Validation of the transcript quantification from the RNA-seq data was carried out through quantitative real-time PCR (qRT-PCR). For extraction of skin RNAs, berry skins from another subsample of 50 berries randomly selected from each biological replicate were manually separated from pulps. Total RNA from berry skins was isolated using the same method mentioned above. The subsequent cDNA synthesis and qRT-PCRs were performed as described by Sun et al. (2015a). Gene-specific primers used for qRT-PCR are listed in Supplementary Table S3 (Downey et al., 2003; Castellarin et al., 2006; Fujita et al., 2006; Reid et al., 2006; Bogs et al., 2007; Czemmel et al., 2009; Shimazaki et al., 2011; Azuma et al., 2012; Sun et al., 2015b, 2016). All reactions were run in triplicate, and the normalized relative expression levels of target genes were calculated by 2−δCt (1Ct = CtTarget – CtControl, Ct: cycle threshold). VviUbiquitin1 and Vviβ-Actin genes were selected as endogenous controls for normalization and CtControl was the geometric mean of their threshold cycles.

1http://genomes.cribi.unipd.it/grape/

2http://www.genome.ad.jp/kegg/

# RESULTS AND DISCUSSION

fpls-08-00547 April 19, 2017 Time: 17:49 # 6

### Berry Development and Ripening

Changes in berry fresh weight, TSS and TA of Cabernet Sauvignon grape berries collected from E-L 31 stage until harvest during the first growing season (2011) and from E-L 29 stage until harvest during the subsequent two growing seasons (2012 and 2013) are shown in Supplementary Figure S1. The date of véraison and harvest were set at approximately eight and sixteen waf, respectively, during all three seasons. Previous studies on several grape species and cultivars reported little or no effect of leaf removal treatments on fruit weights and juice soluble solid contents at harvest (Haselgrove et al., 2000; Main and Morris, 2004; Chorti et al., 2010; Kotseridis et al., 2012). In this study, weight of berries for LR-PS was significantly lower than that of the control at harvest in the 2011 season, while an opposite result was observed in the 2012 season. LM-V and LM-AV significantly increased berry weights from 1 week after each treatment until berry ripening during the 2011 and 2012 seasons, however, there were no discernible differences between each of the two treatments and the control during the third experimental season (Supplementary Table S4). Overall, there were no consistent trends in the differences of berry fresh weight between each of the light-exposure treatment group and the control group during berry development in all the three experimental seasons. Similarly, the differences in the level of juice soluble solids between the exposed and shaded berries during berry development were also inconsistent among different seasons. No treatment differences were found in soluble solid contents at harvest in all the 3 years, except for those of berries from LR-PS, which was higher than that of the control in the 2011 season (Supplementary Figure S1). Specifically, ripening berries from the control and all light-exposure treated clusters have lower soluble solid contents in the 2013 season compared with the other two seasons, which could be attributed to the relative lower level of flowering-to-harvest growing degree days (GDD) accumulation in 2013 than those in the other 2 years (Supplementary Table S1), similar with the results from a previous study (Spayd et al., 2002). The decrease of the TA in the berries from LR-PS treatment during véraison was slightly faster than that of the control during the 2011 and 2012 seasons. However, the influence of LR-PS treatment on TA was negligible during the 2013 season. At harvest, there was no significant difference in the TA among treatments in all the three seasons (Supplementary Figure S1), which was inconsistent with previous studies that leaf removal treatments reduced TA (Zoecklein et al., 1992; Percival et al., 1994), suggesting that TA was differently affected by sunlight exposure depending on cultivar and climate.

#### Influence of Cluster Sunlight Exposure Treatments on Phenolic Concentration and Composition

Of the many environmental factors that affect the phenolic biosynthesis in many plants, light has been regarded as one of the major influences (Downey et al., 2004; Cominelli et al., 2008; Wang et al., 2012; Jaakola, 2013). The present study shows that the concentration of total HCAs was significantly increased in the sunlight exposure berries in comparison with the control berries at both véraison and harvest in all the three experimental seasons, except for berries from LR-PS and LR-AV treatments, in which the level of HCAs was slightly higher during véraison while lower at the harvest stage compared with the control in both the 2011 and 2012 seasons (**Figure 2A** and Supplementary Table S4). The results obtained in our study are inconsistent with a previous study conducted on Pinot Noir, which showed that the concentration of HCAs throughout maturation was effectively enhanced by leaf removal at berry set but slight influenced by leaf removal at véraison (Lemut et al., 2011). Of the three classes of flavonoids, flavan-3-ols are present in the greatest proportion in grapes, followed by anthocyanins, with flavonols being present at relatively low levels (Downey et al., 2004). Sunlight exposure did not consistently affect the accumulation of total flavonoids or flavan-3-ols in the skins throughout berry development or at harvest among different seasons (**Figures 2B**, **3A**). The concentration of total flavonoids and flavan-3-ols in the skins was slightly lower in LR-PS, LR-V, LR-AV, and HLR-V, while the former was higher in HLR-AV and LM-V treated berries than those from control berries at harvest in the 2011 season. Each of the seven sunlight exposure treatments resulted in decrease in concentration of skin total flavonoids as well as flavan-3-ols at harvest in the 2012 season, while an opposite result was observed in the 2013 season, except for berries from LR-PS, in which the concentration of total flavonoids and flavan-3-ols showed a significant decrease and no discernible difference compared with the control, respectively (Supplementary Table S4). The seasonal variations in the level of flavan-3-ol compounds might be caused by differences of temperature, GDD or the level of PAR during berry ripening among seasons (Supplementary Tables S1, S2), which suggests an ambiguous effect of light-exposure on the flavan-3-ol biosynthesis in grape berry skins.

Anthocyanin biosynthesis has been found to be variably regulated in response to light conditions in a number of sunlight exposure studies, which is possibly confounded by varying experimental settings and other factors, such as cultivar, vineyard location, timing of leaf removal, and growing season. For instance, leaf removal at berry set increased skin anthocyanins in grapes of Merlot, Cabernet Sauvignon and Pinot noir (Lemut et al., 2011; Kotseridis et al., 2012), and pre-bloom leaf removal also substantially increased anthocyanin concentration in Barbera, Lambrusco salamino, Graciano, and Carignan grapes when compared with no leaf removal (Poni et al., 2009; Tardaguila et al., 2010). However, another exposure study indicated that no significant differences among treatments were observed in anthocyanin levels in Nebbiolo grapes at harvest, although leaf removal caused a temporary acceleration of anthocyanin accumulation throughout ripening (Chorti et al., 2010). In the present study, sunlight exposure increased the level of skin anthocyanins at the initiation of grape coloration compared to the control in at least two of the three experimental seasons, while no consistent differences among treatments were observed in the concentration of anthocyanins at harvest over the three experimental seasons (**Figure 3B**). In the 2011 season, almost all exposure treatments could significantly (LR-V, LR-AV,

biological replicates. Light gray background represents the phenological phase of véraison from 5 to 100% of colored berries.

HLR-AV, and LM-V) or slightly (LR-PS and HLR-V) increase skin anthocyanin amount at harvest, whereas no significant differences in the concentration of skin anthocyanins at harvest among treatments were observed in the 2013 season. On the contrary, slight or significant decreases in the skin anthocyanin amount were observed from all of the light exposed berries in the 2012 season (Supplementary Table S4). The diametrically opposed result was possibly due to increased levels of PAR, solar radiation and average temperature for exposed berries during coloration and the maturation phase (Supplementary Table S2), accompanied with a relative high temperature during berry ripening in this experimental season (Supplementary Table S1), which could lead to the inhibit formation or induce degradation of anthocyanins (Yamane et al., 2006; Tarara et al., 2008; Pastore et al., 2013).

Among the three major classes of flavonoid compounds, the accumulation of flavonols was most drastically affected in berry skins under the sunlight exposure treatments. Flavonols have been found to accumulate in sun-exposed tissue of grapes and are thought to act as UV protectants and free radical scavengers (Price et al., 1995; Downey et al., 2004). Leaf removal at all three phenological stages resulted in a dramatic increase in flavonol concentration in the grape skin throughout berry development during the three experimental seasons, similar to the results conducted on Sangiovese berries previously (Pastore et al., 2013), but the degree of their effect was variable among seasons in our experiments (**Figure 3C**). The level of flavonol compounds was also moderately increased by HLR-AV and LM-V treatments, except that there was no significant difference between the control and HLR-AV in the 2011 season. HLR-V and LM-AV did not significantly influence the concentration of skin flavonols at harvest compared with the control, although there was a temporary acceleration of flavonol accumulation throughout ripening in HLR-V treated berries (**Figure 3C** and Supplementary Table S4). However, opposite results regarding changes in flavonol contents under different sunlight exposure treatments have been found previously, in which leaf moving at véraison increased flavonol synthesis greater than leaf removal treatment (Matus et al., 2009), suggesting that the enhanced accumulation of flavonols under treatments is strongly associated with the severity degree of sunlight exposure, and also climate divergences in different years and regions.

In the flavonoid biosynthesis pathway, two metabolic branches leading to the biosynthesis of B-ring dihydroxylated (3,30 -OH) and trihydroxylated (3,30 ,50 -OH) subunits were reported to have different sensitivities in response to various lighting conditions in many previous researches (Downey et al., 2004; Azuma et al., 2012; Guan et al., 2015). Our results showed that the molar ratio of dihydroxylated to trihydroxylated flavonoids was continuously decreased throughout berry ripening, while changes of the ratio among treatments were inconsistent during the three seasons (**Figure 3D**). In the first experimental season, the ratio of dihydroxylated/trihydroxylated flavonoids in berries from almost all of the sunlight exposure treatments was decreased compared with the control across development, except for LR-PS at E-L 33 stage, LR-V at E-L 35 stage and LM-AV at harvest. In contrast, however, light-exposure caused a slight or marked increase in the ratio of dihydroxylated/trihydroxylated flavonoids during berry ripening in the 2012 growing season. In the last experimental season, no significant differences in the ratio of dihydroxylated/trihydroxylated flavonoids were observed among treatments except for berries from the leaf removal treatments, which was increase in LR-V and LR-AV treated berries at harvest and in LR-PS treated berries during coloration (**Figure 3D** and Supplementary Table S4). In contrast to the results obtained from

cluster shading studies (Koyama and Goto-Yamamoto, 2008; Guan et al., 2015), the effect of light-exposure was possibly influenced by the temperature or other climate variables of the year.

# Light-induced Transcriptional Changes of Phenolic Biosynthetic Pathway Genes

To investigate the responses of phenylpropanoid/flavonoid biosynthetic pathway related structural and regulatory genes to different light-exposure treatments, berries of three distinct development stages (E-L 36, 37, and 38) from the LR-V and LM-V treated and the control groups during the 2012 growing season were selected to characterize the changes in gene expression at the transcript level by RNA-seq. Results showed that the general structural genes of phenylpropanoid and flavonoid metabolic pathways, including some members of phenylalanine ammonialyase (PAL, EC <IP_ADDRESS>), 4-coumarate: CoA ligase (4CL, EC <IP_ADDRESS>) and flavanone 3-hydroxylase (F3H, EC <IP_ADDRESS>), were significantly or moderately (fold-change ≥ 2 while divergence probability ≤ 0.8) up-regulated in berries from the LR-V and LM-V treatment groups at E-L 36 and 38 stages while downregulated at E-L 37 stage. Furthermore, almost all members of CHS (EC <IP_ADDRESS>) and chalcone isomerase (CHI, EC <IP_ADDRESS>) were moderately down-regulated in sunlight exposed grapes in comparison with those from the control group across the three developmental stages. The expression of members of specific structural genes required for HCA and flavonol biosynthesis, including cinnamyl-alcohol dehydrogenase (CAD, EC <IP_ADDRESS>) and FLS (EC <IP_ADDRESS>) across the three developmental stages as well as bifunctional UDP-glucose/UDP-galactose:flavonol-3- O-glucosyltransferase/galactosyltransferase (GT6, EC <IP_ADDRESS>) at

E-L 38 stage, and members of dihydroflavonol reductase (DFR, EC <IP_ADDRESS>) and UFGT (EC <IP_ADDRESS>) required for anthocyanin biosynthesis at E-L 36 and 37 stages were significantly or moderately up-regulated in LR-V and LM-V treated berries, leading to the increased accumulation of corresponding phenolic products. The lower contents of flavan-3-ols in LR-V and LM-V treated berry skins compared with the control group at E-L 37 and 38 stages is supported by the moderately down-regulation of members of leucoanthocyanidin reductase (LAR, EC <IP_ADDRESS>) and anthocyanidin reductase (ANR, EC <IP_ADDRESS>), which are directly involved in flavan-3-ol biosynthesis (**Figures 3**, **4A**).

It was previously reported that VviFLS4 was the sole member of the grapevine FLS family which specifically responded to different light regimes and showed a clear expression pattern corresponding to the accumulation of flavonols in the berry skins (Fujita et al., 2006; Matus et al., 2009; Koyama et al., 2012; Pastore et al., 2013). In our study, the transcription of several other members of FLS family in addition to VviFLS4 (VIT\_218s0001g03470) was also drastically induced (fold-change ≥ 2) by LR-V and/or LM-V treatments in different developmental stages, such as VIT\_202s0012g00390, VIT\_202s0012g00400 and VIT\_202s0012g00450 in LR-V treated berries at E-L 36 stage, as well as VIT\_208s0007g00750 and VIT\_213s0067g01020 in LR-V and LM-V treated berries at both E-L 36 and 38 stages (**Figure 4A**). These results were consistent with the increased accumulation of flavonols in lightexposure berries, which indicates that grapevine FLS gene family may be functionally redundant in response to light signal. The hydroxylation pattern of flavonoids is known to be mediated by the enzyme activity of F30H (EC <IP_ADDRESS>) and F30 5 0H (EC <IP_ADDRESS>), which catalyze the hydroxylation of naringenin and dihydrokaempferol at the 30 and 30 5 0 positions of the B-ring, respectively (Bogs et al., 2006; Guan et al., 2015). Our results showed that no significant differences in the expression level of genes encoding F30H among treatments were detected at each sampling point, but the transcription abundance of several F30 5 0H family members was significantly down-regulated by LR-V and moderately down-regulated by LM-V at both E-L 36 and 37 stages (**Figure 4A**), which were in fair agreement with the higher ratio of dihydroxylated/trihydroxylated flavonoids observed in berries from LR-V and LM-V treatments, in comparison with those in berries from the control group (**Figure 3D**).

