Time-Dependent Profiling of Metabolites from Snf1 Mutant and Wild Type Yeast Cells
作者:Elizabeth M. Humston、Kenneth M. Dombek、Jamin C. Hoggard、Elton T. Young、Robert E. Synovec
DOI:10.1021/ac800998j
日期:2008.11.1
The effect of sampling time in the context of growth conditions on a dynamic metabolic system was investigated in order to assess to what extent a single sampling time may be sufficient for general application, as well as to determine if useful kinetic information could be obtained. A wild type yeast strain (W) was compared to a snf1Δ mutant yeast strain (S) grown in high-glucose medium (R) and in low-glucose medium containing ethanol (DR). Under these growth conditions, different metabolic pathways for utilizing the different carbon sources are expected to be active. Thus, changes in metabolite levels relating to the carbon source in the growth medium were anticipated. Furthermore, the Snf1 protein kinase complex is required to adapt cellular metabolism from fermentative R conditions to oxidative DR conditions. So, differences in intracellular metabolite levels between the W and S yeast strains were also anticipated. Cell extracts were collected at four time points (0.5, 2, 4, 6 h) after shifting half of the cells from R to DR conditions, resulting in 16 sample classes (WR, WDR, SR, SDR) × (0.5, 2, 4, 6 h). The experimental design provided time course data, so temporal dependencies could be monitored in addition to carbon source and strain dependencies. Comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS) was used with discovery-based data mining algorithms (Anal. Chem. 2006, 78, 5068–5075 (ref 1); J. Chromatogr., A 2008, 1186, 401–411 (ref 2)) to locate regions within the 2D chromatograms (i.e., metabolites) that provided chemical selectivity between the 16 sample classes. These regions were mathematically resolved using parallel factor analysis to positively identify the metabolites and to acquire quantitative results. With these tools, 51 unique metabolites were identified and quantified. Various time course patterns emerged from these data, and principal component analysis (PCA) was utilized as a comparison tool to determine the sources of variance between these 51 metabolites. The effect of sampling time was investigated with separate PCA analyses using various subsets of the data. PCA utilizing all of the time course data, averaged time course data, and each individual time point data set independently were performed to discern the differences. For the yeast strains examined in the current study, data collection at either 4 or 6 h provided information comparable to averaged time course data, albeit with a few metabolites missing using a single sampling time point.
在动态代谢系统的背景下,研究了采样时间在生长条件下的影响,以评估在一般应用中单一采样时间可能足够到何种程度,以及是否可以获得有用的动力学信息。将野生型酵母菌株(W)与snf1Δ突变型酵母菌株(S)在高葡萄糖培养基(R)和含乙醇的低葡萄糖培养基(DR)中进行比较。在这些生长条件下,预计会激活利用不同碳源的不同代谢途径,因此预期与生长培养基中碳源相关的代谢物水平会发生变化。此外,Snf1蛋白激酶复合体是适应细胞代谢从发酵R条件到氧化DR条件所必需的,因此也预期W和S酵母菌株之间的胞内代谢物水平存在差异。在将一半细胞从R条件转移到DR条件后的四个时间点(0.5、2、4、6小时)收集细胞提取物,产生了16个样本类别(WR、WDR、SR、SDR)×(0.5、2、4、6小时)。实验设计提供了时间进程数据,因此除了碳源和菌株依赖性外,还可以监测时间依赖性。综合二维(2D)气相色谱与飞行时间质谱(GC×GC-TOFMS)结合基于发现的挖掘算法(《分析化学》2006,78,5068–5075(参考文献1);《色谱A》2008,1186,401–411(参考文献2))用于在2D色谱图中定位在16个样本类别之间提供化学选择性的区域(即代谢物)。这些区域通过平行因子分析进行数学解析,以正向鉴定代谢物并获取定量结果。利用这些工具,识别并定量了51种独特代谢物。这些数据中出现了各种时间进程模式,并利用主成分分析(PCA)作为比较工具来确定这51种代谢物之间的变异来源。通过使用数据的不同子集进行单独的PCA分析,研究了采样时间的影响。利用所有时间进程数据、平均时间进程数据以及每个独立时间点数据集分别进行了PCA分析,以辨别差异。对于当前研究中考察的酵母菌株,在4或6小时收集数据提供的信息与平均时间进程数据相当,尽管使用单一采样时间点时会缺失一些代谢物。