Metabolomics analysis of oil palm leaves is a promising strategy to prospect new added-value compounds of this underutilized oil industry by-product. Although previous studies had reported some metabolites identified in this matrix, they had been focused on few compounds using conventional analytical techniques.
This study aimed to develop a new high throughput method based on metabolomics able to detect a wide range of metabolites classes in Elaeis guineensis leaves. Furthermore, we investigate the effects caused by harvesting/sample preparation steps for the metabolites identification.
Metabolites analyses were performed by ultra-high liquid chromatography—mass spectrometry (UHPLC–MS) using both ionization modes, ESI(+)–MS and ESI(−)–MS. ANOVA simultaneous component analysis (ASCA) of the resulting complex multivariate dataset was applied to evaluate metabolic alterations. Identification of major metabolites was performed by high resolution mass spectrometry and MS/MS experiments.
A high throughput method based on UHPLC–MS was successfully developed to E. guineensis leaves, detecting from polar to non-polar acid and basic metabolites. According to ASCA, oil palm leaves metabolic fingerprintings have shown influence of transportation/storage and extraction solvent used chosen. In fact, the most significant effect is due to the solvent composition. A total of thirteen metabolites were assigned based on HRMS and MS/MS experiments. However, only seven metabolites identified were previously reported, which represents a potential field to discover new metabolites.
Sample preparation steps should be carefully performed in metabolomics studies, because metabolites will be extracted and identified based on transport and solvent of extraction conditions. In this study, we established a reliable analytical protocol, from sample preparation to data analyses, to prospect new metabolites in oil palm leaves. This protocol could be further applied to similar oil-bearing crops.
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The authors would like to thanks The Brazilian Agricultural Research Corporation (EMBRAPA), The Federal Foundation for the Brazilian Research and Development (FINEP), Coordination for the Improvement of Higher Education Personnel (CAPES) and National Council for Scientific and Technological Development (CNPQ) for the financial support. The authors thank Daniel Nogoceke Sifuentes for his technical support.
This study was funded by FINEP — Project “DENDEPALM—Estratégias genômicas e agregação de valor para a cadeia produtiva do dendê” (Grant Number 0 1 13 0315 00).
Conflict of interest
Luiz Henrique Galli Vargas, José Antônio de Aquino Ribeiro, Jorge Candido Rodrigues Neto, Maria Esther Ricci-Silva, Manoel Teixeira de Souza Júnior, Clenilson Martins Rodrigues, Anselmo Elcana de Oliveira and Patrícia Verardi Abdelnur declare that they have no conflict of interest.
Research involving human participants and/or animals
This article does not contain any studies with human participants or animals performed by any of the authors.
Luiz Henrique Galli Vargas and Jorge Candido Rodrigues Neto have contributed equally to this manuscript.
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Vargas, L.H.G., Neto, J.C.R., de Aquino Ribeiro, J.A. et al. Metabolomics analysis of oil palm (Elaeis guineensis) leaf: evaluation of sample preparation steps using UHPLC–MS/MS. Metabolomics 12, 153 (2016). https://doi.org/10.1007/s11306-016-1100-z
- Untargeted metabolomics
- Metabolite identification
- Mass Spectrometry
- High throughput analysis
- Oil-bearing crop