The main sugars in grape berries are fructose, glucose and sucrose. In the present study, fructose constantly accumulated during development with significant increases before veraison and during ripening (Figure 2B). Glucose also significantly accumulated around veraison but declined afterwards (Figure 2B). The content of sucrose fluctuated during grape development (Figure 2B) with four inflection points at EL 30, 32, 34, and 36 respectively. Other sugars, sugar alcohols and sugar acids either decreased during development (ribose, xylose, myo-inositol, arabinose, rhamnose, galactaric acid) or showed the highest level at EL 32 (threonic acid, galactonic acid, gluconic acid, erythrirol) (Figure 2B).

Amino acids showed distinct dynamics during grape berry development (Figure 2C). Arginine and asparagine were the most abundant amino acids in young berries, alanine and glutamine in mature berries (Table S2). Lysine, tyrosine, arginine, ornithine and phenylalanine increased significantly from EL 27 to EL 29, however, decreased dramatically until the end of lag phase (EL 34) and remained at a relatively low level during the second sigmoidal growth period (Figure 2C). Asparagine was in high abundance at the first two developing stages followed by a dramatic decline from EL 29 to EL 30 then stayed in low level until the end. Other amino acids fluctuated during grape berry developing and all showed a turning point at EL 34 which is the end of the lag phase and the beginning of the veraison (Figure 2C).

The predominant organic acids detected in grape berry were malic acid, tartaric acid and citric acid which increased before veraison (EL 35) and decreased afterwards (Figure 2D). Other organic acids showed similar changing pattern except pyruvic acid, gallic acid and caffeic acid which were highest in the young berries and then decreased throughout the developmental process (Figure 2D).

Grape and its products are rich in polyphenolics. These secondary metabolites, especially flavonoids, play multiple roles in grape and attract more and more attentions due to their health benefits (Anastasiadi et al., 2010; Kim et al., 2013; Zhang et al., 2013). During grape berry development, the detected flavonoids presented two distinct changing patterns (Figure 2E). All the anthocyanins accumulated during ripening whereas most of the candidates in the other subfamilies like proanthocyanins, flavan-3-ol, flavonol, flavanonol and their glycosides were abundant in young berries and decreased during the time course of development (Figure 2E). The synthesis of anthocyanins splits into three branches, i.e., the monohydroxylated (pelargonidin, Pg), the dihydroxylated (cyanidin, Cy), and the trihydroxylated (delphinidin, Dp) branch. Cy and peonidin (Pn) glycosides which belong to the dihydroxylated branch were detected from stage EL 37 or even EL 36 whereas derivatives of the other two branches started to appear one stage later. Furthermore, in mature berries (EL 38), the relative abundance of Cy- and Dp- derivatives were higher than the corresponding derivatives of other aglycones (Figure S1). For instance, Cy-Cou-diGlc (30) and Dp-Cou-diGlc (25) were more abundant than petunidin- (Pt, 29), malvidin- (Mv, 32) and Pg- (33) coumaroyl-diglucoside; Cy-Glc (2) and Dp-Glc (8) were in higher level than glucoside of other aglycones (Figure S1).

The PCA plot (Figure 4A) of the integrated metabolomics and proteomic dataset revealed a continuous trajectory during grape berry development. The separation of various developmental stages indicated a distinction of metabolism on metabolite and protein levels. The first principal component (PC 1) accounting for 47.44% (Figure 4A) of the total variance characterized metabolic and proteomic specificities of grape berries at different developmental stages. Candidates with high absolute loading scores (Table S5) included metabolites, especially flavonoids, caffeic acid, gallic acid, lysine, asparagine, arginine and methionine together with protein candidates involved in development, lipid metabolism, cell wall construction, TCA cycle and protein degradation which accounted for most of the separation among developmental stages.

