Impact of Fibronectin Knockout on Proliferation and Differentiation of Human Infrapatellar Fat Pad-Derived Stem Cells

Wang, Yiming; Fu, Ya-Wen; Yan, Zuoqin; Zhang, Xiaobing; Pei, Ming

doi:10.3389/fbioe.2019.00321

Cited by 25 publications

(23 citation statements)

References 45 publications

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“…FN1 plays an important role in tissue development and regeneration. Wang et al [62] found that the fat pad stem cells’ cartilage differentiation and fat differentiation ability decreased significantly after knocking out FN1. Gcm1 transcription factor covering FGF signal can promote the terminal differentiation of trophoblast stem cells [63] , in addition, the gene can guide the change of cell fate by activating the glial development program in the multipotent precursor cells of the nervous system [64] .…”

Section: Real Data Analysismentioning

confidence: 99%

Joint learning of multiple gene networks from single-cell gene expression data

Yin

Ou-Yang

et al. 2020

Computational and Structural Biotechnology Journal

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Inferring gene networks from gene expression data is important for understanding functional organizations within cells. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is possible to infer gene networks at single cell level. However, due to the characteristics of scRNA-seq data, such as cellular heterogeneity and high sparsity caused by dropout events, traditional network inference methods may not be suitable for scRNA-seq data. In this study, we introduce a novel joint Gaussian copula graphical model (JGCGM) to jointly estimate multiple gene networks for multiple cell subgroups from scRNA-seq data. Our model can deal with non-Gaussian data with missing values, and identify the common and unique network structures of multiple cell subgroups, which is suitable for scRNA-seq data. Extensive experiments on synthetic data demonstrate that our proposed model outperforms other compared state-of-the-art network inference models. We apply our model to real scRNA-seq data sets to infer gene networks of different cell subgroups. Hub genes in the estimated gene networks are found to be biological significance.

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Section: Real Data Analysismentioning

confidence: 99%

Joint learning of multiple gene networks from single-cell gene expression data

Yin

Ou-Yang

et al. 2020

Computational and Structural Biotechnology Journal

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“…In addition, FN1 exhibits chemotactic activity and attracts monocytes, and is involves in both blood coagulation and wound healing (11). FN1-knockout Fibronectin 1 inhibits the apoptosis of human trophoblasts by activating the PI3K/Akt signaling pathway mice have an embryonic lethal phenotype that is characterized by severe cardiac and vascular disorders (15), indicating that FN1 is essential for embryonic development. As FN1 is highly expressed in tumor vessels and mediates angiogenesis during tumorigenesis, it also plays important roles in cancer progression.…”

Section: Introductionmentioning

confidence: 99%

Fibronectin 1 inhibits the apoptosis of human trophoblasts by activating the PI3K/Akt signaling pathway

Chen

Zhuang

et al. 2020

Int J Mol Med

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The excessive apoptosis of human trophoblasts can cause pregnancy-related diseases. It has been reported that fibronectin 1 (FN1) is closely associated with the invasion of human trophoblasts. The aim of the present study was to examine the effects of FN1 on the apoptosis of human trophoblasts and to investigate the underlying molecular mechanisms. It was found that FN1, a differentially expressed gene (DEG) in the GSE127170 dataset, was identified as the hub gene in a protein-protein interaction (PPI) network generated using the cytoHubba plug-in of Cytoscape software. The Metascape website was used to perform GO enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to perform KEGG pathway analysis. Experimental analyses revealed that FN1 expression was downregulated in the chorionic villus tissues of patients diagnosed with and mice subjected to spontaneous abortion (SA). CCK-8 and flow cytometric assays revealed that the knockdown of FN1 decreased the viability and promoted the apoptosis of JEG-3 and BeWo cells. In vivo experiments demonstrated that the knockdown of FN1 promoted the apoptosis of trophoblasts in the chorionic villus tissues obtained from mice subjected to SA, whereas FN1 overexpression increased cell viability and inhibited cell apoptosis. The protein levels of cleaved caspase-3 and Bax were increased by the silencing of FN1 and decreased by FN1 overexpression. The protein expression levels of Bcl-2, proliferating cell nuclear antigen (PCNA) and Ki67 were decreased by the silencing of FN1; however, the overexpression of FN1 increased these levels. The results of western blot analysis revealed that the knockdown of FN1 inhibited the PI3K/Akt signaling pathway, while the overexpression of FN1 activated the PI3K/Akt signaling pathway. Consistently, the apoptosis-inhibiting effect of FN1 overexpression was reversed by a PI3K/Akt signaling pathway inhibitor, and the apoptosis-promoting effect of FN1 silencing was reversed by a PI3K/Akt signaling pathway activator. On the whole, the findings of the present study demonstrate that the inhibition of FN1 induces the apoptosis of JEG-3 and BeWo cells, and the overexpression of FN1 inhibits cell apoptosis by activating the PI3K/Akt signaling pathway.

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“…Fibronectin (FN), one of the main brillary proteins, has been involved in cell-multitrophic (celldifferentiating) and cellular proliferation and differentiation. Following FN1-KO extension, the number of human infrapatellar fat pad derived stem cells was increased, but this rise was negated by FN1 cells after implantation into ECM [23]. Likewise, recent ndings have found that the glucocorticoid receptor enhances the proliferative mesenchymal progenitor cell's acquisition of broblast matrix-derived signals such as Fn1 through and Wnt-1 and JAK-STAT, concerning their activity in the extracellular environment by other cells, and thus indirectly regulates a subset of those cells responsible for promoting tissue extracellular matrix gene expression, especially the cells [24].…”

Section: Discussionmentioning

confidence: 99%

Identification of Genes During the Reprogramming of Fibroblast using Bioinformatics Analysis

Wu¹,

Sai²,

Tang³

et al. 2021

Preprint

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Bioinformatics method was used to screen Human induced pluripotent stem cells reprogrammed by Human Fibroblasts (FBs). The reprogramming efficiency was explored by difference expressed genes (DEGs) before and after the expression of hiPSCs. Likewise, microarray datasets GSE34309, GSE43996, and GSE56805 datasets were downloaded from Gene Expression Omnibus (GEO) and GEO2R to screen the differentially expressed DEGs. Further, Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways were analyzed by David database. Likewise, to explore the correlation between DEGs, and the STRING database software was used to construct the protein-protein internet (PPI), and the Cytoscape_v3.7.2 Cytohubba plug-in was used to screen out the core genes. A total of 622 DEGs were identified, including 344 up regulated genes and 278 down-regulated genes. DEGs were mainly concentrated in the cell cycle and PI3K-Akt signaling pathway. Among the ten core genes screened, CDK1, IL6, EGFR, FN1, CDH1, SOX2, BRCA1, EZH2, CD44, and CCNB1 were most significant in the reprogramming of FBs into iPSCs. Regardless underline the differential of those gene expression profiles and regulatory network during FBs reprogramming could provide a theoretical basis for efficient reprogramming.

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Impact of Fibronectin Knockout on Proliferation and Differentiation of Human Infrapatellar Fat Pad-Derived Stem Cells

Cited by 25 publications

References 45 publications

Joint learning of multiple gene networks from single-cell gene expression data

Joint learning of multiple gene networks from single-cell gene expression data

Fibronectin 1 inhibits the apoptosis of human trophoblasts by activating the PI3K/Akt signaling pathway

Identification of Genes During the Reprogramming of Fibroblast using Bioinformatics Analysis

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