Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro.
Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro.
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DOI：
10.1016/j.tox.2021.153024
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发表时间：
2022-01-15
期刊：
影响因子：
4.5
通讯作者：
Webster TF
中科院分区：
文献类型：
--
作者：
Nielsen G;Heiger-Bernays WJ;Schlezinger JJ;Webster TF
人类暴露于全氟烷基和多氟烷基物质（PFAS）是普遍存在的，在饮用水、食物、家庭灰尘和其他暴露源中检测到PFAS的混合物。动物毒性研究和人类流行病学表明，PFAS可能通过共同的机制发挥作用，包括激活过氧化物酶体增殖物激活受体α（PPARα）。然而，PFAS混合物对人类相关分子引发事件的影响仍然是PFAS文献中的一个重要数据缺口。在这里，我们使用瞬时转染全长人PPARα（hPPARα）表达构建体和过氧化物酶体增殖物反应元件驱动的荧光素酶报告基因的Cos 7细胞，测试了建模方法预测不同PPARα配体对受体活性影响的能力。用两种完全hPPARα激动剂（pemafibrate和GW 7647）、一种完全和部分hPPARα激动剂（pemafibrate和邻苯二甲酸单（2-乙基己基）酯）或一种完全hPPARα激动剂和一种竞争性拮抗剂（pemafibrate和GW 6471）处理细胞24小时。受体活性用三种加性方法建模：效应总和、相对效价因子（RPF）和广义浓度相加（GCA）。虽然RPF和GCA准确预测了完全hPPARα激动剂混合物的活性，但只有GCA预测了完全和部分hPPARα激动剂以及完全激动剂和拮抗剂的活性。然后，我们生成了7个PFAS的浓度响应曲线，这些曲线与三参数Hill函数拟合良好。四种全氟羧酸（PFCA）倾向于作为完全hPPARα激动剂，而三种全氟磺酸（PFSA）倾向于作为部分激动剂，其有效性在完全激动剂阳性对照水平的28-67%之间变化。GCA和RPF在预测三种PFCA混合物的影响方面表现同样出色，但只有GCA预测了PFSA混合物以及PFCA和PFSA混合物在一般人群中的比例的实验活性。我们的结论是，在三种方法中，GCA最准确地模拟了PFAS混合物对体外hPPARα活性的影响。了解PFAS激活hPPARα的功效差异对于准确预测PFAS混合物的作用至关重要。由于PFAS可以激活多个核受体，未来的分析应检查完整细胞中的混合物效应，其中多个分子引发事件有助于近效和功能变化。
Human exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous, with mixtures of PFAS detected in drinking water, food, household dust, and other exposure sources. Animal toxicity studies and human epidemiology indicate that PFAS may act through shared mechanisms including activation of peroxisome proliferator activated receptor α (PPARα). However, the effect of PFAS mixtures on human relevant molecular initiating events remains an important data gap in the PFAS literature. Here, we tested the ability of modeling approaches to predict the effect of diverse PPARα ligands on receptor activity using Cos7 cells transiently transfected with a full length human PPARα (hPPARα) expression construct and a peroxisome proliferator response element-driven luciferase reporter. Cells were treated for 24 hours with two full hPPARα agonists (pemafibrate and GW7647), a full and a partial hPPARα agonist (pemafibrate and mono(2-ethylhexyl) phthalate), or a full hPPARα agonist and a competitive antagonist (pemafibrate and GW6471). Receptor activity was modeled with three additive approaches: effect summation, relative potency factors (RPF), and generalized concentration addition (GCA). While RPF and GCA accurately predicted activity for mixtures of full hPPARα agonists, only GCA predicted activity for full and partial hPPARα agonists and a full agonist and antagonist. We then generated concentration response curves for seven PFAS, which were well-fit with three-parameter Hill functions. The four perfluorinated carboxylic acids (PFCA) tended to act as full hPPARα agonists while the three perfluorinated sulfonic acids (PFSA) tended to act as partial agonists that varied in efficacy between 28–67% of the full agonist, positive control level. GCA and RPF performed equally well at predicting the effects of mixtures with three PFCAs, but only GCA predicted experimental activity with mixtures of PFSAs and a mixture of PFCAs and PFSAs at ratios found in the general population. We conclude that of the three approaches, GCA most accurately models the effect of PFAS mixtures on hPPARα activity in vitro. Understanding the differences in efficacy with which PFAS activate hPPARα is essential for accurately predicting the effects of PFAS mixtures. As PFAS can activate multiple nuclear receptors, future analyses should examine mixtures effects in intact cells where multiple molecular initiating events contribute to proximate effects and functional changes.
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