Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Campus de El Carmen, 21007, Huelva, Spain; Campus of International Excellence ceiA3, University of Huelva, Campus de El Carmen, 21007, Huelva, Spain; Research Center of Health and Environment (CYSMA), University of Huelva, Campus de El Carmen, 21007, Huelva, Spain.
Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Campus de El Carmen, 21007, Huelva, Spain; Campus of International Excellence ceiA3, University of Huelva, Campus de El Carmen, 21007, Huelva, Spain; Research Center of Health and Environment (CYSMA), University of Huelva, Campus de El Carmen, 21007, Huelva, Spain.
Anal Chim Acta. 2018 Feb 13;1000:41-66. doi: 10.1016/j.aca.2017.10.019. Epub 2017 Nov 2.
The present review focus on the analytical platforms and the workflow for toxicometabolomics with a special emphasis on their strengths and pitfalls presenting as a case study the toxicometabolomics of arsenic in mammals. Although powerful analytical methods and techniques are currently available for metabolomics, the main "bottleneck" is still the absence of unified protocols for sample preparation (e.g. quenching, solvents used) as well as several important factors in toxicometabolomics, which drastically affect the metabolism (e.g. selection of model organisms, xenobiotic doses, chemical form of the xenobiotic, exposure route, biological sample). In this context, the applicability to complex samples, higher sensitivity, specificity and the possibility to perform quantitative analysis offered by MS is crucial to probe xenobiotic induced metabolic changes to evaluate the stress responses. Nowadays, the use of different metabolomic platforms allowed determining important changes in the metabolism induced by arsenic in mammals such as alterations in the energy (e.g. Glycolysis, Kreb's cycle), amino acid, lipid, nucleotide and androgen metabolisms.
本综述重点介绍了毒代代谢组学的分析平台和工作流程，特别强调了它们的优缺点，并以砷在哺乳动物中的毒代代谢组学为例。尽管目前有强大的代谢组学分析方法和技术，但主要的“瓶颈”仍然是缺乏统一的样品制备协议（例如淬灭、使用的溶剂）以及毒代代谢组学中的几个重要因素，这些因素极大地影响了代谢（例如模型生物的选择、外源性物质剂量、外源性物质的化学形式、暴露途径、生物样本）。在这种情况下，MS 具有适用于复杂样品、更高的灵敏度、特异性和进行定量分析的可能性，这对于探测外源性物质诱导的代谢变化、评估应激反应至关重要。如今，不同代谢组学平台的使用使得能够确定砷在哺乳动物中诱导的代谢变化，如能量代谢（例如糖酵解、克雷布斯循环）、氨基酸、脂质、核苷酸和雄激素代谢的改变。
