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Untargeted profiling of tracer-derived metabolites using stable isotopic labeling and fast polarity-switching LC-ESI-HRMS.

Kluger B, Bueschl C, Neumann N, Stückler R, Doppler M, Chassy AW, Waterhouse AL, Rechthaler J, Kampleitner N, Thallinger GG, Adam G, Krska R, Schuhmacher R - Anal. Chem. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Analytical Chemistry, Department for Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences Vienna (BOKU) , Konrad-Lorenz-Strasse 20, 3430 Tulln, Austria.

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 (13)C9-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.

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(a, b)Illustration of two metabolic feature pairs detected forthe same metabolite. (a) Two mass spectra derived from positive andnegative ionization mode for the respective native and corresponding 13C8-labeled features derived from phenylalanine.(b) EIC profiles of the respective metabolic features shown in parta. (c) m/z versus retention timeplot of all 13C9-Phe-derived features detectedin the positive and negative ionization mode and (d) their convolutioninto a feature group. The red dots represent selected metabolic featuresfrom three of the either annotated or identified metabolites. Fordetails, see Supporting Information S1.2.
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fig2: (a, b)Illustration of two metabolic feature pairs detected forthe same metabolite. (a) Two mass spectra derived from positive andnegative ionization mode for the respective native and corresponding 13C8-labeled features derived from phenylalanine.(b) EIC profiles of the respective metabolic features shown in parta. (c) m/z versus retention timeplot of all 13C9-Phe-derived features detectedin the positive and negative ionization mode and (d) their convolutioninto a feature group. The red dots represent selected metabolic featuresfrom three of the either annotated or identified metabolites. Fordetails, see Supporting Information S1.2.

Mentions: To demonstrate the workflow, the metabolicfate of the amino acidphenylalanine (Phe) was studied in Tae cell suspensions cultured in the presence of U-13C9 Phe in the culture medium. Processing of the acquired rawdata resulted in a total of 341 metabolic features, which were convolutedto 139 feature groups, each of which is representing a metabolite.Figure 2a shows two mass spectra containinga metabolism product with eight tracer-derived carbon atoms (nativeM) and its partly 13C-labeled analog M′. The presenceof the M′ + 1 mass peaks indicate that the moiety conjugatedto the tracer also contains several carbon atoms.


Untargeted profiling of tracer-derived metabolites using stable isotopic labeling and fast polarity-switching LC-ESI-HRMS.

Kluger B, Bueschl C, Neumann N, Stückler R, Doppler M, Chassy AW, Waterhouse AL, Rechthaler J, Kampleitner N, Thallinger GG, Adam G, Krska R, Schuhmacher R - Anal. Chem. (2014)

(a, b)Illustration of two metabolic feature pairs detected forthe same metabolite. (a) Two mass spectra derived from positive andnegative ionization mode for the respective native and corresponding 13C8-labeled features derived from phenylalanine.(b) EIC profiles of the respective metabolic features shown in parta. (c) m/z versus retention timeplot of all 13C9-Phe-derived features detectedin the positive and negative ionization mode and (d) their convolutioninto a feature group. The red dots represent selected metabolic featuresfrom three of the either annotated or identified metabolites. Fordetails, see Supporting Information S1.2.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4255957&req=5

fig2: (a, b)Illustration of two metabolic feature pairs detected forthe same metabolite. (a) Two mass spectra derived from positive andnegative ionization mode for the respective native and corresponding 13C8-labeled features derived from phenylalanine.(b) EIC profiles of the respective metabolic features shown in parta. (c) m/z versus retention timeplot of all 13C9-Phe-derived features detectedin the positive and negative ionization mode and (d) their convolutioninto a feature group. The red dots represent selected metabolic featuresfrom three of the either annotated or identified metabolites. Fordetails, see Supporting Information S1.2.
Mentions: To demonstrate the workflow, the metabolicfate of the amino acidphenylalanine (Phe) was studied in Tae cell suspensions cultured in the presence of U-13C9 Phe in the culture medium. Processing of the acquired rawdata resulted in a total of 341 metabolic features, which were convolutedto 139 feature groups, each of which is representing a metabolite.Figure 2a shows two mass spectra containinga metabolism product with eight tracer-derived carbon atoms (nativeM) and its partly 13C-labeled analog M′. The presenceof the M′ + 1 mass peaks indicate that the moiety conjugatedto the tracer also contains several carbon atoms.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Analytical Chemistry, Department for Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences Vienna (BOKU) , Konrad-Lorenz-Strasse 20, 3430 Tulln, Austria.

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 (13)C9-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.

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