In this study, human adipose stem cells were isolated from subcutaneous fat in the thigh (htASCs), abdomen (haASCs) and breast (hbASCs). Flow cytometry was used to detect cell surface markers, and an enzyme-linked immunosorbent assay was used to detect paracrine activity. Paracrine gene expression in the three cell types was examined using real-time qPCR, and adipogenic ability was assessed using Oil Red O staining. RNA from third-passage haASCs and hbASCs was sequenced. The results showed that the differentiation potential marker markers CD49d and CD54 were similar across hbASCs from 10 subjects. The hbASCs showed higher colony forming ability and expression of fibroblast growth factor-2, tissue inhibitor of metalloproteinase-1 and stromal cell derived factor-1 than htASCs and haASCs. Stimulating hbASCs with FGF2 promoted adipogenic differentiation, while treating the cells with the PI3K inhibitor LY294002 inhibited differentiation. These results suggest that the PI3K/Akt signaling pathway can promote proliferation and adipogenic differentiation of adipose stem cells, and that activation of this pathway by FGF2 may explain why hbASCs show greater proliferation and adipogenic differentiation than haASCs and htASCs.
Background: Adipose-derived mesenchymal stem cells (AD-MSCs) are a type of stem cell that is abundant and widely used. The molecular characteristics of AD-MSCs from different passages from donors of different ages have not been well elucidated. Methods: Six kinds of AD-MSCs ((E1, E2, E3, Y1, Y2, and Y3) with E denoting cells derived from an elderly patient, Y denoting cells derived from a young patient, and 1, 2, and 3 representing passages 3, 6, and 10) were obtained from human abdominal adipose tissue. We obtained the protein expression profile, the mRNA expression profile, the lncRNA expression profile, and the methylation profile of each kind of AD-MSC by sequencing. After calculating the stemness indices, genes related to stemness were extracted. The multiomics correlation analysis was performed in the stemness-related genes. In addition, short time-series expression miner (STEM) analysis was performed for all cell passages and donor ages. To further explore the biological functions of the stemness-related genes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, the lncRNA-KEGG network and transcription factor (TF)-KEGG network were constructed based on the RNAInter database and TRRUST v2 database. Results: The stemness of the Y1, E1, and Y2 cells was higher than that of the E2, Y3, and E3 cells. The stemness was the highest for Y1 cells and the lowest for E3 cells. STEM analysis showed that five stemness-related gene clusters were associated with the cell passages, and only one gene cluster was associated with age. The enrichment analysis results showed that the biological processes (BPs) and KEGG pathways were mainly involved in the proliferation, differentiation, and migration of cells. The global regulatory landscape of AD-MSCs was constructed: 25 TFs and 16 lncRNAs regulated 21 KEGG pathways through 27 mRNAs. Furthermore, we obtained a core stemness-related gene set consisting of ITGAV, MAD2L1, and PCNA. These genes were expressed at higher levels in Y1 cells than in E3 cells.
Severe burns are acute wounds caused by local heat exposure, resulting in life-threatening systemic effects and poor survival. However, the specific molecular mechanisms remain unclear. First, we downloaded gene expression data related to severe burns from the GEO database (GSE19743, GSE37069, and GSE77791). Then, a gene expression analysis was performed to identify differentially expressed genes (DEGs) and construct protein-protein interaction (PPI) network. The molecular mechanism was identified by enrichment analysis and Gene Set Enrichment Analysis. In addition, STEM software was used to screen for genes persistently expressed during response to severe burns, and receiver operating characteristic (ROC) curve was used to identify key DEGs. A total of 2631 upregulated and 3451 downregulated DEGs were identified. PPI network analysis clustered these DEGs into 13 modules. Importantly, module genes mostly related with immune responses and metabolism. In addition, we identified genes persistently altered during the response to severe burns corresponding to survival and death status. Among the genes with high area under the ROC curve in the PPI network gene, CCL5 and LCK were identified as key DEGs, which may affect the prognosis of burn patients. Gene set variation analysis showed that the immune response was inhibited and several types of immune cells were decreased, while the metabolic response was enhanced. The results showed that persistent gene expression changes occur in response to severe burns, which may underlie chronic alterations in physiological pathways. Identifying the key altered genes may reveal potential therapeutic targets for mitigating the effects of severe burns.
Background: Advanced platelet-rich fibrin extract (APRFE) contains a high concentration of various cytokines that are helpful for improving stem cells repair function. Objective: However, the underlying mechanism of APRFE improving stem cell repairing is not clear. Methods: We produced APRFE by centrifuging fresh peripheral blood samples and isolated and identified human adipose-derived mesenchymal stem cells (ADMSCs). The abundance of cytokines contained in APRFE was detected by the Enzyme-linked immunosorbent assay (ELISA). The ADMSCs treated with or without APRFE were collected for transcriptome sequencing. Results: Based on the sequencing data, the expression profiles were contracted. The differentially expressed genes and lncRNA (DEGs and DElncRNAs) were obtained using for the differential expression analysis. The lncRNA-miRNA-mRNA network was constructed based on the miRNet database. The further enrichment analysis results showed that the biological functions were mainly related to proliferation, differentiation, and cell-cell function. To explore the role of APRFE, the protein-protein interaction network was constructed among the cytokines included in APRFE and DEGs. Furthermore, we constructed the global regulatory network based on the RNAInter and TRRUST database. The pathways in the global regulatory network were considered as the core pathways. We found that the DEGs in the core pathways were associated with stemness scores. Conclusion: In summary, we predicted that APRFE activated three pathways (tryptophan metabolism, mTOR signaling pathway, and adipocytokine signaling) to promote the proliferation and differentiation of ADMSCs. The finding may be helpful for guiding the application of ADMSCs in the clinic.
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