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Screening for genes, miRNAs and transcription factors of adipogenic differentiation and dedifferentiation of mesenchymal stem cells

Journal of Orthopaedic Surgery and Research volume 18, Article number: 46 (2023) Cite this article

Abstract

Background

The purpose of present study was to reveal the molecular mechanisms responsible for both adipogenic differentiation and dedifferentiation of mesenchymal stem cells (MSCs).

Methods

Microarray data GSE36923 were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between adipogenically differentiated cells vs undifferentiated bone marrow-derived MSCs, adipogenically differentiated cells vs dedifferentiated cells samples at day 7 and adipogenically differentiated cells vs dedifferentiated cells samples at day 35 were screened, and overlapped DEGs across the three groups were analyzed. The underlying functions of the upregulated and downregulated DEGs were investigated by Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. The protein–protein interaction network was constructed, and hub genes were obtained subsequently. Hub genes were verified with GSE113253 dataset, and then miRNA-gene network and TF-gene network were constructed.

Results

A total of 284 upregulated DEGs and 376 downregulated DEGs overlapped across the three groups. PPAR signaling pathway, AMPK signaling pathway, insulin signaling pathway, carbon metabolism, pyruvate metabolism, fatty acid metabolism, regulation of lipolysis in adipocytes, biosynthesis of amino acids, citrate cycle (TCA cycle) and 2-Oxocarboxylic acid metabolism were the top 10 pathways involving in the upregulated DEGs, and graft-versus-host disease, allograft rejection, viral myocarditis, cell adhesion molecules, phagosome, type I diabetes mellitus, antigen processing and presentation, autoimmune thyroid disease, intestinal immune network for IgA production and rheumatoid arthritis were the top 10 pathways in downregulated DEGs. After validation, the 8 hub genes were IL6, PPARG, CCL2, FASN, CEBPA, ADIPOQ, FABP4 and LIPE. Ten key miRNAs were hsa-mir-27a-3p, hsa-mir-182-5p, hsa-mir-7-5p, hsa-mir-16-5p, hsa-mir-1-3p, hsa-mir-155-5p, hsa-mir-21-3p, hsa-mir-34a-5p, hsa-mir-27a-5p and hsa-mir-30c-5p, and 10 key TFs were TFDP1, GTF2A2, ZNF584, NRF1, ZNF512, NFRKB, CEBPG, KLF16, GLIS2 and MXD4.

Conclusion

Our study constructed miRNA-gene network and TF-gene network involved in both adipogenic differentiation and dedifferentiation of MSCs, contributing to enhancing the efficiency of MSCs transplantation in soft tissue defect repair and developing more potent remedies for adipogenesis-related skeletal disorders.

Background

Mesenchymal stem cells (MSCs) are multipotent stromal cells that could differentiate into adipogenic, osteogenic, chondrogenic and other lineages[1]. MSCs derive from many tissues, such as bone marrow, umbilical cord, placenta, skin and fat[2]. MSCs has attracted increasing interest in regenerative medicine for easy isolation, rapid proliferation, low immunogenicity and multilineage differentiation potential[3]. Adipogenic differentiation is an important direction of MSCs differentiation for that it plays an important role in soft tissue defect repair in traumatology[4]. Moreover, excessive adipogenic differentiation of MSCs is related to skeletal diseases such as osteoporosis and osteonecrosis[5, 6]. Therefore, regulation of adipogenic differentiation of MSCs is of great importance for clinical work in orthopedics.

Adipogenic differentiation has been regarded as an irreversible terminal process for a long time[7]. However, Sugihara et al.[8, 9] first found that mature adipocytes could dedifferentiated into fibroblast-like cells using ceiling culture technology. These fibroblast-like cells, termed dedifferentiated fat cells, possess stem cell properties, and can re-differentiate to multiple mesenchymal cell lineages[10]. Adipocyte differentiation and dedifferentiation are two opposite processes, however, the mechanisms responsible for adipocyte dedifferentiation remain largely unknown. Generally, genes and pathways that are required for adipogenesis are also essential for dedifferentiation[11]. The mechanism involved in both adipogenic differentiation and dedifferentiation may become a key to regulating MSC adipogenesis.

Adipogenic differentiation and dedifferentiation may involve signaling pathways including PPARγ pathway[12, 13], Wnt/β-catenin pathway[14, 15], transforming growth factor-β pathway[16, 17], and Notch signaling pathway[13, 18]. Moreover, microRNAs such as miR-377-3p[19] and miR-431[20] and transcription factors (TFs) like FOXN1[21] and TEAD4[22] may also be involved in the adipogenic differentiation. The role of microRNAs and TFs in adipocyte dedifferentiation, however, remains largely unknown. Therefore, molecular mechanism of MSCs adipogenic differentiation and dedifferentiation is complicated, and it is necessary to elucidate that for further application.

