miRViz: a novel webserver application to visualize and interpret microRNA datasets.

MicroRNAs (miRNAs) are small non-coding RNAs that are involved in the regulation of major pathways in eukaryotic cells through their binding to and repression of multiple mRNAs. With high-throughput methodologies, various outcomes can be measured that produce long lists of miRNAs that are often difficult to interpret. A common question is: after differential expression or phenotypic screening of miRNA mimics, which miRNA should be chosen for further investigation? Here, we present miRViz (http://mirviz.

miR-600 Acts as a Bimodal Switch that Regulates Breast Cancer Stem Cell Fate through WNT Signaling.

Breast cancer stem cells (bCSCs) have been implicated in tumor progression and therapeutic resistance; however, the molecular mechanisms that define this state are unclear. We have performed two microRNA (miRNA) gain- and loss-of-function screens to identify miRNAs that regulate the choice between bCSC self-renewal and differentiation. We find that micro-RNA (miR)-600 silencing results in bCSC expansion, while its overexpression reduces bCSC self-renewal, leading to decreased in vivo tumorigenicity.

MicroRNA degeneracy and pluripotentiality within a Lavallière-tie architecture confers robustness to gene expression networks.

Modularity, feedback control, functional redundancy and bowtie architecture have been proposed as key factors that confer robustness to complex biological systems. MicroRNAs (miRNAs) are highly conserved but functionally dispensable. These antinomic properties suggest that miRNAs fine-tune gene expression rather than act as genetic switches.

Φ-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays.

Phenotypic screening monitors phenotypic changes induced by perturbations, including those generated by drugs or RNA interference. Currently-used methods for scoring screen hits have proven to be problematic, particularly when applied to physiologically relevant conditions such as low cell numbers or inefficient transfection. Here, we describe the Φ-score, which is a novel scoring method for the identification of phenotypic modifiers or hits in cell-based screens.

A statistically inferred microRNA network identifies breast cancer target miR-940 as an actin cytoskeleton regulator.

MiRNAs are key regulators of gene expression. By binding to many genes, they create a complex network of gene co-regulation. Here, using a network-based approach, we identified miRNA hub groups by their close connections and common targets.

Analysis of monoclonal antibody by a novel CE-UV/MALDI-MS interface.

mAbs are highly complex proteins that present a wide range of microheterogeneity that requires multiple analytical methods for full structure assessment and quality control. As a consequence, the characterization of mAbs on different levels is particularly product- and time-consuming. CE-MS couplings, especially to MALDI, appear really attractive methods for the characterization of biological samples.

The modulation of attachment, growth and morphology of cancerous prostate cells by polyelectrolyte nanofilms.

The behaviour of cancerous epithelial prostatic cells (PC3) growing on polyelectrolytes (PE) coatings was compared to the behaviour of immortalized normal prostatic cells (PNT-2). The cell behaviour was evaluated and quantified in terms of initial cell attachment, growth, metabolic activity, morphometry, adhesion, apoptosis and stress related gene expression. Both the anionic PSS (poly(sodium 4-styrenesulphonate))-terminated surface and cationic PAH (poly(allylamine hydrochloride))-terminated surfaces were not cytotoxic.

Computational profiling of bioactive compounds using a target-dependent composite workflow.

Computational target fishing is a chemoinformatic method aimed at determining main and secondary targets of bioactive compounds in order to explain their mechanism of action, anticipate potential side effects, or repurpose existing drugs for novel therapeutic indications. Many existing successes in this area have been based on a use of a single computational method to estimate potentially new target-ligand associations. We herewith present an automated workflow using several methods to optimally browse target-ligand space according to existing knowledge on either ligand and target space under investigation.