AERS spider: an online interactive tool to mine statistical associations in Adverse Event Reporting System.

BACKGROUND: Exploration of the Adverse Event Reporting System (AERS) data by a wide scientific community is limited due to several factors. First, AERS data must be intensively preprocessed to be converted into analyzable format. Second, application of the currently accepted disproportional reporting measures results in false positive signals. METHODS: We proposed a data mining strategy to improve hypothesis generation with respect to potential associations. RESULTS: By numerous examples, we illustrate that our strategy controls the false positive signals. We implemented a free online tool, AERS spider (www.chemoprofiling.org/AERS). CONCLUSIONS: We believe that AERS spider would be a valuable tool for drug safety experts.

BioProfiling.de: analytical web portal for high-throughput cell biology.

BioProfiling.de provides a comprehensive analytical toolkit for the interpretation gene/protein lists. As input, BioProfiling.de accepts a gene/protein list. As output, in one submission, the gene list is analyzed by a collection of tools which employs advanced enrichment or network-based statistical frameworks. The gene list is profiled with respect to the most information available regarding gene function, protein interactions, pathway relationships, in silico predicted microRNA to gene associations, as well as, information collected by text mining. BioProfiling.de provides a user friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. The web portal is freely available at http://www.BioProfiling.de/gene_list.

CCancer: a bird's eye view on gene lists reported in cancer-related studies.

CCancer is an automatically collected database of gene lists, which were reported mostly by experimental studies in various biological and clinical contexts. At the moment, the database covers 3369 gene lists extracted from 2644 papers published in approximately 80 peer-reviewed journals. As input, CCancer accepts a gene list. An enrichment analyses is implemented to generate, as output, a highly informative survey over recently published studies that report gene lists, which significantly intersect with the query gene list. A report on gene pairs from the input list which were frequently reported together by other biological studies is also provided. CCancer is freely available at http://mips.helmholtz-muenchen.de/proj/ccancer.

R spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from Reactome and KEGG databases.

R spider is a web-based tool for the analysis of a gene list using the systematic knowledge of core pathways and reactions in human biology accumulated in the Reactome and KEGG databases. R spider implements a network-based statistical framework, which provides a global understanding of gene relations in the supplied gene list, and fully exploits the Reactome and KEGG knowledge bases. R spider provides a user-friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. R spider is freely available at http://mips.helmholtz-muenchen.de/proj/rspider.

GeneSet2miRNA: finding the signature of cooperative miRNA activities in the gene lists.

GeneSet2miRNA is the first web-based tool which is able to identify whether or not a gene list has a signature of miRNA-regulatory activity. As input, GeneSet2miRNA accepts a list of genes. As output, a list of miRNA-regulatory models is provided. A miRNA-regulatory model is a group of miRNAs (single, pair, triplet or quadruplet) that is predicted to regulate a significant subset of genes from the submitted list. GeneSet2miRNA provides a user friendly dialog-driven web page submission available for several model organisms. GeneSet2miRNA is freely available at http://mips.helmholtz-muenchen.de/proj/gene2mir/.

ProfCom: a web tool for profiling the complex functionality of gene groups identified from high-throughput data.

ProfCom is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of 'complex functions'. A 'Complex function' is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes. ProfCom provides a user friendly dialog-driven web page submission available for several model organisms and supports most available gene identifiers. In addition, the web service interface allows the submission of any kind of annotation data. ProfCom is freely available at http://webclu.bio.wzw.tum.de/profcom/.

PPI spider: a tool for the interpretation of proteomics data in the context of protein-protein interaction networks.

Recent advances in experimental technologies allow for the detection of a complete cell proteome. Proteins that are expressed at a particular cell state or in a particular compartment as well as proteins with differential expression between various cells states are commonly delivered by many proteomics studies. Once a list of proteins is derived, a major challenge is to interpret the identified set of proteins in the biological context. Protein-protein interaction (PPI) data represents abundant information that can be employed for this purpose. However, these data have not yet been fully exploited due to the absence of a methodological framework that can integrate this type of information. Here, we propose to infer a network model from an experimentally identified protein list based on the available information about the topology of the global PPI network. We propose to use a Monte Carlo simulation procedure to compute the statistical significance of the inferred models. The method has been implemented as a freely available web-based tool, PPI spider (http://mips.helmholtz-muenchen.de/proj/ppispider). To support the practical significance of PPI spider, we collected several hundreds of recently published experimental proteomics studies that reported lists of proteins in various biological contexts. We reanalyzed them using PPI spider and demonstrated that in most cases PPI spider could provide statistically significant hypotheses that are helpful for understanding of the protein list.

Complex phylogenetic profiling reveals fundamental genotype-phenotype associations.

We have developed a computational technique refereed to as complex phylogenetic profiling. Our approach combines logic analyses of gene phylogenetic profiles and phenotype data. Logic analysis of phylogenetic profiles identifies sets of proteins whose presence or absence follows certain logic relationships. Our approach identifies phenotype specific logic, i.e. it identifies sets of proteins simultaneously present or absent only in genomes with a given phenotype. For example, for most genomes expressing phenotype A, the presence of protein C presumes the presence of protein B, while for other genomes (not expressing phenotype A) the presence of protein C presumes the absence of protein B. Application of complex phylogenetic profiling to bacterial data and several well studied phenotypes reveals genotype-phenotype associations on the level of fundamental biochemical pathways.

