A quantitative metabolomics study of bacterial metabolites in different domains

代谢物 代谢组学 化学 甲酸 代谢组 细菌 微生物代谢 大肠杆菌 代谢途径 色谱法 生物化学 质谱法 新陈代谢 定量分析(化学) 代谢物分析 生物 遗传学 基因
作者
Fanyi Zhong,Mengyang Xu,Patrick Metz,Pratiti Ghosh-Dastidar,Jiangjiang Zhu
出处
期刊:Analytica Chimica Acta [Elsevier BV]
卷期号:1037: 237-244 被引量:9
标识
DOI:10.1016/j.aca.2018.02.046
摘要

Absolute metabolite concentrations are essential information for quantitative metabolomics studies, as concentrations are closely related to metabolic reactions, enzyme kinetics and other important biological activities. A well-performed metabolites extraction procedure, a reliable detection method, and a robust quantitative approach are all critical factors for obtaining absolute metabolite concentrations. Here, we used a HPLC-MS/MS based platform to successfully develop a 13C-labeled quantitative metabolomics approach, and applied this novel method to quantify bacterial metabolite concentrations in three different domains (i.e., intracellularly, extracellularly and to the whole culture), with high accuracy for a model Escherichia coli bacteria. The bacterial culture was grown in universal 13C-labeled medium and the metabolites were extracted by 40/40/20/methanol/ACN/H2O with 0.1% formic acid. One hundred and twenty-five metabolites were initially screened and one hundred and six 13C metabolites were confidently detected from the model bacteria grown in 13C-labeled medium. A subset of twenty-one metabolites was subsequently quantified using 12C-metabolite chemical standards to assist the calculation of 13C metabolite concentration. This rigorous 13C-labled quantitative method was then applied to characterize the metabolic profile changes in three domains of E. coli going through antibiotic treatment. Our results demonstrated that metabolites from all three domains can be used to significantly differentiate the ampicillin treatment group and control group (without ampicillin). In conclusion, our work demonstrated that the quantitative metabolomics approach can be used as a valuable tool to study bacterial metabolism in different domains and to understand their response to environmental perturbations.
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