To identify additional genes that might contribute to alterations in phenolic metabolism in berries grown under different light conditions, the transcription profile of phenolic biosynthesis-related genes was compared with the HCA, total flavonoid, flavonol, flavan-3-ol, and anthocyanin profiles of all samples, respectively. The correlation analysis based on pearson's coefficient revealed that the expression of four members of PAL (VIT\_216s0039g01100, VIT\_216s0039g01110, VIT\_216s0039g01120, and VIT\_216s0039g01130), the first committed enzyme in phenylpropanoid metabolism (Sparvoli et al., 1994), was highly significantly (p-value ≤ 0.01) correlated with the accumulation of flavonoids in berries from different light treated groups. The transcript of two other genes (VIT\_213s0047g00210 and VIT\_206s0061g00450) belonging to the 4CL and F3H families was also significantly (p-value ≤ 0.05) correlated with the changes of flavonoid content in each samples. In addition, there was a significant correlation (p-value ≤ 0.05) between flavonol accumulation and the expression of two members of FLS family (VviFLS4 and VIT\_208s0007g00750) mentioned above (Supplementary Table S5). However, no specific structural genes for phenolic biosynthesis pathway were found to be correlated with the accumulation of HCAs, flavan-3-ols or anthocyanins, which indicates that the biosynthesis or degradation of these compounds in berries under different light regimes might be controlled by the cooperation of multiple enzymes from the entrance to branches.

## Expression Analysis of Flavonoid Biosynthesis-related Transcription Factors

In grapes, some members of R2R3-MYB TF family and their co-activators belonging to other TF families (bHLH and WDR) which could regulate the transcription of downstream target genes required for phenolic biosynthesis pathways have been isolated and characterized recently (Hichri et al., 2011). The expression of these TFs involved in flavonoid metabolism has also been reported to be induced or suppressed by many environmental factors, such as light quality, temperature and water deficit conditions (Castellarin et al., 2007; Cominelli et al., 2008; Azuma et al., 2012). The transcript level of VviMYBF1, which acts as a direct regulator of VviFLS4 expression (Czemmel et al., 2009; Matus et al., 2009), was moderately greater in berries after light-exposure treatments than those in the control berries, except for a slight decrease in LM-V treated berries at E-L 37 stage compared with the control (**Figure 4B**). Expression of two UV-B-inducible grapevine flavonol synthesis regulators, VviHY5 and VviHYH (Loyola et al., 2016), was also significantly and moderately up-regulated in LR-V and LM-V treated berries at postvéraison berry developmental (E-L 38) stage, respectively. These results were well consistent with an increase in the transcript abundances of members of FLS as well as flavonol concentrations after light-exposed treatments, which indicates that light affects flavonol biosynthesis through transcript activation of a series of TFs and structural genes. Several regulators of the general branch and different branches of flavonoid synthesis, including VviMYB5a, VviMYB5b, VviMYBPA1, VviMYBPA2, VviMYBPAR, VviMYBC2-L1, VviMYBC2-L2, VviMYBC2-L3, and a TTG2-like homolog protein VviWRKY26 (Amato et al., 2016), showed large divergent changes in the transcript levels during berry development or under different light-exposure treatments (**Figure 4B**). Transcript abundances for VviMYB5a and VviMYB5b, VviWRKY26, as well as the negative regulator of PA accumulation VviMYBC2-L1 (Huang et al., 2014) in grape berries presented a high level during the three development stages but did not respond to changing light conditions, while low levels of VviMYBPA2, VviMYBPAR, VviMYBC2-L2, and VviMYBC2-L3 transcripts were detected in berries from all treatment groups. In addition, the expression of VviMYBPA1 was significantly and slightly down-regulated in LR-V and LM-V treated berries

FIGURE 4 | Effects of sunlight exposure on the transcript profile of the (A) enzymes and (B) regulatory factors involved in phenolic biosynthesis in grape berries. PAL, phenylalanine ammonia-lyase; C4H, trans-cinnamate 4-monooxygenase; CCR, cinnamoyl-CoA reductase; CAD, cinnamyl-alcohol dehydrogenase; COMT, caffeic acid 3-O-methyltransferase; 4CL, 4-coumarate: CoA ligase; CHS:,chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F30H, flavonoid 30 -hydroxylase; F305 0H: flavonoid 30 ,50 -hydroxylase; FLS, flavonol synthase; GT5, uridine diphosphate (UDP)-glucuronic acid:flavonol-3-O-glucuronosyltransferase; GT6, bifunctional UDP-glucose/UDP-galactose:flavonol-3-O-glucosyltransferase/galactosyltransferase; DFR, dihydroflavonol reductase; LAR, leucoanthocyanidin reductase; LDOX, leucoanthocyanidin dioxygenase; ANR, anthocyanidin reductase; UFGT, UDP-glucose:flavonoid 3-O-glucosyltransferase. The number of expressed family members for each enzyme is indicated in green box. Each square in the heatmap located beside their gene names corresponds to the average FPKM value of the gene in each sample as illustrated in the legend. Genes with significant expression changes compared with the control groups in each developmental stage are indicated by asterisks (∗ ) in the squares. Expression profiles of specific structural genes required for the biosynthesis of HCAs, flavonols, flavan-3-ols and anthocyanins are shown in pink, yellow, and violet dotted boxes, respectively. C, control group; LR-V, leaf removal at véraison; LM-V, leaf moving at véraison.

at E-L 38 stage, respectively, which correlated well with the changes of LDOX expression and total flavan-3-ol contents at this stage. Therefore, it may be speculated that the transcript level of VviMYBPA1 might lead to the difference in the flavan-3-ol contents of grape berries growing under different light conditions. Furthermore, transcript abundances of VviMYBA1 and VviMYBA2, two regulators of the anthocyanin branch (Walker et al., 2007), were slightly up-regulated at E-L 36 stage while down-regulated at E-L 37 or 38 stage in light exposed berries, correlating with the anthocyanin levels responded to light conditions in the grape skin. However, no significant changes in the expression of two bHLH factors, VviMYC1 and VviMYCA1, as well as two WDR proteins VviWDR1 and VviWDR2 among different light-exposure groups were observed (**Figure 4B**), although they have been reported to be involved in anthocyanin and/or PA synthesis (Hichri et al., 2010; Matus et al., 2010) and differentially modulated by different light qualities in other plant species (Sompornpailin et al., 2002; Cominelli et al., 2008).

# Validation of RNA-seq by Quantitative Real-time PCR

To validate the expression profiles obtained from the RNA-seq data, 15 genes relating to our biological focus were selected to subject to qRT-PCR analysis. They included 12 phenylpropanoid/flavonoid biosynthetic pathway related structural genes (VviPAL1, VviPAL2, VviPAL7, VviPAL15, VviF3H1, VviF3H2, VviF3'H, VviF30 5 0H, VviFLS1, VviFLS2, VviFLS3, and VviFLS4), as well as VviMYBF1, VviMYBPA1 and VviMYBA1 TF genes involved in the regulation of flavonol, flavan-3-ol and anthocyanin biosynthesis, respectively (Supplementary Figure S2). Two housekeeping genes in V. vinifera, VviUbiquitin1 and Vviβ-Actin were used as endogenous controls for normalization as their relatively constant expression throughout grape berry development as well as in berries under various stress conditions (Downey et al., 2003; Reid et al., 2006). The results showed that the expression of 15 genes determined by qRT-PCR was significantly correlation with those from the RNA-seq data at the 0.01 level (r = 0.54), thus verifying the method.

### Co-expression Analysis between Metabolic Pathway Genes and Transcription Factor Genes

Transcriptome co-expression analysis, which is based on the assumption that genes with similar expression patterns are most likely to be functionally associated, has proven to be a powerful tool for revealing regulatory networks of genes involved in linked processes (Persson et al., 2005). In plants, this strategy has been applied to identify factors regulating several metabolic pathways, such as two Arabidopsis MYB TFs regulating aliphatic glucosinolate biosynthesis and a rice AP2/EREBP (APETALA 2/ethylene responsive element binding protein) family TF involved in starch biosynthesis (Hirai et al., 2007; Fu and Xue, 2010). To systemically identify unknown putative regulators that control the phenolic biosynthesis in grape berries in response to different light regimes, a genomewide co-expression analysis was employed between metabolic pathway genes and TF genes. Eight phenolic synthesis genes screened previously, including genes encoding PALs, 4CL, F3H, and FLSs, were selected as "guide genes" to identify co-expression relationships specific to the light-induced differentially expressed TF genes using expression data of all light treated and control samples from RNA-seq. Any two genes with an absolute value of the Pearson Correlation Coefficient (PCC) greater than 0.7 (or 0.8) and p-value less than or equal to 0.05 (or 0.01) between their expression profiles were considered as significant (or highly significant) co-expressed genes (Fu and Xue, 2010). The results showed that a total of 120 and 59 TFs were highly co-expressed with the six total flavonoid biosynthesis-related (group I) and the two flavonol biosynthesis-related (group II) guide genes, respectively (**Figure 5**). Among the identified group I co-expressed TFs, the most abundant positively correlated TFs were members of the MYB, WRKY, C2C2, AP2/EREBP, bHLH, and MADS-box families (p-value ≤ 0.01), whereas the most abundant negatively correlated TFs belonging to MYB, NAC (No apical meristem, ATAF 1,2, Cup-shaped cotyledon 2), Cys2/His2 (C2H2) type and CCCH type (C3H) zinc finger protein families (p-value ≤ 0.01). Similarly, specific members of MYB, AP2/EREBP and C2C2 families were also found to be the most abundant significantly positively co-expressed TFs with group II (p-value ≤ 0.05).

In all plant species analyzed to date, MYB TFs, together with bHLH and WDR proteins, act as common denominators in the regulation of flavonoid accumulation under various biotic or abiotic signals, such as high-light, UV, drought, and extreme temperatures (Koes et al., 2005; Xu et al., 2015). Some additional potential transcriptional regulators that belong to WRKY, MADS-box, and bZIP TF families have also been reported to be involved in specific branches of the phenylpropanoid metabolism (Hichri et al., 2011). Besides, several negative regulators of flavonoid synthesis, such as R2R3-MYB, single domain R3-MYB repressors and truncated bHLH, inhibit the formation of MBW complex or modify it, thereby actively repress transcription in plants (Liu et al., 2015). The positively or negatively co-expression of multiple members of MYB, bHLH, WRKY, and MADS-box TF families with those key genes in the present result suggests that light-regulated flavonoid biosynthesis in grape berries is maintained by a complex regulatory network involves both positive and negative feedback loops. By using over-expressing and antisense transgenic plant strategies, it was shown that some DOF (DNA-binding One Zinc Finger) genes from the C2C2 zinc finger-containing TF superfamily putatively involved in regulation of enzymes of the phenylpropanoid and flavonoid pathways in Arabidopsis (Noguero et al., 2013). The plant-specific AP2/EREBP TF family, which was composed of AP2, DREB (cis-acting dehydration responsive element-binding protein), RAV (related to ABI3/VP1), ERF (ethylene responsive factor) and other subfamilies, plays a major role in several developmental processes, and also participates in plant hormone signal transduction as well as plant's responses to pathogens and various environmental stresses (Dietz et al., 2010). Recent studies revealed that a class of repressor-type ERF-subfamily

TFs act as active or passive repressors of transcription via their ERF-associated amphiphilic repression (EAR) domain, which was also found in some C2H2 type zinc-finger proteins and R2R3-MYB repressors of flavonoid synthesis-related genes in various plant species (Aharoni et al., 2001; Ciftci-Yilmaz and Mittler, 2008; Huang et al., 2014). NAC proteins are another plant-specific TFs which have been shown to play an essential role in regulating senescence, cell division, and wood formation, and also participate in plant response to pathogens, viral infections, and various environmental stresses (Nakashima et al., 2012). It was also reported that a NAC protein in Arabidopsis, ANAC078, positively regulates the expression of genes related to the biosynthesis of flavonoids, subsequently leading to the accumulation of anthocyanins in response to high-light (Morishita et al., 2009). Furthermore, VviNAC29, a protein belonging to the grapevine NAC TF superfamily, was demonstrated to act as a cooperative regulator controlling the stress-responsive expression of VviF30H in our previous

study (Sun et al., 2015b). However, their roles in the negative regulation of the flavonoid synthesis-related genes have not been investigated previously, thus, the conclusion that whether they could directly or indirectly regulate the light-response of phenolic biosynthesis still needs to be further characterized.

