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Suppression of superconductivity in disordered systems is a fundamental problem of condensed matter physics. Here we investigate superconducting niobium-titanium-nitride (Nb 1-x Ti x N) thin films grown by the atomic layer deposition (ALD) with slightly different growth process parameters. We observe a smooth crossover from the disorder-driven superconductor-normal metal transition (SMT) to the superconductor-insulator transition (SIT) via the intermediate Bose metal state detected by the low-temperature saturation of the temperature dependence of the sheet resistance. We demonstrate that the SIT via the intervening Bose metal state occurs if the sheet resistance of the film in the maximum, R max prior to the superconducting drop of R(T), exceeds R q  = h/4e 2 .

We report stimulated emission in the 2.8-3.5 μm wavelength range from HgTe/CdHgTe quantum well (QW) heterostructures at temperatures available with thermoelectric cooling. The structures were designed to suppress the Auger recombination by implementing narrow (1.5 - 2 nm wide) QWs. We conclude that Peltier cooled operation is feasible in lasers based on such structures, making them of interest for spectroscopy applications in the atmospheric transparency window from 3 to 5 μm.

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.

p53 is a critical tumor suppressor in humans. It functions mostly as a transcriptional factor and its activity is regulated by numerous post-translational modifications. Among different covalent modifications found on p53 the most controversial one is lysine methylation. We found that human G9a (hG9a) unlike its mouse orthologue (mG9a) potently stimulated p53 transcriptional activity. Both ectopic and endogenous hG9a augmented p53-dependent transcription of pro-apoptotic genes, including Bax and Puma, resulting in enhanced apoptosis and reduced colony formation. Significantly, shRNA-mediated knockdown of hG9a attenuated p53-dependent activation of Puma. On the molecular level, hG9a interacted with histone acetyltransferase, p300/CBP, resulting in increased histone acetylation at the promoter of Puma. The bioinformatics data substantiated our findings showing that positive correlation between G9a and p53 expression is associated with better survival of lung cancer patients. Collectively, this study demonstrates that depending on the cellular and organismal context, orthologous proteins may exert both overlapping and opposing functions. Furthermore, this finding has important ramifications on the use of G9a inhibitors in combination with genotoxic drugs to treat p53-positive tumors.

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., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Malignant mesothelioma (MM) is an aggressive, fatal tumor strongly associated with asbestos exposure. There is an urgent need to improve MM patient outcomes and this requires functionally validated pre-clinical models. Mesothelioma-derived cell lines provide an essential and relatively robust tool and remain among the most widely used systems for candidate drug evaluation. Although a number of cell lines are commercially available, a detailed comparison of these commercial lines with freshly derived primary tumor cells to validate their suitability as pre-clinical models is lacking. To address this, patient-derived primary mesothelioma cell lines were established and characterized using complementary multidisciplinary approaches and bioinformatic analysis. Clinical markers of mesothelioma, transcriptional and metabolic profiles, as well as the status of p53 and the tumor suppressor genes CDKN2A and NF2, were examined in primary cell lines and in two widely used commercial lines. Expression of MM-associated markers, as well as the status of CDKN2A, NF2, the 'gatekeeper' in MM development, and their products demonstrated that primary cell lines are more representative of the tumor close to its native state and show a degree of molecular diversity, thus capturing the disease heterogeneity in a patient cohort. Molecular profiling revealed a significantly different transcriptome and marked metabolic shift towards a greater glycolytic phenotype in commercial compared with primary cell lines. Our results highlight that multiple, appropriately characterised, patient-derived tumor cell lines are required to enable concurrent evaluation of molecular profiles versus drug response. Furthermore, application of this approach to other difficult-to-treat tumors would generate improved cellular models for pre-clinical evaluation of novel targeted therapies.

The integrity of the genome is maintained by a host of surveillance and repair mechanisms that are pivotal for cellular function. The tumour suppressor protein p53 is a major component of the DNA damage response pathway and plays a vital role in the maintenance of cell-cycle checkpoints. Here we show that a microRNA, miR-486, and its host gene ankyrin-1 (ANK1) are induced by p53 following DNA damage. Strikingly, the cytoskeleton adaptor protein ankyrin-1 was induced over 80-fold following DNA damage. ANK1 is upregulated in response to a variety of DNA damage agents in a range of cell types. We demonstrate that miR-486-5p is involved in controlling G1/S transition following DNA damage, whereas the induction of the ankyrin-1 protein alters the structure of the actin cytoskeleton and sustains limited cell migration during DNA damage. Importantly, we found that higher ANK1 expression correlates with decreased survival in cancer patients. Thus, these observations highlight ANK1 as an important effector downstream of the p53 pathway.

