Abstract
Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) represents the most powerful metabolomics platform to investigate biological systems. Reproducible and standardized workflows allow obtaining a meaningful biological interpretation. The purpose of this study was to set up and apply an open-source workflow for LC-HRMS plant metabolomics studies. Key steps of the proposed workflow were as follows: (1) experimental design, (2) sample preparation, (3) LC-HRMS analysis, (4) data processing, (5) custom database search, (6) statistical analysis, (7) compound identification, and (8) biochemical interpretation. Its applicability was evaluated through the study of metabolomics changes of two maize recombinant inbred lines with contrasting phenotypes with respect to disease severity after Fusarium verticillioides infection of seedlings. Analysis of data from the case-control study revealed abundance change in metabolites belonging to different metabolic pathways, including two amino acids (L-tryptophan and tyrosine), five flavonoids, and three N-hydroxynnamic acid amides.
Highlights
Metabolomics is a powerful approach for comprehensive investigation of metabolite variations in biological systems (Blaženovic et al, 2018)
Sharing workflows help to validate the findings reported in publications and, more importantly, let researchers freely reuse the data as they are, or as a reliable basis to move forward
Compounds were classified according to confidence levels as defined by the Compound Identification Workgroup of the Metabolomic Society (Sumner et al, 2007; Blaženovic et al, 2018), which are summarized in Table 4 together with relevant minimum data requirements
Summary
Metabolomics is a powerful approach for comprehensive investigation of metabolite variations in biological systems (Blaženovic et al, 2018). Untargeted metabolite mass profiles can be used for biological interpretations; approaches that do not require the identification of the metabolic features should be used with extreme caution, because they may lead to false interpretations. The identification of metabolites with a high level of confidence is required in order to improve the meaning of metabolomics in biological systems, such as plant–pathogen interaction and possible applications. Knowledge-based workflows for metabolite annotations are highly desirable to complement information relevant to mass spectrometry (MS) peaks relationships (adducts and neutral losses), MS/MS data, and retention time modeling with biochemical knowledge. A priority issue in the metabolomics field is the validation and harmonization of untargeted approaches. Criteria to validate identified markers are proposed such as survey of blind real samples, analysis of reference samples, and integration of multiplatform data
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