Purine metabolism.

<p>Upregulated enzymes in green, downregulated enzymes in red. Upregulated metabolites in dark red, downregulated metabolites in blue.</p

Network of differentially regulated metabolites.

<p>Orange, Blue and Grey boxes indicate metabolites (mean of 5 replicates) upregulated, downregulated, and unchanged in cisplatin treatment, respectively (ANOVA p<0.01 and fold change 1.1); metabolites without a box were not detected. Numbers left (cisplatin) and right (control) under boxes indicate the corresponding intensities in MS-data. Results after 4 hours upper panel) and 8 hours (lower panel) are shown.</p

SAMe centered pathways.

<p>Upregulated enzymes in green, downregulated enzymes in red. Upregulated metabolites in dark red, downregulated metabolites in blue.</p

Differentially regulated metabolic enzymes.

<p>(<b>A</b>) Heatmap indicating metabolic enzymes obtained from Cytoscape metabolic signaling network (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076476#pone.0076476.s004" target="_blank">Fig. S4</a>, highlighted in blue; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076476#pone.0076476.s007" target="_blank">Table S2</a>), differentially regulated after 8 h of cisplatin treatment and enriched metabolic pathways within the dataset. (<b>B</b>) Regulation of metabolic enzymes by the transcription factor p53 obtained with Ingenuity pathway analysis.</p

Integrated signaling network.

<p>(<b>A</b>) Schematic representation of metabolomics and transcriptomics data integration leading to identification of common signaling networks related to nucleotide metabolism, SAMe pathways, polyamine pathways and urea cycle, and arginine & proline metabolism. (<b>B</b>) Integrated signaling network of metabolic enzymes and metabolites obtained with Ingenuity pathway analysis. Clusters of significantly enriched canonical pathways and related enzymes and metabolites are highlighted. Upregulated enzymes in green, downregulated enzymes in red. Upregulated metabolites in dark red, downregulated metabolites in blue.</p

Pyrimidine metabolism.

<p>Upregulated enzymes in green, downregulated enzymes in red. Upregulated metabolites in dark red, downregulated metabolites in blue.</p

Automated Analysis of NF-κB Nuclear Translocation Kinetics in High-Throughput Screening

<div><p>Nuclear entry and exit of the NF-κB family of dimeric transcription factors plays an essential role in regulating cellular responses to inflammatory stress. The dynamics of this nuclear translocation can vary significantly within a cell population and may dramatically change e.g. upon drug exposure. Furthermore, there is significant heterogeneity in individual cell response upon stress signaling. In order to systematically determine factors that define NF-κB translocation dynamics, high-throughput screens that enable the analysis of dynamic NF-κB responses in individual cells in real time are essential. Thus far, only NF-κB downstream signaling responses of whole cell populations at the transcriptional level are in high-throughput mode. In this study, we developed a fully automated image analysis method to determine the time-course of NF-κB translocation in individual cells, suitable for high-throughput screenings in the context of compound screening and functional genomics. Two novel segmentation methods were used for defining the individual nuclear and cytoplasmic regions: watershed masked clustering (WMC) and best-fit ellipse of Voronoi cell (BEVC). The dynamic NFκB oscillatory response at the single cell and population level was coupled to automated extraction of 26 analogue translocation parameters including number of peaks, time to reach each peak, and amplitude of each peak. Our automated image analysis method was validated through a series of statistical tests demonstrating computational efficient and accurate NF-κB translocation dynamics quantification of our algorithm. Both pharmacological inhibition of NF-κB and short interfering RNAs targeting the inhibitor of NFκB, IκBα, demonstrated the ability of our method to identify compounds and genetic players that interfere with the nuclear transition of NF-κB.</p> </div

Stepwise demonstration of the image analysis method.

<p>The original nuclear Hoechst channel (A) is pre-processed by image sharpening and background subtraction (B), followed by WMC and nuclear mask definition (C). Subsequently, the Voronoi diagram (D) is generated based on the disjointed nuclear masks. For the GFP-p65 channel, the original image (E) is preprocessed by a smoothing filter (F) for global cell location definition (G). By multiplication of the global cell masks (G) with the Voronoi diagram (D), the Voronoi mask is defined for the each cell (H). Within each Voronoi masks the cytoplasmic areas are redefined as the best-fit ellipse in each Voronoi cell (I). Figure (J) shows the composite view of original Hoechst channel, GFP-p65 channel and the BEVC segmentation result.</p

Application of the individual cell NF-κB nuclear translocation analysis in siRNA screening assays.

<p>(A) The average nuclear translocation response graphs for negative controls siCASP8, siCntrl#1, transfection reagent without siRNA (mock), and positive control siNFKBIA. Inset: representative images of mock and siNFKBIA treated GFP-p65 cells, at 0 and 30 minutes after TNFα stimulation (B) Table showing the univariate Z'-factors of all 32 individual parameters. The definitions of the 26 analogue parameters are given in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052337#pone.0052337.s008" target="_blank">Table S2</a></b>. Absolute Curve Difference: the absolute point-by-point difference between control and treatment averages. (C) Multivariate Z'-factor calculation based on top-scoring univariate Z'-factors. Both the conventional as well as the robust multivariate Z'-factors exceed the confidence threshold of 0.5 by combining ≥5 top-scoring univariate Z'-factors by linear projection.</p

NF-κB oscillation is regulated by an auto-regulatory negative feedback loop.

<p>Simplified schematic overview of the TNFα-induced canonical NF-κB response. TNFα binding to the TNF receptor (TNFR) activates the inhibitor of kappa-B kinase (IKK) complex, leading to phosphorylation of the inhibitor of NF-κB, IκB, upon which NF-κB is free to enter the nucleus to activate transcription of its target genes. One of the primary NF-κB target genes is IκB, which may retrieve NF-κB from the nucleus to maintain inactive IκB::NF-κB complex in the cytoplasm. Ongoing TNFR signaling can re-initiate the induction-inhibition cycle.</p