Abstract
The screening of real features among thousands of ions remains a great challenge in the study of metabolomics. In this research, a workflow designed based on the MetaboFR tool and “feature-rating” rule was developed to screen the real features in large-scale data analyses. Seventy-four reference standards were used to test the feasibility, with 83.21% of real features being obtained after MetaboFR processing. Moreover, the full workflow was applied for systematic characterization of 14 species of the genus Isatis, with the result that 87.72% of real features were retained and 69.19% of the in-source fragments were removed. To gain insights into metabolite diversity within this plant family, 1697 real features were tentatively identified, including lipids, phenylpropanoids, organic acids, indole derivatives, etc. Indole derivatives were demonstrated to be the best chemical markers with which to differentiate different species. The rare existence of indole derivatives in Isatis cappadocica (cap) and Isatis cappadocica subsp. Steveniana (capS) indicates that the biosynthesis of indole derivatives could play a key role in driving the chemical diversity and evolution of genus Isatis. Our workflow provides the foundations for the exploration of real features in metabolomics, and has the potential to reveal the chemical composition and marker metabolites of secondary metabolites in plant fields.
Highlights
Metabolomics has been widely applied by a broad spectrum of researchers interested in defining the biological roles of biomarkers, novel metabolites, and new drug candidates, as well as for disease diagnosis [1–3]
In order to measure the diversity of indole-related compounds, we evaluated the contents of indole in plants of genus Isatis (Figure S11), proving that the existence of interspecific variability mainly occurred via downstream indole-related biosynthesis pathways among different species
MetaboFR (Supplementary file S13), based on the MS-DIAL/MS-CleanR suite, was developed to screen real features and a comprehensive workflow, from raw data to final annotated peak list, was provided. The utility of this workflow is demonstrated by the fact that by analyzing secondary metabolites in 14 species of genus Isatis, 87.72% of real features were retained and 69.19% of the in-source fragments were removed
Summary
Metabolomics has been widely applied by a broad spectrum of researchers interested in defining the biological roles of biomarkers, novel metabolites, and new drug candidates, as well as for disease diagnosis [1–3]. Untargeted and targeted metabolomics are the two most common methodologies for comprehensive and targeted analyses to provide global or simple metabolic overviews [4,5]. For sample sets of complex mixtures for which little information is available, untargeted metabolomics can provide an unbiased discriminatory analysis for global metabolite detection, and may yield insights into biochemical functions [6]. Advances in liquid chromatography coupled with mass spectrometry (LCMS) have become integral metabolomics platforms, with each resulting signal commonly being referred to as a “feature”. Numerous open-source tools have been developed to define features for the generation of peak tables, with the most common ones being XCMS, MZmine, and MS-DIAL [8–10]
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