Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling
Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling
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DOI：
10.1021/jf9009137
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发表时间：
2009-08-26
影响因子：
6.1
通讯作者：
Gomez-Cadenas, Aurelio
中科院分区：
文献类型：
--
作者：
Arbona, Vicent;Iglesias, Domingo J.;Gomez-Cadenas, Aurelio
代谢组的表征是基础研究和植物育种的一个重要方面。在这项工作中，代谢组学对密切相关的基因型进行表型分析的假设应用已经进行了测试。采用LC-MS和GC-MS对粗提物进行分析，并用XCMS软件进行大量数据提取。采用主成分分析（PCA）对结果进行验证。利用层次聚类分析（HCA）对植物基因型分析方法进行了评价。采用多尺度自举重采样方法评估聚类稳健性。在PCA和HCA后的表型划分方面，LC-MS分析比GC-MS表现更好，柑橘的划分同样独立于种植植物的环境条件。此外，当所有不同的位置汇集在一个单一的实验设计中，仍然有可能区分三种密切相关的基因型。所提出的方法提供了一个快速和无目标的工作流程，作为一个强大的工具来区分相关的植物表型。该技术的新颖性依赖于使用质量信号作为独立于假定代谢物身份的表型划分标记，以及相对简单的分析策略，可适用于广泛的植物基质，无需先前的优化。
The characterization of the metabolome is a critical aspect in basic research and plant breeding. In this work, the Putative application of metabolomics for phenotyping closely related genotypes has been tested. Crude extracts were profiled by LC-MS and GC-MS, and mass data extraction was performed with XCMS software. Result validation was achieved with principal component analysis (PCA). The ability of the profiling methodologies to discriminate plant genotypes was assessed after hierarchical clustering analysis (HCA). Cluster robustness was assessed by a multiscale bootstrap resampling method. A better performance of LC-MS profiling over GC-MS was evidenced in terms of phenotype demarcation after PCA and HCA, Citrus demarcation was similarly achieved independently of the environmental conditions used to grow plants. In addition, when all different locations were pooled in a single experimental design, it was still possible to differentiate the three closely related genotypes. The presented methodology provides a fast and nontargeted workflow as a powerful tool to discriminate related plant phenotypes. The novelty of the technique relies on the use of mass signals as markers for phenotype demarcation independent of putative metabolite identities and the relatively simple analytical strategy that can be applicable to a wide range of plant matrices with no previous optimization.