Stephen Harris

Rat toxicogenomic study reveals analytical consistency across microarray platforms

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.

ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research.

The mapping of the human genome and the determination of corresponding gene functions, pathways, and biological mechanisms are driving the emergence of the new research fields of toxicogenomics and systems toxicology. Many technological advances such as microarrays are enabling this paradigm shift that indicates an unprecedented advancement in the methods of understanding the expression of toxicity at the molecular level. At the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration, core facilities for genomic, proteomic, and metabonomic technologies have been established that use standardized experimental procedures to support centerwide toxicogenomic research. Collectively, these facilities are continuously producing an unprecedented volume of data. NCTR plans to develop a toxicoinformatics integrated system (TIS) for the purpose of fully integrating genomic, proteomic, and metabonomic data with the data in public repositories as well as conventional (Italic)in vitro(/Italic) and (Italic)in vivo(/Italic) toxicology data. The TIS will enable data curation in accordance with standard ontology and provide or interface a rich collection of tools for data analysis and knowledge mining. In this article the design, practical issues, and functions of the TIS are discussed through presenting its prototype version, ArrayTrack, for the management and analysis of DNA microarray data. ArrayTrack is logically constructed of three linked components: a) a library (LIB) that mirrors critical data in public databases; b) a database (MicroarrayDB) that stores microarray experiment information that is Minimal Information About a Microarray Experiment (MIAME) compliant; and c) tools (TOOL) that operate on experimental and public data for knowledge discovery. Using ArrayTrack, we can select an analysis method from the TOOL and apply the method to selected microarray data stored in the MicroarrayDB; the analysis results can be linked directly to gene information in the LIB.

ISA software suite

Summary: The first open source software suite for experimentalists and curators that (i) assists in the annotation and local management of experimental metadata from high-throughput studies employing one or a combination of omics and other technologies; (ii) empowers users to uptake community-defined checklists and ontologies; and (iii) facilitates submission to international public repositories. Availability and Implementation: Software, documentation, case studies and implementations at http://www.isa-tools.org Contact: isatools@googlegroups.com

Microarray scanner calibration curves: characteristics and implications

BackgroundMicroarray-based measurement of mRNA abundance assumes a linear relationship between the fluorescence intensity and the dye concentration. In reality, however, the calibration curve can be nonlinear.ResultsBy scanning a microarray scanner calibration slide containing known concentrations of fluorescent dyes under 18 PMT gains, we were able to evaluate the differences in calibration characteristics of Cy5 and Cy3. First, the calibration curve for the same dye under the same PMT gain is nonlinear at both the high and low intensity ends. Second, the degree of nonlinearity of the calibration curve depends on the PMT gain. Third, the two PMTs (for Cy5 and Cy3) behave differently even under the same gain. Fourth, the background intensity for the Cy3 channel is higher than that for the Cy5 channel. The impact of such characteristics on the accuracy and reproducibility of measured mRNA abundance and the calculated ratios was demonstrated. Combined with simulation results, we provided explanations to the existence of ratio underestimation, intensity-dependence of ratio bias, and anti-correlation of ratios in dye-swap replicates. We further demonstrated that although Lowess normalization effectively eliminates the intensity-dependence of ratio bias, the systematic deviation from true ratios largely remained. A method of calculating ratios based on concentrations estimated from the calibration curves was proposed for correcting ratio bias.ConclusionIt is preferable to scan microarray slides at fixed, optimal gain settings under which the linearity between concentration and intensity is maximized. Although normalization methods improve reproducibility of microarray measurements, they appear less effective in improving accuracy.

ArrayTrack: An FDA and Public Genomic Tool

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data, and the availability of functional information for data interpretation. At the FDAs National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack, that is also used in the routine review of genomic data submitted to the FDA. ArrayTrack stores a full range of information related to DNA microarrays and clinical and non-clinical studies as well as the digested data derived from proteomics and metabonomics experiments. In addition, ArrayTrack provides a rich collection of functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation. Many data analysis and visualization tools are available with ArrayTrack for individual platform data analysis, multiple omics data integration, and integrated analysis of omics data with study data. Importantly, gene expression data, functional information, and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. Using ArrayTrack, users can select an analysis method from the ArrayTrack tool box, apply the method to selected microarray data, and the analysis of results can be directly linked to individual gene, pathway, and Gene Ontology analysis. ArrayTrack is publicly available online ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/index.htm ) and the prospective user can also request a local installation version by contacting the authors.

