Metabolomics analysis of oil palm (Elaeis guineensis) leaf: evaluation of sample preparation steps using UHPLC–MS/MS

• Published in: Metabolomics Vol. 12; no. 10; p. 1
• Main Authors: Vargas, Luiz Henrique Galli, Neto, Jorge Candido Rodrigues, de Aquino Ribeiro, José Antônio, Ricci-Silva, Maria Esther, Souza, Manoel Teixeira, Rodrigues, Clenilson Martins, de Oliveira, Anselmo Elcana, Abdelnur, Patrícia Verardi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2016; Springer Nature B.V
• Subjects: Biochemistry; Biomedical and Life Sciences; Biomedicine; Cell Biology; Developmental Biology; Life Sciences; Molecular Medicine; Original Article; Bioprospection; Metabolite identification; Mass Spectrometry; High throughput analysis; Untargeted metabolomics; Oil-bearing crop
• ISSN: 1573-3882; 1573-3890
• DOI: 10.1007/s11306-016-1100-z

Introduction 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. Objectives 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. Method 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. Result 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. Conclusion 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.