ABSTRACT
BACKGROUND: Checkpoint kinase 2 (CHEK2) and its regulated tumor protein p53 (TP53) have been correlated with osteogenic differentiation of osteoblast-like cells. Based on bioinformatics predictions, this study aims to investigate the effect of the CHEK2/TP53 axis on osteogenic differentiation of periodontal ligament stem cells (PDLSCs) and to explore the regulatory mechanism. METHODS: PDLSCs were isolated from human impacted wisdom teeth, and they were cultured in normal medium (NM) or osteogenic medium (OM). Protein levels of CHEK2 and TP53 were examined using western blot analysis. Osteogenic differentiation ability of PDLSCs was analyzed by measuring marker proteins (RUNX2, OCN, and OSX), ALP activity, and ALP staining. Molecular interaction between NEDD4 like E3 ubiquitin protein ligase (NEDD4L) and CHEK2 was examined by ubiquitination and co-immunoprecipitation assays. Gain- and loss-of function assays of NEDD4L, CHEK2, and TP53 were performed to analyze their function in osteogenic differentiation of PDLSCs. A rat model of mandibular bone defect was generated for in vivo validation. RESULTS: NEDD4L was upregulated, while CHEK2 and TP53 were downregulated in PDLSCs cultured in OM. CHEK2 protected TP53 from degradation, while NEDD4L reduced CHEK2 protein level by ubiquitination modification. NEDD4L silencing reduced osteogenic differentiation ability of PDLSCs both in vitro and in vivo, which was restored by CHEK2 silencing. By contrast, CHEK2 overexpression blocked the osteogenic differentiation of PDLSCs in vitro. CONCLUSION: This study demonstrates that NEDD4L affects protein stability of the CHEK2/TP53 axis through ubiquitination modification, thus increasing osteogenic differentiation of PDLSCs.
ABSTRACT
Paclitaxel is one of the key chemotherapeutic drugs widely used to treat various types of cancer. Many cervical cancer patients exhibit selectivity in response to thereapy, however, which is considered to be correlated with drug-gene-pathways. The aim of this study was to identify pathways involved in paclitaxel activity in cervical cancer. Gene expression data was obtained from the NCBI Gene Expression Omnibus and the associations between paclitaxel and genes from DrugBank, MATADOR, TTD, CTD and SuperTarget databases. Differentially expressed genes in cervical cancer were identified using the significance analysis of microarrays (SAM) statistical technique. Pathway analysis was performed according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using the software package SubpathwayMiner to predict target genes of paclitaxel in cervical cancer and regulated pathways. We found that paclitaxel, which exhibits anticancer activity in cervical cancer, may interact with these differentially expressed genes and their corresponding signaling pathways. Our study presents the first in-depth, large-scale analysis of pathways involved in paclitaxel activity in cervical cancer. Interestingly, these pathways have not been reported to be involved in other tumors. Thus our findings may contribute to the understanding of the mechanisms underlying paclitaxel resistance in cervical cancer.