1 short title: transcriptomics of grape berry development · 3 50 abstract 51 grapevine berry...

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Short title: Transcriptomics of grape berry development 1

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Corresponding author details: Sara Zenoni, Department of Biotechnology, University of 3 Verona, Strada le Grazie 15, 37134, Verona, Italy, Tel. +39-045-8027941; Fax +39-045-4

8027929 5

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Transcriptomic differences in grapevine varieties correlate with berry anthocyanin skin 7

Mélanie Massonnet1, Marianna Fasoli1, Giovanni Battista Tornielli1, Mario Altieri1, Marco 8

Sandri1, Paola Zuccolotto2, Paola Paci3,4, Massimo Gardiman5, Sara Zenoni1*, Mario 9

Pezzotti1 10

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1Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134, Verona, 12

Italy 13

2Big&Open Data Innovation Laboratory (BODaI-Lab), University of Brescia, Via Branze 45, 14

25123 Brescia, Italy 15

3Institute for Systems Analysis and Computer Science "Antonio Ruberti," National Research 16

Council, 00185 Rome, Italy SysBio Centre for Systems Biology, 00185 Rome, Italy 17

4SysBio Centre for Systems Biology, 00185 Rome, Italy 18

5 (CREA-VE), Viale XXVIII Aprile 26, Conegliano (TV) 31015, Italy 19

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One sentence summary 21

Grapevine berry development features both common and skin color-dependent transcriptomic 22

changes. 23

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List of author contributions 25

M.M performed the experiments, analyzed the data, interpreted the results and wrote the 26

manuscript; M.F interpreted the results and wrote the manuscript; G.B.T. interpreted the 27

results and was involved in the experimental design; M.A analyzed the sequence data; M.S 28

and P.Z. performed gene clustering analysis and analyzed PCA loadings; P.P analyzed the 29

Plant Physiology Preview. Published on June 26, 2017, as DOI:10.1104/pp.17.00311

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switch genes; M.G. sampled the berries and was involved in the experimental design; S.Z was 30

involved in the experimental design, supervised the study and wrote the manuscript; M.P. 31

conceived the study and reviewed the manuscript. 32

All authors contributed to the discussion of the results, reviewed the manuscript, and 33

approved the final article. 34

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Funding information 36

This work was supported by the Valorizzazione dei Principali Vitigni Autoctoni Italiani e dei 37

loro Terroir (Vigneto) project funded by the Italian Ministry of Agricultural and Forestry 38

Policies and by the European funded COST ACTION FA1106 Quality fruit. 39

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Present addresses 41

Mélanie Massonnet: Department of Viticulture and Enology, University of California Davis, 42

Davis, CA 95616, USA 43

Marianna Fasoli: E & J Gallo Winery Modesto, CA 95353, USA. 44

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Corresponding author email 46

[email protected] 47

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ABSTRACT 50

Grapevine berry development involves a succession of physiological and biochemical 51

changes reflecting the transcriptional modulation of thousands of genes. Although recent 52

studies have investigated the dynamic transcriptome during berry development, most have 53

focused on a single grapevine variety so there is a lack of comparative data representing 54

different cultivars. Here we report the first genome-wide transcriptional analysis of 120 RNA 55

samples corresponding to 10 Italian grapevine varieties collected at four growth stages. The 56

10 varieties, representing five red-skinned and five white-skinned berries, were all cultivated 57

in the same experimental vineyard, to reduce environmental variability. The comparison of 58

transcriptional changes during berry formation and ripening allowed us to determine the 59

transcriptomic traits common to all varieties, thus defining the core transcriptome of berry 60

development, as well as the transcriptional dynamics underlying differences between red and 61

white berry varieties. A greater variation among the red cultivars than between red and white 62

cultivars at the transcriptome level was revealed, suggesting that anthocyanin accumulation 63

during berry maturation has a direct impact on the transcriptomic regulation of multiple 64

biological processes. The expression of genes related to phenylpropanoid/flavonoid 65

biosynthesis clearly distinguished the behavior of red and white berry genotypes during 66

ripening but also reflected the differential accumulation of anthocyanins in the red berries, 67

indicating some form of cross talk between the activation of stilbene biosynthesis and the 68

accumulation of anthocyanins in ripening berries. 69

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INTRODUCTION 73

Grapevine (Vitis vinifera L.) is one of the most widely cultivated fruit crops, accounting for 74

7.5 million hectares of arable land in 2015 and producing 75.1 million metric tons of berries 75

(http://www.oiv.int/). Grape berries are grown commercially for fresh fruit, juice and raisins, 76

but mostly for fermentation into wine, with almost 274 million hectoliters produced in 2015. 77

Although winemaking techniques and equipment have improved over recent decades, the 78

attributes of wine still rely predominantly on berry quality, i.e. the metabolic composition at 79

harvest (Bindon et al., 2014). Berry quality traits depend mainly on sugars, organic acids, and 80

secondary metabolites such as tannins, flavonols, anthocyanins, aroma precursors, and 81

volatile compounds that accumulate during berry development (Conde et al., 2007). The 82

metabolic profile depends on the genotype and environment, but also on the specific 83

‘genotype x environment’ interaction (Jackson and Lombard, 1993; Duchêne and Schneider, 84

2005; van Leeuwen et al., 2004). Viticulture practices also affect berry ripening and final 85

quality, and strategies that can be used to promote maturation include irrigation, canopy 86

management and cropping levels (Jackson, 2014). These strategies are often applied without a 87

full understanding of the impact at the molecular level, with the exception of a few studies 88

that have correlated biochemical and physiological outcomes with transcriptomic changes 89

(Deluc et al., 2009; Leng et al., 2016; Pastore et al., 2011 and 2013; Savoi et al., 2016). 90

Berry development is a complex process involving profound physiological and metabolic 91

changes. The grape berry undergoes two distinct growth phases: the herbaceous (green) phase 92

from flowering to ~60 days, and the ripening/maturation phase, during which the berry 93

reaches full maturity (Coombe, 1992). During the herbaceous phase, the berry grows rapidly 94

by cell division then cell expansion and the seeds emerge. Accordingly, the berry is hard, 95

green due to the presence of chlorophyll, highly acidic due to the accumulation of organic 96

acids, and bitter and astringent due to the high concentration of tannins in the skin (Conde et 97

al, 2007). The end of the herbaceous phase is characterized by a slowing of berry growth 98

during which malic acid accumulates in the flesh and the overall organic acid concentration 99

reaches its highest level. The transition to the ripening phase is known as véraison, and this 100

occurs when the seeds are mature. During the ripening phase, further metabolic changes make 101

the fruit edible and attractive to promote seed dispersal, including a change of skin color, cell 102

expansion coupled with water influx, berry softening, the accumulation of sugars in the pulp, 103

the loss of organic acids and tannins, and the synthesis of volatile aromas (Conde et al, 2007). 104

At harvest, fully ripe berries manifest cultivar-specific features defining their shape, weight 105



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and metabolic composition, and most of all the skin color, which results in red or white 106

skinned varieties. Red berries accumulate anthocyanins in the skin cell vacuoles whereas 107

white berries do not due to mutations affecting the transcription factors (MybA1 and MybA2) 108

that activate the gene encoding UDP glucose:flavonoid 3-o-glucosyltransferase (UFGT), 109

which catalyzes a rate-limiting step in anthocyanin synthesis (Kobayashi et al., 2004; Walker 110

et al., 2007). The relative stability of the quantitative and qualitative anthocyanin profiles in 111

each variety, together with the widespread distribution of anthocyanins among grape 112

cultivars, means that the anthocyanin profile is a valuable chemotaxonomic parameter for the 113

classification of red berry varieties (Wenzel et al., 1987; Mattivi et al., 2006). 114