Further, k-means clustering analysis was applied to group candidates according to their changing patterns. Figure S3 presents the 12 clusters with a bold red line indicating the averaged pattern of all the candidates in each cluster. Cluster 2 and 6 with 86 and 124 candidates, respectively, present candidates with higher abundance at early developmental stages (EL 27 to 31) (Figure 4B, Figure S3). Candidates in these two clusters include amino acids (methionine, phenylalanine, asparagine, ornithine, arginine, lysine, tyrosine), organic acids (caffeic acid, pyruvic acid, gallic acid), sugars and sugar alcohols (arabinose, rhamnose, myo-inositol), (dihydro)flavonol derivatives (epicatechin, 3, 20, 36, 49, 12, 31, 38, 39, 43, 45, compound number see in Table 1) and proteins involved in amino acid metabolism (Table S6). The candidates in cluster 10 and 9 are with highest abundance at EL 32 and veraison (EL 34 and 35), respectively. The most abundant organic acids, i.e., malic acid and citric acid, together with some proteins involved in photosynthesis in cluster 5 were in a relatively higher level during the lag phase and veraison than during the two sigmoidal growth phases. In contrast, those candidates in cluster 3 and 4 showed opposite dynamic patterns including organic acids (shikimic acid, ascorbic acid, fumaric acid, maleic acid), amino acids (valine, leucine, proline isoleucine) and proteins involved in stress response (Figure S3, Table S6). All the annotated anthocyanins and four flavonol derivatives (5, 10, 21, 26, compound number see in Table 1), together with a high abundance of proteins involved in secondary metabolism and lipid metabolism in cluster 1, 8, and 12 mainly accumulated after veraison (EL 36 to 38) (Figure S3, Table S6). The candidates in cluster 7 show a continuous elevation in their abundance whereas those in cluster 11 decreased (Figure S3, Table S6).

Correlation network analysis has been widely applied in omics studies to investigate molecular correlations and connections in metabolism (Steuer et al., 2003; Weckwerth, 2003; Weckwerth et al., 2004a; Sun and Weckwerth, 2012). Those biomolecules with a higher node degree have more connections with other molecules and are therefore regarded as essential connection points in a metabolic network (Weckwerth, 2003; Weckwerth et al., 2004a). The connection degree of nodes might also vary in grape berries at different developmental stages, different cultivars (Cuadros-Inostroza et al., 2016) or under distinct growth conditions (Hochberg et al., 2013; Savoi et al., 2016). Thus, correlation network analysis is an appropriate method for searching important biomolecules involved in specific metabolism processes. Time-lagged correlation analysis by Granger causation represents an advanced level of correlation network analysis (Doerfler et al., 2013). Granger causality analysis was initially introduced by Granger (1969) to predict events based on time series data and time-lagged correlations in economics. It was also applied to some biological studies to interpret directed and nonlinear correlations between metabolites, transcripts and proteins (Walther et al., 2010; Doerfler et al., 2013; Valledor et al., 2014). To further extend the understanding of the dynamic correlations of all the identified metabolites and proteins during the developmental time course, Granger causality analysis was applied to all the candidates and clusters discussed above. The results indicated significant metabolite-metabolite, metabolite-protein and protein-protein correlations. Figure 4C shows the directed correlation of phenylalanine and phloretin (p-value = 0.02187) which indicated a strong effect of phenylalanine concentration on phloretin synthesis. Not only precursors but also enzymes showed significant correlation to their product synthesis. One example is the accumulation of anthocyanin synthase (ANS, A2ICC9) prior to anthocyanins (Figure 4D, Table S7). Figure 4E presents a significant granger correlation (p-value = 0.00538) between protein E0CSB6 (malate dehydrogenase) and D7T300 (ATPase) indicating the close relationship between TCA cycle and ATP production. Other time series correlations with p-values less than 0.05 were summarized in Table S7. The Granger causation network (Figure S4) includes 674 nodes (Table S7) with 21 neighbors in average. Many amino acids (ornithine, arginine, phenylalanine, lysine, tyrosine, asparagine), organic acids (acid_like2, shikimic acid) and their metabolism related proteins (Table S7) show highest node degrees revealing them as potential biochemical hub during grape berry development. The application of Granger analysis to the 12 clusters obtained from the k-means clustering analysis revealed significant time lagged correlations between these clusters. Significant directed connections among these clusters are shown in Figures 4F–H with the time lag set as 1, 2, and 3, respectively (one, two or three time points shifted). The Granger causality correlations from clusters 2 to 6, 6 to 10, 10 to 9, 9 to 12, and 12 to 8 (Figure 4G indicated with red arrows) were visualized as a line chart (Figure 4B) clearly showing their dynamic shift over the developmental time course, especially a significant correlation between cluster 9 and 12. Such combined Granger causality analysis with clustering analysis indicated a general metabolic shift from the metabolism of amino acids, sugars and some flavonoids to organic acid accumulation and finally to lipid and anthocyanin synthesis during grape berry development.

