How consistent are we? Interlaboratory comparison study in fathead minnows using the model estrogen 17α‐ethinylestradiol to develop recommendations for environmental transcriptomics. (19th April 2017)

• Record Type: Journal Article
• Authors: Feswick, April; Isaacs, Meghan; Biales, Adam; Flick, Robert W.; Bencic, David C.; Wang, Rong‐Lin; Vulpe, Chris; Brown‐Augustine, Marianna; Loguinov, Alex; Falciani, Francesco; Antczak, Philipp; Herbert, John; Brown, Lorraine; Denslow, Nancy D.; Kroll, Kevin J.; Lavelle, Candice; Dang, Viet; Escalon, Lynn; Garcia‐Reyero, Natàlia; Martyniuk, Christopher J.; Munkittrick, Kelly R.
• Is Part Of: Environmental toxicology and chemistry. Volume 36:Number 10(2017)
• Page Start: 2614
• Page End: 2623
• Publication Date: 2017-04-19
• Subjects: Transcriptomics -- Risk assessment -- Endocrine disruptor -- Estrogenic compound -- Interlaboratory comparison; Pollution -- Environmental aspects -- Periodicals; Environmental chemistry -- Periodicals; 615.902
• DOI: 10.1002/etc.3799
• Languages: English
• ISSNs: 0730-7268

Abstract

Abstract: 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 R 2 > 0.9, p < 0.001). Ten estrogen‐responsive genes encompassing a fold change range from dramatic (>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. Environ Toxicol Chem 2017;36:2593–2601. © 2017 SETAC