and presented in the PPI network at relatively high degrees. Moreover, the TF RAD21 was predicted to target both shared up- and downregulated genes as well as specific downregulated genes in the myogenic and the osteogenic lineages. In addition, miRNA-DEG interaction analysis revealed that hsa-miR-1 regulated the most shared DEGs in the two lineages, such as FOXP1 and CCL13.
Previous findings have suggested that hASCs secrete significant numbers of angiogenic factors, including VEGFA (30). VEGFA is known to promote both angiogenesis and osteogenesis (31,32). More recently, VEGFA has been proved to play an integral role in the crosstalk between endothelial cells and osteoblasts and is also considered as being of great importance for vascularization (33). VEGFA has been found to increase bone formation, promote osteoblast differentiation and inhibit the apoptosis of osteoblasts (32,34). In addition, Song et al have identified VEGF as a critical factor in cardiomyogenesis in hASCs (35). FGF2, a member of the FGF family, has been identified as a major candidates for the regulation of self-renewal in human embryonic stem cells (36,37). FGF2 may also be important in increasing the lifespan of bone marrow stromal cells and for supporting proliferation as well as the chondrogenic and osteogenic differentiation potential (38,39). Moreover, previous studies have shown that the exposure of hASCs to FGF2 led to the enhancement of chondrogenic lineage differentiation and the inhibition of osteogenic lineage differentiation, as well as the stimulation of adipogenic differentiation (10,40,41). Notably, IL1B, which encodes an inflammatory cytokine, has been shown to be suppressed by mesenchymal stem cell (MSC) transplantation at the transcriptional and the post-transcriptional levels in myocardial infarction (42). NGF is also reported to be associated with many pathologic and physiologic processes, such as differentiation of stem cells (43). In this study, VEGFA, FGF2, IL1B and NGF were found to be downregulated in the myogenic and osteogenic lineages compared with hASCs and connected with relatively more DEGs in the PPI networks, which supports the hypothesis that there may be a correlation between these genes and the differentiation of hASCs.
Additionally, TFs and miRNAs are essential regulatory molecules after DNA replication involved in the differentiation of hASCs. The TF RAD21 has been proved to be associated with the maintenance of embryonic stem cell identity through association with the pluripotency transcriptional network (44). Consistent with our analysis, chromatin immunoprecipitation analysis was used in a previous study to confirm that VEGFA and SMAD6 expression is regulated by RAD21 (45). SMAD6, an inhibitory SMAD, has been reported to inhibit the TGF-β signaling pathway that suppresses osteoblast and myogenic differentiation (46). The data from the present study revealed that RAD21 mediates the differentiation of hASCs by regulating the expression of VEGFA and SMAD6.
In a previous study, miR-1 was shown to strongly enhance myogenesis following the transfection of myoblasts with hsa-miR-1 by modulating skeletal muscle proliferation and differentiation (47). More importantly, hsa-miR-1 is required for smooth muscle cell lineage differentiation from embryonic stem cells by binding with the 3′ untranslated region of the gene encoding Kruppel-like factor 4 (48). Following the construction of an miRNA-target gene interaction network, we found that miR-1 targeted FOXP1 in the differentiation of hASCs into osteocytes and myocytes, which is in agreement with the results of a previous study (49). Additionally, it was demonstrated that knockdown of FOXP1 suppressed the self-renewal capacity of MSCs and reduced the osteogenic potential (50). In the hASC-derived myocytes and osteocytes, CCL13 was upregulated which is consistent with the findings of a previous study revealing a 12-fold change after culturing hASCs with proinflammatory cytokines (51). Our results suggest that miR-1 modulates the differentiation of hASCs into myocytes and osteocytes by regulating FOXP1 and CCL13.
In conclusion, we performed a comprehensive bioinformatics analysis of the expression profiles of in vitro-induced osteogenic and myogenic lineages and hASC cell lines from healthy donors. There may be a correlation between four shared downregulated genes in the two lineages, VEGFA, FGF2, IL1B and NGF, and the differentiation of hASCs. Notably, the TF RAD21 and hsa-miR-1 may play important roles in regulating the expression of differentiation-associated genes. This study may provide new insight into the underlying molecular mechanisms of hASC differentiation, which may help to repair and reconstruct damaged organs. However, further studies are warranted to confirm these results and to clarify their roles in the differentiation of hASCs.
Acknowledgments
The present study was supported by the Liaoning Province Science and Technology Research Project (no. 2013225220).
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