PCA and k-means clustering analysis presented the systemic dynamics of the metabolites and proteins during grape berry development. To understand the metabolism progress of developing grape berry in a biochemical context, we mapped the sugars, amino acids, organic acids, flavonoids and the related proteins on their corresponding biosynthetic pathways (Figure 5). The integration of dynamics of metabolites as well as proteins involved in both primary and secondary metabolisms presents metabolic checkpoints during grape berry development. This is further discussed below.

Sugars, especially fructose, glucose and sucrose determine the sweetness of grapes, moreover, the alcohol concentration of wine. In grape berries, sucrose is mainly imported via phloem from source organs. Subsequently, the imported sucrose is either hydrolyzed to glucose and fructose by invertase or converted to glycolysis substrates via sucrose synthase (Susy) and UDP-glucose pyrophosphorylase (UGPase). The fluctuation of sucrose content during grape berry development might be caused by a disproportionate ratio of the import to the consumption. Furthermore, synthesis of sucrose from malate via the gluconeogenic pathway (Ruffner et al., 1975; Dai et al., 2013) might also contribute to the fluctuation of sucrose concentration, especially after veraison. The accumulation of glucose and fructose during grape berry development was reported previously (Wu et al., 2011; Dai et al., 2013). In the present study, fructose accumulated throughout the developmental process whereas glucose concentration did not continue to rise after veraison. Similar glucose dynamics were also observed in some table grape varieties i.e., “Thompson Seedless,” “Crimson Seedless,” and “Red Globe” (Muñoz-Robredo et al., 2011). In contrast to these findings, both glucose and fructose concentration constantly increased during grape berry development in some grape varieties and cultivars (Wu et al., 2011; Dai et al., 2013). The discrepancy in glucose accumulation patterns could be explained by the differences in the ripening process among varieties. The distinct expression pattern of invertase and Susy might explain the unequal accumulation of glucose and fructose. The decline in abundance of invertase since EL 31 caused a decrease in the production of glucose and fructose whereas the increasing expression of Susy ensured the continuous accumulation of fructose.

The substances generated from sugar metabolism are subsequently incorporated into glycolysis. Metabolism along this process generates energy (ATP), reducing equivalents (NADH) as well as intermediates for amino acid biosynthesis, lipids and secondary metabolite production. The abundance of most glycolytic enzymes increased through the development and ripening process (Figure 5). Phosphoglycerate kinase (PGK) was the only glycolytic enzyme, which declined in abundance (Figure 5). The concentration of pyrophosphate-fructose 6-phosphate 1-phosphotransferase (PFP), enolase and pyruvate kinase (PK) in the cytosol strongly increased after veraison (Figure 5). Phosphoglycerate mutase (PGM) was increased during the first sigmoidal growing phase (EL 27 to EL 32) and maintained a relatively constant level afterward. The increase in abundance of glycolytic proteins was consistent with some former reports that studied the proteomic profile of grape skins and berry tissue without seeds (Negri et al., 2008; Kambiranda et al., 2014). However, some studies reported a decrease in abundance of glycolytic enzymes (Davies and Robinson, 2000; Giribaldi et al., 2007; Martinez-Esteso et al., 2011) or glycolytic intermediates (Dai et al., 2013) during berry ripening. Variety and differences in growth conditions might explain these different observations. Additionally, isoforms of enzymes may play different roles at a particular developmental stage (Chaturvedi et al., 2013; Ischebeck et al., 2014; Wang et al., 2016a,b), thus displaying varying dynamics during development. For instance, Fraige et al reported three isoforms of UDPase, of which two candidates decreased in abundance after veraison whereas one increased (Fraige et al., 2015).