The publication of the grapevine reference genome (Jaillon et al., 2007) and the development 115

of new tools for transcriptomics have facilitated recent advances in the genome-wide analysis 116

of dynamic gene expression during berry development (Zamboni et al., 2010; Zenoni et al., 117

2010; Fortes et al., 2011; Guillaumie et al., 2011; Fasoli et al., 2012; Lijavetzky et al., 2012; 118

Sweetman et al., 2012; Dal Santo et al., 2013b; Cramer et al., 2014; Palumbo et al., 2014; Dal 119

Santo et al., 2016; Wong et al., 2016). A deep transcriptome shift driving the berry into the 120

maturation program was demonstrated in the red cultivar Corvina, showing that more genes 121

are downregulated than upregulated during veraison (Fasoli et al., 2012). The application of 122

different network methods to berry development transcriptome datasets identified so-called 123

switch genes, which probably encode key regulators of the developmental transition (Palumbo 124

et al., 2014). However, further molecular changes not strictly related to skin color have been 125

observed during berry development in different genotypes, suggesting that the core 126

transcriptomic changes may be supplemented by variety-dependent effects (Degu et al, 2014; 127

Corso et al., 2015). The transcriptomic plasticity of berry ripening has been reported in both 128

red and white cultivars growing in different areas, emphasizing the need to account for 129

environmental variability when comparing berry ripening in different genotypes (Dal Santo et 130

al., 2013a; 2016). 131

In this study, we selected five red and five white Italian grapevine varieties, typical of 132

different Regions, representing diverse agronomic traits and different environmental 133

adaptations. Transcriptional changes during berry ripening were compared when the 10 134

varieties were grown under the same environmental conditions, allowing us to distinguish 135

core transcriptomic traits from those dependent on the berry skin color. Multivariate analysis 136

revealed significant variation among the red cultivars, prompting us to focus on the 137

transcriptional regulation of the general phenylpropanoid pathway and the influence of 138



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anthocyanin during berry maturation. 139

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RESULTS 141

Transcriptomic profiling of berry development in 10 grapevine varieties 142

We analyzed the berry transcriptomes of 10 grapevine varieties (five red and five white) 143

cultivated under the same conditions and collected during the same year (Fig. 1A). Berries 144 were sampled in triplicate at four developmental stages, two before and two after veraison 145

(Fig. 1B): the pea-sized berry stage (Bbch 75) at 20 days after flowering (P), the ‘berries 146 beginning to touch’ stage (Bbch77) just prior to veraison (PV), the berry softening stage 147 (Bbch 85) at the end of veraison (EV), and the fully-ripe berry stage (Bbch 89) at harvest (H). 148 Brix values were measured in all varieties at the EV and H stages, revealing diverse sugar 149

accumulation trends, with Refosco, then Barbera, Negro amaro and Moscato bianco showing 150

the highest sugar concentrations at H, and Passerina the lowest (Fig. 1C). The total 151 anthocyanin levels were measured in the red varieties at the same stages, also revealing 152

different trends, with Refosco accumulating the highest level of anthocyanins at H (Fig. 1C). 153

The triplicate sampling of four development stages in 10 varieties yielded 120 RNA samples 154

for RNA sequencing (RNA-Seq) analysis, and 28,607,535 ± 5,342,814 reads were mapped 155

onto the 12x reference genome PN40024 (Jaillon et al., 2007) (Supplemental Table S1). The 156 mean normalized expression value per transcript (FPKM – fragments per kilobase of mapped 157

reads) of the three biological replicates was calculated for each sample using the geometric 158

normalization method and genes with a mean FPKM value >1 in at least one of the 40 159

triplicate samples were considered to be expressed. The resulting dataset comprising 21,746 160

transcripts was used for subsequent analysis (Supplementary Dataset S1). 161

The entire dataset was analyzed first by identifying the number of expressed genes in each 162

variety at each growth stage, revealing that the number of expressed genes decreased slightly 163

after veraison in all varieties (Fig. 2 and Supplemental Figure S1). This decline seemed to 164 affect more particularly genes with high and medium expression levels (>100 and >10 FPKM, 165

respectively) whereas genes with low and very low expression levels (< 10 and

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distinction between the transcriptomes before and after veraison, regardless of the variety or 173

skin color (Fig. 3A). A dendrogram generated by converting correlation coefficients into 174 distance values confirmed the strong clustering of the samples independent of the genotype 175



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and skin color during the herbaceous phase (Fig. 3B), whereas during maturation the Refosco, 176 Barbera and Negro amaro berry transcriptomes grouped separately from the other varieties. 177



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We investigated these relationships in more detail by principal component analysis (PCA) and 178

orthogonal projection of latent structures discriminant analysis (O2PLS-DA), revealing 179

biomarkers specific to the herbaceous or maturation phases, and biomarkers specific to each 180

of the four stages. These results are presented in the Supplemental Data. 181



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Definition of the core berry development transcriptome 183

In order to define genes with similar expression profiles in all the 10 varieties during berry 184

development, we first identified differentially expressed genes (DEGs) between each pair of 185

consecutive developmental stages (i.e. P-PV, PV-EV, EV-H) using Cuffdiff v2.0.2 (Roberts 186

et al., 2011). The number of DEGs ranged from 11,997 in Passerina to 16,709 in Barbera, 187

indicating that berry development involves the transcriptional modulation of a large number 188

of genes (Supplemental Dataset S2). A further inspection of the number of DEGs in each 189 pairwise comparison showed that the dynamics of gene expression modulation differed 190

among the 10 varieties (Fig. 4A). Negro amaro, Garganega, Sangiovese and Passerina were 191 characterized by an enormous increase in the number of DEGs between PV and EV, followed 192

by a marked decline in the number of DEGs from EV to H. Primitivo, Vermentino, Glera and 193

Moscato bianco showed a similar trend, but with a smaller amplitude than the other varieties. 194

Refosco showed a marked increase of the number of DEGs from PV to EV followed by slight 195

further increase from EV to H, whereas Barbera was characterized by the progressive 196

decrease of DEGs throughout development (Fig. 4A). These trends were mostly conserved 197 when applying a cut-off of gene expression of FPKM > 1 in at least one developmental stage 198

for each variety, indicating that the analysis was not biased by any variety. In addition, the 199

trends were similar with higher gene expression level cut-offs such as FPKM > 10 and FPKM 200

> 100 (Supplemental Figure S2). 201

By comparing DEGs in the 10 cultivars, we found that 4,613 genes were commonly 202

upregulated or downregulated in at least one pairwise comparison in all 10 varieties, and 203

exhibited an expression intensity >1 FPKM in at least one growth stage in all varieties (Fig. 204 4B and Supplemental Dataset S3). The most intense transcriptional modulation occurred 205 between PV and EV, underscoring the dramatic metabolic changes that characterize veraison. 206

Moreover, we found that more genes were downregulated rather than upregulated during 207

berry development, in particular during the transition from EV to H (Fig. 4B), confirming the 208 previous results reported for Corvina berries (Fasoli et al., 2012). 209

In order to establish the core berry development transcriptome, clustering analysis was 210

applied to the 4,613 genes commonly modulated during berry development, allowing the 211

identification of 913 genes showing similar expression trends in all 10 varieties, including 858 212

genes (94%) and 746 genes (82%) with FPKM > 5 and FPKM > 10 in at least one sample, 213



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respectively. These genes grouped into six clusters: the first three clusters contained genes 214

with expression peaks within one herbaceous stage or throughout the herbaceous phase, 215

whereas the last three clusters contained genes with expression peaks within one ripening 216

stage or throughout the maturation phase (Fig. 4C and Supplemental Dataset S4). The 217



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VitisNet functional annotations were used to assign Gene Ontology (GO) categories to these 218

913 genes (Grimplet et al., 2009) and enrichment analysis for the GO categories among each 219

cluster was performed using the BiNGO tool with PlantGOslim categories (Maere et al., 220