# Light Response of Plant Hormone Signal Transduction Related Genes

Phytohormones have been implicated in controlling various aspects of grape berry development, in particular, the important processes of ripening and adaptation to adverse environmental conditions, including harmful UV radiation (Jeong et al., 2004). In some cases, hormone pathways act downstream of the light signal pathways to regulate growth, whereas in other cases they interact with each other reciprocally (Alabadí and Blázquez, 2009). In LR-V treated berries, the transcript abundances of some members of PYR/PYL (VIT\_208s0058g00470 and VIT\_210s0003g01335) and abscisic acid (ABA) responsive element binding factor (ABF; VIT\_208s0007g03420) involved in ABA signal transduction (Klingler et al., 2010), xyloglucan:xyloglucosyl transferase TCH4 (VIT\_211s0052g01190) involved in BR signal transduction (Clouse, 2015), jasmonate ZIM domain-containing protein (JAZ; VIT\_201s0146g00480) and MYC2 TF (VIT\_211s0052g00100) involved in jasmonic acid (JA) signal transduction (Fernández-Calvo et al., 2011), as well as TGA TF (VIT\_207s0031g02670 and VIT\_208s0007g06160) and pathogenesis-related protein 1 (PR-1; VIT\_203s0088g00780, VIT\_203s0088g00810, VIT\_203s0088g00910, and VIT\_203s0097g00700) involved in salicylic acid (SA) signal transduction (Eulgem, 2005) were significantly or moderately higher compared with that of the control at nearly all the three developmental stages (**Figure 6**). In addition, the transcription of some proteins involved in other plant hormone signal transductions was also significantly or moderately up-regulated in LR-V treated berries at specific developmental stages, such as members of the histidine kinase receptors CRE1 (VIT\_213s0019g01180 and VIT\_217s0000g04920) and histidine-containing phosphotransfer protein (AHP; VIT\_211s0016g03170) required for cytokinin (CTK) signal transduction (Choi and Hwang, 2007) and gibberellin receptor GID1 (VIT\_213s0084g00130) required for Gibberellin (GA) signal transduction (Hirano et al., 2008) at E-L 36 stage, members of the auxin influx carrier AUX1 (VIT\_208s0007g02030) and auxin-responsive protein IAA (AUX/IAA; VIT\_211s0016g03540) required for auxin signal transduction (Lau et al., 2008) at E-L 38 stage. Notably, a member of serine/threonine-protein kinase CTR1 (VIT\_218s0001g07700), a negative regulator of the ethylene (ETH) response pathway (Chen et al., 2005), was up-regulated in LR-V treated berries at E-L 38 stage. Meanwhile, a member of 1-aminocyclopropane-1-carboxylic acid oxidase (ACO, EC <IP_ADDRESS>; VIT\_211s0016g02380), the last enzyme in the ETH production pathway which controlled the biosynthesis of ETH in plants (Chervin et al., 2004), was significantly down-regulated in the same developmental stage. In berries from LM-V treatment, a member of TCH4 (VIT\_211s0052g01190) was significantly up-regulated, while members of AUX/IAA (VIT\_214s0030g02310) and PR-1 (VIT\_203s0088g00710) were significantly down-regulated at E-L 37 stage. No significant differences in the expression of plant hormone signal transduction related genes between berries from LM-V treatment and the control group were detected at both E-L 36 and 38 stages. Nevertheless, a member of 9-cis-epoxycarotenoid dioxygenase (NCED, EC <IP_ADDRESS>; VIT\_219s0093g00550), which limits the level of ABA in the biosynthesis pathway (Zhang et al., 2009), was up-regulated in LM-V treated berries at E-L 36 stage, while a member of ACO (VIT\_211s0016g02380) was down-regulated at E-L 38 stage (**Figure 6**).

Previous studies indicate that the accumulation of phenolics in berry skin during the ripening stage, as well as the expression of structural genes and their transcriptional regulators considered to be involved in the phenylpropanoid and flavonoid pathways, were enhanced by exogenous ABA and ETH treatments, while suppressed by synthetic auxins, NAA (Ban et al., 2003; El-Kereamy et al., 2003; Jeong et al., 2004; Fujita et al., 2006; Koyama et al., 2010). The acceleration of berry ripening and flavonoid accumulation in LR-V treated grape berries was well correlated with the enhancement of ABA signal transduction, which might act as a protective mechanism induced by enhanced light irradiation (Berli et al., 2011), while the increased auxin signal transduction and decreased biosynthesis of ETH might result in the suppression of flavonoid biosynthesis, especially flavan-3-ols and anthocyanins at harvest. Moreover, the different expression of metabolic enzymes of phytohormones at E-L 36 and 38 stages, and the transcriptional changes of AUX/IAA at E-L 37 stage, were shown to be perfectly correlated with changes in the accumulation of flavonoids in LM-V treated berries. A number of studies have indicated that light signaling affects the biosynthesis and/or signaling of multiple phytohormones such as auxin, GA, CTKs, ETH, and BRs (Carvalho et al., 2011). The interactions between light and hormones pathways operate through distinct molecular mechanisms in plants and play an important role in the adjustment of developmental programs and behavior of the plants to the environment (Alabadí and Blázquez, 2009). Taken together, our results suggest that phenolic metabolic in berry skins of the Cabernet Sauvignon grape is precisely controlled by a series of phytohormones in response to exchanged light irradiation.

# CONCLUSION

In the present study, the transcriptional profiles and metabolite profiles of phenolic biosynthesis pathway were analyzed in Cabernet Sauvignon grapes under different sunlight exposure treatments during berry development. Leaf removal or leaf moving at different berry development stages did not show consistent effects on the accumulation of flavan-3-ol, anthocyanin or total flavonoids in grape berries over three seasons. However, the concentrations of HCAs and flavonols were moderately and drastically increased in sunlight exposed grape berries, respectively, which is well correlated with changes

in transcriptional abundance of PAL, 4CL, F3H, and FLS family members as well as large amounts of regulatory genes. Furthermore, the transcriptional changes of genes required for the biosynthesis and signal transduction of auxin, ETH and ABA were found to be exactly in accordance with the accumulation of phenolics in light exposed berries during development, confirmed the importance of phytohormones on berry phenolic biosynthesis of grapes in response to light environment. Taken together, our results provide new valuable insights into understanding of the complex regulatory network of sunlightresponsive phenolic biosynthesis in grape berries, as well as theoretical foundations for cultivation management and wine production.

#### AUTHOR CONTRIBUTIONS

JW and WC conceived and designed the experiments. GC and Y-NH did the field experiments. R-ZS, GC, and QL analyzed the transcriptome sequencing data. GC, QL, YW, Y-RZ, W-FS, XZ, and X-DC performed the HPLC quantification. Y-BL and S-YL provided technical support. R-ZS and JW wrote the paper. All the authors revised it critically for important

#### REFERENCES

intellectual content and approved the final version of this manuscript.

#### FUNDING

This work was supported by China Agriculture Research System (CARS-30).

#### ACKNOWLEDGMENTS

The authors thank to Prof. Chang-Qing Duan, Prof. Qiu-Hong Pan, Dr. Fei He (China Agricultural University) and Dr. Bao-Qing Zhu (Beijing Forestry University) for information support. The authors are also grateful to CITIC Guoan Wine Co. Ltd for technical assistance.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2017.00547/ full#supplementary-material

of white-fleshed and teinturier grape berries. Planta 243, 23–41. doi: 10.1007/ s00425-015-2391-4

fpls-08-00547 April 19, 2017 Time: 17:49 # 16

of public microarray data sets. Proc. Natl. Acad. Sci. U.S.A. 102, 8633–8638. doi: 10.1073/pnas.0503392102

fpls-08-00547 April 19, 2017 Time: 17:49 # 17

heterozygous grapevine variety. PLoS ONE 2:e1326. doi: 10.1371/journal.pone. 0001326

during abscisic acid biosynthesis from peach and grape fruits. J. Plant Physiol. 166, 1241–1252. doi: 10.1016/j.jplph.2009.01.013

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Sun, Cheng, Li, He, Wang, Lan, Li, Zhu, Song, Zhang, Cui, Chen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Sunlight Modulates Fruit Metabolic Profile and Shapes the Spatial Pattern of Compound Accumulation within the Grape Cluster

#### Noam Reshef, Natasha Walbaum, Nurit Agam\* and Aaron Fait\*

*French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel*

Vineyards are characterized by their large spatial variability of solar irradiance (SI) and temperature, known to effectively modulate grape metabolism. To explore the role of sunlight in shaping fruit composition and cluster uniformity, we studied the spatial pattern of incoming irradiance, fruit temperature and metabolic profile within individual grape clusters under three levels of sunlight exposure. The experiment was conducted in a vineyard of Cabernet Sauvignon cv. located in the Negev Highlands, Israel, where excess SI and midday temperatures are known to degrade grape quality. Filtering SI lowered the surface temperature of exposed fruits and increased the uniformity of irradiance and temperature in the cluster zone. SI affected the overall levels and patterns of accumulation of sugars, organic acids, amino acids and phenylpropanoids, across the grape cluster. Increased exposure to sunlight was associated with lower accumulation levels of malate, aspartate, and maleate but with higher levels of valine, leucine, and serine, in addition to the stress-related proline and GABA. Flavan-3-ols metabolites showed a negative response to SI, whereas flavonols were highly induced. The overall levels of anthocyanins decreased with increased sunlight exposure; however, a hierarchical cluster analysis revealed that the members of this family were grouped into three distinct accumulation patterns, with malvidin anthocyanins and cyanidin-glucoside showing contrasting trends. The flavonol-glucosides, quercetin and kaempferol, exhibited a logarithmic response to SI, leading to improved cluster uniformity under high-light conditions. Comparing the within-cluster variability of metabolite accumulation highlighted the stability of sugars, flavan-3-ols, and cinnamic acid metabolites to SI, in contrast to the plasticity of flavonols. A correlation-based network analysis revealed that extended exposure to SI modified metabolic coordination, increasing the number of negative correlations between metabolites in both pulp and skin. This integrated study of micrometeorology and metabolomics provided insights into the grape-cluster pattern of accumulation of 70 primary and secondary metabolites as a function of spatial variations in SI. Studying compound-specific responses against an extended gradient of quantified conditions improved our knowledge regarding the modulation of berry metabolism by SI, with the aim of using sunlight regulation to accurately modulate fruit composition in warm and arid/semi-arid regions.

Keywords: solar irradiance, microclimate, spatial heterogeneity, grape composition, phenylpropanoids, metabolite profiling, climate change, shading

#### Edited by:

*Giovanni Battista Tornielli, University of Verona, Italy*

#### Reviewed by:

*Laurent Deluc, Oregon State University, USA Marianna Fasoli, E & J Gallo Winery, USA*

#### \*Correspondence:

*Nurit Agam<EMAIL_ADDRESS>Aaron Fait<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Crop Science and Horticulture, a section of the journal Frontiers in Plant Science*

Received: *01 November 2016* Accepted: *12 January 2017* Published: *01 February 2017*

#### Citation:

*Reshef N, Walbaum N, Agam N and Fait A (2017) Sunlight Modulates Fruit Metabolic Profile and Shapes the Spatial Pattern of Compound Accumulation within the Grape Cluster. Front. Plant Sci. 8:70. doi: 10.3389/fpls.2017.00070*

# INTRODUCTION

Plasticity is the ability of an organism to adapt its phenotype in response to changes in the environment. For plants, it is an important adaptive strategy to cope with environmental variability (Svanback and Eklov, 2006) and facilitates the use of the same cultivar in a wide range of growing conditions (Dai et al., 2011). However, at the single-field scale, phenotypic plasticity may be regarded as a significant cause for within-field variation. Field systems, such as vineyards experience a large range of microclimate conditions manifested in significant spatial variation of SI and temperature (Oyarzun et al., 2007; Matese et al., 2014).

Grape and wine quality reflects the levels and composition of a large number of primary and secondary metabolites that shape the overall sensory experience of its derived product. Primary metabolites are largely accumulated in the pulp and include sugars, organic acids, and amino acids. Grape secondary metabolites predominantly include the phenylpropanoids, typically found in the berry skin, and comprise flavonoids, phenolic acids, stilbenes, and viniferins (Anesi et al., 2015). While sugars are largely imported to the fruit through the plant vascular tissues (Ollat et al., 2002), the overall levels and composition of organic acids, amino acids, and secondary metabolites at harvest are determined by the sum of complex metabolic processes occurring in the fruit during its lifetime (Conde et al., 2007; Deluc et al., 2007; Dai et al., 2013). Indeed, metabolic shifts caused by changes in climatic conditions in the immediate vicinity of the fruit (i.e., microclimate), such as light and temperature, are well-known to affect fruit composition (Jackson and Lombard, 1993; Downey et al., 2006).

Studies exploring the effect of microclimate on fruit primary metabolites largely agree that sunlight, and specifically an increase in the temperature load of the fruit, highly modulate malic acid levels, leading to a corresponding decrease in overall fruit acidity (Kliewer, 1965; Jackson and Lombard, 1993; Conde et al., 2007; Sweetman et al., 2014). However, results regarding the effect on the relatively stable tartaric acid are still controversial (Rienth et al., 2016; Young et al., 2016). Discrepancies also exist regarding the effect on amino acids, owing in part to the differential response of cultivated varieties. For example, filtering UV-B irradiance was found to increase the overall levels of amino acids in the juice of Riesling cv. (Schultz et al., 1998), but had no effect on the levels and composition of amino acids in Sauvignon Blanc cv. grapes at harvest (Gregan et al., 2012).

With regards to grape specialized metabolism, studies underline the flavonoids as a highly responsive group to light and temperature perturbations (Chalker-Scott, 1999; Winkel-Shirley, 2002). For instance, flavonol glucoside concentrations in the fruit positively correlated with increased SI on the cluster (Haselgrove et al., 2000; Spayd et al., 2002), and were found to be the most significant metabolites distinguishing the metabolic profile of shaded berries from that of exposed berries (Pereira et al., 2006). Fruit sunlight exposure was found to modulate the composition of anthocyanins, such as the proportion of acylated and coumarylated forms, the ratio between di-hydroxylated and tri-hydroxylated anthocyanins (Haselgrove et al., 2000; Spayd et al., 2002; Downey et al., 2004; Tarara et al., 2008; Chorti et al., 2010), and the proportion of ortho-di phenol anthocyanins (Rustioni et al., 2011). However, the overall accumulation of anthocyanins showed higher dependency on temperature conditions than on SI (Downey et al., 2006), with the effect of the interaction between incoming SI and fruit temperature on grape anthocyanin levels exhibiting a synergistic or antagonistic character, depending on the ranges of both factors (Tarara et al., 2008; Azuma et al., 2012).