A systematic approach to infer biological relevance and biases of gene network structures.

The development of high-throughput technologies has generated the need for bioinformatics approaches to assess the biological relevance of gene networks. Although several tools have been proposed for analysing the enrichment of functional categories in a set of genes, none of them is suitable for evaluating the biological relevance of the gene network. We propose a procedure and develop a web-based resource (BIOREL) to estimate the functional bias (biological relevance) of any given genetic network by integrating different sources of biological information. The weights of the edges in the network may be either binary or continuous. These essential features make our web tool unique among many similar services. BIOREL provides standardized estimations of the network biases extracted from independent data. By the analyses of real data we demonstrate that the potential application of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.

Complex functionality of gene groups identified from high-throughput data.

Relating experimental data to biological knowledge is necessary to cope with the avalanches of new data emerging from recent developments in high-throughput technologies. Automatic functional profiling becomes the de facto standard approach for the secondary analysis of high-throughput data. A number of tools employing available gene functional annotations have been developed for this purpose. However, current annotations are derived mostly from traditional analysis of the individual gene function. The complex biological phenomena carried out by the concerted activity of many genes often requires the definition of new complex functionality (related to a group of genes), which is, in many cases, not available in current annotation vocabularies. Functional profiling with annotation terms related to the description of individual biological functions of a gene may fail to provide reasonable interpretation of biological relationships in a set of genes involved in complex biological phenomena. We introduce a novel procedure to profile a complex functionality of a gene set. Complex functionality is constructed as a combination of available annotation terms. By profiling ChIP-chip data from Saccharomyces cerevisiae we demonstrate that this technique produces deeper insights into the results of high-throughput experiments that are beyond the known facts described in the functional classifications.

Polypharmacology of Approved Anticancer Drugs.

The major drug discovery efforts in oncology have been concentrated on the development of selective molecules that are supposed to act specifically on one anticancer mechanism by modulating a single or several closely related drug targets. However, a bird's eye view on data from multiple available bioassays implies that most approved anticancer agents do, in fact, target many more proteins with different functions. Here we will review and systematize currently available information on the targets of several anticancer drugs along with revision of their potential mechanisms of action. Polypharmacology of the current antineoplastic agents suggests that drug clinical efficacy in oncology can be achieved only via modulation of multiple cellular mechanisms.

BIOREL: the benchmark resource to estimate the relevance of the gene networks.

The progress of high-throughput methodologies in functional genomics has lead to the development of statistical procedures to infer gene networks from various types of high-throughput data. However, due to the lack of common standards, the biological significance of the results of the different studies is hard to compare. To overcome this problem we propose a benchmark procedure and have developed a web resource (BIOREL), which is useful for estimating the biological relevance of any genetic network by integrating different sources of biological information. The associations of each gene from the network are classified as biologically relevant or not. The proportion of genes in the network classified as "relevant" is used as the overall network relevance score. Employing synthetic data we demonstrated that such a score ranks the networks fairly in respect to the relevance level. Using BIOREL as the benchmark resource we compared the quality of experimental and theoretically predicted protein interaction data.

Exploration of individuality in drug metabolism by high-throughput metabolomics: The fast line for personalized medicine.

In many cases, individuality in metabolism of a drug is a reliable predictor of the drug efficacy/safety. Modern high-throughput metabolomics is an ideal instrument to track drug metabolism in an individual after treatment. Productivity and low cost of the metabolomics are sufficient to analyse a large cohort of patients to explore individual variations in drug metabolism and to discover drug metabolic biomarkers indicative of drug efficacy/safety. The only potential disadvantage of metabolomics becoming a routine clinical procedure is a need to treat the patient once before making a prognosis. However, in many clinical applications this would not be a limitation. Here, we explore current opportunities and challenges for translating high-throughput metabolomics into the platform for personalized medicine.

Exploiting scale-free information from expression data for cancer classification.

Most studies concerning expression data analyses usually exploit information on the variability of gene intensity across samples. This information is sensitive to initial data processing, which affects the final conclusions. However expression data contains scale-free information, which is directly comparable between different samples. We propose to use the pairwise ratio of gene expression values rather than their absolute intensities for a classification of expression data. This information is stable to data processing and thus more attractive for classification analyses. In proposed schema of data analyses only information on relative gene expression levels in each sample is exploited. Testing on publicly available datasets leads to superior classification results.

Polypharmacology of small molecules targeting the ubiquitin-proteasome and ubiquitin-like systems.