2005; Supplemental Dataset S4). 221

Cluster 1 contained 542 genes whose expression declined throughout berry development, and 222

the GO category “Cell cycle” was significantly overrepresented (adjusted P-value ≤ 0.01), 223

including a cyclin B gene and five cyclin D genes that are essential parts of the plant cell 224

cycle machinery (De Veylder et al., 2007). Another enriched GO category was “Cellular 225

component organization”, including several cellulose synthase genes, some xyloglucan 226

endotransglucosylase/hydrolase genes and genes involved in pectin metabolism (Desprez et 227

al., 2007; Rose et al., 2002). Cluster 2 contained 94 genes whose expression peaked at the PV 228

stage. A large proportion of these genes represented the GO categories “DNA/RNA metabolic 229

process” and “Transcription factor activity” (Fig. 4C), including 6 genes encoding ribosomal 230 proteins and 10 encoding transcription factors. Cluster 3 comprised genes whose expression 231

declined after veraison, and as expected the GO category “Photosynthesis” was significantly 232

overrepresented, including two genes encoding photosystem I light harvesting chlorophyll a/b 233

binding proteins (VIT_15s0024g00040; VIT_18s0001g10550) and one encoding a 234

photosystem II oxygen-evolving enhancer protein (VIT_12s0028g01080). The same cluster 235

also contained genes involved in auxin transport and gene regulation, including two genes 236

encoding auxin efflux carriers and four encoding auxin response factors. Cluster 4 contained 237

20 genes whose expression peaked at the EV stage, including the sugar transporter gene 238

HT6/TMT2 (VIT_18s0122g00850) and a SWEET gene (VIT_17s0000g00830), a tonoplast 239

intrinsic aquaporin (TIP) gene (VIT_13s0019g00330), a pectinesterase gene 240

(VIT_09s0002g00330) and the expansin gene EXPA18 (VIT_17s0053g00990), the latter two 241

being involved in cell wall loosening (Seymour and Gross, 1996; Dal Santo et al., 2013b). 242

Cluster 5 contained 134 genes whose expression increased between EV and H. Interestingly, 243

we noted the presence of the expansin gene EXPB4 (VIT_15s0021g02700), which is probably 244

involved in cell wall expansion and modification during ripening (Dal Santo et al., 2013b), 245

and the germacrene D synthase gene TPS07 (VIT_18s0001g04280), which is involved in 246

sesquiterpene production (Martin et al., 2010 and 2012). Cluster 6 comprised only three genes 247

whose expression increased after veraison and then remained constant until harvest. These 248

genes encoded a xyloglucan endotransglucosylase/hydrolase (VIT_01s0011g06250), a 249



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predicted E3 ubiquitin-protein ligase (VIT_19s0015g00670, VIT_19s0015g00680) and an 250

uncharacterized protein (VIT_00s0265g00130). 251

The entire dataset was also searched to find genes with a constant expression level throughout 252

berry development in all genotypes. Transcripts were classified according to their coefficient 253

of variation across the 40 triplicate samples as described by Massa et al. (2011). We identified 254

80 genes with a constant level of expression (coefficient of variation < 0.1) throughout berry 255

development in all 10 varieties (Supplemental Dataset S5). The four genes with the lowest 256 coefficients of variation (Supplemental Figure S3) encoded a ubiquitin-specific protease 257 (VIT_13s0073g00610), actin (VIT_04s0044g00580), a β-adaptin (VIT_02s0025g00100) and 258

a phosphoinositide 4-kinase (VIT_04s0044g01210), with expression intensities of 259

approximately 58, 1295, 86, and 14 FPKM, respectively. Moreover, a comparison of the 80 260

constitutively expressed genes with the 76 non-plastic and constitutive transcripts in Corvina 261

berries from veraison to full maturity identified by Dal Santo et al. (2013a) revealed two 262

genes common to both studies: one encoding a ubiquitin-specific protease 263

(VIT_00s0174g00150) and the other encoding an oligo-uridylate-binding protein 264

(VIT_04s0008g02930). These latter two genes are therefore strong candidate reference genes 265

for the quantitative analysis of gene expression in the berry pericarp throughout ripening. 266

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Red and white berry varieties express common and specific switch genes that may 268 regulate berry development 269

We previously used integrated network analysis to compare the transcriptomic datasets of the 270

five red-skinned berry varieties (20 samples), revealing the presence of switch genes 271

potentially involved in the regulation of the developmental transition at veraison (Palumbo et 272

al., 2014). The SWIM tool (Paci et al., 2017) used to unveil the switch genes in that 273

investigation, which included the identification of fight-club hubs and the heat cartography 274

approach (Palumbo et al., 2014; Paci et al., 2017), was therefore applied to the transcriptomic 275

datasets of the white-skinned berries described herein. 276

We compared the transcriptomic profiles of the white berry samples representing the 277

herbaceous and maturation phases and identified 1,824 genes showing significant differential 278

expression, including 1,464 downregulated and 360 upregulated after veraison 279

(Supplemental Figure S4 and Supplemental Dataset S6). This confirms that the shift to the 280 maturation phase predominantly involves the suppression of gene expression and only a few 281



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genes are activated, as previously reported (Palumbo et al., 2014). A co-expression network 282

comprising 1,752 nodes and 327,052 edges was generated with 1,824 DEGs using a predicted 283

Pearson correlation threshold of 0.91 (Supplemental Figure S5A and B and Supplemental 284 Dataset S7). The specific topological proprieties of the co-expression network revealed the 285 tri-modal distribution of the average Pearson correlation coefficients (APCCs) and confirmed 286

the identification of three types of hubs (Supplemental Figure S5C), as previously observed 287 for the red berries and for the grapevine atlas (Palumbo et al., 2014). Specifically, 1,318 genes 288

were classified as party hubs, 55 as date hubs and 258 as fight-club hubs (Supplemental 289 Dataset S8) (Han et al., 2004). The relationship between structure and function within this 290 co-expression network was then investigated by k-means clustering analysis, allowing the 291

identification of three modules in the network. By assigning a color scale proportional to the 292

APCC values, we obtained a heat cartography map (Supplemental Figure S6) that reveals 293 the switch genes – i.e. genes that are more strongly linked to inversely correlated rather than 294

positively correlated genes in the network – expressed at low levels during the herbaceous 295

phase and at a significantly higher level during maturation. We identified 212 switch genes in 296

the white berry varieties, representing diverse functions as observed among the switch genes 297

in the red berry varieties (Supplemental Table S2). The comparison of switch genes in the 298 white and red varieties showed that 131 switch genes were shared by all varieties, including 299

31 previously identified in the grapevine transcriptomic atlas (Supplemental Dataset S9). 300 The 131 common switch genes mainly belonged to the GO categories “Transcription factor 301

activity”, “Carbohydrate metabolic process”, “Response to stress” and “Cell wall 302

metabolism”, including several genes already known to play major roles during berry ripening 303

(Palumbo et al., 2014). The common switch genes included ERF1 (VIT_05s0049g00510) and 304

two other ethylene response factor genes, and an auxin-responsive gene 305

(VIT_16s0098g01150), suggesting that ethylene and auxin play a key role in the berry 306

developmental transition. The list of common switch genes also included 19 genes encoding 307

transcription factors from various families (Table 1) that are likely to represent master 308 regulators of the developmental shift from the herbaceous phase to the maturation phase. 309

Interestingly, the original list also included MYBA1, which encodes a transcription factor 310

required for anthocyanin synthesis in red varieties. The gene is mutated and inactive in white 311

varieties, hence the lack of anthocyanins in the berry skin. By reviewing the alignment of 312

reads onto the reference genome, we found that MYBA2 reads were wrongly attributed to 313

MYBA1 in the white-skinned berries, as explained in detail in the Supplemental Data. We 314 also identified genes encoding a basic helix-loop-helix (bHLH) protein, two proteins 315