To explore the molecular mechanisms responsible for the adipogenic differentiation and dedifferentiation of MSCs, we screened the differentially expressed genes (DEGs) of microarray data GSE36923. Subsequently, comprehensive bioinformatics methods were used to analyze the functions and potential pathways of DEGs. In addition, protein–protein interaction (PPI) network was performed to elucidate the underlying molecular mechanisms. Hub genes were calculated and then validated using dataset GSE113253. Furthermore, TFs and miRNAs targeting the hub genes were predicted, and miRNA-gene network and TF-gene network were constructed.

Methods

Microarray data

The gene expression profile of GSE36923 was downloaded from Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/). GSE36923 was based on the Affymetrix GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array, Santa Clara, CA, USA). The GSE36923 dataset contained 12 samples, including 3 undifferentiated bone marrow-derived MSCs (BMSCs) samples, 3 adipogenically differentiated cells samples at d 15, 3 dedifferentiated cells samples at d 7 and 3 dedifferentiated cells samples at d 35. MSCs features, adipogenic differentiation and dedifferentiation method were showed as Ullah, M et al. described[23].

Screening of differentially expressed genes

NetworkAnalyst (https://www.networkanalyst.ca/NetworkAnalyst/), an online platform of transcriptome profiling, network analysis, and meta-analysis for gene expression data[24], was used to screen DEGs from the gene expression data. Data was normalized by variance stabilizing normalization followed by quantile normalization. Box plots for expression data were generated. DEGs (adipogenically differentiated cells vs undifferentiated MSCs, adipogenically differentiated cells vs dedifferentiated cells samples at d 7 and adipogenically differentiated cells vs dedifferentiated cells samples at d 35) were screened with the criteria of adjust P value < 0.05 and |logFC (fold change)|≥ 1. Overlapped DEGs across the three groups were analyzed and visualized using R software (version 3.6.3; https://www.r-project.org/) and ggplot2 package (version 3.3.3). Heatmap indicating all overlaps between up- and downregulated genes for all comparisons was visualized using R software (version 3.6.3; https://www.r-project.org/) and pheatmap package (version 1.0.12).

Gene ontology enrichment and KEGG pathway analysis

Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of upregulated and downregulated DEGs were performed using R software (version 3.6.3; https://www.r-project.org/) and clusterProfiler packages (version 3.14.3)[25]. P value < 0.05 was considered to indicate a statistical significance.

PPI network construction and hub genes selection

The PPI network was constructed using The Search Tool for the Retrieval of Interacting Genes/Proteins (http://string-db.org)[26] with the threshold of minimum required interaction score > 0.4. Network and node file were generated and further analyzed using Cytoscape software (version 3.8.2; www.cytoscape.org). Cytoscape's plug-in cytoHubba was used to identify top 100 key genes by degree algorithm, and the PPI network of top 100 key genes was constructed.

Hub genes validation

Top ten hub genes were further validated in GSE113253, downloaded from the GEO database. The dataset was based on the platform of GPL18460 (Illumina HiSeq 1500), in which 3 undifferentiated BMSCs and 3 adipogenically differentiated cells samples at d 14 were selected. MSCs features and adipogenic differentiation method were showed as Rauch et al. described [27]. DEGs were identified with Student’s t-test. P value of < 0.05 was considered statistically significant.

MiRNA-target gene network and TF-target gene network construction

After validation, eight genes (IL6, PPARG, CCL2, FASN, CEBPA, ADIPOQ, FABP4, LIPE) were selected as target genes. MiRNA-target gene network and TF-target gene network were analyzed using TarBase (version 8.0; https://dianalab.e-ce.uth.gr/html/diana/web/index.php?r=tarbasev8) and ENCODE software package (http://cistrome.org/BETA/) in NetworkAnalyst, and were visualized by employing Cytoscape software. Top 10 key miRNAs and TFs were identified with cytoHubba by degree algorithm.

Results

Identification of DEGs during MSC adipogenesis and dedifferentiation

After normalization (Fig. 1), 659, 869 and 686 DEGs were upregulated and 933, 1258 and 1042 DEGs were downregulated in adipogenically differentiated cells vs undifferentiated MSCs, adipogenically differentiated cells vs dedifferentiated cells samples at d 7 and adipogenically differentiated cells vs dedifferentiated cells samples at d 35, respectively. A total of 284 upregulated DEGs and 376 downregulated DEGs overlapped across the three groups (Fig. 2) and were shown in the heatmap (Fig. 3).

Fig. 1
figure 1

Box plots before normalization (A) and after normalization (B). The vertical axis is the name of samples while the horizontal axis stands for the values of expression. The black line stands for the median of data and represents the normalization degree. After normalization, black line in each group was almost collinear, which indicates an excellent degree of normalization

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Fig. 2
figure 2

The Venn diagram of overlapped upregulated (A) and downregulated (B) differentially expressed genes of adipogenically differentiated cells vs undifferentiated MSCs, adipogenically differentiated cells vs dedifferentiated cells samples at d 7 and adipogenically differentiated cells vs dedifferentiated cells samples at d 35

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Fig. 3
figure 3

Heatmap indicating all overlap genes. Red represents high expression, and the deeper the red color, a higher expression value. Green represents low expression, and a deeper green color, a lower the expression value

