Discrimination of modes of action of antifungal substances by use of metabolic footprinting
Discrimination of modes of action of antifungal substances by use of metabolic footprinting
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DOI：
10.1128/aem.70.10.6157-6165.2004
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发表时间：
2004-10-01
影响因子：
4.4
通讯作者：
Kell, DB
中科院分区：
文献类型：
--
作者：
Allen, J;Davey, HM;Kell, DB
酿酒酵母的二倍体细胞在受控条件下用Bioscreen仪器生长，其允许通过光密度测量基本上连续记录其生长。将一些培养物暴露于具有不同靶点或作用模式（甾醇生物合成、呼吸链、氨基酸合成和解偶联剂）的多种抗真菌物质的浓度。取培养物上清液并通过使用直接注射质谱法分析其“代谢足迹”。判别函数分析和层次聚类分析允许这些抗真菌化合物进行区分和分类，根据其作用模式。遗传编程是一种规则进化的机器学习策略，它允许呼吸抑制剂仅使用两种质量来区分。因此，代谢足迹法是一种快速，方便，信息丰富的方法，用于分类抗真菌物质的作用模式。
Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.