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首页> 外文期刊>Environmental toxicology and chemistry >HOW CONSISTENT ARE WE? INTERLABORATORY COMPARISON STUDY IN FATHEAD MINNOWS USING THE MODEL ESTROGEN 17 alpha-ETHINYLESTRADIOL TO DEVELOP RECOMMENDATIONS FOR ENVIRONMENTAL TRANSCRIPTOMICS
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HOW CONSISTENT ARE WE? INTERLABORATORY COMPARISON STUDY IN FATHEAD MINNOWS USING THE MODEL ESTROGEN 17 alpha-ETHINYLESTRADIOL TO DEVELOP RECOMMENDATIONS FOR ENVIRONMENTAL TRANSCRIPTOMICS

机译：我们如何保持一致？使用模型雌激素17α-乙炔雌二醇开发环境转录组学建议的无头小LAB的实验室间比较研究

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摘要

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 17 alpha-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 (similar to 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. (C) 2017 SETAC
机译：关于组学在环境风险评估中的应用，仍然存在根本性的问题，例如各个实验室数据的一致性。本研究的目的是确定6个独立实验室的转录数据的一致性。将雄性黑头min鱼暴露于15.8 ng / L 17α-炔雌醇（EE2）的测量浓度下96小时。将肝脏均分，并使用相同的黑头min鱼微阵列将其送至参与实验室进行转录组分析。每个实验室均可自由选择其选择的生物信息学渠道。一个或多个实验室鉴定出对EE2有反应的12 491个转录本。其中，所有实验室均检测到587个转录本（占4.7％）。实验室之间差异表达基因的平均重叠约为50％，使用标准化分析流程可将其提高至约59.0％。倍数变化估计值的动态范围在实验室之间是可变的，但是通过转录本的相对倍数差异对成绩单进行排名会导致任意两个实验室之间的比较具有正相关关系（平均值R-2> 0.9，p <0.001）。十个雌激素反应性基因的表达范围从急剧变化（> 20倍；例如卵黄蛋白生成素）到细微变化（类似于2倍；即增殖阻滞1），被认为是差异表达的，这表明实验室可以一致地鉴定先验已知的转录本会受到化学应激物的干扰。因此，注意力应该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，应考虑就生物信息学管道达成共识并根据倍数变化（相对于p值）对基因进行排名。这些建议有望改善实验室之间的比较，并促进法规中使用组学技术。 （C）2017年SETAC

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  • 来源
    《Environmental toxicology and chemistry》 |2017年第10期|2614-2623|共10页
  • 作者

    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 Natalia; Martyniuk Christopher J.; Munkittrick Kelly R.;

  • 作者单位

    Univ New Brunswick, Dept Biol, St John, NB, Canada;

    Univ New Brunswick, Dept Biol, St John, NB, Canada;

    Natl Exposure Res Lab, Mol Indicators Res Branch, Cincinnati, OH USA;

    Natl Exposure Res Lab, Mol Indicators Res Branch, Cincinnati, OH USA;

    Natl Exposure Res Lab, Mol Indicators Res Branch, Cincinnati, OH USA;

    Natl Exposure Res Lab, Mol Indicators Res Branch, Cincinnati, OH USA;

    Univ Calif Berkeley, Dept Nutr Sci & Toxicol, Berkeley, CA 94720 USA;

    Univ Calif Berkeley, Dept Nutr Sci & Toxicol, Berkeley, CA 94720 USA;

    Univ Calif Berkeley, Dept Nutr Sci & Toxicol, Berkeley, CA 94720 USA;

    Univ Liverpool, Inst Integrat Biol, Liverpool, Merseyside, England;

    Univ Liverpool, Inst Integrat Biol, Liverpool, Merseyside, England;

    Univ Liverpool, Inst Integrat Biol, Liverpool, Merseyside, England;

    Pacific Environm Sci Ctr, N Vancouver, BC, Canada;

    Univ Florida, Coll Vet Med, Dept Physiol Sci, Ctr Environm & Human Toxicol,UF Genet Inst, Gainesville, FL 32610 USA;

    Univ Florida, Coll Vet Med, Dept Physiol Sci, Ctr Environm & Human Toxicol,UF Genet Inst, Gainesville, FL 32610 USA;

    Univ Florida, Coll Vet Med, Dept Physiol Sci, Ctr Environm & Human Toxicol,UF Genet Inst, Gainesville, FL 32610 USA;

    Univ Florida, Coll Vet Med, Dept Physiol Sci, Ctr Environm & Human Toxicol,UF Genet Inst, Gainesville, FL 32610 USA;

    US Army, Engineer Res & Dev Ctr, Vicksburg, MS USA;

    US Army, Engineer Res & Dev Ctr, Vicksburg, MS USA|Mississippi State Univ, Inst Gen Biocomp & Biotechnol, Starkville, MS USA;

    Univ New Brunswick, Dept Biol, St John, NB, Canada|Univ Florida, Coll Vet Med, Dept Physiol Sci, Ctr Environm & Human Toxicol,UF Genet Inst, Gainesville, FL 32610 USA;

    Univ New Brunswick, Dept Biol, St John, NB, Canada;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Transcriptomics; Risk assessment; Endocrine disruptor; Estrogenic compound; Interlaboratory comparison;

    机译：转录组学;风险评估;内分泌干扰物;雌激素化合物;实验室间比较;

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