While the overall environmental effect on grape has been investigated, few studies have addressed the within-cluster spatial profile of compound accumulation and variability. Pisciotta et al. (2013) characterized berry anthocyanin content considering its vertical position on the rachis and external and internal faces of the cluster (i.e., facing the inter-row or the canopy). However, no data regarding micrometeorological conditions were presented, hampering the interpretation of the results with respect to environmental conditions. Zhang et al. (2015) found greater polyphenol content in regions of the cluster characterized by lower berry surface temperature in Shiraz cv. More recently, by using model-generated microclimate data, Pieri et al. (2016) related the overall levels of berry flavonols, anthocyanins, and amino acids to cluster exposition and its impact on the estimated levels of SI and berry surface temperature (BST). Taken together, these works indicate that micro-scale environments play an active role in generating the spatial patterns of metabolite accumulation that affect fruit composition, as well as the uniformity of the crop. Optimizing crop uniformity is generally regarded as an important aspect in the overall improvement of wine quality (Keller, 2010), owing to its significant role in the composition of the harvested grape, and final wine (Kontoudakis et al., 2011; Liu et al., 2016). However, recent attempts to minimize field heterogeneity through the use of common vineyard management techniques, such as deficit irrigation and cluster thinning, were found inefficient (Calderon-Orellana et al., 2014). This stresses the importance of exploring the effectiveness of other potential techniques, including the modulation of clusterzone microclimate, to form more homogeneous conditions. As a key factor determining fruit intercepted irradiance and temperature (Smart and Sinclair, 1976), practical interventions to ameliorate fruit microclimate generally involve the regulation of sunlight exposure. Practices, such as basal leaf removal, or shading, whether by the use of artificial nets or by the use of

**Abbreviations:** GABA, gamma-Aminobutyric acid; Cyan-3-glu, Cyanidin-3- O-glucoside; Pet-3-glu, Petunidin-3-O-glucoside; Peo-3-glu, Peonidin-3-Oglucoside; Mal-3-glu, Malvidin-3-O-glucoside; Delph-3-glu, Delphindin-3-Oglucoside; Delph-3-acet, Delphinidin-3-O-(6′′-acetyl-glucoside); Cyan-3-acet, Cyanidin-3-O-(6′′-acetyl-glucoside); Pet-3-acet, Petundin-3-O-(6′′-acetylglucoside); Mal-3-acet, Malvindin-3-O-(6′′-acetyl-glucoside); Peo-3-acet, Peonidin-3-O-(6′′-acetyl-glucoside); Delph-3-coum, Delphinidin-3-O-(6′′ p-coumaroyl-glucoside); Mal-3-caffe, Malvidin-3-O-(6′′-caffeoyl-glucoside); Cyan-3-coum, Cyanidin-3-O-(6′′-p-coumaroyl-glucoside); Pet-3-coum, Petunidin-3-O-(6′′-p-coumaroyl-glucoside); Peo-3-coum, Peonidin-3-O-(6′′-pcoumaroyl-glucoside); Mal-3-coum, Malvidin-3-O-(6′′-p-coumaroyl-glucoside); Myr-3-glr, Myricetin-3-O-glucuronide; Rutin, Quercetin-3-O-rutinoside; Myr-3-glu, Myricetin-3-O-glucoside; Quer-3-glr, Quercetin-3-O-glucuronide; Quer-3-glu, Quercetin-3-O-glucoside; Kaemp-3-glr, Kaempferol-3-Oglucuronide; Kaemp-3-glu, Kaempferol-3-O-glucoside; Narin-chalc-glu, Naringenin-chalcone-4-O-glucoside; Hex., Hexoside.

extended canopies, are routinely used in the industry. However, integrated studies encompassing a gradient of sunlight exposure, profiling of metabolic data, and a quantitative characterization of microclimate conditions are lacking. Hence, our understanding of how sunlight regulation influences the spatial heterogeneity of fruit microclimate and its effect on fruit metabolic processes and coordination is currently limited.

In this study, we performed a spatial characterization of micrometeorological conditions and metabolic profiles within individual grape clusters subjected to differing levels and directions of sunlight exposure. The extended range of SI intensities allowed us to explore the responses of a large number of primary and secondary (phenylpropanoids) metabolites to a range of microclimate conditions typically found in the field. This study's insights will aid in defining strategies for sunlight regulation aimed at improving fruit composition and uniformity in challenging environments.

## MATERIALS AND METHODS

#### Site Description

The experiment was conducted during the 2014 and 2015 growing seasons, in a vineyard located in the heart of the Negev Desert, Israel (30◦ 36′ 55.22′′N, 34◦ 45′ 12.00′′E, 800 m altitude). This is an arid region with an average annual precipitation rate of 70 mm (Israel Meteorological Service), which, during the growing season, is characterized by stable meteorological conditions including high SI and elevated midday temperatures (Supplementary Figure 1). The vineyard was planted in 2007 with Vitis Vinifera L. cv. Cabernet Sauvignon grafted on 140 Ruggeri rootstock, irrigated using a covered drip-irrigation system as is common in the region. Rows are orientated north-south with a 30◦ angle to the north-east\south-west, and are trained in vertical shoot positioning (VSP). Three experimental rows were selected, with one border row between every two experimental rows. In each row, three groups of nine adjacent vines were marked as field repetitions, and the basal leaves in the vicinity of the clusters (up to 30 cm above the cordon) were completely removed at the onset of veraison. Each field repetition was further assigned one of three shading treatments: fully exposed, i.e., no net (Exposed), 30% shading net (30% shaded) and 60% shading net (60% shaded), using UV-stabilized woven mesh shading nets for agriculture (Ginegar, Israel) with the percentage representing the PAR filtering capacity as published by the manufacturer. The nets were placed using thin metal support wires, one next to each vine, in a manner that created a shading tunnel with a diameter of about 80 cm around the cluster zone in order to facilitate wind flow and prevent an increase in relative humidity (Supplementary Figure 2). Shading was placed from the onset of veraison (the day of basal leaf removal) until harvest date. Treatments were repeated once on each row in a way that represented all locations along the row to minimize effects of spatial differences both between and within the rows.

#### Meteorological Measurements

During the 2015 growing season, a detailed study of clusterscale micrometeorological conditions was performed. Incoming SI, air temperature, relative humidity (RH%), and wind speed and direction were continuously monitored in 15-min intervals from veraison to harvest by placing a multi-sensor (WS501- UMB, Lufft, Fellbach, Germany) connected to a data logger (CR200, Campbell Scientific, Utah, USA) 1 m above the canopy, positioned in the field to allow for a maximum fetch in the direction of the prevailing winds (northwest). The distance between the sensor and the most distant experimental vine was approximately 90 m.

Cluster-zone air temperature and RH% were measured continuously every 15 min from veraison to harvest by placing sensors equipped with an internal data logger (Hobo ProV2, Onset, MA, USA) at a shaded spot in the immediate vicinity of the clusters. Two sensors were placed at each treatment site to verify repeatability.

The SI intensity on the clusters was measured simultaneously at four horizontal axes: parallel and perpendicular to the vine row, and vertically, to the sky (Supplementary Figure 3). This was done by constructing a box positioning all five pyranometers (LI200R, Li-Core, NE, USA) and connecting them to a single data logger (21X, Campbell Scientific, UT, USA). The sensors were stabilized and leveled on a tripod and placed at the cluster zone, with representative locations and distances from adjacent clusters verified to simulate cluster conditions. This system was continuously rotated every 3 days between field treatments and normalized to the SI measured above the canopy to allow for a comparison between measurements made on different dates. Measurements were done on the east side of the canopy for the shaded treatments and on both sides of the canopy for the exposed treatment.

Berry surface temperature (BST) was measured on 28 July (post-veraison) and 19 August (pre-harvest) 2015, during two diurnal field campaigns from 6:00 to 20:00 using an infrared camera (T640, FLIR systems, OR, USA). Every hour, photos were taken from two representative clusters on each treatment, from each side of the canopy, for a total of 12 clusters. Each cluster was divided into three faces, north- and south-facing, and facing the inter-row (east for east-located clusters and west for westlocated clusters). Data analysis included a careful selection of berries located in the middle section of the cluster's vertical axis (i.e., middle height) in each photo and the exclusion of pixels representing non-grape background data.

# Berry Sampling

Sampling during both seasons was done several days prior to harvest date scheduled at 24◦Brix, to represent final berry composition and the potential of accumulated spatial differences. Samples were taken at pre-dawn in order to prevent any differences between sunlit and shaded berries at the time of sampling. Four vines per shading treatment were sampled, located in two out of the three field repetitions. Selected vines for sampling were at least four vines apart (**Figure 1A**). In each vine, four to six exterior-located clusters (furthest from the trunk toward the inter-row) were selected from each canopy side, to avoid possible shading by the canopy apart from midday hours. Each cluster was dissected into four horizontal orientations (**Figure 1B**), and samples were taken from the middle height

of each orientation. Berries from a single vine and canopy side, located on the same cluster plane, were pooled together and immediately frozen in liquid nitrogen. In the lab, skin, pulp and seeds were carefully separated while kept frozen on dry ice, placed in Eppendorf tubes, and stored at −80◦C until further processing. Analysis included grape skin tissues sampled during both seasons and pulp tissues sampled during the 2015 season.

# Analysis of Skin Phenylpropanoids

Grape skin samples were analyzed using Ultra Performance Liquid Chromatography coupled to a Quadrupole Time-of-Flight Mass-Spectrometer (UPLC QTOF-MS, Waters, MA, USA), following an extraction protocol for metabolite profiling as described in Weckwerth et al. (2004). Skin tissues were lyophilized and ground under liquid nitrogen using a RETSCH-mill (Retsch, Haan, Germany) with prechilled holders and grinding beads. The powder was weighed (40 mg), and metabolites were extracted in a 1-ml pre-chilled methanol:chloroform:water extraction solution (2.5:1:1 v/v). Internal standards, i.e., 0.2 mg/ml ribitol in water, 1 mg/ml ampicillin in water, 1 mg/ml corticosterone in methanol, were subsequently added. The mixture was then briefly vortexed, and 100 µl of methanol was added; the mixture was then placed on a horizontal shaker for 10 min at 1000 rpm. The samples were later

sonicated for 10 min (Elmasonic S30, Elma, Singen, Germany) and centrifuged for 10 min (20,817 × g, microcentrifuge 5417R, Eppendorf, Hamburg, Germany). The supernatant was decanted into new tubes, mixed with 300 µl of chloroform and 300 µl of MiliQ water (Millipore, MA, USA), vortexed for 10 s and then centrifuged at 20,817 × g for 5 min. Next, the water/methanol phase was separated, filtered (0.22 µm Millipore, MA, USA) and transferred to UPLC vials for analysis.

## LC/MS Conditions

UPLC-QTOF-MS conditions were exactly as described previously by Hochberg et al. (2013).

### LC/MS Annotation

MassLynxTM software (Waters) version 4.1 was used for system control and data acquisition. Metabolite annotation was validated using the standard libraries described in Arapitsas et al. (2012), based on retention time order, given in Degu et al. (2014). Metabolites were also annotated based on fragmentation patterns searched against the Chemspider metabolite database (http:// www.chemspider.com/), the consistency of their retention times with those of identified metabolites, and comparison with the data in the current scientific literature.

#### Analysis of Pulp Primary Metabolites

Pulp samples were manually crushed with a mortar and pestle while kept frozen with liquid nitrogen. Next, 100 mg of crushed, frozen powder was weighed and extracted by adding a methanol:chloroform:water solution (2.5:1:1), similar to the aforementioned phenylpropanoid extraction. Then, 70 µl of the extract were dried using a Concentrator Plus (Eppendorf, Hamburg, Germany) and derivatized exactly as described in Hochberg et al. (2015) with sorbitol as the internal standard. Glucose, fructose, tartaric and malic acids were quantified using a calibration curve of standards (Sigma-Aldrich, MO, USA) with 10 concentration points from 50 to 900 ng for glucose and fructose and 2.5 to 45 ng for malic and tartaric acids. A split ratio of 50:1 was used to correctly determine the levels of glucose, fructose, malic and tartaric acids, due to their relatively high abundance in the pulp. The GC-MS conditions were exactly as described previously by Hochberg et al. (2013).

The Xcalibur data system V2.0.7 was used for system control and data acquisition. Annotation was based on spectral searching supported by the National Institute of Standards and Technology (NIST, Gaithersburg, MA, USA) against RI libraries from the Max-Plank Institute for Plant Physiology (Golm, Germany).

#### Statistical Analysis

Statistical analysis was performed using R v3.3.1 in RStudio. A profile analysis, from the "profileR" package, was used in order to compare the spatial profiles of clusters from exposed and shaded treatments and both canopy sides. This analysis tested the multivariate spatial data with two separate null hypotheses: a) the multivariate profile between groups is parallel; and b) the multivariate levels between groups are equal. This was done separately for each of the 70 annotated metabolites. In addition, within-cluster differences were tested for significance using the aov() function for ANOVA and the post-hoc Tukey test using the "agricolae" package. The same method was used for comparing the within-cluster coefficient of variance for each metabolite between different shading treatments, to highlight significant differences in cluster homogeneity caused by shading. Differences in malate/tartaric acid levels were tested for significance by using the built-in t-test() function.

The Pearson correlation of the "corrplot" package was used in order to construct separate metabolite correlation matrices for fully exposed and 60% shaded clusters, based on the entire set of samples from each treatment.

A regression analysis of each metabolite with daily incoming SI levels was done using the entire set of samples obtained from all treatments and cluster orientation, for which irradiance data was available. Linear and logarithmic regressions were assessed by the built-in lm() function, using non-transformed and log-transformed irradiance data, respectively. The R 2 -values of metabolites found to have significant regressions were then calculated by using the mean of four biological replicates, in order to minimize the effect of within-group variability on this coefficient.

Heatmap figures were generated using TMeV v4.9, using the mean of four biological replicates. Hierarchical clustering of the heatmaps was based on the Pearson correlation.

### Network Analysis

Correlation networks were constructed based on the data obtained from Pearson correlation analyses performed in R (detailed above), separately for exposed and 60% shaded treatments, and for metabolites detected in pulp and skin tissues (i.e., primary and secondary metabolites). Visualization and computation of network properties were performed using the "MetScape" application and the NetworkAnalyzer tool, respectively, available in Cytoscape V3.4.0. Correlations were incorporated into the network if the r-value was r > 0.5 or r < (−0.5).

