Structure-based methods for predicting mutagenicity and carcinogenicity: are we there yet?

There is a great deal of current interest in the use of commercial, automated programs for the prediction of mutagenicity and carcinogenicity based on chemical structure. However, the goal of accurate and reliable toxicity prediction for any chemical, based solely on structural information remains elusive. The toxicity prediction challenge is global in its objective, but limited in its solution, to within local domains of chemicals acting according to similar mechanisms of action in the biological system; to predict, we must be able to generalize based on chemical structure, but the biology fundamentally limits our ability to do so. Available commercial systems for mutagenicity and/or carcinogenicity prediction differ in their specifics, yet most fall in two major categories: (1) automated approaches that rely on the use of statistics for extracting correlations between structure and activity; and (2) knowledge-based expert systems that rely on a set of programmed rules distilled from available knowledge and human expert judgement. These two categories of approaches differ in the ways that they represent, process, and generalize chemical-biological activity information. An application of four commercial systems (TOPKAT, CASE/MULTI-CASE, DEREK, and OncoLogic) to mutagenicity and carcinogenicity prediction for a particular class of chemicals—the haloacetic acids (HAs)—is presented to highlight these differences. Some discussion is devoted to the issue of gauging the relative performance of commercial prediction systems, as well as to the role of prospective prediction exercises in this effort. And finally, an alternative approach that stops short of delivering a prediction to a user, involving structure-searching and data base exploration, is briefly considered.     

Biodiversity informatics: the challenge of linking data and the role of shared identifiers

A major challenge facing biodiversity informatics is integrating data stored in widely distributed databases. Initial efforts have relied on taxonomic names as the shared identifier linking records in different databases. However, taxonomic names have limitations as identifiers, being neither stable nor globally unique, and the pace of molecular taxonomic and phylogenetic research means that a lot of information in public sequence databases is not linked to formal taxonomic names. This review explores the use of other identifiers, such as specimen codes and GenBank accession numbers, to link otherwise disconnected facts in different databases. The structure of these links can also be exploited using the PageRank algorithm to rank the results of searches on biodiversity databases. The key to rich integration is a commitment to deploy and reuse globally unique, shared identifiers [such as Digital Object Identifiers (DOIs) and Life Science Identifiers (LSIDs)], and the implementation of services that link those identifiers.     

Sharing and archiving nucleic acid structure mapping data

Nucleic acids are particularly amenable to structural characterization using chemical and enzymatic probes. Each individual structure mapping experiment reveals specific information about the structure and/or dynamics of the nucleic acid. Currently, there is no simple approach for making these data publically available in a standardized format. We therefore developed a standard for reporting the results of single nucleotide resolution nucleic acid structure mapping experiments, or SNRNASMs. We propose a schema for sharing nucleic acid chemical probing data that uses generic public servers for storing, retrieving, and searching the data. We have also developed a consistent nomenclature (ontology) within the Ontology of Biomedical Investigations (OBI), which provides unique identifiers (termed persistent URLs, or PURLs) for classifying the data. Links to standardized data sets shared using our proposed format along with a tutorial and links to templates can be found at http://snrnasm.bio.unc.edu.     

Protein Information and Knowledge Extractor: Discovering biological information from proteomics data

One of the main goals in proteomics is to solve biological and molecular questions regarding a set of identified proteins. In order to achieve this goal, one has to extract and collect the existing biological data from public repositories for every protein and afterward, analyze and organize the collected data. Due to the complexity of this task and the huge amount of data available, it is not possible to gather this information by hand, making it necessary to find automatic methods of data collection. Within a proteomic context, we have developed Protein Information and Knowledge Extractor (PIKE) which solves this problem by automatically accessing several public information systems and databases across the Internet. PIKE bioinformatics tool starts with a set of identified proteins, listed as the most common protein databases accession codes, and retrieves all relevant and updated information from the most relevant databases. Once the search is complete, PIKE summarizes the information for every single protein using several file formats that share and exchange the information with other software tools. It is our opinion that PIKE represents a great step forward for information procurement and drastically reduces manual database validation for large proteomic studies. It is available at http://proteo.cnb.csic.es/pike .     

