Correlative GC-TOF-MS-based metabolite profiling and LC-MS-based protein profiling reveal time-related systemic regulation of metabolite-protein networks and improve pattern recognition for multiple biomarker selection
Correlative GC-TOF-MS-based metabolite profiling and LC-MS-based protein profiling reveal time-related systemic regulation of metabolite-protein networks and improve pattern recognition for multiple biomarker selection
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DOI：
10.1007/s11306-005-4430-9
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发表时间：
2005-04-01
期刊：
影响因子：
3.6
通讯作者：
Weckwerth, Wolfram
中科院分区：
文献类型：
--
作者：
Morgenthal, Katja;Wienkoop, Stefanie;Weckwerth, Wolfram
提出了一种结合定量代谢物和蛋白质数据以及多变量统计的新方法，用于在系统水平上分析植物代谢的时间相关调节效应。为了分析代谢物，使用气相色谱法与飞行时间质量分析器 (GC-TOF-MS) 联用。使用最近描述的基于鸟枪测序的新程序来鉴定和定量蛋白质（Weckwerth 等人，2004b，Proteomics 4, 78-83）。为了进行比较，在整个昼/夜周期中每隔一定时间对拟南芥野生型植物和缺乏磷酸葡萄糖变位酶(PGM)活性的无淀粉突变体植物的叶子进行取样。使用主成分和独立成分分析，每个数据集（代谢物和蛋白质）显示离散特征。与仅分析代谢物或仅分析蛋白质相比，集成代谢物/蛋白质数据集的独立成分分析 (ICA) 提高了区分 WT 和 PGM 植物（第一个独立成分）的能力，同时观察两种植物的昼夜变化（第二个独立成分）。有趣的是，甘油酸和甘氨酸等光呼吸中间体的水平最能表征昼夜节律的阶段，并且不受PGM植物中高糖积累的影响。与 WT 植物相比，PGM 植物表现出富氮氨基酸代谢物和碳水化合物的反向调节簇，表明 C/N 分配的变化。这一观察结果与PGM植物中尿素循环中间体的利用改变相对应，表明由于生长抑制而增强了蛋白质降解和碳利用。在叶绿体蛋白中，根据位于叶绿体中的突变的主要效应，与胞质亚型 (At1g13440) 相比，叶绿体 GAPDH (At3g26650) 是 WT 和 PGM 植物之间的最佳区分器。所述方法适用于各种生物系统，并且能够公正地识别嵌入相关代谢物-蛋白质网络中的生物标志物。
A novel approach is presented combining quantitative metabolite and protein data and multivariate statistics for the analysis of time-related regulatory effects of plant metabolism at a systems level. For the analysis of metabolites, gas chromatography coupled to a time-of-flight mass analyzer (GC-TOF-MS) was used. Proteins were identified and quantified using a novel procedure based on shotgun sequencing as described recently (Weckwerth et al., 2004b, Proteomics 4, 78-83). For comparison, leaves of Arabidopsis thaliana wild type plants and starchless mutant plants deficient in phosphoglucomutase activity (PGM) were sampled at intervals throughout the day/night cycle. Using principal and independent components analysis, each dataset (metabolites and proteins) displayed discrete characteristics. Compared to the analysis of only metabolites or only proteins, independent components analysis (ICA) of the integrated metabolite/protein dataset resulted in an improved ability to distinguish between WT and PGM plants (first independent component) and, in parallel, to see diurnal variations in both plants (second independent component). Interestingly, levels of photorespiratory intermediates such as glycerate and glycine best characterized phases of diurnal rhythm, and were not influenced by high sugar accumulation in PGM plants. In contrast to WT plants, PGM plants showed an inversely regulated cluster of N-rich amino acid metabolites and carbohydrates, indicating a shift in C/N partitioning. This observation corresponds to altered utilization of urea cycle intermediates in PGM plants suggesting enhanced protein degradation and carbon utilization due to growth inhibition. Among the proteins chloroplastidic GAPDH (At3g26650) was the best discriminator between WT and PGM plants in contrast to the cytosolic isoform (At1g13440) according to the primary effect of mutation located in the chloroplast. The described method is applicable to all kinds of biological systems and enables the unbiased identification of biomarkers embedded in correlative metabolite-protein networks.