Metabolite Profiling Reveals Developmental Inequalities in Pinot Noir Berry Tissues Late in Ripening

Abstract

Uneven ripening in Vitis vinifera is increasingly recognized as a phenomenon of interest, with substantial implications for fruit and wine composition and quality. This study sought to determine whether variation late in ripening (∼Modified Eichhorn-Lorenz stage 39) was associated with developmental differences that were observable as fruits within a cluster initiated ripening (véraison). Four developmentally distinct ripening classes of berries were tagged at cluster véraison, sampled at three times late in ripening, and subjected to untargeted HPLC-MS to measure variation in amino acids, sugars, organic acids, and phenolic metabolites in skin, pulp, and seed tissues separately. Variability was described using predominantly two strategies. In the first, multivariate analysis (Orthogonal Projections to Latent Structures-Discriminant Analysis, OPLS-DA) was used to determine whether fruits were still distinguishable per their developmental position at véraison and to identify which metabolites accounted for these distinctions. The same technique was used to assess changes in each tissue over time. In a second strategy and for each annotated metabolite, the variance across the ripening classes at each time point was measured to show whether intra-cluster variance (ICV) was growing, shrinking, or constant over the period observed. Indeed, berries could be segregated by OPLS-DA late in ripening based on their developmental position at véraison, though the four ripening classes were aggregated into two larger ripening groups. Further, not all tissues were dynamic over the period examined. Although pulp tissues could be segregated by time sampled, this was not true for seed and only moderately so for skin. Ripening group differences in seed and skin, rather than the time fruit was sampled, were better able to define berries. Metabolites also experienced significant reductions in ICV between single pairs of time points, but never across the entire experiment. Metabolites often exhibited a combination of ICV expansion, contraction and persistence. Finally, we observed significant differences in the abundance of some metabolites between ripening classes that suggest the berries that initiated ripening first remained developmentally ahead of the lagging fruit even late in the ripening phase. This presents a challenge to producers who would seek to harvest at uniformity or at a predefined level of variation.

Keywords: HPLC-MS; Vitis vinifera; crop heterogeneity; fruit composition; metabolomics; uneven ripening.

FIGURE 1

Mean total soluble solids (A) and color index (B) for the four ripening classes over time with standard error bars shown. Different letters indicate significant differences at a single time point, Tukey HSD-test, p-value < 0.05.

FIGURE 2

OPLS-DA analysis, by ripening group, of seed (A,B), skin (C), and pulp (D). Score plot (A) shows separation of samples in analysis of seed. Hotelling’s T2 Ellipse (95%) not shown, but samples outside ellipse denoted with asterisk. Samples are distributed along a predictive component (x-axis) and orthogonal component (y-axis) and are colored per their ripening class: GH, green; GS, light green; PS, pink; RS, red. S-plots (B–D) show the influence of metabolites on sample segregation. Metabolites with high variable importance (VIP > 2) are indicated with a cross symbol. Putative biomarkers are labeled. Interactive versions of (B–D) are provided as in Presentation 1.

FIGURE 3

OPLS-DA analysis, by time sampled, of skin (A,B), and pulp (C,D). Score plots (A,C) show separation of samples. For skin, samples are distributed along a predictive component (x-axis) and orthogonal component (y-axis). For pulp, samples are distributed along the first two predictive components. Samples are colored per their collection date: t1, black; t2, white; t3, gray. S-plots (B,D) show the influence of metabolites on sample segregation. Metabolites with high variable importance (VIP > 2) are indicated with a cross symbol. Putative biomarkers are labeled. Interactive versions of B and D are provided in Presentation 1.

FIGURE 4

Three-dimensional VIP scatterplot for proposing in-common biomarkers across all three examined tissues. Pulp, x-axis; seed, y-axis; skin, z-axis. Metabolites for which a VIP score > 2 occurred in two tissues, square; in all tissues, cross; in only one tissue, circle. An interactive version of this figure is provided in Presentation 1.

FIGURE 5

Changes in intra-cluster variance (ICV) for seed (B), skin (C), and pulp (D) between pairs of time points are shown. A schematic (A) illustrates how ICV changes over time for metabolites, depending on their location within (B) through (D). Metabolites are colored by metabolite class. Shape indicates the outcome of an F-test for variance. No significant change between either pair of time points, circle; significant change between either pair of time points, triangle; significant change between both pairs of time points, cross. Significance threshold, p-value < 0.05. Interactive versions of (B–D) are provided in Presentation 1.

FIGURE 6

The log₁₀ magnitude of variances for metabolites with constant variance (no significant change between pairs of time points) for seed (A), skin (B), and pulp (C) tissues. ICVs at t1, t2, and t3 are plotted on axes x, y, and z, respectively. For plot inclusion, F-test for variance, p > 0.05 between both pairs of time points for the metabolite. Interactive versions of these plots are provided in Presentation 1.