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containing a lateral organ boundaries (LOB) domain that might be involved in organ 316

development by integrating developmental changes in response to phytohormone signaling or 317

environmental cues (Xu et al., 2016), four NAC-domain proteins and four proteins with zinc 318

fingers. Interestingly, the list of switch genes also included AGL15a (Grimplet et al., 2016), 319

encoding the MADS box protein Agamous-like 15a (VIT_13s0158g00100), and the 320

transcription factor gene WRKY19 (VIT_07s0005g01710). 321

The switch genes specific to white grape varieties included three belonging to the GO 322

category “Transcription factor activity”, namely WRKY37 (VIT_12s0059g00880) and two 323

zinc finger genes, and several belonging to the GO category “Response to hormone stimulus”, 324

including the 1-aminocyclopropane-1-carboxylate oxidase (ACO) gene 325

(VIT_11s0016g02380) which is involved in ethylene synthesis (Penrose and Glick, 1997). 326

Further genes represented the categories “Lipid metabolic process”, e.g. the lipoxygenase 327

gene LOXA (VIT_06s0004g01510) which is directly involved in the onset of ripening 328

(Podolyan et al., 2010), and “Response to stress”, e.g. three thaumatin genes, the major latex-329

like ripening protein gene grip61 (VIT_01s0011g05140) and a gene encoding an early-330

responsive dehydration protein (VIT_02s0109g00230) (Supplemental Dataset S9). 331

The switch genes specific to red grape varieties were mainly represented by the GO category 332

“Secondary metabolic process”, including UFGT (VIT_16s0039g02230) and the glutathione 333

S-transferase gene GST4 (VIT_04s0079g00690), whose absence among the white switch 334

genes confirmed the misleading identification of MYBA1 (see above). The red switch genes 335

also encompassed several in the category “Transcription factor activity”, including three zinc 336

finger genes and WRKY52 (VIT_17s0000g01280) (Supplemental Dataset S9). 337

338

The greatest transcriptomic differences among genotypes occur during ripening 339

The FPKM dataset representing 40 triplicate samples was investigated by PCA, revealing a 340

clear separation among varieties at the H stage (Fig. 5A). The first two principal components 341 (PC1 and PC2) explained 44.3% of the total transcriptional variance, with PC1 explaining 342

34.7% and PC2 the remaining 9.6%. PC1 can probably be attributed to the time course of 343

berry development from P to H (from left to right), whereas PC2 appears to distinguish stages 344

P and H from PV and EV. Although the berry transcriptomes from P to PV were clearly 345

separated by PC2 regardless of the genotype, the degree of separation between transcriptomes 346

at EV and H tended to be influenced by the genotype. This was particularly evident for the 347



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Barbera, Negro amaro and Refosco berry transcriptomes, which separated more than the other 348

varieties during the maturation phase. 349



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This trend remained evident when PCA was applied separately to the five red-skinned 350

varieties (Fig. 5B) and the five white-skinned varieties (Fig. 5C). The distribution of the 351 samples was almost identical during the herbaceous phase, with the P and PV samples 352

separated by both principal components. During the maturation phase, the red and white 353

varieties behaved differently: the red berry transcriptomes were separated by both components 354

at the EV and H stages, with a wide distribution at H, whereas the white berry transcriptomes 355

were tightly clustered in two groups corresponding to the EV and H stages, and were only 356

slightly separated by PC1. 357

Genes responsible for these color-dependent differences in behavior during the maturation 358

phase were identified by comparing the five red and five white transcriptomes at the EV and 359

H stages using a between-subjects t-test with an overall P-value threshold of 0.01 (Saeed et 360

al., 2003). This revealed 443 DEGs between the red and white varieties at EV, corresponding 361

to 382 genes more strongly expressed in the red berries and 61 more strongly expressed in the 362

white berries (Supplemental Dataset S10). By applying a cut-off of FPKM>5 and of 363 FPKM>10 in at least one variety at EV, we found that the total number of DEGs at EV was 364

reduced to 84% and 72%, respectively. 365

Similarly, 837 DEGs were found at H, including 536 genes more strongly expressed in the red 366

berries and 289 more strongly expressed in the white berries (Supplemental Dataset S10). 367 By applying a cut-off of FPKM>5 and of FPKM>10 in at least one variety at H, we found that 368

the total number of DEGs at H was reduced to 79% and 62%, respectively. 369

These results show that color-specific differences involve more genes specifically 370

overexpressed in the red berries than in the white berries and that only a minor percentage of 371

differentially expressed genes was characterized by a low expression level (FPKM ≤ 10 and 372

>1). Furthermore, data reported in the Supplemental Dataset S10 show that genes 373 overexpressed in red berries reached much higher fold change (FC) at both maturation stages 374

than genes overexpressed in white berries. 375

By focusing on DEGs satisfying the condition FC > 4 at one or both stages, we identified 268 376

genes in red berries and only 41 genes in white berries (Fig. 5D and Supplemental Dataset 377 S10). As expected, the 268 genes strongly overexpressed in red berries included several 378 structural and regulatory genes involved in the biosynthesis and transport of anthocyanins, 379

such as GST4, UFGT, the transcriptional regulator gene MYBA1 and several genes encoding 380

flavonoid 3',5'-hydroxylase (F3'5'H). Interestingly, we also found several genes encoding 381



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stilbene synthase (STS) and phenylalanine ammonia-lyase (PAL) among the DEGs at stage H, 382

as well as several genes belonging to the GO categories “Transcriptional factor activity”, 383

“Response to hormone stimulus”, “Carbohydrate metabolic process”, “Signal transduction” 384

and “Transport” (Supplementary Dataset S10). The 41 genes strongly overexpressed in 385 white berries included several involved in photosynthesis, terpenoid biosynthesis, cell wall 386

metabolism (e.g. three expansin genes and a cellulose synthase gene) and auxin metabolism, 387

suggesting the developmental transition to maturation in white berries features a delay in the 388

shutdown of vegetative metabolism. 389

The influence of these major DEGs on the different transcriptomic behaviors observed in 390

Figure 5A was investigated by applying PCA to the dataset of 40 triplicate samples having 391 first removed the DEGs (Fig. 5E). The removal of these genes from the dataset only 392 marginally affected the relationships among the berry transcriptomes during the maturation 393

phase, suggesting that a larger number of genes must be involved. 394

395

Differences among berry transcriptomes at harvest correlate with anthocyanin 396 accumulation 397

The relationship between samples was investigated further by applying PCA to the averaged 398

10 berry samples at stage H. A unique principal component, describing the 27.4% of the 399

variability, separated the Barbera, Negro amaro and Refosco berry transcriptomes from the 400

other varieties (Supplemental Figure S7). The minimum number of genes driving the sample 401 separation in this PCA was determined by removing sets of genes step by step, each step 402

reducing the loading value cutoff by 0.05 until the computation of a new principal component 403

no longer provided enough variability between the transcriptomes. The threshold was 0.65/–404

0.65, corresponding to 6,003 removed genes: 3,450 with positive loadings and 2,553 with 405

negative loadings, i.e. positive and negative correlation to the principal component 406

(Supplemental Dataset S11). The removal of these genes resulted in the tighter clustering of 407 berry transcriptomes during the maturation phase (Fig. 6A) and in the red berry data subset, 408 showing that the red berries at stages EV and H were slightly distinguished by PC1, and 409

likewise the white berries (Supplemental Figure S8). On the other hand, the 6,003 genes did 410 not significantly contribute to the separation of the white berry samples during the maturation 411

phase (Supplemental Figure S8C and D), strongly suggesting their involvement in 412 maturation programs specific to the red berry varieties, particularly Refosco, Negro amaro 413



