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: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: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 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.
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:In cancer tumors, lactate accumulation was initially attributed to high glucose consumption associated with the Warburg Effect. Now it is evident that lactate can also serve as an energy source in cancer cell metabolism. Additionally, lactate has been shown to promote metastasis, generate gene expression patterns in cancer cells consistent with "cancer stem cell" phenotypes, and result in treatment resistant tumors. Therefore, the goal of this work was to quantify the impact of lactate on metabolism in three breast cell lines (one normal and two breast cancer cell lines-MCF 10A, MCF7, and MDA-MB-231), in order to better understand the role lactate may have in different disease cell types. Parallel labeling metabolic flux analysis (13C-MFA) was used to quantify the intracellular fluxes under normal and high extracellular lactate culture conditions. Additionally, high extracellular lactate cultures were labelled in parallel with [U-13C] lactate, which provided qualitative information regarding the lactate uptake and metabolism. The 13C-MFA model, which incorporated the measured extracellular fluxes and the parallel labeling mass isotopomer distributions (MIDs) for five glycolysis, four tricarboxylic acid cycle (TCA), and three intracellular amino acid metabolites, predicted lower glycolysis fluxes in the high lactate cultures. All three cell lines experienced reductive carboxylation of glutamine to citrate in the TCA cycle as a result of high extracellular lactate. Reductive carboxylation previously has been observed under hypoxia and other mitochondrial stresses, whereas these cultures were grown aerobically. In addition, this is the first study to investigate the intracellular metabolic responses of different stages of breast cancer progression to high lactate exposure. These results provide insight into the role lactate accumulation has on metabolic reaction distributions in the different disease cell types while the cells are still proliferating in lactate concentrations that do not significantly decrease exponential growth rates.
