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Novel aspects of grape berry ripening and post-harvest withering revealed by untargeted LC-ESI-MS metabolomics analysis

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Abstract

We established a step-by-step, experiment-guided metabolomics procedure, based on LC-ESI-MS analysis, to generate a detailed picture of the changing metabolic profiles during late berry development in the important Italian grapevine cultivar Corvina. We sampled berries from four developmental time points and three post-harvest time points during the withering process, and used chromatograms of methanolic extracts to test the performance of the MetAlign and MZmine data mining programs. MZmine achieved a better resolution and therefore generated a more useful data matrix. Then both the quantitative performance of the analytical platform and the matrix effect were assessed, and the final dataset was investigated by multivariate data analysis. Our analysis confirmed the results of previous studies but also revealed some novel findings, including the prevalence of two specific flavonoids in unripe berries and important differences between the developmental profiles of flavones and flavanones, suggesting that specific individual metabolites could have different functions, and that flavones and flavanones probably play quite distinct biological roles. Moreover, the hypothesis-free multivariate analysis of subsets of the wide data matrix evidentiated the relationships between the various classes of metabolites, such as those between anthocyanins and hydroxycinnamic acids and between flavan-3-ols and anthocyanins.

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Acknowledgements

This work was supported by the BACCA Project funded by the ORVIT Consortium, by the Completamento del Centro di Genomica Funzionale Vegetale Project funded by the Cariverona Bank Foundation, and by the Structural and Functional Characterization of the Grapevine Genome (Vigna) Project funded by the Italian Ministry of Agricultural and Forestry Policies. We thank Pasqua Vini e Cantine (Verona, Italy) for allowing us to sample material from its vineyard.

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Correspondence to Flavia Guzzo.

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11306_2010_259_MOESM1_ESM.doc

Putatively identified berry metabolites. id-1 = identification number in the dataset obtained with MZmine; id-2 = identification number in the dataset previously obtained with MetAlign (see Zamboni et al. 2010); rt = retention time; l.m.i. = level of metabolite identification according to Sumner et al. (2007): 1: identified compound; 2: putatively annotated compound (e.g. without chemical reference standards, based upon physicochemical properties and/or spectral similarity with public/commercial libraries); 3: putatively characterized compound class (e.g. based upon characteristic physicochemical properties of a chemical class of compounds or by spectral similarity to known compounds of a chemical class). The fragmentation of the putatively identified molecules is reported (ms/ms and ms3 for the main ms/ms fragment, in bold). For procyanidins (condensed tannins) the following abbreviations are used: P = proanthocyanidin unit, catechin or epicatechin; Pg = proanthocyanidin unit, gallocatechin or epigallocatechin; proanthocyanidin unit, G = gallic acid. (DOC 1764 kb)

11306_2010_259_MOESM2_ESM.ppt

Quantitation of selected classes of molecules. (A-D) Quantitation by MS expressed as intensity in arbitrary units ± standard deviation. (E–H) Quantitation by DAD expressed as absorbance (see Materials and Methods for wavelengths) ± standard deviation. (A,E) 3-O-glucosylated anthocyanins. (B,F) Two coumarated anthocyanins. (C,G) One caffeic and one coumaric acid derivative. (D,H) Taxifolin and quercetin derivatives. (PPT 97 kb)

11306_2010_259_MOESM3_ESM.ppt

O2PLS-DA loading plot for identified molecules with a matrix effect correction factor. (A) original data. (B) corrected data. The four classes of samples are indicated as red squares, as follows: pv, pre-veraison; v, veraison; r, ripening and ripe; w, withering and withered; the identified metabolites are indicated as colored triangles: fuchsia, non-acylated anthocyanins; violet, acylated anthocyanins; yellow, flavanones; orange, flavones; light blue, sugars; black, stilbenes and viniferins; blue, procyanidins and flavan-3-ols; green, organic acids. (PPT 3387 kb)

11306_2010_259_MOESM4_ESM.ppt

Qualitative-quantitative profile (MS data), represented through histogram plots, of different classes of metabolites in three representative stages. d = derivative; h = hexoside; p = pentoside; ti = trans-isomer; ci = cis-isomer; dh = deoxyhexoside; ga = galactoside; rh = rhamnoside; P = catechin/epicatechin unit; G = gallic acid unit; Pg = catechin/epicatechin gallate unit. (PPT 3602 kb)

11306_2010_259_MOESM5_ESM.ppt

O2PLS-DA correlation loading plots obtained by considering one separate dataset for each group of molecules (A: sugars, B: flavanones, C: aromatic organic acids, D: flavones,E: non-acylated anthocyanins, F: flavan-3-ols and procyanidins, G: stilbenes and viniferins). A, B: since the O2PLS-DA models showed at least two latent components, the scatter pq(corr)1-pq(corr)2 correlation loading plots are shown. The classes of samples are indicated with red squares, the individual molecules with black triangles. C-G: since the O2PLS-DA models showed only one latent component, the histogram pq(corr)1 correlation loading plots are shown. The classes of samples are indicated with red columns, the individual molecules with black columns. Identification numbers are taken from Supplementary Table S1 (id-1). Each O2PLS-DA model shows the number of latent components and the P value of the ANOVA. pv = pre-veraison; v = veraison; r = ripening and ripe, w = withering and withered grape. (PPT 183 kb)

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Toffali, K., Zamboni, A., Anesi, A. et al. Novel aspects of grape berry ripening and post-harvest withering revealed by untargeted LC-ESI-MS metabolomics analysis. Metabolomics 7, 424–436 (2011). https://doi.org/10.1007/s11306-010-0259-y

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  • DOI: https://doi.org/10.1007/s11306-010-0259-y

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