González-Ruiz Víctor, Schvartz Domitille, Sandström Jenny, Pezzatti Julian, Jeanneret Fabienne, Tonoli David, Boccard Julien, Monnet-Tschudi Florianne, Sanchez Jean-Charles, Rudaz Serge
Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
Swiss Centre for Applied Human Toxicology, 4055 Basel, Switzerland.
Metabolites. 2019 Apr 24;9(4):79. doi: 10.3390/metabo9040079.
Toxicology studies can take advantage of approaches to better understand the phenomena underlying the phenotypic alterations induced by different types of exposure to certain toxicants. Nevertheless, in order to analyse the data generated from multifactorial studies, dedicated data analysis tools are needed. In this work, we propose a new workflow comprising both factor deconvolution and data integration from multiple analytical platforms. As a case study, 3D neural cell cultures were exposed to trimethyltin (TMT) and the relevance of the culture maturation state, the exposure duration, as well as the TMT concentration were simultaneously studied using a metabolomic approach combining four complementary analytical techniques (reversed-phase LC and hydrophilic interaction LC, hyphenated to mass spectrometry in positive and negative ionization modes). The ANOVA multiblock OPLS (AMOPLS) method allowed us to decompose and quantify the contribution of the different experimental factors on the outcome of the TMT exposure. Results showed that the most important contribution to the overall metabolic variability came from the maturation state and treatment duration. Even though the contribution of TMT effects represented the smallest observed modulation among the three factors, it was highly statistically significant. The MetaCore™ pathway analysis tool revealed TMT-induced alterations in biosynthetic pathways and in neuronal differentiation and signaling processes, with a predominant deleterious effect on GABAergic and glutamatergic neurons. This was confirmed by combining proteomic data, increasing the confidence on the mechanistic understanding of such a toxicant exposure.
毒理学研究可以利用多种方法来更好地理解由不同类型的特定毒物暴露所诱导的表型改变背后的现象。然而，为了分析多因素研究产生的数据，需要专门的数据分析工具。在这项工作中，我们提出了一种新的工作流程，包括因子反卷积和来自多个分析平台的数据整合。作为一个案例研究，将3D神经细胞培养物暴露于三甲基锡（TMT），并使用一种结合四种互补分析技术（反相液相色谱和亲水相互作用液相色谱，与正离子和负离子模式下的质谱联用）的代谢组学方法，同时研究培养成熟状态、暴露持续时间以及TMT浓度的相关性。方差分析多块OPLS（AMOPLS）方法使我们能够分解并量化不同实验因素对TMT暴露结果的贡献。结果表明，对总体代谢变异性的最重要贡献来自成熟状态和处理持续时间。尽管TMT效应的贡献在三个因素中表现为观察到的最小调节作用，但具有高度统计学显著性。MetaCore™ 通路分析工具揭示了TMT诱导的生物合成通路以及神经元分化和信号传导过程的改变，对γ-氨基丁酸能和谷氨酸能神经元具有主要的有害影响。通过结合蛋白质组学数据证实了这一点，增加了对这种毒物暴露机制理解的可信度。