20

and Barbera. These three varieties, especially Refosco, are characterized by the rapid 414

accumulation of anthocyanins during maturation and high anthocyanin levels at stage H (Fig. 415 1A), suggesting an association between anthocyanin accumulation and the divergence of 416 berry transcriptomes during ripening. 417



21

The biological processes underlying these transcriptomic differences were determined by 418

identifying the GO categories significantly overrepresented among the 6,003 selected genes 419

(representing the most strongly and weakly expressed genes during maturation) in varieties 420

Barbera, Negro amaro and Refosco (Fig. 6B and C). Among the 3,450 transcripts with a 421 loading value greater than 0.65, we found that the GO categories “Secondary metabolic 422

process”, “Transcription” and “Catabolic process” were significantly overrepresented (Fig. 423 6B), whereas “Photosynthesis”, “Generation of precursor metabolites and energy” and 424 “Translation” were significantly overrepresented among the 2,553 genes with loading values 425

below the threshold –0.65 (Fig. 6C). A detailed description of these results is provided in the 426 Supplemental Data. Interestingly, among the 3,450 genes strongly expressed in Barbera, 427 Negro amaro and Refosco, those with the greatest loading values belonged mainly to the 428

categories “DNA/RNA metabolic process”, “Transcription factor activity”, “Transport” and 429

“Carbohydrate metabolic process” (Supplemental Dataset S11). This indicates that Barbera, 430 Negro amaro and Refosco berry maturation involves a deep shift in the regulation of genes 431

controlling many metabolic pathways in addition to anthocyanin biosynthesis, the latter being 432

greatly enhanced compared to the varieties Sangiovese and Primitivo. 433

In order to explore the extent to which differences in gene expression among the 10 varieties 434

at stage H reflect the anthocyanin content regardless of the genotype, we consulted the lists of 435

genes that are differentially expressed both when overexpressing the anthocyanin synthesis 436

regulator MYBA1 in the white variety Chardonnay and when silencing its expression in the 437

red variety Shiraz (Rinaldo et al., 2015). This recent study showed that the transgenic grape 438

berries changed color either due to ectopic anthocyanin accumulation in Chardonnay or much 439

lower anthocyanin levels in Shiraz, representing a suitable framework to investigate the effect 440

of anthocyanin accumulation on the berry transcriptome during ripening in the same genetic 441

background. We compared the 3,450 genes expressed most strongly in the three red varieties 442

with the highest anthocyanin levels (loading value > 0.65) with the 787 genes upregulated in 443

ripe Chardonnay berries overexpressing MYBA1 and the 850 genes downregulated in ripe 444

Shiraz berries lacking MYBA1 expression (Rinaldo et al., 2015), representing the genes most 445

likely to be regulated by anthocyanins. We found that 102 and 349 genes, representing 13% 446

and 41% of the DEGs in Chardonnay and Shiraz, respectively, were common to our list of 447

3,450 genes (Supplemental Dataset S11 and Supplemental Figure S9), including 26 DEGs 448 common to both Chardonnay and Shiraz. A similar proportion was found when we compared 449

the 2,553 genes expressed most weakly in the three red varieties (loading values < –0.65) with 450



22

the 478 genes downregulated in transgenic Chardonnay and the 135 genes upregulated in 451

transgenic Shiraz. In this case, 43 Chardonnay genes and 47 Shiraz genes were shared with 452

our list of 2,553 genes (Supplemental Figure S9), representing 9% and 35% of the DEGs in 453 Chardonnay and Shiraz, respectively, and including two DEGs common to both Chardonnay 454

and Shiraz. The expression of these genes is probably affected by the loss of anthocyanins. 455

The distribution of GO categories revealed that the 102 genes shared with Chardonnay berries 456

overexpressing MYBA1 belonged mainly to the categories “Secondary metabolic process”, 457

“Signal transduction”, “Transport”, “DNA/RNA metabolic process” and “Carbohydrate 458

metabolic process”, whereas the 349 genes shared with Shiraz berries lacking MYBA1 459

expression belonged predominantly to the categories “Transcription factor activity”, 460

“DNA/RNA metabolic process”, “Transport”, “Signal transduction” and “Secondary 461

metabolic process” (Supplemental Figure S9). These data strongly suggest that the 462 accumulation of anthocyanins may directly influence the expression of genes with a broad 463

range of functions, especially transcriptional regulation, signal transduction and transport. 464

465

Genes in the phenylpropanoid/flavonoid pathway not directly controlled by MYBA 466 proteins are modulated in berries accumulating high levels of anthocyanins 467

Variety-specific trends in the expression of phenylpropanoid pathway genes during berry 468

development were investigated by preparing heat maps focusing separately on the herbaceous 469

phase (Supplemental Figure S10) and the maturation phase (Figure 7A). Major differences 470 between the white and red berry varieties were clearly shown for the flavonoid structural 471

genes and particularly for genes directly or indirectly under the control of MYBA1 and 472

MYBA2, but differences were also evident in the early general phenylpropanoid pathway. 473

During ripening, some genes were expressed solely in the red varieties, including most F3'5'H 474

genes, UFGT and both anthocyanin O-methyltransferase (AOMT) genes (Figure 7A), the 475 anthocyanin acyltransferase gene 3AT (VIT_03s0017g00870), the anthoMATE3 (AM3) and 476

GST4 genes (Supplemental Figure S10B). The expression of these genes generally peaked at 477 EV followed by a slight decline at stage H, confirming their involvement in anthocyanin 478

biosynthesis, decoration and transport. Differences in expression pattern of these genes were 479

found among red varieties not strictly mirroring the differences in the final anthocyanin 480

accumulation. For example, one AOMT and the AM3 showed high expression level at EV 481

berries in Primitivo, which is one of the low anthocyanin accumulating variety (Figure 7A 482



23

and Supplemental Figure S10B). MYBA1 was strongly expressed in the maturing red berries 483 but a low level of expression was also observed in the white varieties due to the mapping 484

errors described above and in the Supplemental Data. MYBA2 was similarly expressed in the 485 red and white varieties, although the gene is mutated in white varieties and the transcript 486



24

encodes a nonfunctional MYBA2 protein (Walter et al., 2007). Genes encoding chalcone 487

synthase (CHS), chalcone isomerase (CHI), flavonoid-3'-hydroxylase (F3'H), one favanone-3-488

hydroxylase (F3H), dihydroflavonol reductase (DFR) and leucoanthocyanidin dioxygenase 489

(LDOX) were expressed in both the red and white varieties albeit at higher levels in the reds 490

(Fig. 7A). These data indicate that the expression of genes involved in flavonoid biosynthesis 491 are probably responsible for the differences between red and white varieties during berry 492

maturation, but not for the differences in total anthocyanin levels at maturity among the five 493

red varieties (Fig. 7B). 494

Concerning the general phenylpropanoid pathway, several PAL, cinnamate-4-hydroxylase 495

(C4H) and 4-coumaroyl:CoA-ligase (4CL) genes were expressed more strongly in the red 496

varieties and this distinguished them from the white varieties during ripening (Fig. 7A). 497 Among the 15 members of the PAL gene family, only two clearly showed an expression 498

profile similar to the anthocyanin-related genes, i.e. peaking at the EV stage. The other 13 499

members reached a peak of expression at full ripening, mirroring the expression profile of 500

other phenylpropanoid genes (three C4H and three 4CL genes) and in particular the 44 STS 501

genes and their regulators MYB14 and MYB15 (Höll et al., 2013) (Fig. 7A). This behavior 502 was particularly pronounced in Refosco, characterized by the highest anthocyanin level at H 503

