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三种模式生物组织提取方案的深度代谢谱分析评估

Deep Metabolic Profiling Assessment of Tissue Extraction Protocols for Three Model Organisms.

作者信息

Gegner Hagen M, Mechtel Nils, Heidenreich Elena, Wirth Angela, Cortizo Fabiola Garcia, Bennewitz Katrin, Fleming Thomas, Andresen Carolin, Freichel Marc, Teleman Aurelio A, Kroll Jens, Hell Rüdiger, Poschet Gernot

机构信息

Metabolomics Core Technology Platform, Centre for Organismal Studies (COS), Heidelberg University, Heidelberg, Germany.

Institute of Pharmacology, Heidelberg University, Heidelberg, Germany.

出版信息

Front Chem. 2022 Apr 25;10:869732. doi: 10.3389/fchem.2022.869732. eCollection 2022.

Abstract

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimer's, Parkinson's disease or COVID-19. To better understand such diseases and their intricate metabolic pathways in human studies, model animals are regularly used. There, standardized rearing conditions and uniform sampling strategies are prerequisites towards a successful metabolomic study that can be achieved through model organisms. Although metabolomic approaches have been employed on model organisms before, no systematic assessment of different conditions to optimize metabolite extraction across several organisms and sample types has been conducted. We address this issue using a highly standardized metabolic profiling assay analyzing 630 metabolites across three commonly used model organisms (Drosophila, mouse, and zebrafish) to find an optimal extraction protocol for various matrices. Focusing on parameters such as metabolite coverage, concentration and variance between replicates we compared seven extraction protocols. We found that the application of a combination of 75% ethanol and methyl tertiary-butyl ether (MTBE), while not producing the broadest coverage and highest concentrations, was the most reproducible extraction protocol. We were able to determine up to 530 metabolites in mouse kidney samples, 509 in mouse liver, 422 in zebrafish and 388 in Drosophila and discovered a core overlap of 261 metabolites in these four matrices. To enable other scientists to search for the most suitable extraction protocol in their experimental context and interact with this comprehensive data, we have integrated our data set in the open-source shiny app "MetaboExtract". Hereby, scientists can search for metabolites or compound classes of interest, compare them across the different tested extraction protocols and sample types as well as find reference concentration values.

摘要

代谢谱分析有潜力更好地理解各种疾病实体,如癌症、糖尿病、阿尔茨海默病、帕金森病或新冠肺炎。为了在人体研究中更好地理解此类疾病及其复杂的代谢途径,常使用模式动物。在那里,标准化的饲养条件和统一的采样策略是通过模式生物实现成功代谢组学研究的先决条件。虽然代谢组学方法此前已应用于模式生物,但尚未对不同条件进行系统评估,以优化跨多种生物和样本类型的代谢物提取。我们使用一种高度标准化的代谢谱分析方法来解决这个问题,该方法分析了三种常用模式生物(果蝇、小鼠和斑马鱼)中的630种代谢物,以找到针对各种基质的最佳提取方案。我们关注代谢物覆盖范围、浓度和重复样本间的差异等参数,比较了七种提取方案。我们发现,75%乙醇和甲基叔丁基醚(MTBE)组合的应用虽然不能产生最广泛的覆盖范围和最高浓度,但却是最可重复的提取方案。我们能够在小鼠肾脏样本中测定多达530种代谢物,在小鼠肝脏中测定509种,在斑马鱼中测定422种,在果蝇中测定388种,并发现这四种基质中共有261种代谢物的核心重叠。为了使其他科学家能够在他们的实验环境中搜索最合适的提取方案并与这些全面的数据进行交互,我们已将我们的数据集整合到开源的闪亮应用程序“MetaboExtract”中。通过这种方式,科学家们可以搜索感兴趣的代谢物或化合物类别,在不同的测试提取方案和样本类型之间进行比较,并找到参考浓度值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29ca/9083328/23c6ca4bf1d7/fchem-10-869732-g001.jpg

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