# RESULTS

### The Spatial Distribution of Micrometeorological Conditions across the Cluster Differed in Response to Shading and Position

Cluster-zone shading successfully filtered the incoming SI as shown in **Table 1**. Clusters shaded with 30% and 60% shading nets received 64% and 34%, on average, of the incoming SI measured for fully exposed clusters, respectively. This did not lead to measured differences in the cluster-zone air temperature and relative humidity between treatments (data not shown). Comparing the different orientations in the east-facing cluster, the east-south orientation (orientation 1) received the largest amount of daily SI, followed by east-north (orientation 2) and finally west-south (orientation 3). The higher SI recorded for east-north compared to west-south may be attributed to the sun's zenith relative to the row orientation at the time of direct sunlight exposure on the corresponding sensors.

Interestingly, a comparison between the exposed and the 30% shaded clusters, and the 30% shaded and the 60% shaded clusters revealed that incoming SI differences within a cluster exceeded the differences between treatments. Finally, while the percentage of SI intercepted by the different cluster orientations, compared to the reference of each treatment, remained unaffected, lowering the overall incoming SI levels to the cluster, by shading, effectively minimized the within-cluster heterogeneity in SI. Shading reduced the incoming energy differences between orientations 1 and 3 by 3-fold, from 6.8 to 2.3 MJ/m2 /day for exposed and 60% shaded clusters, respectively.

As expected, filtering the incoming SI caused a reduction in BST (**Figure 2A**). As shown by the percentiles, during direct sun exposure hours (e.g., morning for east-facing clusters), shading reduced the maximum BST, while having no effect on the minimum values, which represent the temperature of the shaded cluster faces. Notably, during the afternoon, when the east-facing clusters were exposed only to diffused radiation, the minimum BST of the 60% shaded clusters increased, while it remained stable for the 30% shaded and fully exposed clusters. Shading clusters with 60% shading nets decreased their maximum temperature by up to 7.1◦C and daytime (6:00–20:00) mean BST by 0.7◦C (**Table 2**). Examining the distribution of BST within a cluster, presented in histograms (**Figure 2B**), revealed that shaded clusters were more homogeneous than exposed ones. While maximum differences in BST within an exposed cluster

*Sums of daily irradiance, measured within the cluster-zone to represent the different cluster orientations.Values marked by* \* *are estimated based on the mean filtering efficiency of each shading net. Relative sums of daily irradiance values represent the percentage compared to the daily irradiance in orientation 1 of each treatment separately. Values enclosed in parentheses represent the percentage compared to the daily irradiance of the exposed treatment for each orientation separately. Data were not collected for orientations 4 and 5.*

TABLE 2 | Berry surface temperature by cluster orientation in the three shading treatments.

*Berry surface temperature (BST) calculations based on hourly spatial measurements of representative clusters acquired on August 19, 2015. Data were not collected for orientations 4 and 5.*

reached 17◦C (33 to 50◦C at 11:00), they were 9.6◦C in 60% shaded clusters (34.3 to 43.9◦C at 12:00).

## Cluster Spatial Profile of Metabolite Level Was Affected by SI Intensity and Cluster Position

The spatial profile analysis, i.e., the levels and pattern of metabolite accumulation in response to the berry position/orientation, was conducted separately for the primary (**Figure 3**) and secondary metabolites (**Figure 4**), and summarized in Supplementary Table 1.

#### The Impact of Cluster Shading on the Spatial Pattern and Overall Levels of Fruit Metabolites

#### **Pulp primary metabolites**

Shading affected the spatial pattern (i.e., the pattern of accumulation in the different cluster orientations) of leucine, beta-alanine, and citrate in east-located clusters, increasing from internal to external orientations in exposed clusters, while shaded clusters had the opposite trend. Shading increased the levels of maleate and aspartate by 1.6- and 1.8-folds, respectively, and decreased the levels of tartaric acid, valine, and leucine by 1.3-, 2.2-, and 2.2-folds, respectively, in east-located clusters. In westlocated clusters, shading decreased the levels of phosphoric acid, erythritol, beta-alanine, valine, leucine, and GABA by 1.3-, 1.4-, 1.3-, 1.9-, 1.6-, and 2.1-folds, respectively.

#### **Skin phenylpropanoids**

Shading significantly affected the levels of 11 and 12 phenylpropanoids in the east- and west-located clusters, respectively, in 2015. Shading increased the levels of procyanidin B1 and epicatechin by 1.5- and 1.2-folds, respectively, and decreased the levels of the anthocyanins cyan-3-glu and cyan-3-acet (1.8- and 2-folds, respectively), phenylalanine and narin-chalc-glu (2.1- and 3-folds), and the flavonols myr-3-glu, myr-3-glr, quer-3-glu, kaemp-3-glu, and kaemp-3-glr by 1.7-, 1.4-, 2.1-, 4-, and 2.5-folds, respectively, in east-located clusters. In west-located clusters, shading significantly increased the levels of both mal-3-acet and mal-3-coum by 1.3-fold, and decreased the levels of cyan-3-glu, phenylalanine, narin-chalc-glu, and hydroxybenzoate hex (1.8-, 1.8-, 3-, and 1.5-folds), the stilbenes delta-viniferin and piceid (1.8- and 4.2-folds), and the flavonols myr-3-glu, quer-3-glu, kaemp-3-glu and kaemp-3-glr by 1.6-, 2.6-, 5.6-, and 2.8-folds, respectively.

#### The Impact of Cluster Canopy Side on the Spatial Pattern and Overall Levels of Fruit Metabolites **Pulp primary metabolites**

The canopy side of the cluster significantly affected the spatial pattern of malate, galactarate, maleate, threonine, glucose, and fructose in shaded clusters, while no significant effect was found in exposed clusters (**Figure 3B**). All mentioned metabolites followed a trend in which external cluster orientations had lower values than the internal ones, resulting in an opposite eastnorth-south-west pattern between the two sides of the canopy, corresponding with SI. In addition, canopy side significantly affected the overall levels of glucose and fructose in shaded clusters, and of lumichrome, beta-alanine, and GABA in exposed clusters. With the exception of lumichrome, all were found to be higher in clusters from the west side of the canopy than in those from the east.

#### **Skin phenylpropanoids**

Canopy side affected the spatial profile of 15 and 24 phenylpropanoid metabolites in exposed and shaded clusters, respectively, in the 2015 season (**Figure 4**). These included all the anthocyanins, with the exception of cyan-3-glu and vitisin A, the flavan-3-ols epigallocatechin and catechin, narin-chalcglu and phenylalanine, and the entire set of flavonols, with the exception of myr-3-glu. In the 2014 season, canopy side significantly affected the spatial profile of only four and five phenylpropanoid metabolites, in exposed and shaded clusters, respectively; of these, mal-3-glu and peo-3-coum, in exposed clusters, and the flavonols rutin, quer-3-glr, quer-3-glu, and kaemp-3-glu, in shaded clusters, repeated in both years. Canopy side had no significant effect on the levels of phenylpropanoids

FIGURE 3 | Cluster spatial level results for primary metabolites detected by GC-MS in berry pulp tissues sampled from different cluster orientations and shading treatments. (A) Illustrations of the daily irradiance levels in each cluster orientation including estimations based on diffusive light measurements and the measured mean filtering capacity of the shading nets. (B) Organic acids, amino acids, sugars, sugar alcohols, and other compounds found to be significantly affected by SI levels, cluster position and/or cluster orientation. Levels represent relative abundance based on ion count. Numbers on the X axis represent cluster orientations. Yellow bars represent fully exposed clusters, and dark gray bars represent 60% shaded clusters. Error bars are standard error (*n* = 4). Bars of the same cluster location and treatment, marked by different letters, represent significantly different values (α < 0.05). Information given in the boxes details the significant effects of treatment (upper box) for clusters located on the east (E) and the west (W) side of the canopy, as well as canopy side (lower box), on the spatial pattern and profile levels of metabolite accumulation in fully exposed (Ex) and 60% shaded (Sh) clusters. Asterisks in upper box indicate a significant effect of treatment on the spatial pattern of compound accumulation. Yellow and gray boxes indicate a significantly higher overall compound level in the exposed and 60% shaded treatments, respectively. Asterisks in lower boxes indicate a significant effect of canopy side on the spatial pattern and overall compound levels.

FIGURE 4 | Cluster spatial level results for phenylpropanoid metabolites detected by UPLC-QTOF-MS in berry skin tissues sampled from different cluster orientations and shading treatments. Included are pivotal phenylpropanoids and stilbenes, flavan-3-ols, flavonols, and anthocyanins found to be significantly affected by SI levels, cluster position and/or cluster orientation. Levels represent relative abundance based on ion count. Numbers on the X axis represent cluster orientations. Yellow bars represent fully exposed clusters, and dark gray bars represent 60% shaded clusters. Error bars are standard error (*n* = 4). Bars of the same cluster location and treatment, marked by different letters, represent significantly different values (α < 0.05). Information given in the boxes details the significant effects of treatment (upper box) for clusters located on the east (E) and the west (W) side of the canopy, as well as canopy side (lower box), on the spatial pattern and profile levels of metabolite accumulation in fully exposed (Ex) and 60% shaded (Sh) clusters. Asterisks in upper box indicate a significant effect of treatment on the spatial pattern of compound accumulation. Yellow and gray boxes indicate a significantly higher overall compound level in the exposed and 60% shaded treatments, respectively. Asterisks in lower boxes indicate a significant effect of canopy side on the spatial pattern and overall compound levels. Illustrations of the daily irradiance levels in the corresponding cluster orientation are given in Figure 3A.

in 2015, while in 2014, mal-3-glu was higher in the eastlocated clusters than in the west; the opposite was found for phenylalanine.

Overall, the proportion of metabolites showing significant responses to canopy side and shading were higher in the phenylpropanoids than in the primary metabolites, with sugars and hydroxy-cinnamates being the least affected chemical groups in pulp and skin tissues, respectively.

#### Common and Differential Responses of Metabolite Groups to SI

Hierarchical clustering of primary metabolites (**Figure 5A**) highlighted three major compound groups. The first group was composed mainly of nitrogenous compounds, including the amino acids leucine, valine, alanine, serine, and GABA, the biogenic amines putrescine and ethanolamine, and two sugar alcohols, galactinol, and erythritol. This group's compounds showed an increase from shaded to fully exposed clusters. In addition, in 30% shaded and fully exposed clusters, a generally higher abundance was found in external cluster orientations (i.e., facing the inter-row), especially for clusters located at the west side of the canopy, than in the internal orientations (i.e., facing the canopy), as seen for orientations 7 and 8 (external) compared to 4 and 5 (internal). The second group included metabolites associated with the TCA cycle, such as citrate, fumarate, malate, as well as the closely linked aspartate and maleate, in addition to glycolate, gluconate, and raffinose. These compounds showed a gradual decrease from the densely shaded to the fully exposed samples. In addition, their content was greater in the internal cluster orientations than in the external ones, irrespective of the treatment, as seen for orientations 4 and 5 compared to 1 and 8, respectively. The third group, comprising tartaric acid, the amino acids proline, beta-alanine and threonine, sucrose, glucose-6-phosphate, phosphoric acids, galactarate, malonate, transcaffeate, pyroglutamate, anhydro-gluopyranose, myo-inositol, and lumichrome, had a less pronounced pattern of change between treatments and cluster orientations.

Clustering the phenylpropanoids using the 2015 season data (**Figure 5B**) grouped together compounds according to their chemical properties and biochemical pathways, such as those belonging to the flavonols, flavan-3-ols and hydroxy-cinnamates. In general, the flavonols showed a strong increase from the densely shaded to the exposed clusters, while the flavan-3 ols and cinnamates had the opposite trend. In contrast to the mentioned groups, the anthocyanins showed metabolitespecific trends. Cyanidin (glycosylated and acetylated) and peonidin (glycosylated) increased from the densely shaded to the exposed clusters and were grouped with the flavonols, as well as phenylalanine and the stilbene piceid, exhibiting an opposite trend to that of the malvidin metabolites. The rest of the anthocyanins were grouped together and exhibited an optimum in the 30% shaded cluster samples.

The 2014 season data (Supplementary Figure 4), based on fully exposed and 60% shaded clusters, yielded similar trends. However, in contrast to the 2015 results, in the 2014 season, the coumaroylated forms of cyaniding, delphinidin, and petunidin clustered with the flavonols, while the glycosylated cyanidin and peonidin, together with the majority of the anthocyanins, formed a separate group.

# Within-Cluster/within-Vine Heterogeneity of Metabolite Abundance Was Affected by Exposure to SI

To study how the within-cluster heterogeneity of metabolite content was affected by shading, metabolite abundance values in samples taken from different orientations of an individual cluster were normalized to that specific cluster median. A hierarchical clustering of the samples (i.e., based on cluster orientations) from the 2015 season (Supplementary Figure 5) was used as a qualitative assessment of cluster heterogeneity where the magnitude of change from the cluster median is represented by the intensity of red and blue colors. Spatial patterns common to all treatments were evident, as samples from the internal orientations (i.e., samples 4 and 5) were clustered together and apart from the external ones (i.e., samples 1 and 8). Among the primary metabolites (Supplementary Figure 5A), organic and amino acids were the most heterogeneous compounds in all treatments, in contrast to the sugars glucose, fructose, and sucrose that were relatively uniform across cluster orientations. Finally, a subtle gradual increase in cluster heterogeneity was visible from the more uniform 60% shaded clusters to the more heterogeneous exposed clusters for the nitrogenous compounds putrescine, pyroglutamate, and ethanolamine, and the compounds trans-caffeate, lumichrome, and erythritol. Among the different phenylpropanoid groups (Supplementary Figure 5B), the flavonols were the most heterogeneous; furthermore, their within-cluster variability clearly increased with increasing levels of shading.