(Fig. 7B). The expression of LDOX, UFGT, one PAL member (VIT_16s0039g01120) and the 504 STS27 (VIT_16s0100g00750), was validated in red varieties by RT-qPCR (Supplemental 505 Figure S11). Overall, these results highlight the coordinated strong activation of the stilbene 506 biosynthesis pathway only during the late ripening stage of red berry varieties. Interestingly, 507

the expression of genes in this pathway reflected the differential accumulation of 508

anthocyanins in the red berries, suggesting there may be some form of cross-talk between the 509

activation of early phenylpropanoid genes and anthocyanin accumulation in berries (Fig. 7A 510 and B). 511

During the herbaceous phase, the expression of phenylpropanoid-related genes could not 512

distinguish the red and white varieties (Supplemental Figure S10A). Some of the genes 513 described above that are expressed more strongly in red grapes during ripening, are recruited 514

in both red and white grapes during the herbaceous phase, probably for flavan-3-ol and 515

pro-anthocyanidin biosynthesis (Boss et al., 1996a and b). Accordingly, genes encoding CHS, 516

CHI, one F3H, DFR, LDOX, at least three PALs, one C4H, two 4CLs (Supplemental Figure 517 S10A), seven flavonol-synthases (FLS), the leuco-anthocyanidin reductases (LAR1 and 518 LAR2), the anthocyanidin reductase (ANR) (Supplemental Figure S10B) plus the regulator 519



25

genes MybPA1/2 and Myb5a (Cavallini et al., 2014) are more strongly expressed at the P 520

stage in all varieties. Interestingly, among the three PAL genes expressed at the P stage in all 521

varieties, PAL2 and VIT_06s0004g02620 appear to be recruited for anthocyanin synthesis, 522

whereas PAL1 appears to be related to stilbene biosynthesis in ripening red berries 523

(Supplemental Figure S10A). 524

525

526



26

DISCUSSION 527

The 10 berry varieties showed diverse ripening parameters during maturation 528

Previous investigations of berry development have focused on single varieties or a 529

comparison of two varieties, and have considered a range of environments, which makes the 530

genotype-specific effects more difficult to evaluate (Zenoni et al., 2010; Fasoli et al., 2012; 531

Lijavetzky et al., 2012; Da Silva et al., 2013; Degu et al., 2014). Here we considered 10 532

diverse berry varieties grown in the same environment, and carried out an exhaustive 533

transcriptomic comparison at four developmental stages, focusing on traits relevant to the 534

berry skin color (particularly the extent of anthocyanin accumulation). 535

Five red-skinned berry varieties (Sangiovese, Barbera, Negro amaro, Refosco and Primitivo) 536

and five white-skinned berry varieties (Vermentino, Garganega, Glera, Moscato bianco and 537

Passerina) were selected from among several hundred Italian varieties for their agronomical 538

and oenological diversity and their importance in the Italian wine production sector. These 539

varieties are traditionally cultivated in different Italian regions, but in this experiment all 540

varieties were grafted onto the same rootstock and cultivated in the same vineyard under 541

identical conditions in order to focus on the genotype-specific effects while eliminating 542

variation caused by the environment and agricultural practices, both of which have previously 543

been shown to have a direct effect on berry metabolism during ripening as recently reported 544

for the cultivars Corvina (Dal Santo et al., 2013a; Anesi et al., 2015) and Garganega (Dal 545

Santo et al., 2016). 546

Despite the uniform environmental conditions and agricultural practices, the 10 varieties 547

differed substantially in terms of sugar accumulation and (in the red berry varieties) the 548

accumulation and final content of anthocyanins. The total anthocyanin content at harvest 549

under our experimental conditions supported previous results reported by Mattivi et al. 550

(2006). Nevertheless, the Negro amaro berries did not achieve the color content expected for 551

this cultivar at harvest when grown in its typical production area in Southern Italy (3.15 g/kg), 552

indicating a slight ‘genotype x environment’ interaction effect (Mattivi et al., 2006). 553

554

Determination of a core berry development transcriptome 555

The diverse ripening characteristics of the 10 varieties grown under the same environmental 556

conditions provided a framework for the determination of common and genotype-dependent 557

transcriptional changes during development. In all 10 varieties, the number of expressed 558



27

genes declined after veraison, particularly genes with high and medium expression levels. 559

Another common feature was that post-veraison development featured a greater number of 560

downregulated genes than upregulated genes, with a peak of modulation between the PV and 561

EV stages. The massive downregulation of genes during the transition from green to ripening 562

berries has previously been reported for the variety Corvina (Palumbo et al., 2014). 563

Clustering analysis identified 913 genes with one of six shared expression profiles among the 564

10 varieties, probably representing the core transcriptome of berry development. Clusters 565

showing downward expression trends during development encompassed the majority of 566

common DEGs, reflecting the suppression of vegetative growth processes, whereas few genes 567

representing secondary metabolism and stress responses were induced during maturation. 568

Accordingly, we identified a much greater number of biomarkers representing the herbaceous 569

development stages than the maturation stages (Supplemental Data), suggesting that the 570 general downregulation of genes after veraison was common to all varieties whereas the 571

upregulation of metabolic genes during maturation tended to be more variety specific. 572

Most of the genes in the six clusters represented physiological processes that are well known 573

to occur during berry development. For example, many genes involved in photosynthesis, 574

cellular component organization and the cell-cycle machinery were downregulated after stage 575

P (cluster 1) or at the end of the herbaceous phase (cluster 3), reflecting the shutdown of 576

photosynthesis and cell division typically observed after veraison (Dokoozlian, 2000). 577

Likewise, the decline in berry acidity during maturation was indicated by the downregulation 578

of genes encoding phosphoenolpyruvate carboxylase (PEPC) and L-idonate dehydrogenase, 579

which are required for the synthesis of malic and tartaric acids, respectively (Famiani et al., 580

2014; Steiner and McKinley, 2015). Three sucrose synthase genes (SUC1, SUS3 and SUS5) 581

were downregulated after stage P (cluster 1) and three sugar transporter genes including the 582

tonoplast monosaccharide transporter gene TMT3 were also downregulated during the green 583

phase (clusters 1 and 3). Conversely, genes characterized by an expression peak at the end of 584

veraison (cluster 4) included the hexose transporter gene HT6/TMT2, which may thus 585

facilitate the vacuolar accumulation of hexose at the inception of ripening (Lecourieux et al., 586

2014), and the aquaporin gene TIP1-3, which may promote berry enlargement by increasing 587

the absorption of water. 588

Gene modulation relating to cell wall metabolism was observed at several stages, showing 589

that each growth event, i.e. rapid cell division and cell enlargement during the herbaceous 590

phase, and berry expansion and softening during the maturation phase, involved distinct sets 591



28

of genes. A large number of cell wall-related genes was expressed at stage P (cluster 1), 592

including those involved in cytokinesis and xylem formation, the latter required for 593

symplastic unloading (Jürgens, 2000; Zhang et al., 2006). Cluster 1 also contained several 594

cellulose synthase genes, including A3 (VIT_08s0007g08380), which is required for primary 595

cell wall synthesis (Desprez et al., 2007), and other members of subfamily A, which may be 596

involved in secondary cell wall metabolism (Taylor et al., 1999 and 2000). 597

The distribution of modulated expansin genes across multiple clusters indicates the 598

involvement of expansin proteins in both vegetative growth (EXPA6, EXPA16 and EXPB2 in 599

cluster 1) and berry expansion and softening during maturation (EXPA18 and EXPB4 in 600

clusters 4 and 5, respectively), as previously described in Corvina berries (Dal Santo et al., 601

2013a). Other common transcriptional modulations appear to regulate the xyloglucan 602

components of the hemicellulose matrix, which may control morphogenesis (Xiao et al., 603

2016). Four xyloglucan endotransglucosylase/hydrolase genes were commonly upregulated at 604

stage P, suggesting a role in cell wall synthesis during cell division, whereas another was 605

commonly upregulated during maturation (cluster 6), suggesting a role in cell wall loosening 606

and tissue softening (Nishitani and Vissenberg, 2006). 607

Berry softening is a crucial physiological event marking the onset of ripening, together with 608

sugar accumulation and color development. A delay in berry softening was recently 609

associated with the inhibition of sugar accumulation and anthocyanin biosynthesis 610

