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
Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada.
Molecular Indicators Research Branch, National Exposure Research Laboratory, Cincinnati, Ohio, USA.
Environ Toxicol Chem. 2017 Oct;36(10):2614-2623. doi: 10.1002/etc.3799. Epub 2017 Apr 19.
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 > 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.
关于组学在环境风险评估中的应用仍存在一些基本问题，比如不同实验室数据的一致性。本研究的目的是确定6个独立实验室转录组数据的一致性。将雄性黑头软口鲦鱼暴露于浓度为15.8纳克/升的17α-乙炔雌二醇（EE2）中96小时。肝脏被平均分成几份，送往参与研究的实验室，使用相同的黑头软口鲦鱼微阵列进行转录组分析。每个实验室可自由应用其选择的生物信息学流程。有12491个转录本被一个或多个实验室鉴定为对EE2有反应。其中，587个转录本（4.7%）在所有实验室中均被检测到。各实验室之间差异表达基因的平均重叠率约为50%，使用标准化分析流程后提高到约59.0%。各实验室之间倍数变化估计的动态范围各不相同，但按相对倍数差异对转录本进行排序后，任意两个实验室之间的比较呈现正相关（平均R>0.9，p<0.001）。鉴定出10个雌激素反应基因差异表达，其倍数变化范围从显著（>20倍；如卵黄蛋白原）到细微（约2倍；即增殖阻滞1），这表明实验室能够一致地鉴定出事先已知会受到化学应激源干扰的转录本。因此，应将注意力转向使用针对特定化学物质的聚焦阵列来识别核心转录网络。此外，在环境风险评估中应考虑商定的生物信息学流程以及基于倍数变化（而非p值）的基因排序。这些建议有望改善不同实验室之间的比较，并推动组学在法规中的应用。《环境毒理学与化学》2017年；36：2593 - 2601。©2017 SETAC。
