Allen Jess, Davey Hazel M, Broadhurst David, Rowland Jem J, Oliver Stephen G, Kell Douglas B
Department of Biological Sciences, University of Wales, Aberystwyth, UK.
Appl Environ Microbiol. 2004 Oct;70(10):6157-65. doi: 10.1128/AEM.70.10.6157-6165.2004.
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.
酿酒酵母的二倍体细胞在可控条件下使用Bioscreen仪器进行培养，该仪器可通过光密度测量基本连续记录其生长情况。一些培养物暴露于多种具有不同靶点或作用方式的抗真菌物质（甾醇生物合成、呼吸链、氨基酸合成和解偶联剂）的浓度下。采集培养上清液，并使用直接进样质谱法分析其“代谢足迹”。判别函数分析和层次聚类分析使这些抗真菌化合物能够根据其作用方式进行区分和分类。遗传编程是一种规则演化的机器学习策略，仅使用两个质量数就能将呼吸抑制剂与其他物质区分开来。因此，代谢足迹分析是一种快速、便捷且信息丰富的抗真菌物质作用方式分类方法。
