Jennifer Fostel

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project aims to foster the coordinated development of minimum-information checklists and provide a resource for those exploring the range of extant checklists.

Toxicogenomics and systems toxicology: aims and prospects.

Toxicogenomics combines transcript, protein and metabolite profiling with conventional toxicology to investigate the interaction between genes and environmental stress in disease causation. The patterns of altered molecular expression that are caused by specific exposures or disease outcomes have revealed how several toxicants act and cause disease. Despite these success stories, the field faces noteworthy challenges in discriminating the molecular basis of toxicity. We argue that toxicology is gradually evolving into a systems toxicology that will eventually allow us to describe all the toxicological interactions that occur within a living system under stress and use our knowledge of toxicogenomic responses in one species to predict the modes-of-action of similar agents in other species.

Modeling biomedical experimental processes with OBI

BackgroundExperimental descriptions are typically stored as free text without using standardized terminology, creating challenges in comparison, reproduction and analysis. These difficulties impose limitations on data exchange and information retrieval.ResultsThe Ontology for Biomedical Investigations (OBI), developed as a global, cross-community effort, provides a resource that represents biomedical investigations in an explicit and integrative framework. Here we detail three real-world applications of OBI, provide detailed modeling information and explain how to use OBI.ConclusionWe demonstrate how OBI can be applied to different biomedical investigations to both facilitate interpretation of the experimental process and increase the computational processing and integration within the Semantic Web. The logical definitions of the entities involved allow computers to unambiguously understand and integrate different biological experimental processes and their relevant components.AvailabilityOBI is available at http://purl.obolibrary.org/obo/obi/2009-11-02/obi.owl

The metabolomics standards initiative (MSI)

In 2005, the Metabolomics Standards Initiative has been formed. An outline and general introduction is provided to inform about the history, structure, working plan and intentions of this initiative. Comments on any of the suggested minimal reporting standards are welcome to be sent to the open email list Msi-workgroups-feedback@lists.sourceforge.net

The MGED Ontology: a resource for semantics-based description of microarray experiments

MOTIVATION The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. RESULTS Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. AVAILABILITY The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICBs Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). CONTACT Stoeckrt@pcbi.upenn.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Compound Cytotoxicity Profiling Using Quantitative High-Throughput Screening

Background The propensity of compounds to produce adverse health effects in humans is generally evaluated using animal-based test methods. Such methods can be relatively expensive, low-throughput, and associated with pain suffered by the treated animals. In addition, differences in species biology may confound extrapolation to human health effects. Objective The National Toxicology Program and the National Institutes of Health Chemical Genomics Center are collaborating to identify a battery of cell-based screens to prioritize compounds for further toxicologic evaluation. Methods A collection of 1,408 compounds previously tested in one or more traditional toxicologic assays were profiled for cytotoxicity using quantitative high-throughput screening (qHTS) in 13 human and rodent cell types derived from six common targets of xenobiotic toxicity (liver, blood, kidney, nerve, lung, skin). Selected cytotoxicants were further tested to define response kinetics. Results qHTS of these compounds produced robust and reproducible results, which allowed cross-compound, cross-cell type, and cross-species comparisons. Some compounds were cytotoxic to all cell types at similar concentrations, whereas others exhibited species- or cell type–specific cytotoxicity. Closely related cell types and analogous cell types in human and rodent frequently showed different patterns of cytotoxicity. Some compounds inducing similar levels of cytotoxicity showed distinct time dependence in kinetic studies, consistent with known mechanisms of toxicity. Conclusions The generation of high-quality cytotoxicity data on this large library of known compounds using qHTS demonstrates the potential of this methodology to profile a much broader array of assays and compounds, which, in aggregate, may be valuable for prioritizing compounds for further toxicologic evaluation, identifying compounds with particular mechanisms of action, and potentially predicting in vivo biological response.

CEBS—Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data

Abstract CEBS (Chemical Effects in Biological Systems) is an integrated public repository for toxicogenomics data, including the study design and timeline, clinical chemistry and histopathology findings and microarray and proteomics data. CEBS contains data derived from studies of chemicals and of genetic alterations, and is compatible with clinical and environmental studies. CEBS is designed to permit the user to query the data using the study conditions, the subject responses and then, having identified an appropriate set of subjects, to move to the microarray module of CEBS to carry out gene signature and pathway analysis. Scope of CEBS: CEBS currently holds 22 studies of rats, four studies of mice and one study of Caenorhabditis elegans. CEBS can also accommodate data from studies of human subjects. Toxicogenomics studies currently in CEBS comprise over 4000 microarray hybridizations, and 75 2D gel images annotated with protein identification performed by MALDI and MS/MS. CEBS contains raw microarray data collected in accordance with MIAME guidelines and provides tools for data selection, pre-processing and analysis resulting in annotated lists of genes of interest. Additionally, clinical chemistry and histopathology findings from over 1500 animals are included in CEBS. CEBS/BID: The BID (Biomedical Investigation Database) is another component of the CEBS system. BID is a relational database used to load and curate study data prior to export to CEBS, in addition to capturing and displaying novel data types such as PCR data, or additional fields of interest, including those defined by the HESI Toxicogenomics Committee (in preparation). BID has been shared with Health Canada and the US Environmental Protection Agency. CEBS is available at http://cebs.niehs.nih.gov. BID can be accessed via the user interface from https://dir-apps.niehs.nih.gov/arc/. Requests for a copy of BID and for depositing data into CEBS or BID are available at http://www.niehs.nih.gov/cebs-df/.