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GO enrichment and KEGG pathway analysis

In order to elucidate the functions of overlapped DEGs, upregulated and downregulated genes were further analyzed for functional enrichment, respectively. With regard to the molecular function, upregulated DEGs (Fig. 4A) mainly showed enrichment in the coenzyme binding, oxidoreductase activity, acting on CH-OH group of donors, oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor, lyase activity, NAD binding and oxidoreductase activity, acting on the CH-CH group of donors, and downregulated DEGs (Fig. 4B) mainly showed enrichment in the peptide antigen binding, metalloendopeptidase inhibitor activity, extracellular matrix structural constituent, MHC class II receptor activity, collagen binding and glycosaminoglycan binding. For the cellular component ontology (Fig. 4A), commonly enriched categories were associated with mitochondrial matrix, lipid droplet, mitochondrial inner membrane, peroxisome, microbody and organelle outer membrane in upregulated DEGs, and were associated with MHC protein complex, integral component of lumenal side of endoplasmic reticulum membrane, lumenal side of endoplasmic reticulum membrane, MHC class II protein complex, collagen-containing extracellular matrix and ER to Golgi transport vesicle membrane in downregulated DEGs (Fig. 4B). For the biological process (Fig. 5A), fatty acid metabolic process, alcohol metabolic process, response to peptide hormone, response to insulin, regulation of small molecule metabolic process and regulation of lipid metabolic process were the commonly enriched categories in upregulated DEGs, and interferon-gamma-mediated signaling pathway, response to interferon-gamma, antigen processing and presentation of peptide antigen, antigen processing and presentation of exogenous peptide antigen, tissue remodeling and cellular response to interferon-gamma in downregulated DEGs (Fig. 4B). PPAR signaling pathway, AMPK signaling pathway, insulin signaling pathway, carbon metabolism, pyruvate metabolism, fatty acid metabolism, regulation of lipolysis in adipocytes, biosynthesis of amino acids, citrate cycle (TCA cycle) and 2-Oxocarboxylic acid metabolism were the top 10 pathways involving in the upregulated DEGs (Fig. 5A), and graft-versus-host disease, allograft rejection, viral myocarditis, cell adhesion molecules, phagosome, type I diabetes mellitus, antigen processing and presentation, autoimmune thyroid disease, intestinal immune network for IgA production and rheumatoid arthritis in downregulated DEGs (Fig. 5B).

Fig. 4
figure 4

Gene Ontology (GO) enrichment results of overlapped upregulated (A) and downregulated (B) differentially expressed genes (DEGs). Bubble charts show GO enrichment significance items of DEGs in three functional groups: molecular function (MF), cellular component (CC) and biological process (BP). The x-axis label represents the gene ratio, and the y-axis label represents GO terms. The size of circle stands for gene count and the color of circle stands for adjusted p value

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Fig. 5
figure 5

Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis of overlapped upregulated (A) and downregulated (B) differentially expressed genes (DEGs). Bubble charts show enrichment of DEGs in signaling pathways. The x-axis label represents the gene ratio, and the y-axis label represents pathway. The size and color of circle stand for gene count in pathway and adjusted p value, respectively

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Protein–protein interaction network

The protein–protein interaction network of top 100 key genes is shown in Fig. 6. The top 10 high-degree hub nodes were IL6, PPARG, CCL2, FASN, SREBF1, SCD, CEBPA, ADIPOQ, FABP4 and LIPE.

Fig. 6
figure 6

Protein–protein interaction network. The circle represents genes and the line indicates the interactions among genes. A thicker line stands for a higher edge confidence, and a larger node size stands for a higher degree

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Validation of hub genes expression

Ten hub genes were validated using the GSE113253 dataset. For upregulated hub genes, compared to undifferentiated MSCs, the expressions of PPARG, FASN, CEBPA, ADIPOQ, FABP4 and LIPE were consistently increased in adipogenically differentiated cells (Fig. 7B, D, G–J). For downregulated hub genes, the expression of IL6 and CCL2 also decreased during adipogenesis (Fig. 7A, C). Results demonstrated that IL6, PPARG, CCL2, FASN, CEBPA, ADIPOQ, FABP4 and LIPE were pivotal genes involved in adipogenic differentiation and dedifferentiation of MSCs (Table 1).

Fig. 7
figure 7

Validation of top 10 hub genes in dataset GSE113253. Expression levels of PPARG (B), FASN (D), CEBPA (G), ADIPOQ (H), FABP4 (I) and LIPE (J) were significantly upregulated in adipogenically differentiated cells. Expression levels of IL6 (A) and CCL2 (C) were significantly downregulated in adipogenically differentiated cells. There were no statistically significant differences in the expression levels of SREBF1 (E) and SCD (F) between undifferentiated MSCs and adipogenically differentiated cells

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Table 1 Hub genes and their descriptions
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MiRNA-target gene interaction network construction

174 miRNAs targeting 8 target hub genes were obtained in NetworkAnalyst using TarBase (version 8.0), and an additional table file shows this in more detail [see Additional file 1]. The miRNA-target gene interaction network was constructed with cytoscape (Fig. 8). According to the nodes’ degree value, top 10 key miRNAs were hsa-mir-27a-3p, hsa-mir-182-5p, hsa-mir-7-5p, hsa-mir-16-5p, hsa-mir-1-3p, hsa-mir-155-5p, hsa-mir-21-3p, hsa-mir-34a-5p, hsa-mir-27a-5p and hsa-mir-30c-5p.

