Abstract: Systematic investigations of complex pathological cascades during ischemic brain injury help to elucidate novel therapeutic targets against cerebral ischemia. Although some transcription factors (TFs) involved in cerebral ischemia, systematic surveys of their changes during ischemic brain injury have not been reported. Moreover, some multi-target agents effectively protected against ischemic stroke, but their mechanisms, especially the targets of TFs, are still unclear. Therefore, a comprehensive approach by integrating network pharmacology strategy and a new concatenated tandem array of consensus transcription factor response elements method to systematically investigate the target TFs critical in the protection against cerebral ischemia by a medication was first reported, and then applied to a multi-target drug, Danhong injection (DHI). High-throughput nature and depth of coverage, as well as high quantitative accuracy of the developed approach, make it more suitable for analyzing such multi-target agents. Results indicated that pre-B-cell leukemia transcription factor 1 and cyclic AMP-dependent transcription factor 1, along with six other TFs, are putative target TFs for DHI-mediated protection against cerebral ischemia. This study provides, for the first time, a systematic investigation of the target TFs critical to DHI-mediated protection against cerebral ischemia, as well as reveals more potential therapeutic targets for ischemic stroke.

Abstract: Although STK11 (LKB1) mutation is a major mediator of lung cancer progression, targeted therapy has not been implemented due to STK11 mutations being loss-of-function. Here, we report that targeting the Na(+)/K(+)-ATPase (ATP1A1) is synthetic lethal with STK11 mutations in lung cancer. The cardiac glycosides (CGs) digoxin, digitoxin and ouabain, which directly inhibit ATP1A1 function, exhibited selective anticancer effects on STK11 mutant lung cancer cell lines. Restoring STK11 function reduced the efficacy of CGs. Clinically relevant doses of digoxin decreased the growth of STK11 mutant xenografts compared to wild type STK11 xenografts. Increased cellular stress was associated with the STK11-specific efficacy of CGs. Inhibiting ROS production attenuated the efficacy of CGs, and STK11-AMPK signaling was important in overcoming the stress induced by CGs. Taken together, these results show that STK11 mutation is a novel biomarker for responsiveness to CGs. Inhibition of ATP1A1 using CGs warrants exploration as a targeted therapy for STK11 mutant lung cancer.

Abstract: Toxin-antitoxin systems constitute a native survival strategy of pathogenic bacteria and thus are potential targets of antibiotic drugs. Here, we target the Zeta-Epsilon toxin-antitoxin system, which is responsible for the stable maintenance of certain multiresistance plasmids in Gram-positive bacteria. Peptide ligands were designed on the basis of the epsilon(2)zeta(2) complex. Three alpha helices of Zeta forming the protein-protein interaction (PPI) site were selected and peptides were designed conserving the residues interacting with Epsilon antitoxin while substituting residues binding intramolecularly to other parts of Zeta. Designed peptides were synthesized with an N-terminal fluoresceinyl-carboxy-residue for binding assays and provided active ligands, which were used to define the hot spots of the epsilon(2)zeta(2) complex. Further shortening and modification of the binding peptides provided ligands with affinities <100 nM, allowing us to determine the most relevant PPIs and implement a robust competition binding assay.

Abstract: MOTIVATION: Finding genes which are directly perturbed or targeted by drugs is of great interest and importance in drug discovery. Several network filtering methods have been created to predict the gene targets of drugs from gene expression data based on an ordinary differential equation model of the gene regulatory network (GRN). A critical step in these methods involves inferring the GRN from the expression data, which is a very challenging problem on its own. In addition, existing network filtering methods require computationally intensive parameter tuning or expression data from experiments with known genetic perturbations or both. RESULTS: We developed a method called DeltaNet for the identification of drug targets from gene expression data. Here, the gene target predictions were directly inferred from the data without a separate step of GRN inference. DeltaNet formulation led to solving an underdetermined linear regression problem, for which we employed least angle regression (DeltaNet-LAR) or LASSO regularization (DeltaNet-LASSO). The predictions using DeltaNet for expression data of Escherichia coli, yeast, fruit fly and human were significantly more accurate than those using network filtering methods, namely mode of action by network identification (MNI) and sparse simultaneous equation model (SSEM). Furthermore, DeltaNet using LAR did not require any parameter tuning and could provide computational speed-up over existing methods. CONCLUSION: DeltaNet is a robust and numerically efficient tool for identifying gene perturbations from gene expression data. Importantly, the method requires little to no expert supervision, while providing accurate gene target predictions. AVAILABILITY AND IMPLEMENTATION: DeltaNet is available on http://www.cabsel.ethz.ch/tools/DeltaNet CONTACT: rudi.gunawan@chem.ethz.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: We employed virtual screening followed by in vitro evaluation to discover novel inhibitors of ER aminopeptidase 1, an important enzyme for the human adaptive immune response that has emerged as an attractive target for cancer immunotherapy and the control of autoimmunity. Screening hits included three structurally related compounds carrying the (E)-N'-((1H-indol-3-yl)methylene)-1H-pyrazole-5-carbohydrazide scaffold and (2-carboxylatophenyl)sulfanyl-ethylmercury as novel ERAP1 inhibitors. The latter, also known as thimerosal, a common component in vaccines, was found to inhibit ERAP1 in the submicromolar range and to present strong selectivity versus the homologous aminopeptidases ERAP2 and IRAP. Cell-based analysis indicated that thimerosal can effectively reduce ERAP1-dependent cross-presentation by dendritic cells in a dose-dependent manner.

Abstract: The discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.

Abstract: Metabolites Biological Role (MBROLE) is a server that performs functional enrichment analysis of a list of chemical compounds derived from a metabolomics experiment, which allows this list to be interpreted in biological terms. Since its release in 2011, MBROLE has been used by different groups worldwide to analyse metabolomics experiments from a variety of organisms. Here we present the latest version of the system, MBROLE2, accessible at http://csbg.cnb.csic.es/mbrole2 MBROLE2 has been supplemented with 10 databases not available in the previous version, which allow analysis over a larger, richer set of vocabularies including metabolite-protein and drug-protein interactions. This new version performs automatic conversion of compound identifiers from different databases, thus simplifying usage. In addition, the user interface has been redesigned to generate an interactive, more intuitive representation of the results.

Abstract: We present systemsDock, a web server for network pharmacology-based prediction and analysis, which permits docking simulation and molecular pathway map for comprehensive characterization of ligand selectivity and interpretation of ligand action on a complex molecular network. It incorporates an elaborately designed scoring function for molecular docking to assess protein-ligand binding potential. For large-scale screening and ease of investigation, systemsDock has a user-friendly GUI interface for molecule preparation, parameter specification and result inspection. Ligand binding potentials against individual proteins can be directly displayed on an uploaded molecular interaction map, allowing users to systemically investigate network-dependent effects of a drug or drug candidate. A case study is given to demonstrate how systemsDock can be used to discover a test compound's multi-target activity. systemsDock is freely accessible at http://systemsdock.unit.oist.jp/.

Abstract: Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using 'gold standard' gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene-phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/.