Global network construction.

<p><b>(a)</b> Conversion of binary data to a network representation. All continuous data are mapped to a binary representation with ‘1’ (colored squares) corresponding to a gene with a value deviating from normal for a particular sample. Each ‘1’ in the binary datasets is converted to an undirected link (solid line) between a gene node and a sample node. Prior knowledge, derived from public gene interaction repositories, is available in the form of undirected links (dashed grey line) between genes. Characters a-g correspond to gene IDs, S₁-S₃ represent sample IDs. <b>(b)</b> Construction of the global network. The network representations of the binary datasets and the prior knowledge network are merged to constitute a single comprehensive network representation. Gene nodes originating from the input datasets are connected to the corresponding gene in the prior knowledge interaction network (dashed yellow lines). <b>(c)</b> The resulting adjacency matrix representation of the undirected global network. For clarity, individual gene and sample identifiers are omitted. NET (grey) = genes from the prior knowledge interaction network, S (dark blue) = samples, EXP (green) = genes from the gene expression dataset, CNV (pink) = genes from the copy number dataset, MUT (light blue) = mutated genes, MET (orange) = methylated genes. <b>(d)</b> The similarity matrix derived from the adjacency matrix, indicating the parts of the similarity matrix that are relevant for the pathway ranking task.</p

The 20 highest ranking pathways for the two most extreme ovarian cancer survival-based subtypes.

<p>The contribution of each component to the total score is indicated in a different color bar: mRNA expression (dark blue), copy number (light blue), mutation (green) and methylation (yellow).</p

Pathway scores compared across breast cancer subtypes for a selection of pathways.

<p>Dark blue = Basal-like, light blue = HER2, green = Luminal A and yellow = Luminal B.</p

The 20 highest ranking pathways for each of the four breast cancer subtypes.

<p>The aggregate score assigned to each pathway can be decomposed into 4 probabilistic components. The contribution of each component to the total score is indicated in a different color bar: mRNA expression (dark blue), copy number (light blue), mutation (green) and methylation (yellow).</p

Pathway relevance scoring.

<p>Given a subset of the global similarity matrix (S_exp S_cnv, S_mut, S_met, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133503#pone.0133503.g001" target="_blank">Fig 1</a>) and a set of genes (a,b,d) constituting a pathway <i>P</i>, a score for each input dataset is calculated by first removing genes from S_exp S_cnv, S_mut, S_met that do not belong to the pathway and then taking the average of all remaining values in S_exp S_cnv, S_mut, S_met. This process is repeated for <i>n</i> randomly generated gene sets (with the same number of genes as the pathway <i>P</i>) yielding <i>n</i> scores for each input dataset. The random pathway scores are used to calculate a <i>p</i>-value for obtaining the pathway scores purely by chance. The resulting <i>p</i>-values are multiplied, resulting in a single aggregated pathway score.</p

Global network construction.

<p><b>(a)</b> Conversion of binary data to a network representation. All continuous data are mapped to a binary representation with ‘1’ (colored squares) corresponding to a gene with a value deviating from normal for a particular sample. Each ‘1’ in the binary datasets is converted to an undirected link (solid line) between a gene node and a sample node. Prior knowledge, derived from public gene interaction repositories, is available in the form of undirected links (dashed grey line) between genes. Characters a-g correspond to gene IDs, S₁-S₃ represent sample IDs. <b>(b)</b> Construction of the global network. The network representations of the binary datasets and the prior knowledge network are merged to constitute a single comprehensive network representation. Gene nodes originating from the input datasets are connected to the corresponding gene in the prior knowledge interaction network (dashed yellow lines). <b>(c)</b> The resulting adjacency matrix representation of the undirected global network. For clarity, individual gene and sample identifiers are omitted. NET (grey) = genes from the prior knowledge interaction network, S (dark blue) = samples, EXP (green) = genes from the gene expression dataset, CNV (pink) = genes from the copy number dataset, MUT (light blue) = mutated genes, MET (orange) = methylated genes. <b>(d)</b> The similarity matrix derived from the adjacency matrix, indicating the parts of the similarity matrix that are relevant for the pathway ranking task.</p

Structure of compound SPI031.

<p>Structure of compound SPI031.</p

Microscopic analysis of the cell membrane after treatment with SPI031.

<p>Fluorescent images of <i>S</i>. <i>aureus</i> cells (upper row) and <i>P</i>. <i>aeruginosa</i> cells (lower row) stained with FM 4–64 in the absence or presence of 1x MIC of SPI031. Scale bar corresponds to 2 μm. Images were processed with unsharp mask of Zen 2.0.</p

Effect of SPI031 on membrane permeability.

<p>(A) Effect of increasing concentrations of SPI031 on the membrane permeability of <i>S</i>. <i>aureus</i>, monitored by the uptake of SYTOX green. Cells treated with melittin (MEL) (1x MIC) served as a positive control. (B) Inner membrane permeabilization of <i>P</i>. <i>aeruginosa</i> after treatment with different concentrations of SPI031, determined by measuring SYTOX green uptake. Melittin (MEL) (1x MIC) was used as a positive control. (C) Outer membrane permeabilization of <i>P</i>. <i>aeruginosa</i> after treatment with different concentrations of SPI031, assessed by quantifying NPN uptake. Cells treated with polymyxin B (PMB) (1x MIC) were used as a positive control. Data represent the means of three independent replicates ± SEM (*p < 0.05; **p < 0.01; ***p < 0.001 compared to untreated control).</p

Time-kill kinetics of SPI031 against <i>S</i>. <i>aureus</i> and <i>P</i>. <i>aeruginosa</i>.

<p>(A) Concentration-dependent killing of <i>S</i>. <i>aureus</i> by SPI031 and vancomycin (VAN). (B) Concentration-dependent killing of <i>P</i>. <i>aeruginosa</i> by SPI031 and polymyxin B (PMB). All data represent means ± standard error of the mean (SEM) from 3 independent experiments (*p < 0.05; **p < 0.01; ***p < 0.001). The black dotted lines indicate the lower limit of detection.</p