p53MutaGene is the first online tool for statistical validation of hypotheses regarding the effect of p53 mutational status on gene regulation in cancer. This tool is based on several large-scale clinical gene expression data sets and currently covers breast, colon and lung cancers. The tool detects differential co-expression patterns in expression data between p53 mutated versus p53 normal samples for the user-specified genes. Statistically significant differential co-expression for a gene pair is indicative that regulation of two genes is sensitive to the presence of p53 mutations. p53MutaGene can be used in 'single mode' where the user can test a specific pair of genes or in 'discovery mode' designed for analysis of several genes. Using several examples, we demonstrate that p53MutaGene is a useful tool for fast statistical validation in clinical data of p53-dependent gene regulation patterns. The tool is freely available at http://www.bioprofiling.de/tp53.

The reconstruction of the geological (tectonic) structure and environments of subglacial Lake Vostok is based on geophysical surveys and the study of mineral particles found in cores of accreted ice and frozen lake water (sampled after the lake was unsealed). Seismic reflection and refraction investigations conducted in the southern part of Lake Vostok show very thin (200-300 m) sedimentary cover overlying a crystalline basement. Most of this thin veneer is thought to have been deposited during temperate-glacial conditions in Oligocene to Middle Miocene time (ca 34-14 Ma). The composition of the lake-bottom sediments can be deduced from mineral inclusions found in cores of accreted ice. Inclusions are represented by soft aggregates consisting mainly of clay-mica minerals and micrometre-sized quartz grains. Some of these inclusions contain subangular to semi-rounded rock clasts (siltstones and sandstones) ranging from 0.3 to 8 mm in size. In total, 31 zircon grains have been identified in two rock clasts and dated using SHRIMP-II. The ages of the studied zircons range from 0.6 to 2.0 Ga with two distinct clusters between 0.8 and 1.15 Ga and between 1.6 and 1.8 Ga. Rock clasts obviously came from the western lake shore, which is thus composed of terrigenous strata with an age of not older than 600 Ma. The sedimentary nature of the western lake shore is also confirmed by seismic refraction data showing seismic velocities there of 5.4-5.5 km s(-1) at the bedrock surface. After Lake Vostok was unsealed, its water (frozen and sampled next season) was also studied with scanning electron microscopy and X-ray microprobe analysis. This study showed the existence of calcium carbonate and silica microparticles (10-20 μm across) in frozen water., (© 2015 The Author(s).)

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., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

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.

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.

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.

During the recent years lysine methyltransferase Set7/9 ((Su(var)-3-9, Enhancer-of-Zeste, Trithorax) domain containing protein 7/9) has emerged as an important regulator of different transcription factors. In this study, we report a novel function for Set7/9 as a critical co-activator of E2 promoter-binding factor 1 (E2F1)-dependent transcription in response to DNA damage. By means of various biochemical, cell biology, and bioinformatics approaches, we uncovered that cell-cycle progression through the G1/S checkpoint of tumour cells upon DNA damage is defined by the threshold of expression of both E2F1 and Set7/9. The latter affects the activity of E2F1 by indirectly modulating histone modifications in the promoters of E2F1-dependent genes. Moreover, Set7/9 differentially affects E2F1 transcription targets: it promotes cell proliferation via expression of the CCNE1 gene and represses apoptosis by inhibiting the TP73 gene. Our biochemical screening of the panel of lung tumour cell lines suggests that these two factors are critically important for transcriptional upregulation of the CCNE1 gene product and hence successful progression through cell cycle. These findings identify Set7/9 as a potential biomarker in tumour cells with overexpressed E2F1 activity.

Activation of serine biosynthesis supports growth and proliferation of cancer cells. Human cancers often exhibit overexpression of phosphoglycerate dehydrogenase (PHGDH), the metabolic enzyme that catalyses the reaction that diverts serine biosynthesis from the glycolytic pathway. By refueling serine biosynthetic pathways, cancer cells sustain their metabolic requirements, promoting macromolecule synthesis, anaplerotic flux and ATP. Serine biosynthesis intersects glutaminolysis and together with this pathway provides substrates for production of antioxidant GSH. In human lung adenocarcinomas we identified a correlation between serine biosynthetic pathway and p73 expression. Metabolic profiling of human cancer cell line revealed that TAp73 activates serine biosynthesis, resulting in increased intracellular levels of serine and glycine, associated to accumulation of glutamate, tricarboxylic acid (TCA) anaplerotic intermediates and GSH. However, at molecular level p73 does not directly regulate serine metabolic enzymes, but transcriptionally controls a key enzyme of glutaminolysis, glutaminase-2 (GLS-2). p73, through GLS-2, favors conversion of glutamine in glutamate, which in turn drives the serine biosynthetic pathway. Serine and glutamate can be then employed for GSH synthesis, thus the p73-dependent metabolic switch enables potential response against oxidative stress. In knockdown experiment, indeed, TAp73 depletion completely abrogates cancer cell proliferation capacity in serine/glycine-deprivation, supporting the role of p73 to help cancer cells under metabolic stress. These findings implicate p73 in regulation of cancer metabolism and suggest that TAp73 influences glutamine and serine metabolism, affecting GSH synthesis and determining cancer pathogenesis.