MScreen: An Integrated Compound Management and High-Throughput Screening Data Storage and Analysis System

High-throughput screening (HTS) has historically been used by the pharmaceutical industry to rapidly test hundreds of thousands of compounds to identify potential drug candidates. More recently, academic groups have used HTS to identify new chemical probes or small interfering RNA (siRNA) that can serve as experimental tools to examine the biology or physiology of novel proteins, processes, or interactions. HTS presents a significant challenge with the vast and complex nature of data generated. This report describes MScreen, a Web-based, open-source cheminformatics application for chemical library and siRNA plate management, primary HTS and dose-response data handling, structure search, and administrative functions. Each project in MScreen can be secured with passwords or shared in an open-information environment that enables collaborators to easily compare data from many screens, providing a useful means to identify compounds with desired selectivity. Unique features include compound, substance, mixture, and siRNA plate creation and formatting; automated dose-response fitting and quality control (QC); and user, target, and assay method administration. MScreen provides an effective means to facilitate HTS information handling and analysis in the academic setting so that users can efficiently view their screening data and evaluate results for follow-up.     

Toxicogenomic approach for assessing toxicant-related disease

The problems of identifying environmental factors involved in the etiology of human disease and performing safety and risk assessments of drugs and chemicals have long been formidable issues. Three principal components for predicting potential human health risks are: (1) the diverse structure and properties of thousands of chemicals and other stressors in the environment; (2) the time and dose parameters that define the relationship between exposure and disease; and (3) the genetic diversity of organisms used as surrogates to determine adverse chemical effects. The global techniques evolving from successful genomics efforts are providing new exciting tools with which to address these intractable problems of environmental health and toxicology. In order to exploit the scientific opportunities, the National Institute of Environmental Health Sciences has created the National Center for Toxicogenomics (NCT). The primary mission of the NCT is to use gene expression technology, proteomics and metabolite profiling to create a reference knowledge base that will allow scientists to understand mechanisms of toxicity and to be able to predict the potential toxicity of new chemical entities and drugs. A principal scientific objective underpinning the use of microarray analysis of chemical exposures is to demonstrate the utility of signature profiling of the action of drugs or chemicals and to utilize microarray methodologies to determine biomarkers of exposure and potential adverse effects. The initial approach of the NCT is to utilize proof-of-principle experiments in an effort to "phenotypically anchor" the altered patterns of gene expression to conventional parameters of toxicity and to define dose and time relationships in which the expression of such signature genes may precede the development of overt toxicity. The microarray approach is used in conjunction with proteomic techniques to identify specific proteins that may serve as signature biomarkers. The longer-range goal of these efforts is to develop a reference relational database of chemical effects in biological systems (CEBS) that can be used to define common mechanisms of toxicity, chemical and drug actions, to define cellular pathways of response, injury and, ultimately, disease. In order to implement this strategy, the NCT has created a consortium of research organizations and private sector companies to actively collaborative in populating the database with high quality primary data. The evolution of discrete databases to a knowledge base of toxicogenomics will be accomplished through establishing relational interfaces with other sources of information on the structure and activity of chemicals such as that of the National Toxicology Program (NTP) and with databases annotating gene identity, sequence, and function.     

Field validation of laboratory-derived IOBC toxicity ratings for natural enemies in commercial vineyards

Management of pests and diseases remains a key issue for agricultural profitability and environmental health. Moves towards sustainability require a reduction in chemical toxicity loadings and conservation of natural enemies to maintain pest control. There is a lot of information from laboratory tests regarding the effects of chemicals on beneficial predators and parasitoids but very few translations of these effects into field impacts particularly under commercial conditions. To address this issue we calculated a chemical toxicity score for 19 commercial vineyards based on IOBC toxicity ratings and application number, and compared this to extensive field collections to determine if natural enemy populations can be related to predicted toxicity loadings. Invertebrates were sampled four times during the growing season using canopy sticky traps and ground level pitfall traps. Ordination analyses using non-metric multidimensional scaling indicated community structure in vineyards correlated to site chemical use, while principal components analyses identified the taxa involved. One ordination axis from canopy data and two axes from ground level data were correlated to overall IOBC ratings for the vineyards. Principal components analyses indicated that spiders, lacewings, carabids and parasitoids were all affected by chemical use. IOBC rating based on laboratory studies therefore correlated with chemical effects on field populations of natural enemies in commercial vineyards where complexes of pesticides were applied. The use of chemicals with low toxicity to beneficials as predicted by IOBC ratings will contribute to preservation and maintenance of natural enemies in vineyard ecosystems.     