EADB: An Estrogenic Activity Database for Assessing Potential Endocrine Activity

Endocrine-active chemicals can potentially have adverse effects on both humans and wildlife. They can interfere with the bodys endocrine system through direct or indirect interactions with many protein targets. Estrogen receptors (ERs) are one of the major targets, and many endocrine disruptors are estrogenic and affect the normal estrogen signaling pathways. However, ERs can also serve as therapeutic targets for various medical conditions, such as menopausal symptoms, osteoporosis, and ER-positive breast cancer. Because of the decades-long interest in the safety and therapeutic utility of estrogenic chemicals, a large number of chemicals have been assayed for estrogenic activity, but these data exist in various sources and different formats that restrict the ability of regulatory and industry scientists to utilize them fully for assessing risk-benefit. To address this issue, we have developed an Estrogenic Activity Database (EADB; http://www.fda.gov/ScienceResearch/BioinformaticsTools/EstrogenicActivityDatabaseEADB/default.htm) and made it freely available to the public. EADB contains 18,114 estrogenic activity data points collected for 8212 chemicals tested in 1284 binding, reporter gene, cell proliferation, and in vivo assays in 11 different species. The chemicals cover a broad chemical structure space and the data span a wide range of activities. A set of tools allow users to access EADB and evaluate potential endocrine activity of chemicals. As a case study, a classification model was developed using EADB for predicting ER binding of chemicals.

atBioNet– an integrated network analysis tool for genomics and biomarker discovery

BackgroundLarge amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity.ResultsatBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis.ConclusionatBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

An integrated bioinformatics infrastructure essential for advancing pharmacogenomics and personalized medicine in the context of the FDA's Critical Path Initiative.

Pharmacogenomics (PGx) is identified in the FDA Critical Path document as a major opportunity for advancing medical product development and personalized medicine. An integrated bioinformatics infrastructure for use in FDA data review is crucial to realize the benefits of PGx for public health. We have developed an integrated bioinformatics tool, called ArrayTrack, for managing, analyzing and interpreting genomic and other biomarker data (e.g. proteomic and metabolomic data). ArrayTrack is a highly flexible and robust software platform, which allows evolving with technological advances and changing user needs. ArrayTrack is used in the routine review of genomic data submitted to the FDA; here, three hypothetical examples of its use in the Voluntary eXploratory Data Submission (VXDS) program are illustrated.:

A unifying ontology to integrate histological and clinical observations for drug-induced liver injury.

Drug-induced liver injury (DILI) may present any morphologic characteristic of acute or chronic liver disease with no standardized terminology in place. Defining lexemes of DILI histopathology would allow the development of advanced knowledge discovery and data mining tools for across comparisons of publicly available information. For these purposes, a DILI ontology (DILIo) was developed by using the Unified Medical Language System tool and the standardized terminology of the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). The DILIo was entrained on findings of 114 US Food and Drug Administration-approved drugs by extracting all clinically DILI-related histopathologic descriptions for 1082 liver biopsy samples, which were then analyzed using the Unified Medical Language System MetaMap and subsequently mapped to the SNOMED CT. The DILIo provides a standard means to describe and organize liver injury induced by drugs, enabling comparative analysis of drugs within and across histopathologic terms. The analysis showed that flutamide, troglitazone, diclofenac, isoniazid, and tamoxifen were reported to have the most diverse histopathologic observations in liver biopsy. Necrosis, cholestasis, fatty degeneration, fibrosis, infiltrate, and hepatic necrosis were the most frequent terms used as descriptors of histopathologic features of DILI. In conclusion, DILIo entrains different algorithms for an efficient meta-analysis of published findings for an improved understanding of mechanisms and clinical characteristics of DILI.

FDA drug labeling: rich resources to facilitate precision medicine, drug safety, and regulatory science

Here, we provide a concise overview of US Food and Drug Administration (FDA) drug labeling, which details drug products, drug-drug interactions, adverse drug reactions (ADRs), and more. Labeling data have been collected over several decades by the FDA and are an important resource for regulatory research and decision making. However, navigating through this data is challenging. To aid such navigation, the FDALabel database was developed, which contains a set of approximately 80000 labeling data. The full-text searching capability of FDALabel and querying based on any combination of specific sections, document types, market categories, market date, and other labeling information makes it a powerful and attractive tool for a variety of applications. Here, we illustrate the utility of FDALabel using case scenarios in pharmacogenomics biomarkers and ADR studies.