Abstract
An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC–HRMS-based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for nonlabeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching, and (iii) metabolic feature annotation. These extensions enable the automated, unbiased, and global detection of tracer-derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of 13C9-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total, 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident, with 32 and 58 of these metabolites having exclusively been detected in the positive and negative modes, respectively. Moreover, for 19 of the remaining 49 phenylalanine-derived metabolites, the assignment of ion species and, thus, molecular weight was possible only by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites.
Highlights
Phenylalanine was chosen as a tracer because it serves as precursor for the biosynthesis of hydroxycinnamic acids, phenylpropanoids, and flavonoids in plants, many of which are known to be involved in the defense against fungal pathogens such as Fusarium
stable isotopic labeling (SIL) has been increasingly used in many fields of targeted and untargeted metabolomics research
All metabolites derived from the studied native and 13C-labeled tracers show unique isotope patterns, which enable their untargeted detection and provide high confidence that the detected metabolites are truly derived from the studied tracer substance
Summary
For LC−HRMS-based tracer metabolization approaches, several software tools designed for the untargeted detection and analysis of isotope patterns of metabolites derived from native and partly isotopically labeled tracers are available (e.g., mzMatch-ISO, X13CMS11). A LC−HRMS-based workflow for the unbiased detection of known and unknown metabolites derived from U-13C-SIL guided tracer metabolism was developed. It is based on our recently published workflow for the detection of metabolic features derived from native and fully labeled biological samples, which has been further developed to support fast polarity switching and automated annotation of metabolic features of the detected metabolites. Phenylalanine was chosen as a tracer because it serves as precursor for the biosynthesis of hydroxycinnamic acids, phenylpropanoids, and flavonoids in plants, many of which are known to be involved in the defense against fungal pathogens such as Fusarium.
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