Prediction of toxicity from chemical structure

The basis for the prediction of toxicity from chemical structure is that the properties of a chemical are implicit in its molecular structure. Biological activity can be expressed as a function of partition and reactivity, that is, for a chemical to be able to express its toxicity, it must be transported from its site of administration to its site of action and then it must bind to or react with its receptor or target. This process may also involve metabolic transformation of the chemical. The application of these principles to the prediction of the toxicity of new or untested chemicals has been achieved in a number of different ways covering a wide range of complexity, from computer systems containing databases of hundreds of chemicals, to simple "reading across" between chemicals with similar chemical/toxicological functionality. The common feature of the approaches described in this article is that their starting point is a mechanistic hypothesis linking chemical structure and/or functionality with the toxicological endpoint of interest. The prediction of toxicity from chemical structure can make a valuable contribution to the reduction of animal usage in the screening out of potentially toxic chemicals at an early stage and in providing data for making positive classifications of toxicity. This revised version was published online in July 2006 with corrections to the Cover Date.     

An automated system designed for large scale NMR data deposition and annotation: application to over 600 assigned chemical shift data entries to the BioMagResBank from the Riken Structural Genomics/Proteomics Initiative internal database

Biomolecular NMR chemical shift data are key information for the functional analysis of biomolecules and the development of new techniques for NMR studies utilizing chemical shift statistical information. Structural genomics projects are major contributors to the accumulation of protein chemical shift information. The management of the large quantities of NMR data generated by each project in a local database and the transfer of the data to the public databases are still formidable tasks because of the complicated nature of NMR data. Here we report an automated and efficient system developed for the deposition and annotation of a large number of data sets including (1)H, (13)C and (15)N resonance assignments used for the structure determination of proteins. We have demonstrated the feasibility of our system by applying it to over 600 entries from the internal database generated by the RIKEN Structural Genomics/Proteomics Initiative (RSGI) to the public database, BioMagResBank (BMRB). We have assessed the quality of the deposited chemical shifts by comparing them with those predicted from the PDB coordinate entry for the corresponding protein. The same comparison for other matched BMRB/PDB entries deposited from 2001-2011 has been carried out and the results suggest that the RSGI entries greatly improved the quality of the BMRB database. Since the entries include chemical shifts acquired under strikingly similar experimental conditions, these NMR data can be expected to be a promising resource to improve current technologies as well as to develop new NMR methods for protein studies.     

Clinicians’ Involvement of Patients in Decision Making. A Video Based Comparison of Their Behavior in Public vs. Private Practice

Background

Little is known about the extent to which Peruvian physicians seek to involve patients in shared decision making, or about the variation in these efforts across different settings.

Objective

To measure the extent to which Peruvian clinicians involve their patients in decision making and to explore the differences between clinicians’ behavior in private vs. public practice.

Design

Videographic analysis.

Participants and Setting

Seven academic physicians who provided care to patients in a public and a private setting participate in this study. All the encounters in both settings were filmed on one random day of February 2012. Approach: Two raters, working independently and in duplicate used the 12-item OPTION scale to quantify the extent of physician effort to involve patients in shared decision making (with 0 indicating no effort and 100 maximum possible effort) in 58 video recordings of usual clinical encounters in private and public practice.

Results

The mean OPTION score was 14.3 (SD 7.0). Although the OPTION score in the private setting (mean 16.5, SD 7.3) was higher than in the public setting (mean 12.3 SD 6.1) this difference was not statistically significant (p = .09).

Conclusion

Peruvian academic physicians in this convenience sample barely sought to involve their patients in shared decision making. Additional studies are required to confirm these results which suggest that patient-centered care remains an unfulfilled promise and a source of inequity within and across the private and the public sectors in Peru.     

EU-OPENSCREEN—chemical tools for the study of plant biology and resistance mechanisms

EU-OPENSCREEN is an academic research infrastructure initiative in Europe for enabling researchers in all life sciences to take advantage of chemical biology approaches to their projects. In a collaborative effort of national networks in 16 European countries, EU-OPENSCREEN will develop novel chemical compounds with external users to address questions in, among other fields, systems and network biology (directed and selective perturbation of signalling pathways), structural biology (compound-target interactions at atomic resolution), pharmacology (early drug discovery and toxicology) and plant biology (response of wild or crop plants to environmental and agricultural substances). EU-OPENSCREEN supports all stages of a tool development project, including assay adaptation, high-throughput screening and chemical optimisation of the ‘hit’ compounds. All tool compounds and data will be made available to the scientific community. EU-OPENSCREEN integrates high-capacity screening platforms throughout Europe, which share a rationally selected compound collection comprising up to 300,000 (commercial and proprietary compounds collected from European chemists). By testing systematically this chemical collection in hundreds of assays originating from very different biological themes, the screening process generates enormous amounts of information about the biological activities of the substances and thereby steadily enriches our understanding of how and where they act.     

