A novel predicted NFκB pathway specific to prostate cancer.

<p>(A) A pathway of putative molecular activities surrounding NFκB as predicted by our computational framework (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.g001" target="_blank">Fig 1</a>): the pathway results from a genome-wide functional interaction specific to the NFκB pathway in human prostate cancer and contains 50 genes connected by 112 mechanism-specific interactions. To generate this novel NFκB pathway, we extracted a high-confidence subnetwork from the genome-wide functional interaction network (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#sec015" target="_blank">Methods</a>) around 18 query genes in total (blue type), including five NFκB complex genes (NFκB1, NFκB2, REL, RELA, RELB), five NFκB specific inhibitors (NFκBIA, NFκBIE, IκBKB, IκBKG, CHUK), and eight genes found to be differentially expressed between lethal and indolent prostate cancer (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.t001" target="_blank">Table 1</a>). (B) We recovered all known molecular interaction mechanisms between NFκB complex members and their inhibitors (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.s024" target="_blank">S17 Table</a>).</p

Mechanistic interaction networks specific to prostate cancer and the NFκB pathway.

<p>(A) We constructed an interaction ontology containing seven interaction types: a functional relationship can be a physical interaction (two genes directly bind to each other and interact physically), a complex (two genes form a protein binding complex), a covalent modification (a kinase posttranslationally modifies a substrate), a phosphorylation (a kinase adds a phosphate group to a substrate at a phosphorylation site), a shared pathway (two genes react in the same pathway which can be an indirect regulation), a regulatory interaction (a gene is activating or inhibiting another gene), or a synthetic interaction (two genes simultaneously regulate another gene, whereas the two genes individually would not regulate the third gene). (B) The performance (AUC values) of the seven trained mechanistic interaction networks after performing a 10-fold gene-holdout-based cross-validation of each of the networks revealed that the mechanistic interaction networks are generally more accurate than the global functional network.</p

NEDD9 / ZFP36 co-immunoprecipitation supports predicted physical interaction and knockdown regulates cell proliferation in a prostate cancer line.

<p>(A) Anti-ZFP36 was used to co-immunoprecipitate ZFP36-NEDD9 complex, confirming the presence of NEDD9 by western blot; IgG-rabbit antibody was included as a negative control. (B) LAPC4 cells were transfected with NEDD9 and control siRNAs (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#sec015" target="_blank">Methods</a>), with knockdown efficiency verified by western blot. (C) Cell proliferation rate measured after NEDD9 knockdown by WST-1 optical density (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#sec015" target="_blank">Methods</a>). Depletion of NEDD9 transcript consistently downregulates proliferation rates and suggests a possible role in growth regulation.</p

List of genes significantly upregulated in lethal prostate cancer and highly confidently associcated with NFκB in multiple biological contexts.

<p>We integrated 860 total datasets (651 gene expression datasets and 225 gene interaction networks) using a Bayesian framework in different biological contexts (including cell death, cell differentiation, cell cycle, cell proliferation, cell migration, and NFκB regulation; <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.s010" target="_blank">S3 Table</a>). From such context-specific networks, we extracted the subnetworks of genes most confidently associated with NFκB (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#pcbi.1004820.s012" target="_blank">S5 Table</a>), which were subsequently analyzed in a differential expression study for significant (FDR corrected <i>p<0</i>.<i>05)</i> changes between lethal and indolent prostate cancer (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004820#sec015" target="_blank">Methods</a>); this resulted in eight total genes.</p

DataSheet_1_DNases improve effectiveness of antibiotic treatment in murine polymicrobial sepsis.pdf

IntroductionNeutrophil extracellular traps (NETs) have various beneficial and detrimental effects in the body. It has been reported that some bacteria may evade the immune system when entangled in NETs. Thus, the aim of the current study was to evaluate the effects of a combined DNase and antibiotic therapy in a murine model of abdominal sepsis.MethodsC57BL/6 mice underwent a cecum-ligation-and-puncture procedure. We used wild-type and knockout mice with the same genetic background (PAD4-KO and DNase1-KO). Mice were treated with (I) antibiotics (Metronidazol/Cefuroxime), (II) DNAse1, or (III) with the combination of both; mock-treated mice served as controls. We employed a streak plate procedure and 16s-RNA analysis to evaluate bacterial translocation and quantified NETs formation by ELISA and immune fluorescence. Western blot and proteomics analysis were used to determine inflammation.ResultsA total of n=73 mice were used. Mice that were genetically unable to produce extended NETs or were treated with DNases displayed superior survival and bacterial clearance and reduced inflammation. DNase1 treatment significantly improved clearance of Gram-negative bacteria and survival rates. Importantly, the combination of DNase1 and antibiotics reduced tissue damage, neutrophil activation, and NETs formation in the affected intestinal tissue.ConclusionThe combination of antibiotics with DNase1 ameliorates abdominal sepsis. Gram-negative bacteria are cleared better when NETs are cleaved by DNase1. Future studies on antibiotic therapy should be combined with anti-NETs therapies.</p

An Integrative Computational Approach for Prioritization of Genomic Variants

<div><p>An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.</p></div

Docked folate conformations (blue: native, red: H27R mutant-bound folate molecules) showing the distinct change in the optimal conformation between native and the mutant.

<p>Docked folate conformations (blue: native, red: H27R mutant-bound folate molecules) showing the distinct change in the optimal conformation between native and the mutant.</p

Analytical workflow for identification of spina bifida candidate genes.

<p>Analytical workflow for identification of spina bifida candidate genes.</p

Volume and surface area of the of the cleft in the native and the mutant structures as calculated by 3V.

<p>Volume and surface area of the of the cleft in the native and the mutant structures as calculated by 3V.</p

Access to the analytical tools within the described bioinformatics environment.

<p>Access to the analytical tools within the described bioinformatics environment.</p