Fig. 8
figure 8

The miRNA-target gene interaction network. The circle represents genes and the line indicates the interactions among genes. A thicker line stands for a higher edge betweenness, and a larger node size stands for a higher degree

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TF-target gene interaction network construction

135 TFs targeting 8 hub genes were obtained in NetworkAnalyst using ENCODE software package, and an additional table file shows this in more detail [see Additional file 2]. The TF-target gene interaction network was also constructed with cytoscape (Fig. 9). According to the nodes’ degree value, top 10 key TFs were TFDP1, GTF2A2, ZNF584, NRF1, ZNF512, NFRKB, CEBPG, KLF16, GLIS2 and MXD4.

Fig. 9
figure 9

The TF-target gene interaction network. The circle represents genes and the line indicates the interactions among genes. A thicker line stands for a higher edge betweenness, and a larger node size stands for a higher degree

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Discussion

MSCs are promising seed cells in tissue engineering. These cells are usually applied in restoring large-sized soft tissue defects resulting from trauma, musculoskeletal tumor resection and congenital defects for their adipogenetic ability [28]. However, adipocyte dedifferentiation would occur after transplantation [29]. Moreover, MSCs adipogenesis is correlated with diseases like osteoporosis and osteonecrosis. Complete elucidation of the mechanism underlying the adipogenetic differentiation and dedifferentiation could contribute to designing better seed cells based on MSCs in tissue engineering and discovering more effective treatments for skeletal diseases related to adipogenesis [11].

In this study, we analyzed DEGs of adipogenically differentiated MSCs and undifferentiated MSCs, adipogenically differentiated MSCs and dedifferentiated MSCs at d 7, and adipogenically differentiated MSCs vs dedifferentiated MSCs at d 35, respectively. Overlapped DEGs across the three groups contained 284 upregulated genes and 376 downregulated genes. GO and KEGG pathway analyses were carried out to find the interactions of DEGs. For the upregulated DEGs, pathway enrichment analysis revealed that PPAR signaling pathway was significantly enriched. PPAR-γ has been reported to be one of the important genetic factors in the MSCs adipogenic differentiation and dedifferentiation, and PPAR signaling pathway plays a role in the regulation of adipocyte differentiation and dedifferentiation [11, 30,31,32]. For the downregulated DEGs, pathway enrichment analysis revealed that the graft-versus-host disease (GVHD) was significantly enriched. Qi et al. [33] revealed that the adipogenic differentiation capacity was decreased in the active chronic GVHD MSCs compared with no-cGVHD MSCs. The adipogenesis of MSCs may be negatively associated with GVHD disease, which was in line with the results of the present study.

After PPI network construction and validation, we found 8 hub genes including IL6, PPARG, CCL2, FASN, CEBPA, ADIPOQ, FABP4 and LIPE. PPARG, FASN, SREBF1, SCD, CEBPA, ADIPOQ, FABP4 are key adipogenic markers, and activation expression of these genes could enhance the adipogenic differentiation of MSCs [34,35,36,37,38]. PPARG, CEBPA, ADIPOQ and FABP4 are involved in adipogenesis, and FASN is involved in lipogenesis. Ullah et al. [23] had confirmed that the expression of ADIPOQ and FABP4 upregulated at adipogenic differentiation d 15 compared to undifferentiated MSCs d 0, and downregulated during dedifferentiation. LIPE, a gene related to lipolysis, plays a key role in the regulation of adipose tissue deposition [39, 40]. Yi et al. [41] found that the expression of ADIPOQ, CEBPA, FABP4, FASN AND LIPE were significantly higher during adipogenesis of MSCs by the RNA-Seq technique, which is consistent with our results. Liao et al. [42] found that the expression of PPARG, CEBPA and ADIPOQ was significantly downregulated in vivo dedifferentiation of adult adipose cells. Cote et al. [43] explored the changes in gene expression profile during human mature adipocyte dedifferentiation, they also found that the expression of LIPE, ADIPOQ, CEBPA AND FABP4 decreased when human mature adipocyte dedifferentiated from d 7 to d 12. However, they used ceiling culture technique for adipocyte dedifferentiation, which is different with our study. IL6 is a known regulator of adipose homeostasis in obesity and is high secreted from adipose tissue [44, 45]. It involves in obesity-associated metabolic complications [45]. Moreover, IL6 involves in differentiation of MSCs. IL6 could enhance osteogenetic and myogenetic differentiation of MSCs [46,47,48]. Huang et al. [49] found that IL6 potentiated BMP-2-induced osteogenesis and adipogenesis of BMSCs by amplifying BMPR1A-mediated BMP/Smad and p38 MAPK pathways, respectively. CCL2 is a chemokine that plays an important role in inflammation and indirectly involves in the process of adipogenesis via inflammatory responses. Zhu et al. [50] revealed that CCL2 directly promoted adipogenesis of MSCs in vitro possibly by enhancing AKT phosphorylation.