To further verify this trend, the coefficient of variance of each metabolite was calculated per vine (i.e., within all samples originating in a single vine). Values were compared between treatments, and a significant treatment effect on the coefficient of variance was found for five out of the total 70 metabolites (Supplementary Figure 6). Among the primary metabolites, putrescine had a significantly lower coefficient of variance in the 60% shaded clusters than in the 30% shaded and fully exposed clusters, showing an improvement in uniformity with shading. In the phenylpropanoid group, the glucuronide and glycosylated forms of the flavonols quercetin and kaempferol had a significantly higher coefficient of variance in the 60% shaded clusters than in the 30% shaded and fully exposed clusters, showing an improvement in cluster uniformity with an increasing degree of sunlight exposure.

### Modulation of Metabolite-Specific Abundance by Sunlight

The metabolite profiles of berries exposed to a gradient of SI intensity resulted in a significant linear regression between 33, out of 70, metabolites and the sum of daily SI. **Figure 6** shows the regression of 12 metabolites of interest that obtained R 2 > 0.5, highlighting major metabolic shifts in the fruit in response to SI. Among the primary metabolites (**Figure 6A**),

the two most abundant organic acids, malate and tartaric acid, were found to have contrasting responses to incoming SI levels, showing negative and positive regressions with incoming SI levels, respectively. This caused a significant increase in the ratio of tartaric acid/malate in fully exposed clusters compared to shaded ones (Supplementary Figure 7). As with malate, fumarate and aspartate showed similar negative responses. In contrast, a large number of nitrogenous compounds, including betaalanine, the branch-chained amino acids, valine and leucine, stress-related proline and GABA, and the biogenic amines,

putrescine and ethanolamine, had positive regressions with SI levels (**Figure 6A** and Supplementary Table 2).

Among the phenylpropanoids (**Figure 6B** and Supplementary Table 2), the flavan-3-ols catechin, epicatechin, epigallocatechin, and procyanidin B1 had negative regressions with SI levels. A similar negative trend was found for malvidin anthocyanins, and acetylated and coumaroylated peonidin, while cyanidinglucoside showed a contrasting response, verifying the preceding results shown in **Figure 5B**. As expected, the flavonols showed positive regressions with incoming SI. In addition, the logarithmic trend that best fitted the response of the glycosylated kaempferol and quercetin was in accordance with the higher variability of these metabolites found by within-cluster hierarchical clustering (Supplementary Figure 5), and calculated as an increase in the coefficient of variance, observed under low-light conditions (i.e., 60% shaded clusters) in Supplementary Figure 6.

#### Metabolic Coordination in Response to SI Differed between Exposed and Shaded Clusters

Correlation matrices were separately constructed for primary and phenylpropanoid metabolites based on samples obtained during the 2015 season, from all cluster orientations and canopy sides of the same treatment. This allowed a comparison of the metabolite coordination of exposed vs. 60% shaded clusters in response to changes in SI (**Figures 7A–D**). For both metabolite groups, exposed clusters had a higher number of negative correlations between metabolites and a lower number of positive correlations compared to the 60% shaded clusters. The same trend was found in the phenylpropanoid data obtained in 2014.

Primary metabolites (**Figures 7A,B**) had 136 negative correlations with r < −0.5 in exposed clusters compared to 36 in the 60% shaded clusters, and 270 positive correlations with r > 0.5 compared to 384, respectively. In the exposed clusters, the amino acids serine, alanine, leucine and valine, the biogenic amine putrescine, and galactinol were found to negatively correlate with glucose-6-phophate, glycolate, myo-inositol, glucopyranose, trans-caffeate, and gluconate, as well as with malate, fumarate, maleate, aspartate, and threonine. Fumarate, gluconate and aspartate were also negatively correlated with tartaric acid, glucose, fructose, GABA, beta-alanine, erythritol, and raffinose. Tartaric acid and galactarate were negatively correlated in addition to a strong negative correlation of proline and ethanolamine with malate. GABA, beta-alanine and erythritol were strongly positively correlated, as well as glucose and fructose, fumarate and gluconate and the group of glucose-6-phosphate, malate, glycolate, maleate, myoinositol, and threonine. In the 60% shaded clusters, alanine was negatively correlated with the associated citrate, malate and maleate, the amino acids GABA and proline and trans-caffeate. Pyroglutamate had a strong negative correlation with leucine, while gluconate was negatively correlated with raffinose. In contrast, a large group of metabolites, comprising serine, threonine, aspartate, GABA, myo-inositol, fumarate, phosphoric acid, maleate, glycolate, citrate, malonate, galactarate, sucrose, glucopyranose, and erythritol, were positively correlated, in addition to a strong positive correlation between tartaric acid, malate and glucose.

The phenylpropanoids (**Figures 7C,D**) shared 88 negative correlations with r < −0.5 in exposed clusters compared to 74 in the 60% shaded clusters and 506 positive correlations with r > 0.5 compared to 610, respectively. In the exposed clusters, phenylalanine was found to negatively correlate with a large number of metabolites, including all the annotated flavan-3-ols and anthocyanins, and to positively correlate with the flavonols of kaempferol, as well as quercetin conjugates (excluding quer-3-glr). The latter flavonols, in addition to hydroxy-benzoate, were negatively correlated with a large number of anthocyanins, including all malvidin metabolites and the acetylated forms of peonidin, petunidin and delphinidin, but not with their glycosylated and coumaroylated forms, nor with any of the cyanidin metabolites. In contrast, in the 60% shaded clusters, phenylalanine showed only few, weak negative correlations, and correlations between flavonols and anthocyanins were strongly positive. Instead, epicatechin and coumarate hex showed differing degrees of negative correlations with the entire set of annotated anthocyanins. Finally, correlations between the stilbene delta-viniferin and flavonols, which were slightly positive in the exposed clusters, were strongly negative in the shaded clusters.

Four networks were created based on the correlation matrices of the four datasets (Supplementary Figures 8A–D), two treatments (exposed and 60% shaded clusters), and two metabolic groups (primary and phenylpropanoid metabolites), across the different cluster locations and orientations. At r > 0.5 and r < −0.5, shading slightly increased the number of edges in the primary metabolite network from 157 to 169, the network density from 0.28 to 0.301, the clustering coefficient from 0.486 to 0.537 and the average node degree from 9.24 to 9.94. In the phenylpropanoid networks, shading increased the number of edges from 303 to 340, the network density from 0.481 to 0.54, the clustering coefficient from 0.71 to 0.77 and the average node degree from 16.83 to 18.89. Shading caused an at least 2-fold increase in the nodal degree of the primary metabolites glycolate, galactarate, phosphoric acid, maleate, malonate, beta-alanine, and fructose, and an at least 2-fold decrease in the nodal degree of the nitrogenous compounds valine, serine, leucine, proline, putrescine, ethanolamine and pyroglutamate, as well as glycerate and tartaric acid. In the phenylpropanoids, shading caused an at least 2-fold increase in the nodal degree of the flavonols quer-3-glu, rutin, kaemp-3-glu, and kaemp-3-glr, the flavan-3 ols catechin and Epicatechin, and the stilbene delta-viniferin, and an at least 2-fold decrease in the nodal degree of phenylalanine, the hydroxy-cinnamates p-coumarate and coutarate, hydroxybenzoate hex, procyanidin B1, vitisin A, and the stilbene piceid.

#### DISCUSSION

The accumulation levels of the major primary and secondary metabolites that determine a grape berry's sensorial profile vary as a function of the micrometeorological conditions in its immediate vicinity (Jackson and Lombard, 1993; Downey

et al., 2006). Nevertheless, few studies have investigated how these conditions vary within a vine and across a single grape cluster, and how their variation affects the spatial pattern of the fruit metabolic profile in the vineyard and crop uniformity at harvest. This study provided a detailed spatial characterization of metabolite abundance in clusters located on both canopy sides and subjected to different degrees of sun exposure. The coupling of this sampling layout with high resolution measurements of both SI and BST was used to assess the role of sun exposure as a determining factor in the spatial pattern and variability in grape clusters' chemical composition, and to expand current knowledge regarding compound-specific responses and plasticity to SI.

SI filtering was proven to be an efficient tool to minimize the within-cluster variability of both incoming irradiance and BST. Artificial shading resulted in decreased BST of sunlit berries and an increased BST of shaded berries. This phenomenon could not be explained by diffusive light or air temperature, since the diffusive light intensity was lower in the shaded treatment and air temperature within the cluster-zone showed no differences (data not shown). For both SI and BST, differences between the orientations of the same cluster exceeded those between treatments, stressing the extent of within-cluster variability and the range of conditions that can be investigated within a single grape cluster. Finally, due to the strong correlation between SI and BST, as previously suggested (Smart and Sinclair, 1976) and found here, and since SI plays an important role in both the direct triggering of fruit-located photoreceptors and in fruit thermal balance**,** SI was selected as the explanatory variable in this work.

Elevated fruit temperatures have previously been suggested to increase the metabolic flux toward the TCA cycle and to modify its regulation, utilizing malate to enhance its anaplerotic capacity in grapevine berries (Sweetman et al., 2014; Rienth et al., 2016). The former work related the flux increment to the enhanced biosynthesis of pyruvate (valine, leucine, serine and glycine), oxaloacetate (aspartate, threonine and isoleucine) and 2-oxoglutarate-driven compounds (GABA, proline and putrescine). Indeed, increased TCA flux and the accumulation of all or part of the mentioned metabolites have been described in response to a wide array of abiotic stresses in a number of plant species, as summarized by Krasensky and Jonak (2012) and Obata and Fernie (2012). Here, SI conditions (i.e., cluster location and sun exposure levels) triggered responses in malate and oxaloacetate-associated compounds (malate, fumarate and aspartate) that contrasted with those in pyruvate (valine, leucine and serine) and 2-oxoglutarate-derived (proline, GABA and putrescine) nitrogenous compounds. Increased SI levels involve a concomitant increase in tissue temperature, and their combination is expected to exert oxidative stress (Foyer et al., 1994). Indeed, redox homeostasis was recently suggested to be the link between the metabolic modulation of grapevine berries, found in different leaf-removal studies (Young et al., 2016). This may explain the increase in the levels of proline, GABA and the polyamine putrescine, which share a role in mitigating oxidative stress (Krasensky and Jonak, 2012). Furthermore, the decrease in TCA cycle metabolites and the increase in specific amino acids may also have resulted from an arrest in glycolytic activity coupled with protein degradation, generating amino acids in a non-biosynthetic manner (Araujo et al., 2011; Lehmann et al., 2012; Obata and Fernie, 2012). Unfortunately, current knowledge is lacking regarding the direct effect of SI on fruit primary metabolism. Hence, such metabolic shifts are currently attributed to the secondary effects of temperature, UV-B irradiance and combined stress responses. Future research on the photo-receptor-mediated modulation of primary metabolism can greatly contribute to our understanding.

When considering the results of the network analysis, a higher coordination of the metabolic processes in the central metabolism was observed in this study in shaded grapes. Compared to fully exposed grapes, glycolysis, the TCA cycle, and amino acid metabolism shared a greater number of correlations, and the relations were mainly positive. The fact that the acclimation of the exposed and shaded clusters to perturbations in SI levels resulted in opposite types of correlations between nitrogen and carbon metabolites points to a differential coordination, possibly resulting from a comparison between non-stress (shaded clusters) and stress-related (fully exposed) responses. As described above, and as shown previously in potted grapevines and grape cell cultures subjected to shortterm heat treatment (Sweetman et al., 2014; Ayenew et al., 2015), and berries of vines exposed to water deficit (Hochberg et al., 2013), a combined stress response could lead to the specific accumulation of nitrogen metabolites. In contrast, a positive coordination was maintained between glycolysis sugars and TCA cycle intermediates in both conditions, emphasizing the tight co-regulation existing between these two metabolic pathways.

The induction of the flavonoids biosynthesis by fruit-located photo-receptors was shown in a number of species, including grapevine, as reviewed in Zoratti et al. (2014). Summarizing a large number of studies, it can be concluded that while the R2R3 MYB transcription factors family includes both positive and negative regulators of the flavonoid biosynthesis, light seemed to strictly induce the expression of positive ones. However, a more complex interaction is evident, considering the consequent increase in fruit temperature. As found by a number of studies (Spayd et al., 2002; Mori et al., 2007; Tarara et al., 2008; Azuma et al., 2012), above a certain threshold, temperature causes a reduction in the anthocyanin levels, possibly through degradation, whether enzymatic or non-enzymatic (Vaknin et al., 2005; Mori et al., 2007; Chassy et al., 2015). This results in an expected antagonistic effect of elevated BST and strong SI conditions, as found in the exposed berries. Under these assumptions, the response of anthocyanins to increasing SI levels is expected to reflect the metabolite turnover between biosynthesis and degradation. The negative linear effect of SI on the levels of acylated peonidin and malvidin and mal-3 glu, measured in this study, might not be solely attributed to the consequent increase in temperature, as the proportion of acylated anthocyanins was found to increase with rising temperatures (Downey et al., 2004; Tarara et al., 2008), and malvidin metabolites were found to be exceptionally stable to high temperatures in Mori et al. (2007). Instead, considering the contrasting linear increase in the levels of cyanidin-glucoside, it is possible that the combined heat and light modulation of the biosynthesis-related genes' expression patterns, as found in Azuma et al. (2012), was involved in the preferential biosynthesis of upstream metabolites as was observed for cyanidin-glucoside. This may explain the overall positive net turnover of cyanidinglucoside, while downstream metabolites, such as acylated peonidin and malvidin, showed a clear negative result.

The induction of flavonol accumulation by increasing SI levels, measured here, is in accordance with a large number of studies regarding light-induced flavonol biosynthesis in grape berries (Czemmel et al., 2009; Matus et al., 2009; Carbonell-Bejerano et al., 2014; Liu et al., 2015) and overall accumulation (Haselgrove et al., 2000; Downey et al., 2004; Cortell and Kennedy, 2006; Pereira et al., 2006; Matus et al., 2009; Azuma et al., 2012)**,** yet the regression between SI and flavonol-glucoside accumulation in grape skins has been found so far to be purely linear (Haselgrove et al., 2000). The extensive gradient and relatively high SI intensity present in our study may be the cause for this discrepancy, yet whether this trend is due to a stagnation in biosynthesis or to an increase in temperaturedriven degradation remains unclear.

