Optimization of cDNA microarrays procedures using criteria that do not rely on external standards-5

<p><b>Copyright information:</b></p><p>Taken from "Optimization of cDNA microarrays procedures using criteria that do not rely on external standards"</p><p>http://www.biomedcentral.com/1471-2164/8/377</p><p>BMC Genomics 2007;8():377-377.</p><p>Published online 18 Oct 2007</p><p>PMCID:PMC2147032.</p><p></p

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards-4

<p><b>Copyright information:</b></p><p>Taken from "Optimization of cDNA microarrays procedures using criteria that do not rely on external standards"</p><p>http://www.biomedcentral.com/1471-2164/8/377</p><p>BMC Genomics 2007;8():377-377.</p><p>Published online 18 Oct 2007</p><p>PMCID:PMC2147032.</p><p></p> lines NRK52E and AR42J) compared to self-self hybridization (rat cell line AR42J). The samples were hybridized to rat 15 k cDNA duplicates under six different blocking conditions including no blocker, 1000 ng poly(dA), and 25 to 1000 ng LNA dT blocker. Dye-swap and self self were performed for all blocking conditions (total of 24 hybridizations). Green-labelled samples are placed at the tail and red labelled samples at the head of the arrows

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards-1

<p><b>Copyright information:</b></p><p>Taken from "Optimization of cDNA microarrays procedures using criteria that do not rely on external standards"</p><p>http://www.biomedcentral.com/1471-2164/8/377</p><p>BMC Genomics 2007;8():377-377.</p><p>Published online 18 Oct 2007</p><p>PMCID:PMC2147032.</p><p></p

Prior knowledge network representing the cell fate decision network governing growth of AGS gastric adenocarcinoma cells.

<p>The network receives no external input but encompasses two outputs <i>Antisurvival</i> and <i>Prosurvival</i> (phenotypic readouts, colored in red for Antisurvival and green for Prosurvival). Activating regulations are denoted by green arrows, while red T arrows denote inhibition. Signaling component nodes (proteins, protein complexes or genes) associated with Boolean variables (taking the values 0, 1) are represented by ellipses, while rectangles depict nodes encoded with multilevel variables. Yellow nodes represent drug targets and are subjected to inhibitory perturbations during simulations.</p

Experimentally confirmed synergies, where the effect of combining two inhibitors at half GI50 concentrations (violet) outperforms each of the single inhibitor at the full GI50 concentration.

<p>A) AKT inhibitor (green) and TAK1 inhibitor (blue). B) MEK inhibitor (blue) and AKT inhibitor (green). C) MEK inhibitor (green) and PI3K inhibitor (blue). D) PI3K inhibitor (green) and TAK1 inhibitor (blue). Cells growing in the absence of inhibitors are shown in red. One standard deviation is indicated by error bars. Inhibitors (and concentrations) used: MEK inhibitor PD0325901 (35 nM), TAK1 inhibitor (5Z)-7-oxozeaenol (0.5 μM), PI3K inhibitor PI103 (0.7 μM) and AKT inhibitor AKTi-1,2 (10 μM). See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004426#sec011" target="_blank">Materials and Methods</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004426#pcbi.1004426.s001" target="_blank">S1 Text</a> for all growth curves of combinations of inhibitors, and dose-response curves of individual inhibitors.</p

Chemical inhibitors and their corresponding protein kinase targets.

<p>Chemical inhibitors and their corresponding protein kinase targets.</p

Workflow of model construction and synergy prediction followed by experimental validation.

<p>We started with a signaling network built from general database and literature knowledge (upper left), which was refined with published experimental data on protein activities in AGS cells (upper right) to generate the logical model. Next, we generated a formally reduced version of the logical model, focusing on the drug target nodes and valid for systematic simulations of combinatorial inhibitions. Predicted synergies were challenged with observations from AGS cell growth experiments. Cartoons on the right refer to each of the Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004426#pcbi.1004426.g002" target="_blank">2</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004426#pcbi.1004426.g005" target="_blank">5</a> further down.</p

Effects of combined inhibitors on cell growth.

<p>Synergistic (yellow) and non-synergistic (blue) combinations are shown both as predicted by model simulations (upper panel of boxes, value of model parameter “growth”) and as verified by cell growth experiments (lower panel of boxes; combinatorial indexes (synergy indicated by CI < 1) or “n” when non-synergy was observed).Synergy was proposed whenever the predicted growth for a combination of inhibitors was lower than the modeled effect of single drug perturbations, shown in the outer diagonal (grey, value of model parameter “growth”).</p

Reduced logical model obtained by semi-automated reduction of the comprehensive logical model shown in Fig 2.

<p>The reduced model encompasses all seven drug targets (yellow) and the two phenotypic outputs (red for Antisurvival and green for Prosurvival). In addition the ERK node (blue) had to be preserved to maintain dynamical consistency with the large model. Activating regulations are denoted by green arrows, while red T arrows denote inhibition. The blue arc with both arrow and T head (p38alpha to Antisurvival) indicates a dual regulation, i.e. activating and inhibiting, depending on context. In some contexts p38alpha inhibition will increase Antisurvival, while in others p38alpha inhibition will decrease Antisurvival (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004426#pcbi.1004426.s001" target="_blank">S1 Text</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004426#pcbi.1004426.s011" target="_blank">S7</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004426#pcbi.1004426.s012" target="_blank">S8</a> Tables). Note that after model reduction two members of the Wnt/β-catenin pathway, β-catenin and GSK3, became non-regulated and fixed at either the on-state (β-catenin) or off-state (GSK3).</p

SIK1 inhibits migration in AGS-G_R cells via suppression of MMP-9.

<p><b>A–B:</b> AGS-G_R cells (<b>A</b>) and MKN45 cells (<b>B</b>) were treated with gastrin, and phospho-LKB1 (Ser-428) protein levels determined by Western blot. The phospho-LKB1 bands from a representative experiment are shown. <b>C–D:</b> AGS-G_R cells (<b>C</b>) and MKN45 (<b>D</b>) were treated with gastrin, and phospho-SIK1 (Thr-182) protein levels determined by Western blot. The phospho-SIK1 bands from a representative experiment are shown. <b>E:</b> AGS-G_R cells transfected with siSIK1 or siCtr and real-time cell migration monitored (0–24 h). Results show one representative of three independent experiments (mean ±SD of three technical replicates). <b>F:</b> MMP-9 mRNA expression in cells transfected with pSIK1 and treated with gastrin. Results show one representative of three independent experiments, (mean ± SD).</p