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How consistent are we? Interlaboratory comparison study in fathead minnows using the model estrogen 17α‐ethinylestradiol to develop recommendations for environmental transcriptomics
Feswick, A., M. Isaacs, A. Biales, R. Flick, D. Bencic, R. Wang, C. Vulpe, M. Brown-Augustine, A. Loguinov, F. Falciani, P. Antczak, J. Herbert, L. Brown, N. Denslow, K. Kroll, C. Lavelle, V. Dang, L. Escalon, N. Garcia-Reyero, C. Martyniuk, AND K. Munkittrick. How consistent are we? Interlaboratory comparison study in fathead minnows using the model estrogen 17α‐ethinylestradiol to develop recommendations for environmental transcriptomics. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, 36(10):2614-2623, (2017). https://doi.org/10.1002/etc.3799
The use of biological indicators for environmental regulations, risk assessments, or monitoring programs requires careful investigation of the experimental method to generate reliable and reproducible data. Interlaboratory assays are conducted to validate the effectiveness and reliability of a method, and to demonstrate how an established protocol performs under different conditions. In the context of ecotoxicology and environmental monitoring, interlaboratory comparisons have been conducted for physiological endpoints such as testosterone (T), 17β-estradiol (E2) and 11-ketotestosterone (11KT) plasma steroids [1, 2], ethoxyresorufin-O-deethylase; ERODs [3, 4] and vitellogenin , as well as for acute and short-term chronic whole effluent toxicity test methods .
Fundamental questions remain about the application of omics in environmental risk assessments, such as the consistency of data across laboratories. The objective of the present study was to determine the congruence of transcript data across 6 independent laboratories. Male fathead minnows were exposed to a measured concentration of 15.8 ng/L 17α‐ethinylestradiol (EE2) for 96 h. Livers were divided equally and sent to the participating laboratories for transcriptomic analysis using the same fathead minnow microarray. Each laboratory was free to apply bioinformatics pipelines of its choice. There were 12 491 transcripts that were identified by one or more of the laboratories as responsive to EE2. Of these, 587 transcripts (4.7%) were detected by all laboratories. Mean overlap for differentially expressed genes among laboratories was approximately 50%, which improved to approximately 59.0% using a standardized analysis pipeline. The dynamic range of fold change estimates was variable between laboratories, but ranking transcripts by their relative fold difference resulted in a positive relationship for comparisons between any 2 laboratories (mean R2 > 0.9, p 20‐fold; e.g., vitellogenin) to subtle (∼2‐fold; i.e., block of proliferation 1) were identified as differentially expressed, suggesting that laboratories can consistently identify transcripts that are known a priori to be perturbed by a chemical stressor. Thus, attention should turn toward identifying core transcriptional networks using focused arrays for specific chemicals. In addition, agreed‐on bioinformatics pipelines and the ranking of genes based on fold change (as opposed to p value) should be considered in environmental risk assessment. These recommendations are expected to improve comparisons across laboratories and advance the use of omics in regulations.
Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY
EXPOSURE METHODS & MEASUREMENT DIVISION