The accumulation of monomeric and condensed Flavan-3 ols in grape skin peaks around veraison, followed by a decrease during the final stages of ripening (Downey et al., 2004; Fujita et al., 2007; Cohen et al., 2012). Increased SI exposure was found to increase flavan-3-ols levels at veraison, yet, since sunlit berries showed a faster decline during ripening, differences were no longer evident at harvest (Downey et al., 2004; Fujita et al., 2007). In this study, conducted between veraison and harvest, the linear negative effect of SI on the levels of flavan-3-ols metabolites was in accordance with the cited literature. A possible explanation could be a delay in the typical post-veraison decrease in the expression of the biosynthesis-related genes LAR and ANR under shading, as found in Fujita et al. (2007).

The stilbenes, which play a role in antifungal activity (Langcake, 1981; Pont and Pezet, 1990; Pezet et al., 2004) and offer potential health promoting effects (Bradamante et al., 2004; Baur et al., 2006; Gresele et al., 2011), accumulate in grapevine tissues in response to biotic and abiotic stresses, such as mold development, wounding, and UV-C irradiation treatments (Fritzemeier and Kindl, 1981; Bais et al., 2000; Vannozzi et al., 2012). Attempts to understand the impact of environmental factors on the accumulation of stilbenes in grape skin tissues have yielded confounding results, possibly owing to the importance of genetic, developmental, and pedological factors (Versari et al., 2001; Bavaresco et al., 2007, 2008, 2012; Berli et al., 2008; Carbonell-Bejerano et al., 2014; Degu et al., 2016). In this study, no significant correlation was found between SI and the levels of the stilbenes piceid and delta-viniferin. However, their levels were significantly higher in the exposed, compared to the shaded, clusters that were positioned on the west side of the canopy but not the east, an opposite trend to that of the major anthocyanins Mal-3-acet and Mal-3-coum. Considering the UV-B induction of stilbene synthase (STS) expression and stilbene accumulation (Versari et al., 2001; Carbonell-Bejerano et al., 2014), and the fact that chalcone synthase and STS compete for the same substrate (Jeandet et al., 1995; Vannozzi et al., 2012), the measured increase in stilbene accumulation in the exposed, compared to the shaded, berries may have depended on a combination of increased STS expression and higher substrate availability, provided by a possible inhibition of anthocyanin biosynthesis.

The differences in metabolic coordination between the exposed and shaded clusters in phenylpropanoid metabolism are intriguing. Exposing clusters to high SI generated a strong negative association between the precursor of the polyphenol pathway, phenylalanine, and the anthocyanins, which was not evident in the shaded clusters. In addition, in the exposed clusters, anthocyanins were more strongly correlated with narinchalc-glu, but less with flavonols, and the correlation between flavonols and stilbenes shifted from highly negative (shaded) to partially positive (exposed). Anthocyanin accumulation in grape-tissue culture was recently shown to be decoupled from phenylalanine under conditions in which biosynthesis-related gene expression was down-regulated (Manela et al., 2015). As a result, the tissue accumulated higher levels of flavonols and stilbenes. It is possible that a similar phenomenon occurred in the fully exposed clusters, as biosynthetic gene expression may have been hampered under conditions that included elevated temperatures (Azuma et al., 2012). Taken together, these lines of evidence are in support of a repartitioning of carbon precursors of the polyphenol pathway from anthocyanin biosynthesis to that of stilbenes and flavonols. This hypothesis should be confirmed in future studies by implementing stable isotopes based flux analysis in detached berries.

Our results revealed that shifting the intensity and direction of solar irradiance (SI) significantly modulated the spatial patterns of the accumulation of organic and amino acids, the main sugars glucose and fructose, and the majority of skin phenylpropanoid metabolites, across the grape-cluster, while hydroxy-cinnamates were not affected. In addition, filtering the irradiance intensity significantly affected the levels of 24 metabolites across the spatial locations, including organic and amino acids, flavonols, flavan-3-ols, anthocyanins and stilbenes. The within-cluster spatial heterogeneity was characterized by large variations in the flavonol levels, found to be significantly affected by sunlight exposure. Overall, the effect of SI conditions on skin phenylpropanoids was comprehensive, while it was found to be more specific in the case of pulp primary metabolites. Together, these findings suggest that sunlight plays a major role in shaping the spatial accumulation of quality-related compounds within a single grape cluster.

The calculated fold changes in metabolite accumulation, across the spatial dataset presented here (Supplementary Table 3), summarizes the compound plasticity of 70 primary and secondary metabolites to light and temperature perturbations, revealing the potential modulating effect of sunlight regulation on fruit composition. Considering that a single layer of grapevine leaves absorbs at least 60–70% of the visible wavelengths (Schultz, 1996), the gradient of SI intensities and metabolite fold change values found in this study may represent, yet possibly underestimate, the range found within a single, non-defoliated, commercial vine.

# CONCLUSIONS

Grape berries' acclimation to their surrounding environment involves local metabolic shifts, which affect their chemical composition and quality at harvest. Sunlight exposure triggers a complex response through both irradiance-mediated signaling and accumulated heat. Here, we show that this metabolic acclimation to sunlight drives the spatial variability of chemical composition between berries on a single cluster, and that it involves the interaction and modulation of partitioning between several biochemical processes in both pulp and skin. These include the accumulation of pyruvate and 2-oxoglutarate-derived nitrogenous compounds, at the expense of malate, fumarate and aspartate in the pulp, and the accumulation of phenylalanine, narin-chalc-glu, cyan-3-glu and the flavonols, accompanied with a decrease in flavan-3-ols, hydroxy-cinnamates and malvidin anthocyanins, in the skin. **Figure 8** illustrates the main findings of the study.

clusters, while mesh trapezoids represent 60% shaded clusters.

This study characterized, yet did not isolate, the accompanying climatic components, such as temperature, that are known to influence fruit metabolic processes. Therefore, our findings may apply to a defined climate, namely warm and arid to semi-arid regions. The existing knowledge gap regarding the intriguing interaction between (SI) and berry temperature, as well as the contribution of temperature-driven processes to the overall fruit metabolic response, may hinder the extrapolation of our findings to systems experiencing different climatic conditions.

At the practical level, (SI) is the most easily and readily controlled climatic factor. In the context of climate change, these results will aid in designing a knowledgebased use of sunlight regulation as tool to improve grape composition, under the conditions that are expected to prevail in an ever-expanding number of commercial vineyards worldwide.

# AUTHOR CONTRIBUTIONS

NR, NA, and AF conceived and planned the study. NR and NW applied the viticultural treatments, and collected the meteorological data and berry samples in the field. NR and NW analyzed the meteorological data and prepared the berry samples for extraction. NW processed and analyzed the thermal images, and NR performed the sample extraction and analysis using the UPLC-QTOF-MS and GC-MS devices. NR integrated the data and performed the data analysis. NR wrote the body of the paper with AF and NA. All authors reviewed and approved the manuscript.

#### FUNDING

This work was partially funded by the Koshland Foundation for the Support of Interdisciplinary Research in Combating Desertification, and the Frances and Elias Margolin Trust.

#### ACKNOWLEDGMENTS

The authors would like to thank the owner of the Nana Farm, Eran Raz, for his professional maintenance of the experimental parcel, and Noga Sikron, Mariela Leiderman, Talya Samani and

#### REFERENCES

Biruk Ayenew for their technical guidance and support in the lab and field.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2017. 00070/full#supplementary-material

stable isotopes. Food Chem. 166, 448–455. doi: 10.1016/j.foodchem.2014. 06.002

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Reshef, Walbaum, Agam and Fait. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Transcriptome-Wide Identification of Novel UV-B- and Light Modulated Flavonol Pathway Genes Controlled by VviMYBF1

Stefan Czemmel1,2, Janine Höll2 , Rodrigo Loyola3,4, Patricio Arce-Johnson3 , José Antonio Alcalde4 , José Tomás Matus5 and Jochen Bogs2,6,7 \*

1 Quantitative Biology Center, University of Tübingen, Tübingen, Germany, 2 Centre for Organismal Studies Heidelberg, Heidelberg, Germany, 3 Departamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, Santiago, Chile, 4 Departamento de Fruticultura y Enología, Pontificia Universidad Católica de Chile, Santiago, Chile, 5 Centre for Research in Agricultural Genomics, CSIC-IRTA-UAB-UB, Barcelona, Spain, 6 Dienstleistungszentrum Ländlicher Raum Rheinpfalz, Viticulture and Enology Group, Neustadt/W, Germany, 7 Fachhochschule Bingen, Bingen am Rhein, Germany

#### Edited by:

Claudio Bonghi, University of Padua, Italy

#### Reviewed by:

Zhanwu Dai, INRA, France Ilaria Filippetti, Università di Bologna, Italy

> \*Correspondence: Jochen Bogs<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Crop Science and Horticulture, a section of the journal Frontiers in Plant Science

> Received: 02 March 2017 Accepted: 06 June 2017 Published: 22 June 2017

#### Citation:

Czemmel S, Höll J, Loyola R, Arce-Johnson P, Alcalde JA, Matus JT and Bogs J (2017) Transcriptome-Wide Identification of Novel UV-B- and Light Modulated Flavonol Pathway Genes Controlled by VviMYBF1. Front. Plant Sci. 8:1084. doi: 10.3389/fpls.2017.01084 Flavonols constitute a group of flavonoids with important photoprotective roles in plants. In addition, flavonol content and composition greatly influences fruit quality. We previously demonstrated that the grapevine R2R3-MYB transcription factor (TF) VviMYBF1 promotes flavonol accumulation by inducing the expression of flavonol synthase (VviFLS1/VviFLS4), a key step of the initial flavonol pathway. Despite this, gene networks underlying flavonol modification in grapevine including both structural and regulatory genes remain poorly understood. In order to identify flavonol modifying genes and TFs acting downstream of VviMYBF1 a microarray-based transcriptome analysis was performed on grapevine hairy roots ectopically expressing VviMYBF1 or a Green Fluorescent Protein as control. VviFLS1 was induced in VviMYBF1 transgenic roots and glycosylated flavonols accumulated significantly compared with control lines. Among the differentially expressed genes, potential flavonol-modifying enzymes with predicted rhamnosyltransferase (e.g., RhaT1) or glycosyltransferase (e.g., GT3) activities were identified. In addition, important TFs of the MYB and bZIP families such as the proanthocyanidin regulator VviMYBPA1 and the UV-B light responsive HY5 homolog VviHYH were significantly altered in their expression pattern by overexpression of VviMYBF1. Co-temporal expression analysis demonstrated positive correlation of VviMYBF1 with VviFLS1, VviGT3, and VviRhaT1 during berry development and in fruits ripened with different light and UV-B radiation conditions at field. These results show that VviMYBF1 overexpression led to the identification of novel genes of the flavonol pathway and that the flavonol modifying machinery can be influenced by agricultural practices to optimize flavonol composition in grapes.

Keywords: flavonols, MYB, gene regulation, HY5, UV-containing light, grapevine

# INTRODUCTION

fpls-08-01084 June 20, 2017 Time: 17:4 # 2

Flavonols are the most ubiquitous flavonoids found in dietary plant-based foods (Middleton et al., 2000) and provide the second most abundant group of flavonoids in grapevine (Vitis vinifera L.) fruits (Georgiev et al., 2014). Flavonols largely accumulate in grape berry skins and show a remarkable facet of chemical diversity. In skins of the cultivar (cv.) 'Shiraz', modification of flavonols were mainly identified as 3-, 7-, and 40 -O-glycosylations of the basic flavonol scaffold (Downey and Rochfort, 2008; **Figure 1**). Flavonol profiles strongly depend on grapevine cultivars (Zhu et al., 2012), but in general, the main representatives in red grapes are quercetin-3-O-glucosides followed by myricetin, whereas quercetin and kaempferol derivatives constitute the most prominent flavonol compounds in white grapes (Mattivi et al., 2006; Castillo-Muñoz et al., 2007). The quality of red wines can be influenced by flavonols as the color of the wines is positively influenced by the copigmentation phenomenon, which is due to molecular associations between anthocyanins and flavonols or other uncolored phenolic compounds in solutions (Boulton, 2001). In recent years, it has been shown that biomedical activities of flavonols are tightly linked to their chemical diversity. The efficiency of flavonols as antioxidant compounds greatly depends on their chemical structure demonstrating a decrease in antioxidant and antibacterial capacity of flavonol glycosides compared to flavonol aglycones (Mori et al., 1987; Vinson et al., 1999; Burda and Oleszek, 2001; Montoro et al., 2005).

Although flavonols are produced as secondary metabolites, the relationship between their biological function and modification in planta still remains elusive. In grapevine a boost in flavonol accumulation is often observed in response to any agronomical practice that favors the exposure of grape brunches to sunlight (Teixeira et al., 2013). Consequently, flavonol biosynthesis has been extensively studied in response to its induction by UV-containing light in grapes (Price et al., 1995; Czemmel et al., 2009; Matus et al., 2009). In Arabidopsis it was shown that the tt4 and tt5 mutants, which are defective in the gene CHALCONE SYNTHASE and CHALCONE ISOMERASE, respectively, are sensitive to high-irradiance UV-containing light (Li et al., 1993).

exclusively glycosylated in position 3 of the C ring.

the flavan skeleton are abbreviated as follows: Glc, glucose; Gal, galactose; Rha, rhamnose; Me, methyl group. Note that flavonols detected in grape so far are

The authors showed that this effect was correlated with low content or even absence of kaempferol derivatives in leaves (Li et al., 1993), suggesting an important role of flavonols as UV screens in planta. Outside its role in UV protection, modified flavonols were found to act as endogenous flavonol inhibitors of polar auxin transport in the model plant Arabidopsis (Yin et al., 2014). Conversely the expression or activity of flavonol glycosyltransferases have been shown to be influenced by auxin levels (Santelia et al., 2008). Moreover flavonol aglycones but not flavonol 3-O-glycosides were able to restore pollen fertility in conditionally male-fertile petunia pollen both in vivo and in vitro (Vogt and Taylor, 1995).

