Project description:We used HeLa cells that overexpress GFP-tagged SRSF6 (4-fold) were grown in normal conditions (Normoxia, 21% oxygen) or hypoxic conditions (Hypoxia, 0.2% oxygen). compared the binding pattern of SRSF6 between normoxia, 4h hypoxia and 24h hypoxia.
Project description:We used HeLa cells that express GFP-tagged SRSF6 at physiological levels and subjected them to 4 h or 24 h hypoxia (0.2% oxygen). Cells grown in normal conditions were used as control (Normoxia, 21% oxygen). We performed iCLIP using anti-GFP antibodies and compared the binding pattern of SRSF6 between normoxia, 4h hypoxia and 24h hypoxia.
Project description:BackgroundYaks are animals that have lived in plateau environments for generations. Yaks can adapt to the hypoxic plateau environment and also pass this adaptability on to the next generation. The lungs are the most important respiratory organs for mammals to adapt to their environment. Pulmonary artery smooth muscle cells play an important role in vascular remodeling under hypoxia, but the genetic mechanism underpinning the yak's ability to adapt to challenging plateau conditions is still unknown.MethodsA tandem mass tag (TMT) proteomics study together with an RNA-seq transcriptome analysis were carried out on pulmonary artery smooth muscle cells (PASMCs) that had been grown for 72 hours in both normoxic (20% O2) and hypoxic (1% O2) environments. RNA and TP (total protein) were collected from the hypoxic and normoxic groups for RNA-seq transcriptome sequencing and TMT marker protein quantification, and RT-qPCR validation was performed.ResultsA total of 17,711 genes and 6,859 proteins were identified. Further, 5,969 differentially expressed genes (DEGs) and 531 differentially expressed proteins (DEPs) were identified in the comparison group, including 2,924 and 186 upregulated genes and proteins and 3,045 and 345 down-regulated genes and proteins, respectively. The transcriptomic and proteomic analyses revealed that 109 DEGs and DEPs were highly positively correlated, with 77 genes showing the same expression trend. Nine overlapping genes were identified in the HIF-1 signaling pathway, glycolysis / gluconeogenesis, central carbon metabolism in cancer, PPAR signaling pathway, AMPK signaling pathway, and cholesterol metabolism (PGAM1, PGK1, TPI1, HMOX1, IGF1R, OLR1, SCD, FABP4 and LDLR), suggesting that these differentially expressed genes and protein functional classifications are related to the hypoxia-adaptive pathways. Overall, our study offers abundant data for further analysis of the molecular mechanisms in yak PASMCs and their adaptability to different oxygen concentrations.
Project description:Hypoxia, which characterizes most tumor tissues, can alter the function of different immune cell types, favoring tumor escape mechanisms. In this study, we show that hypoxia profoundly acts on NK cells by influencing their transcriptome, affecting their immunoregulatory functions, and changing the chemiotactic responses of different NK cell subsets.
Project description:Hypoxia is a low oxygen condition that occurs in the developing tumor mass and that is associated with poor prognosis and resistance to chemo- and radio-therapy. The definition of the hypoxia gene signature is fundamental for the understanding of tumor biology, as in the case of neuroblastoma, the most common pediatric solid tumor. The issue of identifying a significant group of variables in microarray gene expression experiments is particularly difficult due to the typical high dimensional nature of the data and great effort has been spent in the development of feature selection techniques. Our main goal is to define a robust hypoxia gene signature in neuroblastoma cell lines. A set of 9 neuroblastoma cell lines were cultured under normoxic and hypoxic conditions for 18 hours, and their gene expression profiles were measured with Affymetrix GeneChip HG-U133 Plus 2.0. The clustering analysis of the expression profiles based on different clustering methods consistently revealed that hypoxia was not the major factor characterizing the data set. T-test analysis with multiple testing correction fails to identify significantly differentially expressed genes. Conversely the l1-l2 regularization selects 11 significant probesets while building an effective classification rule. The algorithm is cast within a cross-validation framework in order to achieve an unbiased analysis. The estimated cross-validation error is 17% (3 out of 18). We show that the use of l1-l2 regularization allowed us to model the effect of hypoxia, which was not detected by conventional t-test based approaches and we find a panel of genes able to properly discriminate the normoxic versus the hypoxic status of neuroblastoma cell lines.
Project description:Outcome prediction classifiers were successfully constructed through expression profiling of a total of 1,329 miRNAs in MKN1, gastric cancer cell line under normoxic and hypoxic conditions.
Project description:Hypoxia is a low oxygen condition that occurs in the developing tumor mass and that is associated with poor prognosis and resistance to chemo- and radio-therapy. The definition of the hypoxia gene signature is fundamental for the understanding of tumor biology, as in the case of neuroblastoma, the most common pediatric solid tumor. The issue of identifying a significant group of variables in microarray gene expression experiments is particularly difficult due to the typical high dimensional nature of the data and great effort has been spent in the development of feature selection techniques. Our main goal is to define a robust hypoxia gene signature in neuroblastoma cell lines. A set of 11 neuroblastoma cell lines were cultured under normoxic and hypoxic conditions for 18 hours, and their gene expression profiles were measured with Affymetrix GeneChip HG-U133 Plus 2.0. We used the l1-l2 regularization framework in order to select the significant probesets defining hypoxic versus normoxic cell lines.
