一种使用OSMAC、Log P和核磁共振指纹图谱的系统方法：一种创新方法。
A systems approach using OSMAC, Log P and NMR fingerprinting: An approach to novelty.
作者信息
Liu Miaomiao, Grkovic Tanja, Liu Xueting, Han Jianying, Zhang Lixin, Quinn Ronald J
机构信息
Griffith Institute for Drug Discovery, Griffith University, Brisbane, QLD, 4111, Australia.
Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
出版信息
Synth Syst Biotechnol. 2017 Oct 21;2(4):276-286. doi: 10.1016/j.synbio.2017.10.001. eCollection 2017 Dec.
The growing number of sequenced microbial genomes has revealed a remarkably large number of secondary metabolite biosynthetic clusters for which the compounds are still unknown. The aim of the present work was to apply a strategy to detect newly induced natural products by cultivating microorganisms in different fermentation conditions. The metabolomic analysis of 4160 fractions generated from 13 actinomycetes under 32 different culture conditions was carried out by H NMR spectroscopy and multivariate analysis. The principal component analysis (PCA) of the H NMR spectra showed a clear discrimination between those samples within PC1 and PC2. The fractions with induced metabolites that are only produced under specific growth conditions was identified by PCA analysis. This method allows an efficient differentiation within a large dataset with only one fractionation step. This work demonstrates the potential of NMR spectroscopy in combination with metabolomic data analysis for the screening of large sets of fractions.
越来越多已测序的微生物基因组揭示了大量次生代谢物生物合成簇，而这些簇所对应的化合物仍然未知。本研究的目的是应用一种策略，通过在不同发酵条件下培养微生物来检测新诱导产生的天然产物。利用核磁共振氢谱（¹H NMR）光谱法和多变量分析，对13株放线菌在32种不同培养条件下产生的4160个馏分进行了代谢组学分析。¹H NMR光谱的主成分分析（PCA）显示，PC1和PC2内的样本之间有明显区分。通过PCA分析鉴定出了仅在特定生长条件下产生的具有诱导代谢物的馏分。该方法仅通过一步分馏就能在大型数据集中实现有效区分。这项工作证明了核磁共振光谱结合代谢组学数据分析在筛选大量馏分方面的潜力。
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