Induction of flavonol biosynthesis by solar radiation can be attributed to an upregulation of flavonol biosynthetic genes leading to an increase in glycosylated flavonols. This process is negatively affected in the grape berry skin in response to light depletion (Matus et al., 2009) or UV-B filtering (Carbonell-Bejerano et al., 2014). As seen from these studies and many others, visible light and UV-B strongly affect the expression of the R2R3-MYB transcription factor (TF) VviMYBF1 and its target, the first flavonol branch gene FLAVONOL SYNTHASE 1 (VviFLS1, also known as FLS4 by Fujita et al., 2006; **Figure 1**), whose relation was demonstrated in grape suspension cells treated with UV-B light (Czemmel et al., 2009). Besides this well-described regulatory mechanism, only two structural genes have been identified so far involved in flavonol modification, namely VviGT5 and VviGT6, which encode a UDP-glucuronic acid:flavonol-3-O-glucuronosyltransferase and a bifunctional UDP-glucose/UDP-galactose:flavonol-3-Oglucosyltransferase/galactosyltransferase, respectively (Ono et al., 2010). From these two genes, GT5 (in addition to MYBF1 and FLS1) has been directly associated with the UV-B signaling pathway in grapevine (Loyola et al., 2016).

In addition to the above-mentioned VviMYBF1, other R2R3-MYB TFs have been described as regulators of the phenylpropanoid pathway (reviewed by Czemmel et al., 2012; Matus, 2016). Similar to other plants, these TFs provide the common denominators in the regulation of structural genes of all flavonoid branches whereas co-factors encoding the basic helix–loop–helix (bHLH) domains (also referred to as MYC proteins) and conserved WD repeats (WDR) have been so far exclusively associated to the regulation of anthocyanin/PA accumulation and not to flavonol biosynthesis (Czemmel et al., 2012). The main players in grapevine are VviMYB5A and VviMYB5B proteins that are considered to be general regulators of the flavonoid biosynthetic pathway while VviMYBPA1, VviMYBPA2, and VviMYBPAR are PA biosynthesis regulators and VviMYBA1, VviMYBA2, VviMYBA6, and VviMYBA7 are specific for anthocyanin biosynthesis (Kobayashi et al., 2002; Bogs et al., 2006, 2007; Walker et al., 2007; Deluc et al., 2008; Terrier et al., 2009; Koyama et al., 2014; Matus et al., 2017).

In Arabidopsis the VviMYBF1 homologes AtMYB12, AtMYB11, and AtMYB111 regulate flavonol biosynthesis in a tissue-specific manner (Stracke et al., 2007). In both grape and Arabidopsis, flavonol related MYBs are regulated by the bZIP TF ELONGATED HYPOCOTYL 5 (AtHY5 and VviHY5) contributing to the establishment of UV-B tolerance in these species (Stracke et al., 2010; Loyola et al., 2016). This is in line with findings that the hy5 mutant shows flavonol-deficient roots and downregulation of AtMYB12 expression (Lee et al., 2007; Stracke et al., 2010).

Besides the well-established control mechanism of the initial flavonol pathway gene VviFLS1 by VviMYBF1, it is unclear how the immense biodiversity of flavonol compounds found in grapes is achieved. In order to elucidate the regulation in biosynthesis and modification of flavonols in grapevine, VviMYBF1 was ectopically expressed in grapevine hairy roots (HRs). Follow up experiments on these HRs allowed to identify novel structural and regulatory genes of the flavonol branch. The hypothesis that genes involved in flavonol modification were under similar transcriptional control by VviMYBF1 and environmental factors such as UV-B light was tested. Microarray analysis revealed several differentially regulated candidate genes, which are potentially involved in flavonol modification, transcriptional regulation and response to light. We selected two genes encoding for a putative flavonol rhamnosyltransferase (VviRhaT1) and a glycosyltransferase (VviGT3), which is closely related to the previously characterized flavonol modifying enzymes GT5 and GT6 (Ono et al., 2010), for downstream analysis. Expression of these candidates correlated with flavonol accumulation and VviMYBF1 and VviFLS1 expression in VviMYBF1 overexpressing grapevine HRs and also in response to UV-containing light in field and greenhouse experiments. These genes are promising VviMYBF1 target structural genes that can be further investigated in relation to their impact on flavonol biosynthesis in response to common agronomical practices in the vineyard.

# MATERIALS AND METHODS

#### Cloning of MYBF1 Construct

The cloning of VviMYBF1 from the cultivar (cv.) 'Shiraz' (GenBank locus accession: FJ948477) into the vector pART7 (Gleave, 1992) to give the construct pART7MYBF1 was described previously (Czemmel et al., 2009). For generation of HRs overexpressing VviMYBF1, pART7MYBF1 was NotI digested and the insert (VviMYBF1 under control of CaMV35S) was cloned into pART27 (Gleave, 1992) to give pART27MYBF1. The binary vector pART27 contains spectinomycin resistance for bacterial selection and kanamycin resistance for selection in planta.

# Plant Material Collection for Developmental Series

The collection of the samples for the grapevine developmental series of V. vinifera cv. 'Pinot Noir' was described before (Höll et al., 2013). In short, sampling was performed from the start of floral initiation until harvest with early flower and later berry samples collected from a commercial vineyard during the 2007 to 2008 season in Schriesheim near Heidelberg, Germany. The berry samples derived from ∼100 berries from 20 different plants and bunches and were sampled at weekly intervals, as described previously (Downey et al., 2003). All samples were frozen in

liquid nitrogen upon collection on the field and stored at −80◦C until analyzed.

# Hairy Root Culture and Transformation

Before HR transformation, pART27-MYBF1 or pKGWFS7 control plasmid expressing GFP-GUS were transferred by electroporation into the Agrobacterium rhizogenes strain ATCC 15834, carrying the Ri plasmid pRi15834 (neither the chromosome nor the plasmid ATCC 15834 of this A. rhizogenes strain carry an antibiotic marker).

Transformation and induction of transgenic VviMYBF1 or GFP-GUS expressing grapevine HRs derived from leaves from in vitro-grown cv. 'Chardonnay' plants was performed exactly as described in Höll et al. (2013). After transformation and induction, transgenic HRs were routinely kept on HR) medium (Höll et al., 2013) and transferred to fresh medium after 4 weeks. HR tissues used for RNA extraction and high performance liquid chromatography (HPLC) analysis were rapidly frozen in liquid nitrogen, ground to a fine powder using a Retchmill (MM20, Retch) and stored at −80◦C until further use.

# RNA Extraction and Quantitative PCR (qPCR) Analysis

Total RNA was extracted from grinded material from the developmental series of V. vinifera cv. 'Pinot Noir' as previously described in Höll et al. (2013). RNA of MYBF1 and GFP transgenic grapevine HRs was isolated following the protocol of the EURx GeneMATRIX Universal RNA purification kit (Roboklon). Prior to RT reactions, RNA samples were heated to 65◦C for 10 min and immediately placed on ice to destroy secondary structures. Synthesis of cDNA was performed with SuperScriptTM III First-Strand Biosynthesis System (Invitrogen, catalog no. 18080-051) according to the instructions in the manual for RT using oligo dT primers. Typically, 1–2 µg of RNA were reverse transcribed in a volume of 20 µl for 1 h at 50◦C followed by termination of the enzyme reaction at 85◦C for 5 min. After digest of the residual RNA strands using RNase H, 30 µl of RNase free H2O were added. This 50 µl solution served as cDNA stock for further application and was stored at −20◦C. PCR reactions were carried out for flower and berry skin samples from the cv. 'Pinot Noir' developmental series as well as HR samples by using 0.5 µl of 10 µM forward and 0.5 µl of 10 µM reverse primer, 5 µl cDNA (diluted 1:20), 7.5 µl of 2x ABsoluteTM QPCR SYBR R Green Fluorescein Mix (ABgene) and 1.5 µl of H2O in a final volume of 15 µl (for all set of qPCR primers used in this work, see Supplementary Table 1). Thermal cycling conditions were identical for all primer pairs: 96◦C for 15 min followed by 96◦C for 30 s, 58◦C for 30 s, and 72◦C for 30 s for 35 cycles, followed by a melt cycle from 50 to 96◦C. QPCR was carried out using SYBR green method for detection of double stranded PCR products on an iCyclerTM optical Module real-time cycler (Bio-Rad). UBIQUITIN1 (previous TIGR database id: TC32075, VIT\_16s0098g01190) was used for normalization of target gene levels in grapevine HRs and light experiments while the three reference genes GAPDH (VIT\_17s0000g10430), UBIQUITIN1 and EF1-alpha (VIT\_06s0004g03220) were used to normalize gene expression during cv. 'Pinot Noir' berry development as done previously (Höll et al., 2013). The primers MYBintF and MYBintR2 were used to detect the transcript level of VviMYBF1 in grapevine by amplification of a 214 bp PCR-fragment from the 3 0 region of the gene. The primers VviFLS1 and VviFLS2 were used to detect a 154 bp amplicon of the VviFLS1/FLS4 gene (for other primer pairs see Supplementary Table 1). The efficiency of the primers was tested in preliminary experiments with dilutions of plasmid or purified PCR products maintaining an r 2 value ≥ 0.96. With all cDNAs used, the primer set gave a single PCR product, which was verified by agarose gel electrophoresis and by determination of the melt curves for the product at the end of each run.

# Flavonol Staining

Fresh cross sections from 3 to 4 weeks old HRs were transferred in a freshly prepared solution of 0.25% (w/v) diphenylboric acid 2-amino-ethyl ester (DBPA, Naturstoffreagenz A, Roth) and 0.00375% (v/v) Triton X-100. Fluorescence was visualized with either an inverted epifluorescence microscope (Leica DM IRB, Leica) or a stereomicroscope (Leica MZ FL III, Leica) using a DAPI fluorescence filter with an excitation wavelength of 340–380 nm and an emission wavelength of 425 nm. Images were captured using the Leica Image Manager (IM) 50 software and handled with Adobe Photoshop version 8.0.1 without changing the color parameters.

## Microarray Analysis

Microarray raw data generation was produced at the Centro Nacional de Biotecnologia (CNB, Madrid) by using the Grapegen GeneChip, which is an Affymetrix custom made chip containing 23,096 probe sets corresponding to ∼15,800 different annotated genes (Pontin et al., 2010) and covering ∼52% of the genome. Quality assessment of microarray data and differential expression (DE) analysis were carried out in the statistical language R (version 3.2.1) mainly using the affy (version 1.48.0) and LIMMA packages (version 3.26.9) by the Quantitative Biology Center (QBiC) in Tübingen1 . Based on PCR analysis for the overexpressed TF VviMYBF1 and its target VviFLS1, four VviMYBF1 (lines no. 32, 33, 34, and 62) and four GFP overexpressing lines (lines no. 77, 79, 83, and 97) out of 10 stable transformants were selected. During raw data control analysis on all eight lines (four GFP and four VviMYBF1 lines each) several sensitive measures to assess array quality such as Normalized Unscaled Standard Error (NUSE) and Relative Log Expression (RLE) confirmed low quality of array corresponding to the GFP control line 79. Since the array for line 79 was detected as an outlier, DE analysis has been performed on four VviMYBF1 and three GFP lines excluding GFP line 79. Raw data (CEL files) and meta-information (e.g., experimental design, sample names) of microarray data were deposited to NCBI's Gene Expression Omnibus (GEO) and can be accessed under the GEO identifier GSE95532.

1https://portal.qbic.uni-tuebingen.de/portal/

### Light and UV-B Radiation Treatments

Four different sunlight or UV-B radiation experiments were conducted in cv. 'Cabernet Sauvignon' commercial plants (either treated at field or uprooted and transferred to a greenhouse).

#### Experiment 1

Three sunlight depletion treatments were conducted at field in commercial cv. 'Cabernet Sauvignon' vines during the 2006–2007 ripening season, as described by Matus et al. (2009). In this study, different sunlight exposures were generated: (i) full shading of fruits by the plant's own canopy (0% exposure), (ii) full sunlight exposure from veraison (ripening onset) onward, generated by displacement of leaves around the cluster region (100% exposure), and (iii) 25% exposure, by shading from veraison until 6 weeks after which leaves were displaced for cluster illumination.

#### Experiments 2 and 3

High (∼0.3 Wm−2 ) and Low (∼0.1 Wm−2 ) UV-B exposure treatments were applied to clusters from 9 year old potted vines in a UV-free greenhouse during 2011–2012 and 2012–2013 growing seasons, respectively, as described by Loyola et al. (2016).

#### Experiment 4

A UV-B filtering radiation treatment (here called -UV-B) was applied in cv. 'Cabernet Sauvignon' plants from a commercial vineyard in Maipo Valley, Chile (33◦ 430 2800 S, 70◦ 450 9.7100 W) during 2011–2012 growing season. The filtering treatment consisted in blocking solar UV-B radiation by installing a 100 µm clear polyester film at the position of grape clusters. Grape clusters from both east and west side of the rows were treated, but only grape clusters from the east side of each experimental row (exposed to sunlight during the morning until midday) were sampled. The experimental design consisted in four blocks with five plants each (biological replicates n = 4). Three berries per cluster (randomly sampled) and four clusters per plant were used for each sample. The treatments started at 6 weeks before veraison. Weeks −3, 0, 3, and 6 after veraison (WAV) were considered for RNA extraction and gene expression quantification while weeks −2, 1, 4, and 7 after veraison were used for HPLC analysis. A total of 60 berries were sampled for each biological replicate per condition at each sampling date. Berries were immediately peeled and deseeded. Berry skins were frozen in liquid nitrogen and stored at −80◦C until RNA was extracted. Total flavonols were measured as quercetin equivalents and environmental parameters such as solar UV-B irradiance, temperature, solar radiation at cluster level and total solar radiation were measured around veraison from 08.00 to 19.45 h in the east side of the row.

### Separation of Hairy Root and Berry Skin Flavonols by HPLC