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High precision mass measurements for wine metabolomics.

Roullier-Gall C, Witting M, Gougeon RD, Schmitt-Kopplin P - Front Chem (2014)

Bottom Line: An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification.UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2).In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

View Article: PubMed Central - PubMed

Affiliation: UMR PAM Université de Bourgogne/AgroSup Dijon, Institut Universitaire de la Vigne et du Vin Jules Guyot, Dijon, France ; Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München Neuherberg, Germany.

ABSTRACT
An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2). In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

No MeSH data available.


Related in: MedlinePlus

(A) Pathways for the biosynthesis of secondary metabolites from KEGG for the vitis vinifera organism and (B) enlargement of a portion of flavonoid biosynthesis pathways, with annotated compounds (blue dots) possibly corresponding to detected masses from all of the (−) FTICR-mass spectra; (C) Venn diagrams showing the convergence between annotations from different data bases (MassTRIX vs. home build wine data base, top).
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Figure 2: (A) Pathways for the biosynthesis of secondary metabolites from KEGG for the vitis vinifera organism and (B) enlargement of a portion of flavonoid biosynthesis pathways, with annotated compounds (blue dots) possibly corresponding to detected masses from all of the (−) FTICR-mass spectra; (C) Venn diagrams showing the convergence between annotations from different data bases (MassTRIX vs. home build wine data base, top).

Mentions: A first overview of potential structures corresponding to detected metabolites was obtained by metabolite annotation using both the MassTRIX interface and a home-build (including grape and wine) metabolite database (Suhre and Schmitt-Kopplin, 2008; Wägele et al., 2012; Roullier-Gall et al., 2014a,b) (Figure 2). A search against KEGG, HMDB and LipidMaps with maximum error of 3 ppm was performed. MassTRIX and KEGG enable the visualization of compounds annotation on pathways of a chosen organism (Vitis vinifera in this example) (Figures 2A,B). In total, 3351 detected masses from FTICR-MS could be annotated using MassTRIX, whereas 2613 detected masses could be annotated using our home-build metabolite database (Figure 2C). Around 22% of total detected features from FTICR-MS are detected by both MassTRIX and homemade data base. Only few features, 119, from UPLC-Q-ToF-MS were annotated using MassTRIX, comparing to the 3351 masses from FTICR-MS (Figure 2C) proving the necessity of the alignment of both datasets.


High precision mass measurements for wine metabolomics.

Roullier-Gall C, Witting M, Gougeon RD, Schmitt-Kopplin P - Front Chem (2014)

(A) Pathways for the biosynthesis of secondary metabolites from KEGG for the vitis vinifera organism and (B) enlargement of a portion of flavonoid biosynthesis pathways, with annotated compounds (blue dots) possibly corresponding to detected masses from all of the (−) FTICR-mass spectra; (C) Venn diagrams showing the convergence between annotations from different data bases (MassTRIX vs. home build wine data base, top).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230200&req=5

Figure 2: (A) Pathways for the biosynthesis of secondary metabolites from KEGG for the vitis vinifera organism and (B) enlargement of a portion of flavonoid biosynthesis pathways, with annotated compounds (blue dots) possibly corresponding to detected masses from all of the (−) FTICR-mass spectra; (C) Venn diagrams showing the convergence between annotations from different data bases (MassTRIX vs. home build wine data base, top).
Mentions: A first overview of potential structures corresponding to detected metabolites was obtained by metabolite annotation using both the MassTRIX interface and a home-build (including grape and wine) metabolite database (Suhre and Schmitt-Kopplin, 2008; Wägele et al., 2012; Roullier-Gall et al., 2014a,b) (Figure 2). A search against KEGG, HMDB and LipidMaps with maximum error of 3 ppm was performed. MassTRIX and KEGG enable the visualization of compounds annotation on pathways of a chosen organism (Vitis vinifera in this example) (Figures 2A,B). In total, 3351 detected masses from FTICR-MS could be annotated using MassTRIX, whereas 2613 detected masses could be annotated using our home-build metabolite database (Figure 2C). Around 22% of total detected features from FTICR-MS are detected by both MassTRIX and homemade data base. Only few features, 119, from UPLC-Q-ToF-MS were annotated using MassTRIX, comparing to the 3351 masses from FTICR-MS (Figure 2C) proving the necessity of the alignment of both datasets.

Bottom Line: An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification.UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2).In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

View Article: PubMed Central - PubMed

Affiliation: UMR PAM Université de Bourgogne/AgroSup Dijon, Institut Universitaire de la Vigne et du Vin Jules Guyot, Dijon, France ; Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München Neuherberg, Germany.

ABSTRACT
An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2). In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

No MeSH data available.


Related in: MedlinePlus