Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project provides a resource for those exploring the range of extant minimum information checklists and fosters coordinated development of such checklists.

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BackgroundThe 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.ObjectiveThe 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.MethodsA 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.ResultsqHTS 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.ConclusionsThe 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.

The cell population with cancer stem cell characteristics increased after prolonged continuous selection for doxorubicin resistance.

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.

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

Enzymes in the ergosterol-biosynthetic pathway are the targets of a number of antifungal agents including azoles, allylamines, and morpholines. In order to understand the response of Saccharomyces cerevisiae to perturbations in the ergosterol pathway, genome-wide transcript profiles following exposure to a number of antifungal agents targeting ergosterol biosynthesis (clotrimazole, fluconazole, itraconazole, ketoconazole, voriconazole, terbinafine, and amorolfine) were obtained. These profiles were compared to the transcript profiles of strains containing deletions of one of the late-stage ergosterol genes: ERG2, ERG5, or ERG6. A total of 234 genes were identified as responsive, including the majority of genes from the ergosterol pathway. Expression of several responsive genes, including ERG25, YER067W, and YNL300W, was also monitored by PCR over time following exposure to ketoconazole. The kinetics of transcriptional response support the conditions selected for the microarray experiment. In addition to ergosterol-biosynthetic genes, 36 mitochondrial genes and a number of other genes with roles related to ergosterol function were responsive, as were a number of genes responsive to oxidative stress. Transcriptional changes related to heme biosynthesis were observed in cells treated with chemical agents, suggesting an additional effect of exposure to these compounds. The expression profile in response to a novel imidazole, PNU-144248E, was also determined. The concordance of responsive genes suggests that this compound has the same mode of action as other azoles. Thus, genome-wide transcript profiles can be used to predict the mode of action of a chemical agent as well as to characterize expression changes in response to perturbation of a metabolic pathway.