You are here:
How consistent are we? Inter-laboratory comparison for male fathead minnows exposed to 17α-ethinylestradiol
Feswick, A., M. Isaacs, A. Biales, R. Flick, D. Bencic, R. Wang, B. Lorraine, M. Brown-Augustine, AND A. Loguinov. How consistent are we? Inter-laboratory comparison for male fathead minnows exposed to 17α-ethinylestradiol. Society of Toxicology and Chemistry, Orlando, FL, November 06 - 10, 2016.
Examines the interlaboratory variability of microarray data, which is essential for their acceptance as a regulatory tool
Transcriptomic approaches are widely used to examine effects of aquatic contaminants in both laboratory and field studies. Fundamental questions remain however for defining the limits of the technology and how it may be used in environmental monitoring programs. Uncertainties exist as to how molecular initiating events translate into adverse effects at the population level, as well as how large a magnitude or transcriptome response constitutes an adverse effect (threshold). Also debated are the most appropriate metrics for quantifying an “omics” response (e.g. fold change, intensity, p-value, pathway, or network). If omics technologies are to be established in governmental programs for environmental monitoring and risk assessment, they must adhere to rigorous standardization that ensures reliability and consistency. To investigate this, male fathead minnows (FHM), a widely used aquatic species for toxicity testing, were exposed to 25 ng/L EE2 for 96 h, and six independent laboratories received frozen livers to conduct a transcriptome analysis using a 60K Agilent custom platform. Independent laboratories were free to use different processing and analysis packages to analyze the dataset. There was a mean congruence of ~50% across laboratories when it came to identifying differentially expressed targets on the microarray platform. This was not surprising given the flexibility in methods used to detect differentially expressed transcript. The range in the magnitude of response of the transcriptome was variable across laboratories, with some laboratories reporting a smaller dynamic range than others for transcript fold change. However, when transcripts were ranked by fold change across laboratories, there was a strong positive relationship (R2 = >0.9, p20-fold) (e.g. vitellogenin, estrogen receptor 1) to more subtle a response (<1.5 fold) (i.e. BOP1, Block of Proliferation 1). These data suggest that transcripts responsive to a chemical stressor can be consistently identified across laboratories, in designs that allow flexibility in the methods used. However, a more complete and rigorous framework of standardization as well as careful reflection as to the expectations about these data are needed to advance omics technologies in environmental monitoring.
Record Details:Record Type: DOCUMENT (PRESENTATION/SLIDE)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY
EXPOSURE METHODS & MEASUREMENT DIVISION
INTERNAL EXPOSURE INDICATORS BRANCH