Targeting the ubiquitin-proteasome system (UPS) and ubiquitin-like signalling systems (UBL) has been considered a promising therapeutic strategy to treat cancer, neurodegenerative and immunological disorders. There have been multiple efforts recently to identify novel compounds that efficiently modulate the activities of different disease-specific components of the UPS-UBL. However, it is evident that polypharmacology (the ability to affect multiple independent protein targets) is a basic property of small molecules and even highly potent molecules would have a number of "off target" effects. Here we have explored publicly available high-throughput screening data covering a wide spectrum of currently accepted drug targets in order to understand polypharmacology of small molecules targeting different components of the UPS-UBL. We have demonstrated that molecules targeting a given UPS-UBL protein also have high odds to target a given off target spectrum. Moreover, the off target spectrum differs significantly between different components of UPS-UBL. This information can be utilized further in drug discovery efforts, to improve drug efficiency and to reduce the risk of potential side effects of the prospective drugs designed to target specific UPS-UBL components.

KMT Set7/9 affects genotoxic stress response via the Mdm2 axis.

Genotoxic stress inflicted by anti-cancer drugs causes DNA breaks and genome instability. DNA double strand breaks induced by irradiation or pharmacological inhibition of Topoisomerase II activate ATM (ataxia-telangiectasia-mutated) kinase signalling pathway that in turn triggers cell cycle arrest and DNA repair. ATM-dependent gamma-phosphorylation of histone H2Ax and other histone modifications, including ubiquitnylation, promote exchange of histones and recruitment of DNA damage response (DDR) and repair proteins. Signal transduction pathways, besides DDR itself, also control expression of genes whose products cause cell cycle arrest and/or apoptosis thus ultimately affecting the sensitivity of cells to genotoxic stress. In this study, using a number of experimental approaches we provide evidence that lysine-specific methyltransferase (KMT) Set7/9 affects DDR and DNA repair, at least in part, by regulating the expression of an E3 ubiquitin ligase, Mdm2. Furthermore, we show that Set7/9 physically interacts with Mdm2. Several cancer cell lines with inverse expression of Set7/9 and Mdm2 displayed diminished survival in response to genotoxic stress. These findings are signified by our bioinformatics studies suggesting that the unleashed expression of Mdm2 in cancer patients with diminished expression of Set7/9 is associated with poor survival outcome.

Perspective on multi-target antiplatelet therapies: high content phenotypic screening as an unbiased source of novel polypharmacological strategies.

Platelets play an important role in cardiovascular thrombosis as well as in many other pathological conditions such as inflammation, atherosclerosis and cancer. While multi-target strategies to treat complex diseases are gaining considerable attention, current development of antiplatelet therapies is mostly oriented towards several single targets, arising from our present understanding of the regulation of platelet activation. Limited efforts to develop multi-target agents or multidrug therapies are mostly due to a lack of a systematic basis to define target combinations with synergistic effects. Here we discuss the perspective to use high content phenotypic screening of in vitro models as a potential source for inference of synergetic multi-target strategies to control platelet activation.

Large scale integration of drug-target information reveals poly-pharmacological drug action mechanisms in tumor cell line growth inhibition assays.

Understanding therapeutic mechanisms of drug anticancer cytotoxicity represents a key challenge in preclinical testing. Here we have performed a meta-analysis of publicly available tumor cell line growth inhibition assays (~ 70 assays from 6 independent experimental groups covering ~ 500 000 molecules) with the primary goal of understanding molecular therapeutic mechanisms of cancer cytotoxicity. To implement this we have collected currently available information on protein targets for molecules that were tested in the assays. We used a statistical methodology to identify protein targets overrepresented among molecules exhibiting cancer cytotoxicity with the particular focus of identifying overrepresented patterns consisting of several proteins (i.e. proteins "A" and "B" and "C"). Our analysis demonstrates that targeting individual proteins can result in a significant increase (up to 50-fold) of the observed odds for a molecule to be an efficient inhibitor of tumour cell line growth. However, further insight into potential molecular mechanisms reveals a multi-target mode of action: targeting a pattern of several proteins drastically increases the observed odds (up to 500-fold) for a molecule to be tumour cytotoxic. In contrast, molecules targeting only one protein but not targeting an additional set of proteins tend to be nontoxic. Our findings support a poly-pharmacology drug discovery paradigm, demonstrating that anticancer cytotoxicity is a product, in most cases, of multi-target mode of drug action.

Mining protein lists from proteomics studies: applications for drug discovery.

IMPORTANCE OF THE FIELD: In recent years, proteomics has become a common technique applied to a wide spectrum of scientific problems, including the identification of diagnostic biomarkers, monitoring the effects of drug treatments or identification of chemical properties of a protein or a drug. Although being significantly different in scientific essence, the ultimate result of the majority of proteomics studies is a protein list. Thousands of independent proteomics studies have reported protein lists in various functional contexts. AREAS COVERED IN THIS REVIEW: We review here the spectrum of scientific problems where proteomics technology was applied recently to deliver protein lists. The available bioinformatics methods commonly used to understand the properties of the protein lists are compared. WHAT THE READER WILL GAIN: The types and common functional properties of the reported protein lists are discussed. The range of scientific problems where this knowledge could be potentially helpful with a focus on drug discovery issues is explored. TAKE HOME MESSAGE: Reported protein lists represent a valuable resource which can be used for a variety of goals, ranging from biomarkers discovery to identification of novel therapeutic implications of known drugs.