沃度保兒謨ニ因セル濕疹

Evaluation of CNA-IC50 correlations by known cancer driver mutations. Distributions of PCCs for cancer cell lines with mutated or wild-type proto-oncogenes or tumor suppressors as depicted in each figure. The rank position of negatively correlated drugs (p < 0.10) is shown. (EPS 975 kb

NetAligner strategy.

<p>1) Pairs of orthologous proteins between the two input networks are identified, with the possibility to include or exclude distant homologs. Each vertex in the network represents a pair of orthologs. Vertex probabilities are indicated by different shades of blue, ranging from 0 (white) to 1 (blue). 2.) The initial alignment graph is constructed by drawing edges between vertices that are involved in a conserved interaction (green). Likely conserved interactions for all pairs of orthologs with an interaction in at least one of the input networks can also be considered (yellow). Edges with a low probability are filtered out based on the given edge probability threshold. 3.) To identify alignment solution seeds, we search for connected components in the initial alignment graph (red ellipses). 4.) The alignment graph is then extended by connecting vertices of different seeds through gap or mismatch edges (dashed lines) if the given orthologs are connected by indirect interactions in one or both input networks, respectively. Again, the edge probability threshold is used to filter out false positives. 5.) Lastly, we search for connected components in the extended alignment graph, which represent the final alignment solutions (red ellipses), and determine their statistical significance (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031220#s3" target="_blank"><i>Materials and Methods</i></a>). These and all subsequent network representations were created with Cytoscape <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031220#pone.0031220-Smoot1" target="_blank">[53]</a>.</p

NetAligner performance in different alignment tasks.

<p>Performance of NetAligner (blue) measured in A) interactome to interactome, B) complex to interactome and C) pathway to interactome alignment benchmarks in comparison to the current standard in the field (NetworkBLAST <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031220#pone.0031220-Sharan2" target="_blank">[12]</a> and IsoRank <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031220#pone.0031220-Singh1" target="_blank">[14]</a>; grey). Precision and recall are shown on the complex/pathway and protein level, separately for each species pair (e.g. H/Y for human vs. yeast), using default parameters (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031220#s3" target="_blank"><i>Materials and Methods</i></a>). We calculated the statistical significance of the performance differences using a two-sided Fisher's exact test (with a standard p-value threshold of 0.05) and marked all significant values with an asterisk.</p

Illustrative examples of different alignment tasks.

<p>A) Interactome to interactome alignment: alignment of the yeast to the human interactome indicates that the COP9 signalosome (CSN) might be able to substitute the lid subcomplex of the proteasome and suggests a functional role of the CSN in cell-cycle control through interaction with cyclins and cyclin-dependent kinases. B) Complex to interactome alignment: alignment of the human DNA polymerase α - primase complex to the yeast interactome reveals a similar topology of the complex in the two organisms and hints towards a potential cross-talk between the DNA polymerases α and δ in yeast. C) Pathway to interactome alignment: alignment of the fly PI3K-AKT-IKK signalling pathway to the human interactome predicts an IKKB homo- to IKKA/IKKB heteromultimer evolution and uncovers different interaction patterns of IKK with the three AKT isoforms in human, indicating different roles in cellular signalling events. See main text for details. Vertices represent pairs of orthologous proteins. Edges denote either conserved interactions (green), interactions in the query (blue) or target species (yellow) that are likely conserved, gaps in the query (magenta) or target network (orange), or mismatches (dark red). The similarity of aligned proteins on the sequence level is represented by the respective vertex probability, ranging from 0 (dissimilar; white) to 1 (highly similar; blue). Phosphorylations (P) are shown as red spheres.</p

Additional file 1: of Exploring the OncoGenomic Landscape of cancer

Contains the Supplementary Figures 1–3. (PDF 1055 kb

Detecting similar binding pockets to enable systems polypharmacology

<div><p>In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.</p></div

Additional file 1 of Global analysis of suppressor mutations that rescue human genetic defects

Additional file 1: Fig. S1. Literature curation process. Fig. S2. Suppressor genes are important for maintaining health and cellular fitness. Fig. S3. Overlap with other interaction networks. Fig. S4. Functional connections between query and suppressor genes. Fig. S5. General mechanistic classes of suppression. Fig. S6. Query gene knockout is associated with large variation in fitness across cell lines. Fig. S7. Suppressor gene prediction

A selective target combination.

<p>(A) Structures of GPI (PDB ID: 1iri, chain C), DLD (PDB ID: 1zmc, chain C), PGD (PDB ID: 2jkv, chain D), SORD (PDB ID: 1pl8, chain C) and RPE (PDB ID: 3ovp, chain C) are displayed, together with cavity residues. Please note that these cavities are representative, as several structures exist for each of the proteins. For a deeper exploration, please refer to Supporting <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005522#pcbi.1005522.s001" target="_blank">S1 Data</a>. (B) Best similarity between cavities in GPI, DLD, PGD, SORD and RPE. Highest similarities represent, in principle, easier cases of polypharmacology design. In the upper triangle, the main metabolic chemotypes related to the enzymes are also displayed. (C) Reduction of biomass production upon the simultaneous inhibition in cancer (red) and normal (blue) cell lines. The effect of individual inhibitions in also showed. (D) Influence of each inhibition on metabolic cancer hallmarks. O₂ stands for oxygen consumption, Lac for lactate secretion, Glu for glucose uptake, and ROS for reactive oxygen species production. The assignment of ‘strong' and ‘mild’ reversals was based on visual inspection of the maximal flux of the corresponding reactions. When the maximal flux approached (>50% of the difference) the healthy cell line, it was classified as ‘strong reversal' ('strong worsening' if it otherwise diverged); `mild' effects were assigned to effects of less than 50%; ‘no effect' was assigned when one could observe essentially no change.</p

Expression of PSMC3IP and EPSTI1 in normal and breast cancer cell lines.

<p>We inspected the endogenous expression of PSMC3IP <b>(A)</b> and EPSTI1 <b>(B)</b> in two types of breast cancer cell lines, MDA-MB-231 and MCF-7, as compared to a normal breast epithelial cell line, MCF-10A. Estimated protein levels based on densitometry (right) of the immunoblots (left) show a PSMC3IP 19- and 15-fold expression in MDA-MB-231 and MCF-7 cells, while EPSTI1 only shows 1.9- and 1.3-fold in each cell line, respectively. Protein levels were normalized based on the loading control protein β-actin. (*<i>P</i> <0.05, **P<0.01, ***<i>P</i> <0.001 vs MCF-10A cells).</p

TRAIL-induced apoptosis in breast cancer cells.

<p><b>(A)</b> MDA-MB-231 cells treated with the apoptosis inducing ligand TRAIL at 80ng/mL for 24h show a moderate decrease in cell viability while <b>(B)</b> MCF-7 cells treated with TRAIL at 100ng/mL.for 24h show a more pronounced decrease in viability. Each bar represents the mean ±SD of three experiments performed in duplicate (*<i>P</i> <0.05, **<i>P</i> <0.01, ***<i>P</i> <0.001 vs untreated cells).</p