How consistent are we? Interlaboratory comparison study in fathead minnows using the model estrogen 17α-ethinylestradiol to develop recommendations for environmental transcriptomics

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 R² > 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.

Keywords: Endocrine disruptor; Estrogenic compound; Interlaboratory comparison; Risk assessment; Transcriptomics.

Figure 1

Mean coefficient of variation (CV; ± standard deviation [SD]) of all spots over all microarrays (n = 8 biological replicates) within an experimental group. (A) Coefficient of variation is plotted separately in the control group and 17α‐ethinylestradiol for each laboratory. (B) Mean probe intensity (± SD) in each experimental group. C = control; EE = 17alpha‐ethinylestradiol exposure.

Figure 2

Metrics of variability and spread of fold changes in differentially expressed genes across laboratories. Mean fold change of differentially expressed genes with 95% confidence internals (vertical bars) shown for increasing (A) and decreasing (B) transcripts, relative to the control group. Median fold change of differentially expressed genes with interquartile range (vertical bars) shown for increasing (C) and decreasing (D) transcripts, relative to the control group. Fold change distribution of differentially expressed genes for increasing (E) and decreasing (F) transcripts, relative to the control group. Uncorrected p value for transcripts was used for laboratory 3 instead of corrected p values because the number of differentially expressed genes identified following a post hoc correction was small relative to the other laboratories. CI = confidence interval; DEG = differentially expressed gene.

Figure 3

Ten estrogen‐responsive genes arranged in order from decreasing to increasing by fold change. Each point represents a laboratory. Only those genes that were identified as differentially expressed are included in these graphs. Horizontal line represents the median expression. Zero represents no fold change relative to control. APOA1 = apolipoprotein A‐I‐1; APOE = apolipoprotein Eb; BOP1 = block of proliferation 1; ESR1 = estrogen receptor 1; IGF1 = insulin‐like growth factor 1; RTN1A = reticulon 1a; VTG = vitellogenin; XBP1 = x‐box binding protein 1.