The tricarboxylic acid cycle (TCA cycle) generates energy, reducing power and carbon skeletons, which makes it a central hub in metabolism. The Pyruvate dehydrogenase complex (PDC) converts pyruvate to acetyl-CoA which serves as fuel to the TCA cycle. During the grape berry development, PDC was in high abundance during the first growing phase (EL 27-32), followed by a significant decline at the lag phase (EL 32-34) and with a subsequent increase (Figure 5). Similar to PDC, isocitrate dehydrogenase (IDH), oxoglutarate dehydrogenase complex (OGDC), and succinyl coenzyme A synthetase (SCS) were in high abundance during the sigmoidal growth phases whereas in low abundance during the lag phase (Figure 5). The high abundance of these enzymes in the young and ripening berries was consistent with the great demand for energy and building blocks at these two phases. Aconitase and succinate dehydrogenase (SDH) were strongly expressed after veraison. In a former report, a sharp expression of aconitase was observed in ripening grape skin (Negri et al., 2008). Fumarase is the only enzyme whose expression gradually declined throughout the berry development (Figure 5). Malate dehydrogenase (MDH), catalyzing a reversible reaction between oxaloacetate and malate, was concentrated at a lower level at phase I whereas it showed a progressive increase in expression during ripening which was in agreement with previous reports (Martinez-Esteso et al., 2011; Kambiranda et al., 2014). The transcript levels of MDH and malic enzyme were reported to increase during grape berry ripening which might contribute to the decline of malate concentration after veraison (Deluc et al., 2007).

The contents of the intermediates, citrate, succinate, fumarate and malate gradually increased in early stages of development, up to a peak in concentration at EL 31 (fumarate and succinate), EL 33 (malate) or around veraison (citrate) with a subsequent decline. Similar dynamic patterns were observed in developing Cabernet Sauvignon berries with a peak in accumulation of most TCA cycle intermediates at veraison (Dai et al., 2013). The accumulation of these organic acids before veraison was parallel to the high abundance of enzymes at phase I. However, the gradually increasing expression of TCA cycle associated enzymes was accompanied by a decrease in the intermediates during grape berry ripening (phase III). The discrepancy between the increase in the abundance of the enzymes and the decrease in the content of the intermediates during grape berry ripening indicates a high metabolic flux through this pathway with an efficient incorporation of the intermediates in the synthesis of amino acids, lipids and secondary metabolites. In grape, organic acids are responsible for the titratable acidity which is an index for fruit quality. High amounts of organic acids endow young berries a sour taste for defense against herbivores. The organic acids in mature berries are essential for wine production as they protect the fermentation process from bacterial contamination. In wine they are responsible for the sour part of the taste. They are also essential for the color of wine by contributing to the stabilization of anthocyanins (Clemente and Galli, 2011). The changing patterns of those dominant organic acids i.e., malic acid, tartaric acid and citric acid, were consistent with previous reports (Deluc et al., 2007; Ali K. et al., 2011; Muñoz-Robredo et al., 2011; Fraige et al., 2015).

Amino acids are major transportable nitrogenous compounds in grape. In source organs, intermediates from glycolysis and TCA cycle can be utilized as precursors for the synthesis of amino acids. For instance, phosphoenolpyruvate is the precursor of aromatic amino acids that derive from the shikimate pathway; α-ketoglutarate and oxaloacetate are the precursors of glutamate and aspartate family amino acids, respectively. Asparagine and glutamine were the major amino acids in young and mature grape berries respectively. Both of them carry an extra amide group making them efficient nitrogen-carriers. They play important roles in the nitrogen assimilation, transportation and storage in plants. Asparagine and glutamine can be converted to Asp and Glu to serve as precursors for biosynthesis of many other amino acids, e.g., proline, arginine (from glutamate); methionine, threonine and lysine (from aspartate). Several enzymes involved in amino acid metabolism were detected and mapped on Figure 5. Aspartate aminotransferase (AspAT) catalyzes the reversible transfer of an amino group between aspartate and glutamate thus plays an important role in nitrogen distribution. In concordance with a previous report (Martinez-Esteso et al., 2011), AspAT abundancy gradually decreased during grape berry development. Methionine synthase (MetH), catalyzing the synthesis of methionine, is another essential amino acid of the aspartate family. The abundance of MetH increased until veraison, then stayed relatively constant during ripening in the present study. However, it decreased during green developmental stages and increased during ripening in the study of Martinez-Esteso et al. (2011). Glutamine synthetase (GS) is another crucial enzyme involved in nitrogen assimilation. GS, which catalyzes the condensation of glutamate and ammonia to generate glutamine, gradually declined in abundance from EL 27 to EL 36 and slightly increased afterwards. The transcript level of GS was shown to be significantly higher in phase I in a previous study (Deluc et al., 2007). Marinez-Esteso et al reported a decline in the level of GS before veraison (Martinez-Esteso et al., 2011) which is consistent with our result. However, the changing trend of GS after veraison was absent in their study.

