Bioinformatics analysis of the biological changes involved in the osteogenic differentiation of human mesenchymal stem cells

Fan, Tao; Qu, Rongmei; Yu, Qinghe; Sun, Bing; Jiang, Xin; Yang, Yuchao; Huang, Xiaolan; Zhou, Zhaoxi; Ouyang, Jun; Zhang, Shizhen; Dai, Jingxing

doi:10.1111/jcmm.15429

Cited by 26 publications

(18 citation statements)

References 60 publications

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“…hMSCs are self-renewing precursor cells that can differentiate into bone, fat, cartilage, and stromal cells of the bone marrow ( Frenette et al, 2013 ). It has been reported that they are ideal seed cells for bone tissue engineering ( Fan et al, 2020 ). Notably, however, the effective cultivation of BMSCs requires a good culture environment and a good in vitro culture technique.…”

Section: Discussionmentioning

confidence: 99%

TGF-Beta Induced Key Genes of Osteogenic and Adipogenic Differentiation in Human Mesenchymal Stem Cells and MiRNA–mRNA Regulatory Networks

Cheng

Zhang

et al. 2021

Front. Genet.

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Background: The clinical efficacy of osteoporosis therapy is unsatisfactory. However, there is currently no gold standard for the treatment of osteoporosis. Recent studies have indicated that a switch from osteogenic to adipogenic differentiation in human bone marrow mesenchymal stem cells (hMSCs) induces osteoporosis. This study aimed to provide a more comprehensive understanding of the biological mechanisms involved in this process and to identify key genes involved in osteogenic and adipogenic differentiation in hMSCs to provide new insights for the prevention and treatment of osteoporosis.Methods: Microarray and bioinformatics approaches were used to identify the differentially expressed genes (DEGs) involved in osteogenic and adipogenic differentiation, and the biological functions and pathways of these genes were analyzed. Hub genes were identified, and the miRNA–mRNA interaction networks of these hub genes were constructed.Results: In an optimized microenvironment, transforming growth factor-beta (TGF-beta) could promote osteogenic differentiation and inhibit adipogenic differentiation of hMSCs. According to our study, 98 upregulated genes involved in osteogenic differentiation and 66 downregulated genes involved in adipogenic differentiation were identified, and associated biological functions and pathways were analyzed. Based on the protein–protein interaction (PPI) networks, the hub genes of the upregulated genes (CTGF, IGF1, BMP2, MMP13, TGFB3, MMP3, and SERPINE1) and the hub genes of the downregulated genes (PPARG, TIMP3, ANXA1, ADAMTS5, AGTR1, CXCL12, and CEBPA) were identified, and statistical analysis revealed significant differences. In addition, 36 miRNAs derived from the upregulated hub genes were screened, as were 17 miRNAs derived from the downregulated hub genes. Hub miRNAs (hsa-miR-27a/b-3p, hsa-miR-128-3p, hsa-miR-1-3p, hsa-miR-98-5p, and hsa-miR-130b-3p) coregulated both osteogenic and adipogenic differentiation factors.Conclusion: The upregulated hub genes identified are potential targets for osteogenic differentiation in hMSCs, whereas the downregulated hub genes are potential targets for adipogenic differentiation. These hub genes and miRNAs play important roles in adipogenesis and osteogenesis of hMSCs. They may be related to the prevention and treatment not only of osteoporosis but also of obesity.

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Section: Discussionmentioning

confidence: 99%

TGF-Beta Induced Key Genes of Osteogenic and Adipogenic Differentiation in Human Mesenchymal Stem Cells and MiRNA–mRNA Regulatory Networks

Cheng

Zhang

et al. 2021

Front. Genet.

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“…In this study, through the DEGs analysis of multiple datasets, the gene changes of PDLSCs after osteoinduction were obtained, and the GO enrichment analysis of DEGs was performed. Fan reported ( Fan et al, 2020 ) that the main enrichment pathways of BMSCs osteogenic differentiation were extracellular matrix organization, ossification, negative regulation of cell proliferation, vasculature development, cell division and spindle. Wang reported ( Yang et al, 2019 ) that regulation of the cellular amino acid metabolic process, DNA metabolic process and regulation of apoptotic process were mostly enriched during the osteogenic differentiation of BMSCs.…”

