Cultivar Diversity of Grape Skin Polyphenol Composition and Changes in Response to Drought Investigated by LC-MS Based Metabolomics

Abstract

Phenolic compounds represent a large family of plant secondary metabolites, essential for the quality of grape and wine and playing a major role in plant defense against biotic and abiotic stresses. Phenolic composition is genetically driven and greatly affected by environmental factors, including water stress. A major challenge for breeding of grapevine cultivars adapted to climate change and with high potential for wine-making is to dissect the complex plant metabolic response involved in adaptation mechanisms. A targeted metabolomics approach based on ultra high-performance liquid chromatography coupled to triple quadrupole mass spectrometry (UHPLC-QqQ-MS) analysis in the Multiple Reaction Monitoring (MRM) mode has been developed for high throughput profiling of the phenolic composition of grape skins. This method enables rapid, selective, and sensitive quantification of 96 phenolic compounds (anthocyanins, phenolic acids, stilbenoids, flavonols, dihydroflavonols, flavan-3-ol monomers, and oligomers…), and of the constitutive units of proanthocyanidins (i.e., condensed tannins), giving access to detailed polyphenol composition. It was applied on the skins of mature grape berries from a core-collection of 279 Vitis vinifera cultivars grown with or without watering to assess the genetic variation for polyphenol composition and its modulation by irrigation, in two successive vintages (2014-2015). Distribution of berry weights and δ¹³C values showed that non irrigated vines were subjected to a marked water stress in 2014 and to a very limited one in 2015. Metabolomics analysis of the polyphenol composition and chemometrics analysis of this data demonstrated an influence of water stress on the biosynthesis of different polyphenol classes and cultivar differences in metabolic response to water deficit. Correlation networks gave insight on the relationships between the different polyphenol metabolites and related biosynthetic pathways. They also established patterns of polyphenol response to drought, with different molecular families affected either positively or negatively in the different cultivars, with potential impact on grape and wine quality.

Keywords: UHPLC-QqQ-MS; Vitis vinifera; grape berry; large-scale studies; metabolomics; phenolic compounds; water deficit.

Figure 1

Correlation network (correlation values >0.8) established from the 105 MRM polyphenol composition variables (in mg berry⁻¹, coded as in Table 1) on the whole data set (2014 and 2015). Clusters of the different polyphenol groups are colored differently: anthocyanins (red), anthocyanin derived pigments (dark red and purple), flavonols (yellow), flavan-3-ols (blue), stilbenes (gray).

Figure 2

Bar plots showing water quantities supplied by rainfall and irrigation.

Figure 3

Distribution of berry weights in the population grown with and without irrigation, in 2014 and 2015.

Figure 4

Distribution of δ¹³C values in the population grown with and without irrigation, in 2014 and 2015.

Figure 5

PCA of the MRM phenolic composition data of berry skin samples collected in 2014 (mg g⁻¹); (A), projection of the samples on PC1 and PC2; red and white cultivars are represented in red and in green, respectively; IR, irrigated, NI, not-irrigated. (B), loadings of the variables (coded as in Table 1) on PC1. AN, native anthocyanins+dimers; AP, pyrano anthocyanins; AF, anthocyanin-flavanol adducts; AC, caftaric-anthocyanin adducts; HF, dihydroflavonols; FO, flavonols; ST, stilbenes; FA, flavanols (tannins); HB, hydroxybenzoic acids; HC, hydroxycinnamic acids; OT, others.

Figure 6

PCA of the MRM phenolic composition data of berry skin samples collected in 2014 (mg berry⁻¹); (A), projection of the samples on PC1 and PC2; red and white cultivars are represented in red and in green, respectively; IR, irrigated; NI, not-irrigated. (B), loadings of the variables (coded as in Table 1) on PC1. AN, native anthocyanins+dimers; AP, pyrano anthocyanins; AF, anthocyanin-flavanol adducts; AC, caftaric-anthocyanin adducts; HF, dihydroflavonols; FO, flavonols; ST, stilbenes; FA, flavanols (tannins); HB, hydroxybenzoic acids; HC, hydroxycinnamic acids; OT, others.

Figure 7

Unsupervised hierarchical clustering of metabolites and cultivars affected by drought; normalized lines (centered and reduced) of Log (content I/content NI), calculated for all variables, with polyphenol concentrations expressed in mg berry⁻¹, on the 2014 data set. Codes for variables and cultivars are provided in Table 1 and Table S2, respectively. Clusters of the different polyphenol groups are colored differently: anthocyanin (red; f1: mono hydroxylated and f2: di-hydroxylated), anthocyanin derived pigments (purple; c), hydroxycinnamic acids and their anthocyanin derivatives(dark red; d).flavonols and dihydroflavonols (yellow; a: mono- and di-hydroxylated; g1 and g2: trihydroxylated), flavan-3-ols (blue; b: tannin subunits and sum of flavan-3-ols; e1 and e2: flavan-3-ol monomers and terminal units), stilbenes (gray; h). Subgroups of cultivars (1-1-1, 1-1-2, …) and significantly different distribution of colors (mostly colored: C, in red; mostly White: W, in green), genetic origin (WW) and precocity (early, late) in individual subgroups compared to the entire population (see Figures 9, 10, and Table S3) are also illustrated.

Figure 8

Correlation network (correlations >0.8) established from the Log (concentration I/concentration NI), calculated from the 105 MRM polyphenol composition variables (in mg berry⁻¹, coded as in Table 1) on the 2014 data set. Clusters of the different polyphenol families are colored differently: anthocyanins (red), anthocyanin derived pigments (dark red), flavonols (yellow), flavan-3-ols (blue), stilbenes (gray), hydroxycinnamic acids (green).

Figure 9

Histogram of the distribution in the eight cultivar subgroups arising from unsupervised hierarchical clustering of metabolites and cultivars affected by drought, calculated for all variables [Log (content I/content NI), with polyphenol concentrations expressed in mg berry⁻¹] on the 2014 data set (Figure 7) and in the whole population of white and colored cultivars (top) and of wine West (WW), wine East (WE), and table East (TE) cultivars (bottom). Significant differences between the distribution in a subgroup and that of the entire population are indicated by the corresponding Chi-2 values (p < 0.1) (cf. Table S3).

Figure 10

Histogram of the distribution of harvest dates in the eight cultivar subgroups arising from unsupervised hierarchical clustering of metabolites and cultivars affected by drought, calculated for all variables [Log (content I/content NI), with polyphenol concentrations expressed in mg berry⁻¹] on the 2014 data set (Figure 7) and in the whole population in not irrigated (Top) and irrigated (Bottom) conditions. Significant differences between the distribution in a subgroup and that of the entire population are indicated by the corresponding Chi-2 values (p < 0.1) (cf. Table S3).