Search

Background: The physiological level of reactive oxygen species (ROS) is necessary for many cellular functions. However, during the in-vitro manipulations, cells face a high level of ROS, leading to reduced cell quality. Preventing this abnormal ROS level is a challenging task. Hence, here we evaluated the effect of sodium selenite supplementation on the antioxidant potential, stemness capacity, and differentiation of rat-derived Bone Marrow MSCs (rBM-MSCs) and planned to check our hypothesis on the molecular pathways and networks linked to sodium selenite's antioxidant properties. Methods: MTT assay was used to assess the rBM-MSCs cells' viability following sodium selenite supplementation (concentrations of: 0.001, 0.01, 0.1, 1, 10 µM). The expression level of OCT-4, NANOG, and SIRT1 was explored using qPCR. The adipocyte differentiation capacity of MSCs was checked after Sodium Selenite treatment. The DCFH-DA assay was used to determine intracellular ROS levels. Sodium selenite-related expression of HIF-1α, GPX, SOD, TrxR, p-AKT, Nrf2, and p38 markers was determined using western blot. Significant findings were investigated by the String tool to picture the probable molecular network. Results: Media supplemented with 0.1 µM sodium selenite helped to preserve rBM-MSCs multipotency and keep their surface markers presentation; this also reduced the ROS level and improved the rBM-MSCs' antioxidant and stemness capacity. We observed enhanced viability and reduced senescence for rBM-MSCs. Moreover, sodium selenite helped in rBM-MSCs cytoprotection by regulating the expression of HIF-1 of AKT, Nrf2, SOD, GPX, and TrxR markers. Conclusions: We showed that sodium selenite could help protect MSCs during in-vitro manipulations, probably via the Nrf2 pathway. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is one of the most prevalent types of cancer in Iranian women and the second cause of death in women worldwide. Gene mutations are the key determinants of the disease; therefore, the genetic study of this disease is of paramount importance. One of the genetic evaluation methods of this disease is microarray technology, which allows the examination of the simultaneous expression of thousands of genes. Clustering is the method for analyzing high-dimension data, which we used in the present research for collecting similar genes in separated clusters. Method: A descriptive and inferential statistical analysis was carried out to evaluate unsupervised learning models of gene expression analysis and five bi-clustering methods (including PLAID (PL), Fabia, Bimax, Cheng & Church (CC), and Xmotif) were compared. For this purpose, we obtained the microarray gene expression data for lapatinib-resistant breast cancer cell lines from previously published research. The enrichment efficacy of the clusters was evaluated with gene ontology, and the results of these five models were compared with the Jaccard index, variance stability, least-square error, and goodness of fit indices. Furthermore, the results of the best model were assessed for building a genes sets network with Bayesian networks. Results: After preprocessing, clustering was performed on the data with the dimension (4710 x 18) of the genes. Four models, except for CC, successfully found bi-clusters in the data set. The data evaluation revealed that the results of the models were almost the same, but the PL model performed better than the others, finding 11 bi-clusters; this model was used to build the network of gene sets. Conclusion: According to the results, the PL method was suitable for clustering the data. Accordingly, it could be recommended for data analysis. In addition, the gene sets network formed on gene expression data was incompetent. [ABSTRACT FROM AUTHOR]