School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.
School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Department of Internal Medicine Specialties, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, Switzerland.
Talanta. 2019 Apr 1;195:77-86. doi: 10.1016/j.talanta.2018.11.019. Epub 2018 Nov 8.
The human adrenal cell line H295R constitutes a well-established model to evaluate potential alterations of steroidogenic pathways as a result of chemical exposure. However, to date most assays are based on the targeted investigation of a limited number of steroid hormones, thus preventing in-depth mechanistic interpretation with respect to steroidogenesis. In that context, analytical strategies coupling liquid chromatography and high-resolution mass spectrometry (LC-HRMS) have been reported as promising methods for an extended monitoring of steroid metabolites. However, unwanted sources of variability occurring during the acquisition process, including batch effects, may prevent relevant biochemical information to be properly highlighted. Dedicated data mining strategies are therefore needed to overcome these limitations, and extract relevant extended steroidomic profiles. The present study combines an untargeted LC-HRMS acquisition strategy with automated steroid metabolite annotation based on accurate mass and isotopic patterns, and a chemometric tool allowing the different sources of variability to be decomposed based on experimental design. This workflow was applied to the extended monitoring of steroidogenic dysregulations due to endocrine disrupting chemicals (EDCs) exposure in H295R cell cultures. A series of six chemicals, including acetyl tributylcitrate, octyl methoxycinnamate, torcetrapib, forskolin, linuron, and octocrylene, and dimethylsulfoxide as solvent control, were investigated through the simultaneous monitoring of 130 potential steroid metabolites, repeating the whole experiment independently three times. A stratified subsampling strategy was carried out to remove efficiently systematic batch variations and highlight subgroups of chemicals with similar steroid patterns. The proposed approach was reported as a potent screening strategy, as it allowed specific alterations of the steroid hormone biosynthesis and metabolism related to distinct mechanisms of action to be distinguished.
人肾上腺细胞系 H295R 构成了一个成熟的模型，可用于评估化学暴露对类固醇生成途径的潜在改变。然而，迄今为止，大多数检测方法都是基于对有限数量的类固醇激素的靶向研究，因此无法深入了解类固醇生成的机制。在这种情况下，结合液相色谱和高分辨率质谱（LC-HRMS）的分析策略已被报道为扩展监测类固醇代谢物的有前途的方法。然而，在采集过程中出现的不受欢迎的变异性源，包括批次效应，可能会阻止适当突出相关的生化信息。因此，需要专门的数据挖掘策略来克服这些限制，并提取相关的扩展类固醇组学谱。本研究将非靶向 LC-HRMS 采集策略与基于精确质量和同位素模式的自动类固醇代谢物注释相结合，并使用化学计量学工具根据实验设计分解不同的变异性源。该工作流程应用于由于内分泌干扰化学物质（EDCs）暴露而导致的 H295R 细胞培养物中类固醇生成失调的扩展监测。研究了一系列六种化学物质，包括乙酰三丁酸甘油酯、辛基甲氧基肉桂酸酯、托瑞司他、毛喉素、环脲和奥克立林，以及作为溶剂对照的二甲基亚砜，通过同时监测 130 种潜在的类固醇代谢物进行重复实验三次。进行了分层抽样策略以有效地去除系统批次变化，并突出具有相似类固醇模式的化学物质组。该方法被报道为一种有效的筛选策略，因为它允许区分与不同作用机制相关的类固醇激素生物合成和代谢的特定改变。