(Castellarin et al., 2016). Softening and a loss of turgor are early events in the ripening 611

process and may even commence before veraison (Coombe and McCarthy, 2000). We 612

therefore focused on genes upregulated during the late stages of the herbaceous development 613

phase, and identified two cellulose synthase-like E1 (CSLE1) genes, which synthesize the 614

backbones of non-cellulosic polysaccharides (hemicelluloses), and a pectin methylesterase 615

inhibitor (PMEI), which has previously been shown to control pectin methylesterase activity 616

in tomato at the post-transcriptional level (Di Matteo et al., 2005). The grapevine PMEI1 617

gene, which is strongly expressed during green berry growth, was recently proposed to inhibit 618

pectin methylesterase activity and thus prevent premature berry softening related to pectin 619

degradation (Lionetti et al., 2015). These genes may thus represent the triggers for ripening 620

by regulating the initial events at veraison, such as softening and loss of turgor (Castellarin et 621

al., 2016). 622

The common berry development transcriptome also featured genes highlighting the interplay 623

between auxin and ethylene signaling during ripening (Giovannoni et al., 2001 Böttcher and 624



29

Davies, 2012). The substantial downregulation of 10 auxin-related genes after stage P (cluster 625

1) and 7 genes after stage PV (cluster 3) correlated with the use of indole-3-acetic acid (IAA) 626

before maturation to delay ripening (Böttcher et al., 2010; 2013; Cawthon and Morris, 1982). 627

Although auxin biosynthesis may reach a peak at the initiation of ripening, the IAA is 628

sequestered into IAA-Asp conjugates that accumulate rapidly at veraison and remain at the 629

same level throughout ripening (Böttcher et al., 2013). The hypothesis of active auxin 630

biosynthesis during berry development is supported by the upregulation of 10 genes involved 631

in auxin metabolism (Supplemental Dataset S4) and an auxin efflux carrier protein at stage 632 PV (cluster 2), and of an auxin-independent growth promoter and an indole-3-acetate 633

β-glucosyltransferase after stage PV (cluster 5). The upregulation of an indole-3-acetate 634

β-glucosyltransferase, putatively involved in auxin inactivation by the IAA sugar conjugation, 635

could take part to the mechanisms by which the endogenous IAA concentration decreased and 636

was maintained at low levels in maturing fruit (Bottcher et al., 2010). No genes involved in 637

IAA catabolism or amino acid conjugation were identified, possibly due to the incomplete 638

functional annotation of the predicted grapevine genes or because a brief period of 639

transcriptional modulation featuring these genes was not covered by our samples. 640

Grape berries are non-climacteric fruits so there is no sharp increase in ethylene production 641

and respiratory activity at the onset of ripening (Böttcher et al., 2013). Nevertheless, the 642

ethylene concentration peaked before the initiation of ripening (Chervin et al., 2004), and 643

ethylene-releasing compounds can influence the ripening time and the auxin concentration 644

(Böttcher et al., 2013). The proposed cross-talk between ethylene and auxin signaling in fruit 645

ripening has also been supported by the observation that auxin depletion in apple can induce 646

the expression of genes required for ethylene biosynthesis (Shin et al. 2016) whereas the 647

inhibition of ethylene signaling can induce the expression of genes related to auxin signaling 648

(Tadiello et al., 2016). The downregulation of the ethylene-responsive transcription factor 649

genes SHINE3 and ERF042 after stage P (cluster 1) and the peak induction of an ethylene 650

responsive protein gene at stage PV (cluster 2) were consistent with a regulatory role for 651

ethylene in berry development. 652

Common genes in clusters 5 and 6 showed an increase in expression after veraison and the 653

GO categories “Transcription factor activity” and “DNA/RNA metabolic process” were 654

prevalent, suggesting a role in the transcriptomic reprograming that accompanies maturation. 655

Three NAC genes were identified: NAC03, a close homologue of LeNOR, one of the master 656

regulators of fruit ripening in tomato (Martel et al., 2011); NAC11, a berry switch gene 657



30

probably involved in the transition from vegetative growth to maturation (Palumbo et al., 658

2014); and NAC37, which may be involved in the regulation of secondary cell wall biogenesis 659

(Wang et al., 2013). The upregulation of WRKY37, another switch gene in white berries and a 660

homolog of AtWRKY6, suggests the induction of cell death-related processes (Robatzek and 661

Somssich, 2001), including the loss of cell vitality observed during the advanced stages of 662

grape berry maturation (Tilbrook and Tyerman, 2008). 663

The core set of 19 switch genes encoding transcription factors among the genes commonly 664

upregulated in all 10 varieties after veraison represents the putative regulators of the shift to 665

maturity in the core berry development transcriptome. These genes are candidates for further 666

functional studies aiming to dissect the molecular mechanisms that govern the important 667

physiological changes that occur during the onset of ripening. 668

669

Transcriptomic behavior during maturation correlates with the anthocyanin content 670

PCA applied to the entire dataset emphasized the deep shift in the berry transcriptome 671

between the herbaceous and maturation phases, as well as the separation between white and 672

red berry varieties after veraison, mostly evident at stage H. Barbera, Negro amaro and 673

particularly Refosco transcriptomes featured a more advanced ripening state described by 674

PC2, reflecting the particularly high levels of sugars and anthocyanins in these varieties (Fig. 675 4B). In contrast, the white berry genotypes showed no evidence of such enhanced 676 transcriptomic changes after veraison, and seemed to be more transcriptionally active during 677

the herbaceous development phase (Fig. 4C). It is likely that the transcriptomic diversity 678 among the red berry varieties can be attributed to the generally more advanced ripening at 679

stage H compared to the white berry varieties. However, the final Brix degrees (Fig. 1C) 680 indicate that Moscato bianco behaved more similarly to Barbera, Negro amaro and Refosco 681

than to the other four white varieties, even though the Moscato bianco berry transcriptome at 682

stage H was more similar to the white varieties than the red ones (Fig. 4A). These data 683 suggest that the sugar concentration, a very important technological ripening parameter for 684

winemakers, only partially indicates the physiological stage of berry ripening revealed by 685

transcriptome analysis. The extent of anthocyanin accumulation appeared to be more directly 686

related to the transcriptomic program of fruit maturation. With this regard, the highest 687

anthocyanin accumulation of Refosco at H fully typifies this behavior being associated with a 688

distinctive transcriptomic trend and a marked induction of early phenypropanoid- and 689



31

stilbenoid-related genes during maturation. 690

It is well known that the red color of grapes reflects the biosynthesis and accumulation of 691

anthocyanins (generally in the skin). The transcription factors MYBA1 and MYBA2 692

additively regulate the structural genes for anthocyanin biosynthesis, and in white cultivars 693

this is prevented by a double mutation that inactivates both proteins (Kobayashi et al., 2004, 694

Walker et al., 2007). When comparing the two color groups at both maturation stages, we 695

found that most of the DEGs were preferentially expressed in red berries, and represented the 696

phenylpropanoid/flavonoid pathway, anthocyanin transport and anthocyanin modification. 697

Nevertheless, several other DEGs were preferentially expressed in red berries, including 698

genes involved in carbohydrate metabolism, such as the cell wall apoplastic invertase gene 699

cwINV4, the neutralization of reactive oxygen species, such as the gene encoding ascorbate 700

oxidase (Pilati et al., 2014), and abiotic stress responses, such as 10 WRKY genes (Liu et al., 701

2011; Wang et al., 2014) and the early responsive dehydration protein 15 gene (ERD15). The 702

