High-throughput classification of yeast mutants for functional genomics using metabolic footprinting
High-throughput classification of yeast mutants for functional genomics using metabolic footprinting
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DOI：
10.1038/nbt823
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发表时间：
2003-06-01
影响因子：
46.9
通讯作者：
Kell, DB
中科院分区：
文献类型：
--
作者：
Allen, J;Davey, HM;Kell, DB
为了解释通过系统基因组测序所发现基因的功能，人们已经开发了许多技术。目前，转录组和蛋白质组研究在大规模功能分析策略中占主导地位。然而，代谢组由于处于“下游”，应该会更明显地体现出基因或生理变化的影响，因而应该更接近生物体的表型。我们之前提出了一种功能分析策略，即利用代谢指纹图谱来揭示酵母基因沉默突变的表型（1）。但是，这种方法难以扩大规模用于高通量筛选。在此，我们提出一种替代方法，它具备所需的通量（每个样本2分钟）。这种“代谢足迹分析”方法认识到在适当培养基中“溢流代谢”的重要性。测量细胞内代谢物既耗时，又会因细胞内代谢物的快速周转以及需要终止代谢并将代谢物与细胞外空间分离而面临技术难题。因此，我们转而专注于对废弃培养基中细胞外代谢物进行直接、无创的质谱监测。代谢足迹分析能够区分野生型酵母的不同生理状态，以及即使来自相关代谢区域的酵母单基因缺失突变体。通过使用适当的聚类和机器学习技术（后者基于遗传编程（2 - 8）），我们表明代谢足迹分析是一种通过遗传缺陷对“未知”突变体进行分类的有效方法。
Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes(1). However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wildtype yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming(2-8), we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect.