System-Level and Granger Network Analysis of Integrated Proteomic and Metabolomic Dynamics Identifies Key Points of Grape Berry Development at the Interface of Primary and Secondary Metabolism

Abstract

Grapevine is a fruit crop with worldwide economic importance. The grape berry undergoes complex biochemical changes from fruit set until ripening. This ripening process and production processes define the wine quality. Thus, a thorough understanding of berry ripening is crucial for the prediction of wine quality. For a systemic analysis of grape berry development we applied mass spectrometry based platforms to analyse the metabolome and proteome of Early Campbell at 12 stages covering major developmental phases. Primary metabolites involved in central carbon metabolism, such as sugars, organic acids and amino acids together with various bioactive secondary metabolites like flavonols, flavan-3-ols and anthocyanins were annotated and quantified. At the same time, the proteomic analysis revealed the protein dynamics of the developing grape berries. Multivariate statistical analysis of the integrated metabolomic and proteomic dataset revealed the growth trajectory and corresponding metabolites and proteins contributing most to the specific developmental process. K-means clustering analysis revealed 12 highly specific clusters of co-regulated metabolites and proteins. Granger causality network analysis allowed for the identification of time-shift correlations between metabolite-metabolite, protein- protein and protein-metabolite pairs which is especially interesting for the understanding of developmental processes. The integration of metabolite and protein dynamics with their corresponding biochemical pathways revealed an energy-linked metabolism before veraison with high abundances of amino acids and accumulation of organic acids, followed by protein and secondary metabolite synthesis. Anthocyanins were strongly accumulated after veraison whereas other flavonoids were in higher abundance at early developmental stages and decreased during the grape berry developmental processes. A comparison of the anthocyanin profile of Early Campbell to other cultivars revealed similarities to Concord grape and indicates the strong effect of genetic background on metabolic partitioning in primary and secondary metabolism.

Keywords: Vitis vinifera; berry development; data integration; flavonoids; mass spectrometry; primary metabolism; secondary metabolism; systems biology.

Figure 1

Grape berries harvested at 12 developmental stages according to the modified E-L system.

Figure 2

Metabolome dynamics of developing grape berry. (A) Overview of the metabolite dynamics with bi-hierarchical-clustering heat map. (B–D) Present the dynamics of sugars (including sugar alcohol and sugar acids), amino acids and organic acids. (E) Presents the dynamics of flavonoids.

Figure 3

Proteomic analysis of developing grape berries. (A) Protein distribution throughout the developmental process. Samples of 12 developing stages were sorted to 3 groups with group 1 including samples at stage EL 27, 29, 30, 31; group 2, EL32, 33, 34, 35 and group 3, EL 36, 37, 37.5, 38. The proteins that specific to group 1, 2, 3 and those common to all groups were functionally summarized with pie charts in yellow, blue, black and red box respectively. (B) Protein frequencies. (C) Protein dynamics. Four distinctive changing patterns were summarized with line charts.

Figure 4

Multivariate statistical and Granger causality analysis of integrated metabolome and proteome data during grape berry development. (A) PCA plot of the integrated dataset shows a trajectory during grape berry development. (B) The averaged dynamic patterns of 6 clusters from k-means clustering analysis. (C–E) Examples of Granger causalities showed directed interactions of metabolite-metabolite, metabolite-protein and protein-protein. (F–H) Present Granger causalities between clusters that resulted from k-means clustering analysis with time lag set as 1, 2, and 3, respectively.

Figure 5

Visualization of metabolite and protein dynamics on their biosynthetic pathways. Metabolites are written in black letters with blue line charts indicating their changing patterns whereas proteins are written in red letters with red line charts. Relative abundance of metabolites and proteins were averaged over three biological replicates. Bars represent standard errors. Susy, sucrose synthase; UGPase, UDP-glucose pyrophosphorylase; PFP, pyrophosphate-fructose 6-phosphate 1-phosphotransferase; FBPase, fructose 1, 6-bisphosphatase; PGK, phosphoglycerate kinase; PGM, phosphoglycerate mutase; PK, pyruvate kinase; PDC, pyruvate dehydrogenase complex; IDH, isocitrate dehydrogenase; OGDC, oxoglutarate dehydrogenase complex; SCS, succinyl coenzyme A synthetase; SDH, succinate dehydrogenase; MDH, malate dehydrogenase; AspAT, aspartate aminotransferase; AS, asparagine synthetase; ASADH, aspartate-semialdehyde dehydrogenase; MetH, methionine synthase; MAT, methionine adenosyltransferase; PHGDH, phosphoglycerate dehydrogenase; PSAT, Phosphoserine transaminase; SHMT, serine hydroxymethyltransferase; OASTL, O-acetylserine (thiol)-lyase; GLDH, Glutamate dehydrogenase; GS, Glutamine synthetase; ASS, argininosuccinate synthase; ALAT, alanine aminotransferase; AGT, alanine-glyoxylate transaminase; CS, chorismate synthase; PAL, phenylalanine ammonia lyase; C4H, cinnamate 4-hydroxylase; 4-coumarate-CoA ligase; CCR, cinnamoyl-CoA reductase; CAD, cinnamyl-alcohol dehydrogenase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; F3′5′H, flavanoid 3′,5′-hydroxylase; FLS, flavonol synthase; DFR, dihydroflavanol 4-reductase; ANS, anthocyanidin synthase; ANR, anthocyanidin reductase; 3-GT, anthocyanidin 3-O-glucosyltransferase.