Among these 10 hub miRNAs, we found that the interactions between adipogenesis and hub miRNAs hsa-mir-21-3p, hsa-mir-27a-5p and hsa-mir-30c-5p have not been reported. MiR-27-3p exerts an inhibitory effect on adipogenesis by repressing PPARγ [51, 52]. Ding et al. [53] found that circPTK2 promoted lipolysis and inhibited adipogenesis through binding to miR-182-5p. Chen et al. [54] found that CDR1as-miR-7-5p-WNT5B axis might play crucial role in adipogenic/osteogenic differentiation disorder of BMSCs from steroid-induced osteonecrosis of the femoral head patients. Xu et al. [55] found that miR-16-5p promoted adipocyte differentiation by suppressing EPT1. Gu et al. [56] found that miR-1-3p was upregulated during osteogenesis but downregulated during adipogenesis of mouse MSCs. Du et al. [57] found that hsa-miR-1-3p was one of hub miRNAs coregulated both osteogenic and adipogenic differentiation. Eseberri et al. [58] found that resveratrol and glucuronide metabolites inhibited adipogenesis through upregulating miR-155 expression. Meruvu et al. [59] found that butyl benzyl phthalate promoted adipogenesis via the miRNA-34a-5p signaling pathway.

Among these 10 hub TFs, we found that the interactions between adipogenesis and hub TFs TFDP1, GTF2A2, ZNF584, ZNF512, NFRKB and GLIS2 have not been reported. Xue et al. [60] found that long isoforms of NRF1 was a negative regulator of PPARγ expression to suppress adipogenesis. CEBPG is one of potential TFs involved in adipogenesis[61]. Jang et al.[62] found that KLF16 inhibited adipogenesis through downregulating PPARγ expression. However, Cui et al. [63] found that when the FGF1 upregulated, adipocytes accumulation accelerated, while the expression of KLF16 increased significantly at the same time. Li et al. [64] found that over-expression of MXD4 could inhibit adipogenesis of human adipose-derived stem cells.

Conclusions

In conclusion, we have identified key genes, miRNAs and TFs involved in both adipogenic differentiation and dedifferentiation of MSCs by bioinformatical analyses and constructed the miRNA-gene network and TF-gene network. These findings could improve our understanding of adipogenic differentiation and dedifferentiation of stem cells, contributing to enhancing the efficiency of MSCs transplantation in soft tissue defect repair and developing more potent remedies for adipogenesis-related skeletal disorders.

Availability of data and materials

The datasets supporting the conclusions of this article are available in the Gene Expression Omnibus repository, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36923 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113253.

Abbreviations

MSCs:

Mesenchymal stem cells

DEGs:

Differentially expressed genes

PPI:

Protein–protein interaction

BMSCs:

Bone marrow-derived mesenchymal stem cells

GO:

Gene ontology

KEGG:

Kyoto encyclopedia of genes and genomes

GVHD:

Graft-versus-host disease

References

  1. Han Y, Li X, Zhang Y, et al. Mesenchymal stem cells for regenerative medicine. Cells. 2019;8.

  2. da Silva ML, Chagastelles PC, Nardi NB. Mesenchymal stem cells reside in virtually all post-natal organs and tissues. J Cell Sci. 2006;119:2204–13.

    Article  Google Scholar 

  3. Chu DT, Phuong TNT, Tien NLB, et al. An update on the progress of isolation, culture, storage, and clinical application of human bone marrow mesenchymal stem/stromal cells. Int J Mol Sci. 2020;21.

  4. Liu X, Li Z, Liu H, et al. Flufenamic acid inhibits adipogenic differentiation of mesenchymal stem cells by antagonizing the PI3K/AKT signaling pathway. Stem Cells Int. 2020;2020:1540905.

    Article  Google Scholar 

  5. Lin Z, He H, Wang M, et al. MicroRNA-130a controls bone marrow mesenchymal stem cell differentiation towards the osteoblastic and adipogenic fate. Cell Prolif. 2019;52: e12688.

    Article  Google Scholar 

  6. Sheng HH, Zhang GG, Cheung WH, et al. Elevated adipogenesis of marrow mesenchymal stem cells during early steroid-associated osteonecrosis development. J Orthop Surg Res. 2007;2:15.

    Article  Google Scholar 

  7. Liu S, Wang L, Ling D, et al. Potential key factors involved in regulating adipocyte dedifferentiation. J Cell Physiol. 2022;237:1639–47.

    Article  CAS  Google Scholar 

  8. Sugihara H, Yonemitsu N, Miyabara S, et al. Proliferation of unilocular fat cells in the primary culture. J Lipid Res. 1987;28:1038–45.

