Modulation of Pathways Underlying Distinct Cell Death Mechanisms in Two Human Lung Cancer Cell Lines in Response to S_N1 Methylating Agents Treatment

Modulation of Pathways Underlying Distinct Cell Death Mechanisms in Two Human Lung Cancer Cell Lines in Response to S_N1 Methylating Agents Treatmen

Validation of Microarray data.

<p><b>(a)</b> Validation of microarray data by RT-PCR. Fold change in expression (in log₂ scale) of the selected genes, as compared between MNU and DMSO treated cells, as mentioned by qRT-PCR and microarrays. Values represent the mean of three independent reactions and error bars the standard deviations. <i>GAPDH</i> was used as an internal control for data normalization. All expressions were statistically significantly different between MNU and DMSO treated cells, except those of ERCC1 which was used as internal control. <b>(b)</b> Immunoblot of A549 cell extracts (40 μg) with a-caspase-1 antibody. Cells were either DMSO (0.1% v/v) or MNU (200 μg/ml) treated and maintained in culture for the indicated length of time. The relative abundance of total protein applied was measured by using as control the amount of actin as assessed by a-actin on the same blot.</p

Statistically significant differentiated genes.

<p>Hierarchical clustering of the expression fold change values, as compared between MNU and DMSO treated cells after 24, 48 and 72h of treatment. The genes found as significantly differentiated in A549 <b>(a)</b> and H157 cells <b>(b)</b> are shown, where red color indicates up-regulated genes and green indicates down-regulated genes. <b>(c)</b> Venn diagram showing the total number of genes found as significantly differentiated in A549 and H157 cells, after MNU treatment. Among the 87 common genes, 63 share a common expression profile (up- or down-regulation) in both cell lines.</p

StRAnGER pathway analysis.

<p>StRAnGER pathway analysis exploiting KEGG database, based on the up-regulated genes after MNU-treatment in A549 cells. The P53 signaling pathway is ranking at the top of the significantly over-represented pathways. Genes found as significantly up-regulated are shown in red.</p

Top up- and down-regulated genes in A549 cells after MNU treatment.

<p>Top up- and down-regulated genes in A549 cells after MNU treatment.</p

Thermodynamic transitions on metabolism and proliferation of glucocorticoid-treated acute leukemia cells

<p>Glucocorticoids play an essential part in anti-leukemic therapies. Resistance is considered crucial for disease prognosis. Glucocorticoids influence the metabolic properties of the cell and consequently the leukemic cells. We have previously outlined the differences that emerge from glucocorticoid treatment used in various concentrations, and lower concentrations manifested a mitogenic effect. A critical established glucocorticoid action is the apoptotic effect they exert on leukemic cells. However, little is known about the molecular response of malignant cells following glucocorticoid exposure. Even less is known about the cell proliferation dynamics governing leukemic cells under glucocorticoid influence. Growth and metabolic features are assumed to be of nonlinear nature. A model based prediction of glucocorticoid effects is derived by applying a non-linear fitting approximation to the measured parameters. Additionally, borrowing principles from the metabolic engineering and thermodynamics disciplines, we calculated the required energetics for cell proliferation under prednisolone treatment. Finally, we utilized a previously reported microarray dataset, to examine whether the predicted and measured parameters of the metabolism and proliferation under glucocorticoids are reflected in gene expression. Hence, making such an approach more pragmatic since those genes could shed light into the mechanisms of glucocorticoid-induced apoptotic resistance action, and subsequently identify novel targets for more efficient glucocorticoid treatments. We have eventually attempted to answer the basic question of what the thermodynamic mechanisms in the transition of the cell population from one state to the next are.</p

Halostability and halotolerance of CelDZ1.

<p><b>(A)</b> CelDZ1 was incubated in 5 M NaCl and 4 M KCl for up to 20 days. At different time intervals aliquots were taken and the residual activity of the enzyme was measured in the standard reaction. <b>(B)</b> The activity of CelDZ1 in the presence of different high-salt concentrations was measured in the standard reaction. The reported values correspond to the mean value from three independent experiments performed in triplicate and the error bars to one standard deviation from the mean value.</p

Thermostability of CelDZ1.

<p><b>(A).</b> Catalytic thermostability of CelDZ1 evaluated by measurements of residual CMC-degrading activity after high-temperature exposure at 65, 70 and 75°C for up to 24 h. <b>(B).</b> Thermal denaturation analysis of CelDZ1 using differential scanning fluorimetry with the conformation-sensitive dye SYPRO Orange. The reported values correspond to the mean value from three independent experiments performed in triplicate and the error bars to one standard deviation from the mean value.</p

The structure of CelDZ1.

<p><b>(A)</b> Folding of the CelDZ1α monomer is presented as a cartoon diagram and viewed from the solvent region towards the active site groove formed by the C-terminal ends of the β-strands of the (β/α)₈-barrel. The α-helices, β-strands and loops are coloured in turquoise, magenta and pink, respectively. The carbohydrate-binding module (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146454#pone.0146454.g001" target="_blank">Fig 1</a>), which contains helix α8 at the C terminus, is highlighted in green. The two catalytic residues are shown as stick models and secondary structural elements are labelled. The Met50 indicates the position of the first N-terminal residue defined in the electron density. The image was prepared using PyMol [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146454#pone.0146454.ref040" target="_blank">40</a>]. <b>(B)</b> A stereo representation of the superimposition of the monomers of CelDZ1, CelK and Cel5a displayed as grey carbon traces. The three different insertion regions are highlighted in red for CelDZ1, magenta for CelK and green for Cel5a. The cellobiose ligand bound to CelK is shown as a magenta stick model. The image was prepared using PyMol [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146454#pone.0146454.ref040" target="_blank">40</a>]. <b>(C)</b> The electrostatic potential surface of the CelDZ1 enzyme around the active site groove as viewed from the solvent region. The positive charge is shown in blue and the negative charge is shown in red. The extended active site groove, which crosses the monomer from left to right, is negatively charged disfavoring the binding of halogen ions thereby increasing halotolerance. The image was prepared with ccp4mg [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146454#pone.0146454.ref041" target="_blank">41</a>].</p

Effect of pH and temperature on the activity of CelDZ1.

<p><b>(A)</b> CelDZ1 activity was measured in the standard reaction at 40°C for 5 min at pH values ranging from 4 to 10 and <b>(B)</b> at temperatures between 40 and 90°C for 5 min in a pH 5 buffer. The reported values correspond to the mean value from three independent experiments performed in triplicate and the error bars to one standard deviation from the mean value.</p