Behrends Volker, Ebbels Tim M D, Williams Huw D, Bundy Jacob G
Imperial College London, Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology, and Anaesthetics, Faculty of Medicine, Sir Alexander Fleming Building, London SW7 2AZ, United Kingdom.
Appl Environ Microbiol. 2009 Apr;75(8):2453-63. doi: 10.1128/AEM.01742-08. Epub 2009 Feb 13.
Untargeted profiling of small-molecule metabolites from microbial culture supernatants (metabolic footprinting) has great potential as a phenotyping tool. We used time-resolved metabolic footprinting to compare one Escherichia coli and three Pseudomonas aeruginosa strains growing on complex media and show that considering metabolite changes over the whole course of growth provides much more information than analyses based on data from a single time point. Most strikingly, there was pronounced selectivity in metabolite uptake, even when the bacteria were growing apparently exponentially, with certain groups of metabolites not taken up until others had been entirely depleted from the medium. In addition, metabolite excretion showed some complex patterns. Fitting nonlinear equations (four-parameter sigmoids) to individual metabolite data allowed us to model these changes for metabolite uptake and visualize them by back-projecting the curve-fit parameters onto the original growth curves. These "uptake window" plots clearly demonstrated strain differences, with the uptake of some compounds being reversed in order between different strains. Comparison of an undefined rich medium with a defined complex medium designed to mimic cystic fibrosis sputum showed many differences, both qualitative and quantitative, with a greater proportion of excreted to utilized metabolites in the defined medium. Extending the strain comparison to a more closely related set of isolates showed that it was possible to discriminate two species of the Burkholderia cepacia complex based on uptake dynamics alone. We believe time-resolved metabolic footprinting could be a valuable tool for many questions in bacteriology, including isolate comparisons, phenotyping deletion mutants, and as a functional complement to taxonomic classifications.
对微生物培养上清液中的小分子代谢物进行非靶向分析（代谢足迹分析）作为一种表型分析工具具有巨大潜力。我们使用时间分辨代谢足迹分析来比较在复杂培养基上生长的一株大肠杆菌和三株铜绿假单胞菌菌株，结果表明，考虑生长全过程中的代谢物变化所提供的信息比基于单个时间点数据的分析要多得多。最引人注目的是，即使细菌明显呈指数生长，代谢物摄取也存在明显的选择性，某些代谢物组直到其他代谢物从培养基中完全耗尽后才被摄取。此外，代谢物排泄呈现出一些复杂的模式。将非线性方程（四参数S形曲线）拟合到单个代谢物数据，使我们能够对代谢物摄取的这些变化进行建模，并通过将曲线拟合参数反向投影到原始生长曲线上来直观呈现这些变化。这些“摄取窗口”图清楚地展示了菌株差异，某些化合物在不同菌株之间的摄取顺序相反。将一种未定义的丰富培养基与一种设计用于模拟囊性纤维化痰液的定义复杂培养基进行比较，结果显示在定性和定量方面都存在许多差异，在定义培养基中排泄代谢物与利用代谢物的比例更高。将菌株比较扩展到一组关系更密切的分离株表明，仅基于摄取动态就有可能区分洋葱伯克霍尔德菌复合体的两个物种。我们认为，时间分辨代谢足迹分析可能是解决细菌学中许多问题的有价值工具，包括分离株比较、缺失突变体表型分析以及作为分类学分类的功能补充。