    Article  CAS  Google Scholar 

  9. Sugihara H, Yonemitsu N, Miyabara S, et al. Primary cultures of unilocular fat cells: characteristics of growth in vitro and changes in differentiation properties. Differentiation. 1986;31:42–9.

    Article  CAS  Google Scholar 

  10. Matsumoto T, Kano K, Kondo D, et al. Mature adipocyte-derived dedifferentiated fat cells exhibit multilineage potential. J Cell Physiol. 2008;215:210–22.

    Article  CAS  Google Scholar 

  11. Song T, Kuang S. Adipocyte dedifferentiation in health and diseases. Clin Sci (Lond). 2019;133:2107–19.

    Article  CAS  Google Scholar 

  12. Yuan Z, Li Q, Luo S, et al. PPARγ and Wnt signaling in adipogenic and osteogenic differentiation of mesenchymal stem cells. Curr Stem Cell Res Ther. 2016;11:216–25.

    Article  CAS  Google Scholar 

  13. Bi P, Yue F, Karki A, et al. Notch activation drives adipocyte dedifferentiation and tumorigenic transformation in mice. J Exp Med. 2016;213:2019–37.

    Article  CAS  Google Scholar 

  14. Ambele MA, Dhanraj P, Giles R, et al. Adipogenesis: a complex interplay of multiple molecular determinants and pathways. Int J Mol Sci. 2020;21.

  15. Gustafson B, Smith U. Activation of canonical wingless-type MMTV integration site family (Wnt) signaling in mature adipocytes increases beta-catenin levels and leads to cell dedifferentiation and insulin resistance. J Biol Chem. 2010;285:14031–41.

    Article  CAS  Google Scholar 

  16. Margoni A, Fotis L, Papavassiliou AG. The transforming growth factor-beta/bone morphogenetic protein signalling pathway in adipogenesis. Int J Biochem Cell Biol. 2012;44:475–9.

    Article  CAS  Google Scholar 

  17. Côté JA, Lessard J, Pelletier M, et al. Role of the TGF-β pathway in dedifferentiation of human mature adipocytes. FEBS Open Bio. 2017;7:1092–101.

    Article  Google Scholar 

  18. Liu MC, Logan H, Newman JJ. Distinct roles for Notch1 and Notch3 in human adipose-derived stem/stromal cell adipogenesis. Mol Biol Rep. 2020;47:8439–50.

    Article  CAS  Google Scholar 

  19. Li X, Yang Y, Yan R, et al. miR-377-3p regulates adipogenic differentiation of human bone marrow mesenchymal stem cells by regulating LIFR. Mol Cell Biochem. 2018;449:295–303.

    Article  CAS  Google Scholar 

  20. Wang Y, Yang L, Liu X, et al. miR-431 inhibits adipogenic differentiation of human bone marrow-derived mesenchymal stem cells via targeting insulin receptor substance 2. Stem Cell Res Ther. 2018;9:231.

    Article  CAS  Google Scholar 

  21. Walendzik K, Kopcewicz M, Bukowska J, et al. The transcription factor FOXN1 regulates skin adipogenesis and affects susceptibility to diet-induced obesity. J Invest Dermatol. 2020;140(1166–75): e9.

    Google Scholar 

  22. Zhang W, Xu J, Li J, et al. The TEA domain family transcription factor TEAD4 represses murine adipogenesis by recruiting the cofactors VGLL4 and CtBP2 into a transcriptional complex. J Biol Chem. 2018;293:17119–34.

    Article  CAS  Google Scholar 

  23. Ullah M, Stich S, Notter M, et al. Transdifferentiation of mesenchymal stem cells-derived adipogenic-differentiated cells into osteogenic- or chondrogenic-differentiated cells proceeds via dedifferentiation and have a correlation with cell cycle arresting and driving genes. Differentiation. 2013;85:78–90.

    Article  CAS  Google Scholar 

  24. Zhou G, Soufan O, Ewald J, et al. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019;47:W234–41.

    Article  CAS  Google Scholar 

  25. Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141.

    CAS  Google Scholar 

  26. Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49:D605–12.

    Article  CAS  Google Scholar 

  27. Rauch A, Haakonsson AK, Madsen JGS, et al. Osteogenesis depends on commissioning of a network of stem cell transcription factors that act as repressors of adipogenesis. Nat Genet. 2019;51:716–27.

    Article  CAS  Google Scholar 

  28. Wu L, Wang T, Ge Y, et al. Secreted factors from adipose tissue increase adipogenic differentiation of mesenchymal stem cells. Cell Prolif. 2012;45:311–9.

    Article  CAS  Google Scholar 

  29. Chai Y, Chen Y, Yin B, et al. Dedifferentiation of human adipocytes after fat transplantation. Aesthet Surg J. 2022;42:np423–31.

    Article  Google Scholar 

  30. Shao X, Wang M, Wei X, et al. Peroxisome proliferator-activated receptor-gamma: master regulator of adipogenesis and obesity. Curr Stem Cell Res Ther. 2016;11:282–9.