Simple phenolic compounds that are synthesized from phenylalanine can be polymerized to lignin which is an essential component of the cell wall. In the present study, three enzymes, i.e., cinnamoyl-CoA reductase (CCR), cinnamyl-alcohol dehydrogenase (CAD) and ferulate 5-hydroxylase (F5H, Q9M4H8) involved in lignin synthesis were annotated. CCR and CAD, which catalyze the last two steps of monolignol synthesis not only impact lignification but also plant development. Absence of CCR and CAD resulted in dwarfism and sterility in Arabidopsis (Thevenin et al., 2011). One protein candidate was annotated as CCR (A5AXM6) and detected after veraison. Four candidates were annotated as CAD. The averaged expression pattern of CAD showed high levels in both young and ripening berries and low level around veraison (Figure 5). Aharoni et al. (2002) reported a comparable pattern of the transcription level of CAD in developing strawberries which are also non-climacteric fruits and undergo color turning phases. In their study, CAD expression level was high in green strawberries followed by a decreasing during the white and turning stages and finally increased again in red strawberries (Aharoni et al., 2002).

Flavonoids are another class of secondary metabolites derived from the phenylpropanoid pathway and share common precursors with lignin. The detected flavonols and flavan-3-ols showed distinct changing patterns with anthocyanins. Parallel phenomena were observed before in other varieties, i.e., Cabernet Sauvigon (Ali M. B. et al., 2011; Degu et al., 2014), Shiraz (Degu et al., 2014) and Norton (V. aestivalis) (Ali M. B. et al., 2011) and supposed to be caused by the competition of precursors between anthocyanins and other subfamily members of flavonoids (Ali K. et al., 2011; Degu et al., 2014). The anthocyanin profile obtained from the present data was consistent with that of “Concord” (Liang et al., 2011) and “Pink Sultana” (Boss et al., 1996) in which Cy and Dp derivatives were the dominant anthocyanins. In contrast, Mv derivatives were the most abundant anthocyanins in the other varieties in these two studies (Boss et al., 1996; Liang et al., 2011) and other reports (an overview of all varieties is provided in Table S8) (Mazza et al., 1999; He et al., 2010; Papini et al., 2010; Ali M. B. et al., 2011; Degu et al., 2014, 2015). Noticeably, Early Campbell is a hybrid of Vitis vinifera and Vitis labrusca by crossing Moore Early with (Belvidere × Muscat of Hamburg). Both Moore Early and Belvidere are seedlings of Concord. The fruit taste and disease resistance of Early Campbell are similar to Concord (Robinson et al., 2012). The similar anthocyanin profile of Early Campbell with that of Concord is probably also due to its genetic background. The annotated proteins that were involved in the flavonoid pathway include chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), dihydroflavanol 4-reductase (DFR) and anthocyanidin synthase (ANS). CHS catalyzes the condensation of p-coumaroyl-CoA with malonyl-CoA to generate chalcone which is further isomerized to naringenin by CHI. Two proteins, annotated as CHS (A2ICC5, G4XGW2), were detected after veraison and increased in abundance during ripening (Table S4). In a previous study, three copies of Chss were found in the grape genome. The mRNA levels of Chs2 and Chs3 significantly coincided with anthocyanin and Chs1 and Chs2 with flavonol biosynthesis (Jeong et al., 2008). The expression of A2ICC5 and G4XGW2 were in accordance with anthocyanin accumulation indicating the involvement of these two CHSs in the coordination of anthocyanin synthesis. The expression level of CHI (A5ANT9) gradually increased before veraison then underwent a sharp decline at veraison (EL 35) with a subsequent recovery to the level before veraison. F3H catalyzes the hydroxylation of flavanones at 3-position to form dihydroflavonols. Both protein candidates annotated as F3H (A2ICC8, A2ICC8) were detected from EL 32 and showed increasing levels during grape berry development. The transcription of F3hs appeared to be induced for the biosynthesis of flavonols and anthocyanins (Jeong et al., 2008). Dihydroflavonols are further converted to either flavonols via flavonol synthase (FLS) or to