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pmid:27547172
Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis.
Elisabete Carvalho · Pietro Franceschi · Antje Feller · Lorena Herrera · Luisa Palmieri · Panagiotis Arapitsas · Samantha Riccadonna · Stefan Martens
Metabolomics : Official journal of the Metabolomic Society · 2016 doi:10.1007/s11306-016-1090-x
Paper
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Experiment
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Data
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Analysis
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Complete file
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Despite the presence of only trace amounts of anthocyanins, yellow varieties seems to show similar or better effects in comparison to conventional raspberries.\nOBJECTIVES: The aim of this work is to characterize the metabolic differences between red and yellow berries, focussing on the compounds showing a higher concentration in yellow varieties.\nMETHODS: The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC-TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC-MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333).\nRESULTS: Among the metabolites showing higher concentration in yellow raspberries it was possible to identify a series of compounds showing a pseudospectrum similar to that of A-type procyanidin polymers. The annotation of this group of compounds was confirmed by specific MS/MS experiments and performing standard injections.\nCONCLUSIONS: In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers.", "claims": [{"id": "claim-method", "statement": "Within this shortlist, 114 markers were identified by a t test corrected for multiplicity (Bonferroni) at the 0.01 confidence level (Fig. 1b).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27547172", "locator": "sections[8] \"Data analysis\" paragraph 5", "quote": "Within this shortlist, 114 markers were identified by a t test corrected for multiplicity (Bonferroni) at the 0.01 confidence level (Fig. 1b)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Raw LC–MS data will be made publicly available on MetaboLights (MTBLS333).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27547172", "locator": "sections[7] \"LC–MS\" paragraph 2", "quote": "Raw LC–MS data will be made publicly available on MetaboLights (MTBLS333)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Yellow raspberries were found to contain higher levels of putative A-type procyanidin polymers than red raspberries. The statistical workflow used untargeted UPLC–TOF-MS metabolomics, a Bonferroni-corrected t test for differential markers, Pearson correlation of extracted ion chromatograms to construct pseudospectra, and principal components analysis to assess sample variation. The raw source data were deposited in MetaboLights under accession MTBLS333.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:27547172", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC–TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC–MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333)."}, {"asset": "source:pmid:27547172", "locator": "sections[0] \"Abstract\" paragraph 5", "quote": "In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Yellow raspberries were found to contain higher levels of putative A-type procyanidin polymers than red raspberries. The statistical workflow used untargeted UPLC–TOF-MS metabolomics, a Bonferroni-corrected t test for differential markers, Pearson correlation of extracted ion chromatograms to construct pseudospectra, and principal components analysis to assess sample variation. 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