    Article  CAS  Google Scholar 

  31. Wang Y, Gao R, Wang PP, et al. The differential effects of aligned electrospun PHBHHx fibers on adipogenic and osteogenic potential of MSCs through the regulation of PPARγ signaling. Biomaterials. 2012;33:485–93.

    Article  CAS  Google Scholar 

  32. Wu L, Ma F, Zhao X, et al. GNPDA2 gene affects adipogenesis and alters the transcriptome profile of human adipose-derived mesenchymal stem cells. Int J Endocrinol. 2019;2019:9145452.

    Article  Google Scholar 

  33. Qi HZ, Ye YL, Suo Y, et al. Wnt/beta-catenin signaling mediates the abnormal osteogenic and adipogenic capabilities of bone marrow mesenchymal stem cells from chronic graft-versus-host disease patients. Cell Death Dis. 2021;12:308.

    Article  CAS  Google Scholar 

  34. Salehpour A, Shidfar F, Hedayati M, et al. Molecular mechanisms of vitamin D plus Bisphenol A effects on adipogenesis in human adipose-derived mesenchymal stem cells. Diabetol Metab Syndr. 2021;13:41.

    Article  CAS  Google Scholar 

  35. Salehpour A, Hedayati M, Shidfar F, et al. 1,25-Dihydroxyvitamin D3 modulates adipogenesis of human adipose-derived mesenchymal stem cells dose-dependently. Nutr Metab (Lond). 2021;18:29.

    Article  CAS  Google Scholar 

  36. Ibrahim M, Jang M, Park M, et al. Capsaicin inhibits the adipogenic differentiation of bone marrow mesenchymal stem cells by regulating cell proliferation, apoptosis, oxidative and nitrosative stress. Food Funct. 2015;6:2165–78.

    Article  CAS  Google Scholar 

  37. Luo J, Dou L, Yang Z, et al. CBFA2T2 promotes adipogenic differentiation of mesenchymal stem cells by regulating CEBPA. Biochem Biophys Res Commun. 2020;529:133–9.

    Article  CAS  Google Scholar 

  38. Comas F, Latorre J, Ortega F, et al. Permanent cystathionine-β-Synthase gene knockdown promotes inflammation and oxidative stress in immortalized human adipose-derived mesenchymal stem cells, enhancing their adipogenic capacity. Redox Biol. 2021;42: 101668.

    Article  CAS  Google Scholar 

  39. Al-Thuwaini TM, Al-Shuhaib MBS, Lepretre F, et al. Co-inherited novel SNPs of the LIPE gene associated with increased carcass dressing and decreased fat-tail weight in Awassi breed. Trop Anim Health Prod. 2020;52:3631–8.

    Article  Google Scholar 

  40. Zhang X, Zhang CC, Yang H, et al. An epistatic interaction between Pnpla2 and Lipe reveals new pathways of adipose tissue lipolysis. Cells. 2019;8.

  41. Yi X, Liu J, Wu P, et al. The whole transcriptional profiling of cellular metabolism during adipogenesis from hMSCs. J Cell Physiol. 2020;235:349–63.

    Article  CAS  Google Scholar 

  42. Liao Y, Zeng Z, Lu F, et al. In vivo dedifferentiation of adult adipose cells. PLoS ONE. 2015;10: e0125254.

    Article  Google Scholar 

  43. Cote JA, Guenard F, Lessard J, et al. Temporal changes in gene expression profile during mature adipocyte dedifferentiation. Int J Genomics. 2017;2017:5149362.

    Article  Google Scholar 

  44. Cuzzone DA, Weitman ES, Albano NJ, et al. IL-6 regulates adipose deposition and homeostasis in lymphedema. Am J Physiol Heart Circ Physiol. 2014;306:H1426–34.

    Article  CAS  Google Scholar 

  45. Wueest S, Konrad D. The role of adipocyte-specific IL-6-type cytokine signaling in FFA and leptin release. Adipocyte. 2018;7:226–8.

    Article  CAS  Google Scholar 

  46. Fukuyo S, Yamaoka K, Sonomoto K, et al. IL-6-accelerated calcification by induction of ROR2 in human adipose tissue-derived mesenchymal stem cells is STAT3 dependent. Rheumatology (Oxford). 2014;53:1282–90.

    Article  CAS  Google Scholar 

  47. Bastidas-Coral AP, Bakker AD, Zandieh-Doulabi B, et al. Cytokines TNF-alpha, IL-6, IL-17F, and IL-4 differentially affect osteogenic differentiation of human adipose stem cells. Stem Cells Int. 2016;2016:1318256.

    Article  Google Scholar 

  48. Bastidas-Coral AP, Hogervorst JMA, Forouzanfar T, et al. IL-6 counteracts the inhibitory effect of IL-4 on osteogenic differentiation of human adipose stem cells. J Cell Physiol. 2019;234:20520–32.

    Article  CAS  Google Scholar 

  49. Huang RL, Sun Y, Ho CK, et al. IL-6 potentiates BMP-2-induced osteogenesis and adipogenesis via two different BMPR1A-mediated pathways. Cell Death Dis. 2018;9:144.

