Tikunov Yury, Lommen Arjen, de Vos C H Ric, Verhoeven Harrie A, Bino Raoul J, Hall Robert D, Bovy Arnaud G
Centre for BioSystems Genomics, 6700 AB Wageningen, The Netherlands.
Plant Physiol. 2005 Nov;139(3):1125-37. doi: 10.1104/pp.105.068130.
To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.
为了充分利用功能基因组学技术的强大功能，特别是代谢组学技术，所选择的分析方法和数据分析策略都需要尽可能做到无偏差且全面。现有的代谢组学数据分析方法仍无法对现代研究中常作为目标的数百种基因型的代谢组成进行快速且无偏差的比较分析。我们现已开发出一种分析此类代谢组学数据的新策略。该方法包括：（1）使用MetAlign软件包对气相色谱（GC）-质谱（MS）代谢谱进行全质谱比对；（2）随后在单个分子片段水平对代谢表型进行多变量比较分析；（3）多变量质谱重建，这是一种可实现代谢物鉴别、识别和鉴定的方法。基于198个GC-MS数据集中>20,000个单个分子片段的强度模式，该方法实现了对94种番茄（Lycopersicon esculentum Mill.）基因型成熟果实挥发性代谢物组成的快速且无偏差的多变量比较分析。发现了果实类型之间以及果实内部代谢物组成的差异，并揭示了具有鉴别性的代谢物。在整个基因型集合中，使用多变量质谱重建可区分出总共322种不同的化合物。对这些代谢物进行层次聚类分析，结果显示源自相同生化前体的结构相关代谢物聚集在一起。所选择的方法将进一步提高基于GC-MS的代谢组学方法的全面性，因此将被证明是对非靶向功能基因组学研究的有益补充。