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., (Copyright © 2014 John Wiley & Sons, Ltd.)

Multiple clinical studies have correlated gene expression with survival outcome in cancer on a genome-wide scale. However, in many cases, no obvious correlation between expression of well-known tumour-related genes (that is, p53, p73 and p21) and survival rates of patients has been observed. This can be mainly explained by the complex molecular mechanisms involved in cancer, which mask the clinical relevance of a gene with multiple functions if only gene expression status is considered. As we demonstrate here, in many such cases, the expression of the gene interaction partners (gene 'interactome') correlates significantly with cancer survival and is indicative of the role of that gene in cancer. On the basis of this principle, we have implemented a free online datamining tool (http://www.bioprofiling.de/PPISURV). PPISURV automatically correlates expression of an input gene interactome with survival rates on >40 publicly available clinical expression data sets covering various tumours involving about 8000 patients in total. To derive the query gene interactome, PPISURV employs several public databases including protein-protein interactions, regulatory and signalling pathways and protein post-translational modifications.

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.

The use of existing drugs for new therapeutic applications, commonly referred to as drug repositioning, is a way for fast and cost-efficient drug discovery. Drug repositioning in oncology is commonly initiated by in vitro experimental evidence that a drug exhibits anticancer cytotoxicity. Any independent verification that the observed effects in vitro may be valid in a clinical setting, and that the drug could potentially affect patient survival in vivo is of paramount importance. Despite considerable recent efforts in computational drug repositioning, none of the studies have considered patient survival information in modelling the potential of existing/new drugs in the management of cancer. Therefore, we have developed DRUGSURV; this is the first computational tool to estimate the potential effects of a drug using patient survival information derived from clinical cancer expression data sets. DRUGSURV provides statistical evidence that a drug can affect survival outcome in particular clinical conditions to justify further investigation of the drug anticancer potential and to guide clinical trial design. DRUGSURV covers both approved drugs (∼1700) as well as experimental drugs (∼5000) and is freely available at http://www.bioprofiling.de/drugsurv.

The tumour suppressor p53 is a crucial regulator of cell cycle arrest and apoptosis by acting as a transcription factor to regulate a variety of genes. At least in part, this control is exerted by p53 via regulating expression of numerous microRNAs. We identified two abundantly expressed microRNAs, miR-16 and miR-26a, whose expression is regulated by p53 during the checkpoint arrest induced by the genotoxic drug, doxorubicin. Importantly, among the targets of these miRs are two critical checkpoint kinases, Chk1 and Wee1. The p53-dependent augmentation of miR-16 and miR-26a expression levels led to the cell cycle arrest of tumour cells in G1/S and increased apoptosis. Strikingly, the bioinformatics analysis of survival times for patients with breast and prostate cancers has revealed that co-expression of mir-16 and miR-26a correlated with a better survival outcome. Collectively, our data provide a novel mechanism whereby p53 represses Chk1 and Wee1 expression, at least partially, via upregulation of miR-16 and miR-26a and thus sensitizes tumour cells to genotoxic therapies.

The article presents an algorithm of diagnostics and treatment of renal cysts and other liquid neoplasms of the retroperitoneal space on an analysis of 270 case histories. The algorithm takes into account the achievements of modern medical technologies developed in the recent years. The application of the proposed algorithm must elevate efficiency of the diagnosis and quality of treatment of patients with renal cysts.

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.

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.

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.

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.

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/.

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.

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.

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 ).

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.

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/.

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.

The results of nephrectomy for hydronephrosis and arteriolosclerotic kidney performed by open (30), laparoscopic (22) and lumboscopic (27) accesses were compared. The advantages of the endovideosurgical method of operation were statistically proved by the indices of invasiveness of surgery. No difference between the transperitoneal and retroperitoneal accesses in nephrectomy was found.

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.