    Article  Google Scholar 

  50. Zhu Z, Guo L, Yeltai N, et al. Chemokine (C-C motif) ligand 2-enhanced adipogenesis and angiogenesis of human adipose-derived stem cell and human umbilical vein endothelial cell co-culture system in adipose tissue engineering. J Tissue Eng Regen Med. 2022;16:163–76.

    Article  CAS  Google Scholar 

  51. Wu H, Pula T, Tews D, et al. microRNA-27a-3p but Not -5p Is a crucial mediator of human adipogenesis. Cells. 2021;10.

  52. Shen X, Tang J, Huang Y, et al. CircRNF111 Contributes to adipocyte differentiation by elevating PPARγ expression via miR-27a-3p. epigenetics. 2022:1–15.

  53. Ding Z, Sun D, Han J, et al. Novel noncoding RNA CircPTK2 regulates lipolysis and adipogenesis in cachexia. Mol Metab. 2021;53: 101310.

    Article  CAS  Google Scholar 

  54. Chen G, Wang Q, Li Z, et al. Circular RNA CDR1as promotes adipogenic and suppresses osteogenic differentiation of BMSCs in steroid-induced osteonecrosis of the femoral head. Bone. 2020;133: 115258.

    Article  CAS  Google Scholar 

  55. Xu J, Zhang L, Shu G, et al. microRNA-16-5p promotes 3T3-L1 adipocyte differentiation through regulating EPT1. Biochem Biophys Res Commun. 2019;514:1251–6.

    Article  CAS  Google Scholar 

  56. Gu H, Shi S, Xiao F, et al. MiR-1-3p regulates the differentiation of mesenchymal stem cells to prevent osteoporosis by targeting secreted frizzled-related protein 1. Bone. 2020;137: 115444.

    Article  CAS  Google Scholar 

  57. Du G, Cheng X, Zhang Z, et al. TGF-beta induced key genes of osteogenic and adipogenic differentiation in human mesenchymal stem cells and MiRNA-mRNA regulatory networks. Front Genet. 2021;12: 759596.

    Article  CAS  Google Scholar 

  58. Eseberri I, Lasa A, Miranda J, et al. Potential miRNA involvement in the anti-adipogenic effect of resveratrol and its metabolites. PLoS ONE. 2017;12: e0184875.

    Article  Google Scholar 

  59. Meruvu S, Zhang J, Choudhury M. Butyl benzyl phthalate promotes adipogenesis in 3T3-L1 cells via the miRNA-34a-5p signaling pathway in the absence of exogenous adipogenic stimuli. Chem Res Toxicol. 2021;34:2251–60.

    Article  CAS  Google Scholar 

  60. Xue P, Hou Y, Zuo Z, et al. Long isoforms of NRF1 negatively regulate adipogenesis via suppression of PPARgamma expression. Redox Biol. 2020;30: 101414.

    Article  CAS  Google Scholar 

  61. Szczerbal I, Chmurzynska A. Chromosomal localization of nine porcine genes encoding transcription factors involved in adipogenesis. Cytogenet Genome Res. 2008;121:50–4.

    Article  CAS  Google Scholar 

  62. Jang MK, Lee S, Jung MH. RNA-Seq analysis reveals a negative role of KLF16 in adipogenesis. PLoS ONE. 2016;11: e0162238.

    Article  Google Scholar 

  63. Cui S, Li X, Li R, et al. FGF1 promotes the differentiation of goat intramuscular and subcutaneous preadipocytes. Anim Biotechnol. 2021:1–13.

  64. Li S, Xue T, He F, et al. A time-resolved proteomic analysis of transcription factors regulating adipogenesis of human adipose derived stem cells. Biochem Biophys Res Commun. 2019;511:855–61.

    Article  CAS  Google Scholar 

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Acknowledgements

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Authors and Affiliations

  1. Department of Traumatic Joint Surgery, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde, Foshan), No.1 Jiazi Road, Lunjiao, Shunde District, Foshan City, Guangdong Province, China

    Yi Ou-yang

  2. Department of Ophthalmology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde, Foshan), No.1 Jiazi Road, Lunjiao, Shunde District, Foshan City, Guangdong Province, China

    Miao-miao Dai

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  1. Yi Ou-yang
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Contributions

OY-Y designed the study, conducted the bioinformatic analysis and performed the statistically analyses. D-MM wrote the draft. OY-Y and D-MM edited and confirmed the final manuscript. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1.

miRNAs and their degree scores

Additional file 2.

TFs and their descriptions

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Ou-yang, Y., Dai, Mm. Screening for genes, miRNAs and transcription factors of adipogenic differentiation and dedifferentiation of mesenchymal stem cells. J Orthop Surg Res 18, 46 (2023). https://doi.org/10.1186/s13018-023-03514-0

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  • DOI: https://doi.org/10.1186/s13018-023-03514-0

Keywords

  • miRNAs
  • Transcription factors
  • Mesenchymal stem cells
  • Adipogenesis
  • Dedifferentiation

Journal of Orthopaedic Surgery and Research

ISSN: 1749-799X