The First RSBI (ISA-TAB) Workshop: 'Can a Simple Format Work for Complex Studies?'

This article summarizes the motivation for, and the proceedings of, the first ISA-TAB workshop held December 6-8, 2007, at the EBI, Cambridge, UK. This exploratory workshop, organized by members of the Microarray Gene Expression Data (MGED) Societys Reporting Structure for Biological Investigations (RSBI) working group, brought together a group of developers of a range of collaborative systems to discuss the use of a common format to address the pressing need of reporting and communicating data and metadata from biological, biomedical, and environmental studies employing combinations of genomics, transcriptomics, proteomics, and metabolomics technologies along with more conventional methodologies. The expertise of the participants comprised database development, data management, and hands-on experience in the development of data communication standards. The workshops outcomes are set to help formalize the proposed Investigation, Study, Assay (ISA)-TAB tab-delimited format for representing and communicating experimental metadata. This article is part of the special issue of OMICS on the activities of the Genomics Standards Consortium (GSC).

Targeted deletion of Nrf2 reduces urethane-induced lung tumor development in mice.

Nrf2 is a key transcription factor that regulates cellular redox and defense responses. However, permanent Nrf2 activation in human lung carcinomas promotes pulmonary malignancy and chemoresistance. We tested the hypothesis that Nrf2 has cell survival properties and lack of Nrf2 suppresses chemically-induced pulmonary neoplasia by treating Nrf2 +/+ and Nrf2 -/- mice with urethane. Airway inflammation and injury were assessed by bronchoalveolar lavage analyses and histopathology, and lung tumors were analyzed by gross and histologic analysis. We used transcriptomics to assess Nrf2-dependent changes in pulmonary gene transcripts at multiple stages of neoplasia. Lung hyperpermeability, cell death and apoptosis, and inflammatory cell infiltration were significantly higher in Nrf2 -/- mice compared to Nrf2 +/+ mice 9 and 11 wk after urethane. Significantly fewer lung adenomas were found in Nrf2 -/- mice than in Nrf2 +/+ mice at 12 and 22 wk. Nrf2 modulated expression of genes involved cell-cell signaling, glutathione metabolism and oxidative stress response, and immune responses during early stage neoplasia. In lung tumors, Nrf2-altered genes had roles in transcriptional regulation of cell cycle and proliferation, carcinogenesis, organismal injury and abnormalities, xenobiotic metabolism, and cell-cell signaling genes. Collectively, Nrf2 deficiency decreased susceptibility to urethane-induced lung tumorigenesis in mice. Cell survival properties of Nrf2 were supported, at least in part, by reduced early death of initiated cells and heightened advantage for tumor cell expansion in Nrf2 +/+ mice relative to Nrf2 -/- mice. Our results were consistent with the concept that Nrf2 over-activation is an adaptive response of cancer conferring resistance to anti-cancer drugs and promoting malignancy.

Gene expression profile exploration of a large dataset on chronic fatigue syndrome

OBJECTIVE To gain understanding of the molecular basis of chronic fatigue syndrome (CFS) through gene expression analysis using a large microarray data set in conjunction with clinically administrated questionnaires. METHOD Data from the Wichita (KS, USA) CFS Surveillance Study was used, comprising 167 participants with two self-report questionnaires (multidimensional fatigue inventory [MFI] and Zung depression scale [Zung]), microarray data, empiric classification, and others. Microarray data was analyzed using bioinformatics tools from ArrayTrack. RESULTS Correspondence analysis was applied to the MFI questionnaire to select the 23 samples having either the most or the least fatigue, and to the Zung questionnaire to select the 26 samples having either the most or least depression; ten samples were common, resulting in a total of 39 samples. The MFI and Zung-based CFS/non-CFS (NF) classifications on the 39 samples were consistent with the empiric classification. Two differentially-expressed gene lists were determined, 188 fatigue-related genes and 164 depression-related genes, which shared 24 common genes and involved 11 common pathways. Principal component analysis based on 24 genes clearly separates 39 samples with respect to their likelihood to be CFS. Most of the 24 genes are not previously reported for CFS, yet their functions are consistent with the prevailing model of CFS, such as immune response, apoptosis, ion channel activity, signal transduction, cell-cell signaling, regulation of cell growth and neuronal activity. Hierarchical cluster analysis was performed based on 24 genes to classify 128 (=167-39) unassigned samples. Several of the 11 identified common pathways are supported by earlier findings for CFS, such as cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction. Importantly, most of the 11 common pathways are interrelated, suggesting complex biological mechanisms associated with CFS. CONCLUSION Bioinformatics is critical in this study to select definitive sample groups, analyze gene expression data and gain insight into biological mechanisms. The 24 identified common genes and 11 common pathways could be important in future studies of CFS at the molecular level.