Complex functionality of gene groups identified from high-throughput data

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.

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

Detecting phenotype-specific interactions between biological processes from microarray data and annotations

High throughput technologies enable researchers to measure expression levels on a genomic scale. However, the correct and efficient biological interpretation of such voluminous data remains a challenging problem. Many tools have been developed for the analysis of GO terms that are over- or under-represented in a list of differentially expressed genes. However, a previously unexplored aspect is the identification of changes in the way various biological processes interact in a given condition with respect to a reference. Here, we present a novel approach that aims at identifying such interactions between biological processes that are significantly different in a given phenotype with respect to normal. The proposed technique uses vector-space representation, SVD-based dimensionality reduction, differential weighting, and bootstrapping to asses the significance of the interactions under the multiple and complex dependencies expected between the biological processes. We illustrate our approach on two real data sets involving breast and lung cancer. More than 88 percent of the interactions found by our approach were deemed to be correct by an extensive manual review of literature. An interesting subset of such interactions is discussed in detail and shown to have the potential to open new avenues for research in lung and breast cancer.

ADGO 2.0: interpreting microarray data and list of genes using composite annotations

ADGO 2.0 is a web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. Some other tools also incorporate composite annotations solely for interpreting lists of genes but usually provide highly redundant information. This new version has the following additional features: first, it provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. Second, it screens redundant composite annotations when generating and prioritizing them. Third, it incorporates union and subtracted sets as well as intersection sets. Lastly, users can upload their own gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets. The first two features are unique to ADGO 2.0. Using our tool, we demonstrate analyses of a microarray dataset and a list of genes for T-cell differentiation. The new ADGO is available at http://www.btool.org/ADGO2.

MIPS: curated databases and comprehensive secondary data resources in 2010

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38 000 000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de).

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.

PLIPS, an automatically collected database of protein lists reported by proteomics studies

The spectrum of problems covered by proteomics studies range from the discovery of compartment specific cell proteomes to clinical applications, including the identification of diagnostic markers and monitoring the effects of drug treatments. In most cases, the ultimate results of a proteomics study are lists of proteins found to be present (or differentially present) at cell physiological conditions under study. Normally, the results are published directly in the article in one or several tables. In many cases, this type of information remains disseminated in hundreds of proteomics publications. We have developed a Web mining tool which allows the collection of this information by searching through full text papers and automatically selecting tables, which report a list of protein identifiers. By searching through major proteomics journals, we have collected approximately 800 independent studies published recently, which reported about 1000 different protein lists. On the basis of this data, we developed a computational tool PLIPS (Protein Lists Identified in Proteomics Studies). PLIPS accepts as input a list of protein/gene identifiers. With the use of statistical analyses, PLIPS infers recently published proteomics studies, which report protein lists that significantly intersect with a query list. PLIPS is a freely available Web-based tool ( http://mips.helmholtz-muenchen.de/proj/plips ).

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/.

TICL--a web tool for network-based interpretation of compound lists inferred by high-throughput metabolomics

High-throughput metabolomics is a dynamically developing technology that enables the mass separation of complex mixtures at very high resolution. Metabolic profiling has begun to be widely used in clinical research to study the molecular mechanisms of complex cell disorders. Similar to transcriptomics, which is capable of detecting genes at differential states, metabolomics is able to deliver a list of compounds differentially present between explored cell physiological conditions. The bioinformatics challenge lies in a statistically valid interpretation of the functional context for identified sets of metabolites. Here, we present TICL, a web tool for the automatic interpretation of lists of compounds. The major advance of TICL is that it not only provides a model of possible compound transformations related to the input list, but also implements a robust statistical framework to estimate the significance of the inferred model. The TICL web tool is freely accessible at http://mips.helmholtz-muenchen.de/proj/cmp.

KEGG spider: interpretation of genomics data in the context of the global gene metabolic network

KEGG spider is a web-based tool for interpretation of experimentally derived gene lists in order to gain understanding of metabolism variations at a genomic level. KEGG spider implements a 'pathway-free' framework that overcomes a major bottleneck of enrichment analyses: it provides global models uniting genes from different metabolic pathways. Analyzing a number of experimentally derived gene lists, we demonstrate that KEGG spider provides deeper insights into metabolism variations in comparison to existing methods.

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/.