Based on the data available in the literature and his own findings, the author determines the urgency of study of environment-dependent diseases in a poor ecological area. The analytical survey presents data available about the ecological situation in the town of Omsk, a center of chemical and oil-refining industries of Western Siberia from 1997 to 2003. Particular emphasis is laid on a child's responses to the combined exposure to unfavorable environmental factors. The signs characterizing ecopathology are considered and the main principles of the treatment and prevention of environment-dependent diseases are given.

A benchmark of several popular methods, Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), Maximal Margin Linear Programming (MMLP), Radial Basis Function Neural Network (RBFNN), and Multiple Linear Regression (MLR), is reported for quantitative-structure property relationships (QSPR) of stability constants logK1 for the 1:1 (M:L) and logbeta2 for 1:2 complexes of metal cations Ag+ and Eu3+ with diverse sets of organic molecules in water at 298 K and ionic strength 0.1 M. The methods were tested on three types of descriptors: molecular descriptors including E-state values, counts of atoms determined for E-state atom types, and substructural molecular fragments (SMF). Comparison of the models was performed using a 5-fold external cross-validation procedure. Robust statistical tests (bootstrap and Kolmogorov-Smirnov statistics) were employed to evaluate the significance of calculated models. The Wilcoxon signed-rank test was used to compare the performance of methods. Individual structure-complexation property models obtained with nonlinear methods demonstrated a significantly better performance than the models built using multilinear regression analysis (MLRA). However, the averaging of several MLRA models based on SMF descriptors provided as good of a prediction as the most efficient nonlinear techniques. Support Vector Machines and Associative Neural Networks contributed in the largest number of significant models. Models based on fragments (SMF descriptors and E-state counts) had higher prediction ability than those based on E-state indices. The use of SMF descriptors and E-state counts provided similar results, whereas E-state indices lead to less significant models. The current study illustrates the difficulties of quantitative comparison of different methods: conclusions based only on one data set without appropriate statistical tests could be wrong.

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.

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.

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.

Motivation: Any development of new methods for automatic functional annotation of proteins according to their sequences requires high-quality data (as benchmark) as well as tedious preparatory work to generate sequence parameters required as input data for the machine learning methods. Different program settings and incompatible protocols make a comparison of the analyzed methods difficult., Results: The MIPS Bacterial Functional Annotation Benchmark dataset (MIPS-BFAB) is a new, high-quality resource comprising four bacterial genomes manually annotated according to the MIPS functional catalogue (FunCat). These resources include precalculated sequence parameters, such as sequence similarity scores, InterPro domain composition and other parameters that could be used to develop and benchmark methods for functional annotation of bacterial protein sequences. These data are provided in XML format and can be used by scientists who are not necessarily experts in genome annotation., Availability: BFAB is available at http://mips.gsf.de/proj/bfab

The Maximal Margin (MAMA) linear programming classification algorithm has recently been proposed and tested for cancer classification based on expression data. It demonstrated sound performance on publicly available expression datasets. We developed a web interface to allow potential users easy access to the MAMA classification tool. Basic and advanced options provide flexibility in exploitation. The input data format is the same as that used in most publicly available datasets. This makes the web resource particularly convenient for non-expert machine learning users working in the field of expression data analysis.

Motivation: Microarray data appear particularly useful to investigate mechanisms in cancer biology and represent one of the most powerful tools to uncover the genetic mechanisms causing loss of cell cycle control. Recently, several different methods to employ microarray data as a diagnostic tool in cancer classification have been proposed. These procedures take changes in the expression of particular genes into account but do not consider disruptions in certain gene interactions caused by the tumor. It is probable that some genes participating in tumor development do not change their expression level dramatically. Thus, they cannot be detected by simple classification approaches used previously. For these reasons, a classification procedure exploiting information related to changes in gene interactions is needed., Results: We propose a MAximal MArgin Linear Programming (MAMA) method for the classification of tumor samples based on microarray data. This procedure detects groups of genes and constructs models (features) that strongly correlate with particular tumor types. The detected features include genes whose functional relations are changed for particular cancer types. The proposed method was tested on two publicly available datasets and demonstrated a prediction ability superior to previously employed classification schemes., Availability: The MAMA system was developed using the linear programming system LINDO http://www.lindo.com. A Perl script that specifies the optimization problem for this software is available upon request from the authors.

The typing of F. tularensis strains by four variable number of tandem repeats (VNTR) loci has been carried out. Among the strains isolated in the Stavropol and Krasnodar Territories seven genotypes have been detected and their spread in different natural foci has been analyzed. The data thus obtained suggest that the VNTR analysis may become an important instrument for studying the structure of the natural foci of tularemia and evolutionary relationships between individual areas of these foci.