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Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach.
Environment International ( IF 11.8 ) Pub Date : 2020-01-13 , DOI: 10.1016/j.envint.2019.105455
Pu Xia 1 , Hanxin Zhang 1 , Ying Peng 1 , Wei Shi 1 , Xiaowei Zhang 1
Affiliation  

The ever-increasing number of chemicals and complex mixtures demands a high-throughput and cost-effective approach for chemical safety assessment. High-throughput transcriptomics (HTT) is promising in investigating genome-scale perturbation of chemical exposure in concentration-dependent manner. However, the application of HTT has been limited due to lack of methodology for single chemicals and mixture assessment. This study aimed to evaluate the ability of a newly-developed human reduced transcriptomics (RHT) approach to assess pathway-based profiles of single chemicals, and to develop a biological pathway-based approach for benchmarking mixture potency using single chemical-based prediction model. First, concentration-dependent RHT were used to qualitatively and quantitatively differentiate pathway-based patterns of different chemicals, using three model toxicants, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), triclosan (TCS) and 5-Chloro-6-hydroxy-2,2',4,4'-tetrabromodiphenyl ether (5-Cl-6-OH-BDE-47). AHR-regulated genes and pathways were most sensitively induced by TCDD, while TCS and 5-Cl-6-OH-BDE-47 were much less potent in AHR-associated activation, which was concordant with known MoA of each single chemical. Second, two artificial mixtures and their components of twelve individual chemicals were performed with concentration-dependent RHT. Concentration addition (CA) and independent action (IA) models were used to predict transcriptional potency of mixtures from transcriptomics of individual chemicals. For overall bioactivity, CA and IA models can both predict potency of observed responses within 95% confidence interval. For specific biological processes, multiple biological processes such as hormone signaling and DNA damage can be predicted using CA models for mixtures. The concentration-dependent RHT can provide a powerful approach for qualitative and quantitative assessment of biological pathway perturbated by environment chemical and mixtures.

中文翻译:

通过高通量转录组学方法对单一化学品和混合物进行基于途径的评估。

化学品和复杂混合物的数量不断增加,要求采用高通量且具有成本效益的方法进行化学品安全评估。高通量转录组学(HTT)有望以浓度依赖的方式研究化学暴露的基因组规模扰动。但是,由于缺乏单一化学品和混合物评估的方法,HTT的应用受到了限制。这项研究旨在评估一种新开发的人类还原转录组学(RHT)方法评估单一化学物质基于途径的概况的能力,并开发一种基于生物途径的方法来使用基于单一化学物质的预测模型对混合物的效能进行基准测试。首先,使用浓度依赖的RHT来定性和定量区分不同化学品的基于途径的模式,使用三种模型毒物,2,3,7,8-四氯二苯并-对-二恶英(TCDD),三氯生(TCS)和5-氯-6-羟基-2,2',4,4'-四溴二苯醚(5- Cl-6-OH-BDE-47)。TCDD最灵敏地诱导了AHR调控的基因和途径,而TCS和5-Cl-6-OH-BDE-47在与AHR相关的激活中的效力要低得多,这与每种化学物质的已知MoA一致。第二,使用浓度依赖的RHT进行了两种人工混合物及其十二种单独化学品的成分。使用浓度加和(CA)和独立作用(IA)模型来预测混合物的转录效力,这些化合物来自各个化学物质的转录组学。对于总体生物活性,CA和IA模型都可以预测在95%置信区间内观察到的反应的效力。对于特定的生物学过程,使用混合物的CA模型可以预测多种生物过程,例如激素信号传导和DNA损伤。浓度依赖性RHT可为定性和定量评估受环境化学物质和混合物干扰的生物途径提供强大的方法。
更新日期:2020-01-13
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