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Evasion of cell death is one crucial capability acquired by tumour cells to ward-off anti-tumour therapies and represents a fundamental challenge to sustaining clinical efficacy for currently available agents. Inhibitor of apoptosis (IAP) proteins use their ubiquitin E3 ligase activity to promote cancer cell survival by mediating proliferative signalling and blocking cell death in response to diverse stimuli. Using immunoaffinity enrichment and MS, ubiquitination sites on thousands of proteins were profiled upon initiation of cell death by IAP antagonists in IAP antagonist-sensitive and -resistant breast cancer cell lines. Our analyses identified hundreds of proteins with elevated levels of ubiquitin-remnant [K-GG (Lys-Gly-Gly)] peptides upon activation of cell death by the IAP antagonist BV6. The majority of these were observed in BV6-sensitive, but not-resistant, cells. Among these were known pro-apoptotic regulators, including CYC (cytochrome c), RIP1 (receptor-interacting protein 1) and a selection of proteins known to reside in the mitochondria or regulate NF-κB (nuclear factor κB) signalling. Analysis of early time-points revealed that IAP antagonist treatment stimulated rapid ubiquitination of NF-?B signalling proteins, including TRAF2 [TNF (tumour necrosis factor) receptor-associated factor 2], HOIL-1 (haem-oxidized iron-regulatory protein 2 ubiquitin ligase-1), NEMO (NF-κB essential modifier), as well as c-IAP1 (cellular IAP1) auto-ubiquitination. Knockdown of several NF-?B pathway members reduced BV6-induced cell death and TNF production in sensitive cell lines. Importantly, RIP1 was found to be constitutively ubiquitinated in sensitive breast-cancer cell lines at higher basal level than in resistant cell lines. Together, these data show the diverse and temporally defined roles of protein ubiquitination following IAP-antagonist treatment and provide critical insights into predictive diagnostics that may enhance clinical efficacy. [ABSTRACT FROM AUTHOR]

Background: To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome. Results: In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several overexpressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides. Conclusion: Our proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, to be used as diagnostic targets. [ABSTRACT FROM AUTHOR]

We discuss the cellular automata approach and its extensions, the lattice Boltzmann and multiparticle methods. The potential of these techniques is demonstrated in the case of modeling complex systems. In particular, we consider applications taken from various fields of physics, such as reaction-diffusion systems, pattern formation phenomena, fluid flows, fracture processes and road traffic models. [ABSTRACT FROM AUTHOR]

Summary: pViz.js is a visualization library for displaying protein sequence features in a Web browser. By simply providing a sequence and the locations of its features, this lightweight, yet versatile, JavaScript library renders an interactive view of the protein features. Interactive exploration of protein sequence features over the Web is a common need in Bioinformatics. Although many Web sites have developed viewers to display these features, their implementations are usually focused on data from a specific source or use case. Some of these viewers can be adapted to fit other use cases but are not designed to be reusable. pViz makes it easy to display features as boxes aligned to a protein sequence with zooming functionality but also includes predefined renderings for secondary structure and post-translational modifications. The library is designed to further customize this view. We demonstrate such applications of pViz using two examples: a proteomic data visualization tool with an embedded viewer for displaying features on protein structure, and a tool to visualize the results of the variant_effect_predictor tool from Ensembl.Availability and implementation: pViz.js is a JavaScript library, available on github at https://github.com/Genentech/pviz. This site includes examples and functional applications, installation instructions and usage documentation. A Readme file, which explains how to use pViz with examples, is available as Supplementary Material A.Contact: masselot.alexandre@gene.comSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]