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Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories

<p>Abstract</p> <p>Background</p> <p>The use of gene expression profiling in both clinical and laboratory settings would be enhanced by better characterization of variance due to individual, environmental, and technical factors. Meta-analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies could yield useful information on baseline fluctuations in gene expression, although control animal data has not been available on a scale and in a form best served for data-mining.</p> <p>Results</p> <p>A dataset of control animal microarray expression data was assembled by a working group of the Health and Environmental Sciences Institute's Technical Committee on the Application of Genomics in Mechanism Based Risk Assessment in order to provide a public resource for assessments of variability in baseline gene expression. Data from over 500 Affymetrix microarrays from control rat liver and kidney were collected from 16 different institutions. Thirty-five biological and technical factors were obtained for each animal, describing a wide range of study characteristics, and a subset were evaluated in detail for their contribution to total variability using multivariate statistical and graphical techniques.</p> <p>Conclusion</p> <p>The study factors that emerged as key sources of variability included gender, organ section, strain, and fasting state. These and other study factors were identified as key descriptors that should be included in the minimal information about a toxicogenomics study needed for interpretation of results by an independent source. Genes that are the most and least variable, gender-selective, or altered by fasting were also identified and functionally categorized. Better characterization of gene expression variability in control animals will aid in the design of toxicogenomics studies and in the interpretation of their results.</p

Guest Editorial: Toxicogenomics in Risk Assessment: Communicating the Challenges-0

<p><b>Copyright information:</b></p><p>Taken from "Guest Editorial: Toxicogenomics in Risk Assessment: Communicating the Challenges"</p><p>Environmental Health Perspectives 2004;112(12):A662-A662.</p><p>Published online Jan 2004</p><p>PMCID:PMC1277120.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.</p

112(4).Tox.Pt2.MON.417-510

DNA microarrays and related tools offer promise for identification of pathways involved in toxic responses to xenobiotics. To be useful for risk assessment, experimental data must be challenged for reliability and interlaboratory reproducibility. Toward this goal, the Hepatotoxicity Working Group of the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Technical Committee on Application of Genomics to Mechanism-Based Risk Assessment evaluated and compared biological and gene expression responses in rats exposed to two model hepatotoxins-clofibrate and methapyrilene. This collaborative effort provided an unprecedented opportunity for the working group to evaluate and compare multiple biological, genomic, and toxicological parameters across different laboratories and microarray platforms. Many of the results from this collaboration are presented in accompanying articles in this minimonograph, whereas others have been published previously. In vivo studies for both compounds were conducted in two laboratories using a standard experimental protocol, and RNA samples were distributed to 16 laboratories for analysis on six microarray platforms. Histopathology, clinical chemistry, and organ weight changes were consistent with reported effects. Gene expression results demonstrated reasonable agreement between laboratories and across platforms. Discrepancies in expression profiles of some individual genes were largely due to platform differences and approaches to data analysis rather than to biological or interlaboratory variability. Despite these discrepancies there was overall agreement in the biological pathways affected by these compounds, demonstrating that transcriptional profiling is reproducible between laboratories and can reliably identify affected pathways necessary to provide mechanistic insight. This effort represents an important first step toward the use of transcriptional profiling in risk assessment