Kiefer Patrick, Schmitt Uwe, Müller Jonas E N, Hartl Johannes, Meyer Fabian, Ryffel Florian, Vorholt Julia A
Institute of Microbiology, ETH Zurich , Zurich, Switzerland 8093.
ID Scientific IT Services, ETH Zurich , Zurich, Switzerland 8093.
Anal Chem. 2015 Oct 6;87(19):9679-86. doi: 10.1021/acs.analchem.5b01660.
Dynamic isotope labeling data provides crucial information about the operation of metabolic pathways and are commonly generated via liquid chromatography-mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We developed DynaMet for fully automated investigations of isotope labeling experiments from LC-high-resolution MS raw data. DynaMet enables untargeted extraction of metabolite labeling profiles and provides integrated tools for expressive data visualization. To validate DynaMet we first used time course labeling data of the model strain Bacillus methanolicus from (13)C methanol resulting in complex spectra in multicarbon compounds. Analysis of two biological replicates revealed high robustness and reproducibility of the pipeline. In total, DynaMet extracted 386 features showing dynamic labeling within 10 min. Of these features, 357 could be fitted by implemented kinetic models. Feature identification against KEGG database resulted in 215 matches covering multiple pathways of core metabolism and major biosynthetic routes. Moreover, we performed time course labeling experiment with Escherichia coli on uniformly labeled (13)C glucose resulting in a comparable number of detected features with labeling profiles of high quality. The distinct labeling patterns of common central metabolites generated from both model bacteria can readily be explained by one versus multicarbon compound metabolism. DynaMet is freely available as an extension package for Python based eMZed2, an open source framework built for rapid development of LC-MS data analysis workflows.
动态同位素标记数据提供了有关代谢途径运作的关键信息，通常通过液相色谱-质谱联用（LC-MS）生成。全代谢组分析具有挑战性，因为它需要对不同样本中的代谢物特征进行分组。我们开发了DynaMet，用于从LC-高分辨率MS原始数据中对同位素标记实验进行全自动研究。DynaMet能够非靶向提取代谢物标记谱，并提供用于富有表现力的数据可视化的集成工具。为了验证DynaMet，我们首先使用了来自（13）C甲醇的模式菌株嗜甲醇芽孢杆菌的时间进程标记数据，这导致多碳化合物中出现复杂的光谱。对两个生物学重复的分析显示了该流程的高稳健性和可重复性。总的来说，DynaMet提取了386个在10分钟内显示动态标记的特征。在这些特征中，357个可以通过实施的动力学模型进行拟合。与KEGG数据库进行特征识别，得到了215个匹配项，涵盖了核心代谢的多个途径和主要生物合成途径。此外，我们用均匀标记的（13）C葡萄糖对大肠杆菌进行了时间进程标记实验，得到了数量相当的检测特征，且标记谱质量很高。由这两种模式细菌产生的常见中心代谢物的不同标记模式可以很容易地用单碳化合物与多碳化合物代谢来解释。DynaMet作为基于Python的eMZed2的扩展包免费提供，eMZed2是一个为快速开发LC-MS数据分析工作流程而构建的开源框架。
