Rivas-Ubach A., Perez-Trujillo M., Sardans J., Gargallo-Garriga A., Parella T., Penuelas J. (2013) Ecometabolomics: Optimized NMR-based method. Methods in Ecology and Evolution. 4: 464-473.LinkDoi: 10.1111/2041-210X.12028
Metabolomics is allowing great advances in biological sciences. Recently, an increasing number of ecological studies are using a metabolomic approach to answer ecological questions (ecometabolomics). Ecometabolomics is becoming a powerful tool which allows following the responses of the metabolome of an organism environmental changes and the comparison of populations. Some Nuclear Magnetic Resonance (NMR) protocols have been published for metabolomics analyses oriented to other disciplines such as biomedicine, but there is a lack of a description of a detailed protocol applied to ecological studies. Here we propose a NMR-based protocol for ecometabolomic studies that provides an unbiased overview of the metabolome of an organism, including polar and nonpolar metabolites. This protocol is aimed to facilitate the analysis of many samples, as typically required in ecological studies. In addition to NMR fingerprinting, it identifies metabolites for generating metabolic profiles applying strategies of elucidation of small molecules typically used in natural-product research, and allowing the identification of secondary and unknown metabolites. We also provide a detailed description to obtain the numerical data from the 1H-NMR spectra needed to perform the statistical analyses. We tested and optimized this protocol by using two field plant species (Erica multiflora and Quercus ilex) sampled once per season. Both species showed high levels of polar compounds such as sugars and amino acids during the spring, the growing season. E. multiflora was also experimentally submitted to drought and the NMR analyses were sensitive enough to detect some compounds related to the avoidance of water loses. This protocol has been designed for ecometabolomic studies. It identifies changes in the compositions of metabolites between individuals and detects and identifies biological markers associated with environmental changes. © 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society.
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