Section: Discussionmentioning

confidence: 99%

“…Microarray techniques and bioinformatics analysis have been widely used to screen for the DEGs, functional pathways and PPI associated with osteogenic differentiation of stem cells ( Yang et al, 2019 ; Fan et al, 2020 ; Bini et al, 2021 ; Shin et al, 2021 ). In this study, three microarray datasets (total 16 samples) were downloaded from Gene Expression Omnibus (GEO) for analysis to identify DEGs and DEMs between the control group and the induction group.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics Analysis Identified miR-584-5p and Key miRNA-mRNA Networks Involved in the Osteogenic Differentiation of Human Periodontal Ligament Stem Cells

et al. 2021

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Human periodontal ligament cells (PDLCs) play an important role in periodontal tissue stabilization and function. In the process of osteogenic differentiation of PDLSCs, the regulation of molecular signal pathways are complicated. In this study, the sequencing results of three datasets on GEO were used to comprehensively analyze the miRNA-mRNA network during the osteogenic differentiation of PDLSCs. Using the GSE99958 and GSE159507, a total of 114 common differentially expressed genes (DEGs) were identified, including 62 up-regulated genes and 52 down-regulated genes. GO enrichment analysis was performed. The up-regulated 10 hub genes and down-regulated 10 hub genes were screened out by protein-protein interaction network (PPI) analysis and STRING in Cytoscape. Similarly, differentially expressed miRNAs (DEMs) were selected by limma package from GSE159508. Then, using the miRwalk website, we further selected 11 miRNAs from 16 DEMs that may have a negative regulatory relationship with hub genes. In vitro RT-PCR verification revealed that nine DEMs and 18 hub genes showed the same trend as the RNA-seq results during the osteogenic differentiation of PDLSCs. Finally, using miR-584-5p inhibitor and mimics, it was found that miR-584-5p negatively regulates the osteogenic differentiation of PDLSCs in vitro. In summary, the present results found several potential osteogenic-related genes and identified candidate miRNA-mRNA networks for the further study of osteogenic differentiation of PDLSCs.

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“…Identification of the DE genes in PAITA. The limma package (http://bioconductor.org/packages/limma/) (23) in r software (version 3.2.11, http://www.bioconductor. org/packages/release/bioc/html/clusterProfiler.html) (24) was used to perform data preprocessing, including background correction, quantile normalization and probe summarization, to obtain the probe expression matrix (25).…”

Section: Methodsmentioning

confidence: 99%

“…The limma package ( ) ( 23 ) in R software (version 3.2.11, ) ( 24 ) was used to perform data preprocessing, including background correction, quantile normalization and probe summarization, to obtain the probe expression matrix ( 25 ). UCSC RefSeq database ( ), miRBase database 21 ( ) and NONCODE V4 database ( ) were used to perform the blast comparison (identity >95%, coverage >95) and obtain the gene ID ( 26 ).…”

Section: Methodsmentioning

confidence: 99%

Effects of Egr1 on pancreatic acinar intracellular trypsinogen activation and the associated ceRNA network

et al. 2020

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acute pancreatitis (aP) is a common digestive disorder with high morbidity and mortality. The present study aimed to investigate the expression of early growth response protein 1 (egr1), and the effect of competing endogenous (ce)rna network on trypsinogen activation. Pancreatic acinar intracellular trypsinogen activation (PaiTa) is an important event in the early stage of aP; however, the underlying mechanisms remain unclear. The present study used taurolithocholic acid 3-sulfate (Tlc-S)-treated ar42J cells (pancreatic cell line) to establish a PaiTa model. a gene microarray and bioinformatics analysis was performed to identify the potential key targets in PaiTa. The results demonstrated that egr1, an important transcription factor, was significantly overexpressed in PAITA. In Egr1 small interfering (si)rna-transfected cells, egr1 expression was decreased and trypsinogen activation was significantly decreased compared with negative control sirna-transfected cells, indicating that in Tlc-S-induced PaiTa, overexpression of egr1 enhanced trypsinogen activation. a cerna network [mrna-microrna (mirna/mir)-long non-coding (lnc)rna] generated using the PaiTa model revealed that the effects of egr1 on PaiTa may be regulated by multiple cerna pairs, and the lncrnas (including nonraTT022624 and nonraTT031002) and mirnas [including Rattus norvegicus (rno)-mir-214-3p and rno-mir-764-5p] included in the cerna pairs may serve roles in PaiTa by regulating the expression of egr1. The results of the present study may provide novel targets for researching the underlying mechanisms of, and developing treatments for aP.

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Bioinformatics analysis of the biological changes involved in the osteogenic differentiation of human mesenchymal stem cells

Cited by 26 publications

References 60 publications

TGF-Beta Induced Key Genes of Osteogenic and Adipogenic Differentiation in Human Mesenchymal Stem Cells and MiRNA–mRNA Regulatory Networks

TGF-Beta Induced Key Genes of Osteogenic and Adipogenic Differentiation in Human Mesenchymal Stem Cells and MiRNA–mRNA Regulatory Networks

Bioinformatics Analysis Identified miR-584-5p and Key miRNA-mRNA Networks Involved in the Osteogenic Differentiation of Human Periodontal Ligament Stem Cells

Effects of Egr1 on pancreatic acinar intracellular trypsinogen activation and the associated ceRNA network

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