leucoanthocyanidins via DFR. The competition between FLS and DFR influences the contents of flavonols and anthocyanins (Tian et al., 2015) which further affects the color (Lou et al., 2014) and the abilities of plants to cope with stress (Hua et al., 2013; Wang et al., 2013). DFR is a crucial enzyme in the flavonoid pathway involved in the synthesis of anthocyanins, proanthocyanins and tannins (Moyano et al., 1998; Zhang et al., 2008; Hua et al., 2013; Wang et al., 2016c). In the present study, two protein candidates (A5BGJ0, A5BIY8) were annotated as DFR. Their combined expression pattern indicated a progressive increase in abundance of DFR before veraison and a slight decrease afterwards (Figure 5). ANS catalyzing the conversion of colorless leucoanthocyanins to colored anthocyanidins was detected in ripening berries. The abundance of ANS (A2ICC9) significantly increased after veraison (Figure 5) in accordance with the anthocyanin accumulation. MYB-related transcription factors (TFs) are involved in regulation of flavonoid synthesis (Czemmel et al., 2009). It was further reported that VvMYB5b was highly expressed after veraison and the anthocyanin synthesis was enhanced in transgenic tobacco due to ectopic expression of VvMYB5b (Deluc et al., 2008). In our study, four proteins i.e., A5ADL7, A5AHA8, F6GTT4, F6I581 were annotated as “MYB-related transcription factor”. Their summarized content was lowest around veraison whereas the highest level was observed before veraison and during ripening (Table S4 sheet 2). This pattern was neither directly correlated to the amount of anthocyanins in the developing grape berries nor to the protein levels of ANS (A2ICC9). Further we detected 7 bZIP family members (A5B427, A5BZF5, D7SUP9, D7TNE5, F6GTA6, F6GUN1, F6HBQ3). bZIP family member are thought to be involved in the regulation of flavonoid biosynthesis (Malacarne et al., 2016). In a recent study Loyola et al. propose that HY5 and HYH are involved in UV-B-dependent flavonol accumulation in grapevine (Loyola et al., 2016). The concentrations of the bZIP proteins in our study showed an increase toward veraison and a decrease afterwards. Because there is often not a direct dependency between transcriptional and translational/posttranslational control (Nukarinen et al., 2016) it is difficult to compare gene expression levels from other studies with protein levels from our study. Furthermore, the analysis of TFs requires in most cases a specific enrichment step before proteomic analysis. Future investigations will focus more on the discussed transcription factors and their control on developmental processes and flavonoid biosynthesis.

Stilbenes are also derived from the phenylpropanoid pathway and were reported to be enriched in grape. However, we did not detect any stilbene or related enzymes in this study. This might be due to different growth and stress conditions or a different genetic background of the variety we studied. Two publications reported stilbene content in the peel of Early Campbell at veraison stage (Islam et al., 2014; Ahn et al., 2015). They also found that hairy vetch and ryegrass extracts and red and blue LED light induced stilbene accumulation as well as the expression of genes involved in stilbene synthesis. We used the whole berry as the study object which is different from using isolated peel. Furthermore there is evidence that support a competition between the synthesis of stilbenes and flavonoids. One evidence is the negative correlation between resveratrol and anthocyanin accumulation in 5 Vitis species at different developmental species (Jeandet et al., 1995). The other evidence is observed in transgenic strawberries. Hanhineva et al transformed a stilbene synthase gene to strawberries (35S:NS-Vitis3 line). While the STS gene was highly expressed in the transgenic strawberry line, CHS expression was down regulated (Hanhineva et al., 2009). These are two examples indicating the competition relationship between flavonoid and stilbene synthesis pathway. It is thus of interest to compare the flavonoid and stilbene content of this variety under different growth conditions and with other grape varieties that produce stilbenes to investigate the competition of stilbene and flavonoid biosynthesis.