Structural determinants of developmental toxicity in hamsters.

A CASE/MULTICASE structure activity relationship (SAR) model of developmental toxicity of chemicals in hamsters (HaDT) was developed. The model exhibited a predictive performance of 74%. The model's overall predictivity and informational content were similar to those of an SAR model of mutagenicity in Salmonella. However, unlike the Salmonella mutagenicity model, the HaDT model did not identify overtly chemically reactive moieties as associated with activity. Moreover, examination of the number and nature of significant structural determinants suggested that developmental toxicity in hamsters was not the result of a unique mechanism or attack on a specific molecular target. The analysis also indicated that the availability of experimental data on additional chemicals would improve the performance of the SAR model.     

Identification of c-myc coding region determinant RNA sequences and structures cleaved by an RNase1-like endoribonuclease

The coding region of c-myc mRNA encompassing the coding region determinant (CRD) nucleotides (nts) 1705-1792 is critical in regulating c-myc mRNA stability. This is in part due to the susceptibility of c-myc CRD RNA to attack by an endoribonuclease. We have previously purified and characterized a mammalian endoribonuclease that cleaves c-myc CRD RNA in vitro. This enzyme is tentatively identified as a 35 kDa RNase1-like endonuclease. In an effort to understand the sequence and secondary structure requirements for RNA cleavage by this enzyme, we have determined the secondary structure of the c-myc CRD RNA nts 1705-1792 using RNase probing technique. The secondary structure of c-myc CRD RNA possesses five stems; two of which contain 4 base pairs (stems I and V) and three consisting of 3 base pairs (stems II, III, and IV). Endonucleolytic assays using the c-myc CRD and several c-myc CRD mutants as substrates led to the following conclusions: (i) the enzyme prefers to cleave in between the dinucleotides UA, CA, and UG in single-stranded regions; (ii) the enzyme is more specific towards UA dinucleotides. These properties further distinguish the enzyme from previously described mammalian endonuclease that cleaves c-myc mRNA in vitro.     

Synthesis and antileishmanial evaluation of thiazole orange analogs

Cyanine compounds have previously shown excellent in vitro and promising in vivo antileishmanial efficacy, but the potential toxicity of these agents is a concern. A series of 22 analogs of thiazole orange ((Z)-1-methyl-4-((3-methylbenzo[d]thiazol-2(3H)-ylidene)methyl)quinolin-1-ium salt), a commercial cyanine dye with antileishmanial activity, were synthesized in an effort to increase the selectivity of such compounds while maintaining efficacy. Cyanines possessing substitutions on the quinolinium ring system displayed potency against Leishmania donovani axenic amastigotes that differed little from the parent compound (IC₅₀ 12–42 nM), while ring disjunction analogs were both less potent and less toxic. Changes in DNA melting temperature were modest when synthetic oligonucleotides were incubated with selected analogs (ΔTm ≤ 5 °C), with ring disjunction analogs showing the least effect on this parameter. Despite the high antileishmanial potency of the target compounds, their toxicity and relatively flat SAR suggests that further information regarding the target(s) of these molecules is needed to aid their development as antileishmanials.     

The ARKdb: genome databases for farmed and other animals

The ARKdb genome databases provide comprehensive public repositories for genome mapping data from farmed species and other animals (http://www.thearkdb.org) providing a resource similar in function to that offered by GDB or MGD for human or mouse genome mapping data, respectively. Because we have attempted to build a generic mapping database, the system has wide utility, particularly for those species for which development of a specific resource would be prohibitive. The ARKdb genome database model has been implemented for 10 species to date. These are pig, chicken, sheep, cattle, horse, deer, tilapia, cat, turkey and salmon. Access to the ARKdb databases is effected via the World Wide Web using the ARKdb browser and Anubis map viewer. The information stored includes details of loci, maps, experimental methods and the source references. Links to other information sources such as PubMed and EMBL/GenBank are provided. Responsibility for data entry and curation is shared amongst scientists active in genome research in the species of interest. Mirror sites in the United States are maintained in addition to the central genome server at Roslin.     

KaBOB: ontology-based semantic integration of biomedical databases

Background

The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources.

Results

We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license.

Conclusions

KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0559-3) contains supplementary material, which is available to authorized users.     

Bioinformatics tools and database resources for systems genetics analysis in mice--a short review and an evaluation of future needs

During a meeting of the SYSGENET working group 'Bioinformatics', currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a 'cloud' should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats.