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海洋放线菌的液质联用高分辨质谱分析和统计化学解析

Chemical dereplication of marine actinomycetes by liquid chromatography-high resolution mass spectrometry profiling and statistical analysis.

机构信息

Nautilus Biosciences Canada Inc., Charlottetown PEI, Canada C1A 4P3.

出版信息

Anal Chim Acta. 2013 Dec 17;805:70-9. doi: 10.1016/j.aca.2013.10.029. Epub 2013 Oct 21.

Abstract

Discovery of novel bioactive metabolites from marine bacteria is becoming increasingly challenging, and the development of novel approaches to improve the efficiency of early steps in the microbial drug discovery process is therefore of interest. For example, current protocols for the taxonomic dereplication of microbial strains generally use molecular tools which do not take into consideration the ability of these selected bacteria to produce secondary metabolites. As the identification of novel chemical entities is one of the key elements driving drug discovery programs, this study reports a novel methodology to dereplicate microbial strains by a metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS). In order to process large and complex three dimensional LC-HRMS datasets, the reported method uses a bucketing and presence-absence standardization strategy in addition to statistical analysis tools including principal component analysis (PCA) and cluster analysis. From a closely related group of Streptomyces isolated from geographically varied environments, we demonstrated that grouping bacteria according to the chemical diversity of produced metabolites is reproducible and provides greatly improved resolution for the discrimination of microbial strains compared to current molecular dereplication techniques. Importantly, this method provides the ability to identify putative novel chemical entities as natural product discovery leads.

摘要

从海洋细菌中发现新的生物活性代谢产物变得越来越具有挑战性,因此,开发新的方法来提高微生物药物发现过程早期步骤的效率是很有意义的。例如,目前用于微生物菌株分类去重的方案通常使用不考虑这些选定细菌产生次生代谢物能力的分子工具。由于鉴定新的化学实体是推动药物发现计划的关键因素之一,因此本研究报告了一种使用代谢组学方法(液相色谱-高分辨率质谱法 [LC-HRMS])通过代谢组学方法对微生物菌株进行去重的新方法。为了处理大型和复杂的三维 LC-HRMS 数据集,所报道的方法除了使用主成分分析(PCA)和聚类分析等统计分析工具外,还使用了分箱和存在-不存在标准化策略。从地理上多样化的环境中分离出的一组密切相关的链霉菌中,我们证明了根据产生代谢物的化学多样性对细菌进行分组是可重复的,并且与当前的分子去重技术相比,大大提高了微生物菌株的分辨能力。重要的是,这种方法提供了识别潜在新化学实体的能力,作为天然产物发现的先导。

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