DEGs preferentially expressed in white berries were mostly involved in photosynthesis, 703

strongly suggesting the presence of residual photosynthetic activity at full maturity, but also 704

included genes responsible for the aroma traits specific to white berries, such as the two 705

terpene synthase genes TPS55 and TPS59 (Martin et al., 2010) (Fig. 8). Other DEGs 706 expressed in white berries encoded a multidrug and toxic compound extrusion (MATE) 707

protein, which probably transports secondary metabolites to the vacuole (Zhao et al., 2011; 708

Marinova et al., 2007), a nitrate transporter, and a major facilitator superfamily protein 709

(MFS), possibly required for the production of volatile thiol precursors that characterize white 710

berry varieties (Tominaga et al., 2000) (Fig. 8). Notably, when the DEGs distinguishing red 711 and white berry varieties were removed, the transcriptomic differences among berry samples 712

were still evident, suggesting that genotypes are distinguished by profound transcriptomic 713

differences affecting many processes in addition to anthocyanin accumulation and related 714

MYBA functions, as previously reported in high-anthocyanin tomatoes (Povero et al., 2011). 715

When PCA was applied to the entire transcriptome dataset, it was clear that the major 716

contribution to the separation of samples at stage H was provided by the red berry genotypes. 717

We therefore focused on those samples and found that 6,003 transcripts were needed to 718

maintain the separation among the red varieties, confirming that a substantial proportion of 719

the berry transcriptome changes during ripening, particularly in the varieties Barbera, Negro 720

amaro and Refosco, and that the transcriptional rearrangement may be related to anthocyanin 721

content. The 6,003 selected genes included many involved in carbohydrate metabolism, 722



32

relating to the differences in sugar content among the genotypes. The HT5 and INV4 genes 723

encoding a hexose transporter and a cell wall invertase, which were identified as DEGs when 724

searching for differences between the red and white varieties, were particularly strongly 725

expressed in Barbera, Negro amaro and Refosco. In contrast, the HT2, HT3/HT7 and 726



33

HT6/TMT2 genes were expressed at higher levels in all the other grapes. Many stress response 727

genes were also strongly expressed in Barbera, Negro amaro and Refosco berries, including 728

37 STS genes, 7 jasmonate ZIM-domain (JAZ/TIFY) genes, and several genes encoding 729

transcription factors or related to transport, signal transduction and secondary metabolism 730

(Supplemental Dataset S11). 731

In order to determine whether these diverse functions could be linked to the anthocyanin 732

content indirectly (i.e. not through the direct activity of MYBA transcription factors) and in a 733

variety-independent manner, we compared the 6,003 selected genes with two external gene 734

sets: genes modulated by the ectopic expression of MYBA1 in the white variety Chardonnay, 735

resulting in a red phenotype, and genes modulated by the silencing of MYBA1 in the red 736

variety Shiraz, resulting in a white phenotype (Rinaldo et al., 2015). The ectopic expression 737

of MYBA1 in Chardonnay driven by the CaMV 35S promoter caused this factor to be 738

expressed also in pulp, which is not directly comparable with our experiment. In contrast, 739

MYBA1 silencing in Shiraz represented a more suitable framework for our investigation: a 740

large number of shared DEGs were identified during ripening in the transgenic Shiraz berries 741

and the Barbera, Negro amaro and Refosco varieties. The commonly modulated genes were 742

mostly related to carbohydrate metabolism, transcriptional activity and transport, suggesting 743

that anthocyanin levels may influence many other processes (Fig. 8). 744

The extent of anthocyanin accumulation in the berry skin might correlate with its opacity to 745

sunlight, profoundly influencing the transcriptomic changes during ripening. This is supported 746

by the preferential expression of several genes related to photosynthesis or the response to 747

visible and UV light in the white berries (and in red berries with low levels of anthocyanins) 748

such as HY5, encoding a bHLH transcription factor that mediates the promotion of flavonol 749

accumulation in the berry in response to UV-B (Loyola et al., 2016), and TPS genes that 750

promote terpenoid accumulation in response to light (Friedel et al., 2016). Anthocyanin levels 751

could also influence other metabolic pathways, similar to the way in which sugars act as 752

signals to promote anthocyanin accumulation (Dai et al., 2014). Anthocyanin biosynthesis 753

could also be part of a more general maturation process controlled by unidentified master 754

regulators that are more active in Refosco, Barbera and Negro amaro than in the other 755

varieties. 756

757

Stilbenoid biosynthesis may influence anthocyanin accumulation during maturation 758



34

Our survey provided a comprehensive profile of the phenylpropanoid/flavonoid biosynthesis 759

pathway in 10 varieties. The quantity and composition of flavonoids differs among grape 760

varieties and contributes to their distinctive traits (Mattivi et al., 2006). Indeed, differences in 761

the expression pattern of genes involved in anthocyanin decoration were found among red 762

varieties, suggesting differences in final anthocyanin profiles. The very high expression level 763

of one isoform of AOMT in Primitivo berries matched the high ratio of methoxyl to hydroxyl 764

anthocyanin forms reported in this variety; likewise, the low 3AT expression level in 765

Sangiovese berries corroborated the low acylated anthocyanins content in this cultivar 766

(Mattivi et al., 2006). 767

As well as distinguishing white and red cultivars, the total anthocyanin content was much 768

higher in Refosco, Barbera, and Negro amaro berries compared to Primitivo and especially 769

Sangiovese. The expression profiles of genes involved in flavonoid biosynthesis mirrored 770

these results: the entire phenylpropanoid pathway was more transcriptionally active in 771

Refosco, Negro amaro, and Barbera berries than Sangiovese and Primitivo, even at the early 772

steps catalyzed by PAL, C4H and 4CL (Figure 7A). In particular, we observed diverse 773 expression profiles among the PAL gene family, characterizing the P stage in both white and 774

red berries (Supplemental Figure S10) and the maturation phase (and specifically stage H) in 775 the red varieties (Fig. 7A). 776

These diverse expression profiles suggest that individual PAL genes serve phase-specific 777

functions, as already shown for the PAL gene family in the grapevine expression atlas (Fasoli 778

et al., 2012). Interestingly, the expression of PAL genes at stage H matched the expression 779

profile of 44 STS genes and the related transcriptional regulators MYB14 and MYB15 (Höll 780

et al., 2013), suggesting that the transcriptional activation of specific members of the PAL 781

gene family diverts flux towards stilbene synthesis. Stilbenes accumulate strongly in response 782

to a wide range of biotic and abiotic stresses (Versari et al., 2001; Vannozzi et al., 2011), and 783

are also part of the late ripening program in healthy unstressed berries (Gatto et al., 2008). 784

Red berries preferentially expressed genes involved in the neutralization of reactive oxygen 785

species and in abiotic stress responses, which correlates with the transcriptional activation of 786

the stilbenoid pathway in different varieties. Varietal-specific stilbene profiles have been 787

reported previously (Gatto et al., 2008; Vincenzi et al., 2013; Zenoni et al., 2016) suggesting 788

that the transcriptional activation of the stilbenoid pathway in Barbera, Negro amaro and 789

Refosco may be specific to the cultivar. 790

Although Refosco, Barbera, and Negro amaro accumulated more anthocyanins than the other 791



35

varieties, this was only slightly reflected by the expression of genes directly involved in 792

anthocyanin biosynthesis such as UFGT and MYBA1/MYBA2 (Fig. 7A and Supplemental 793 Figure S11). The stilbene and flavonoid pathways branch from the general phenylpropanoid 794 pathway and thus share the same precursors. Therefore, whereas the varietal-specific 795

reinforcement of the early steps in the general phenylpropanoid pathway may channel 796

precursors into the stilbene branch, the flavonoid branch may benefit indirectly, resulting in 797

the higher accumulation of anthocyanins. Nevertheless, the possibility that differences in 798

anthocyanin accumulation may also rely on different biosynthetic enzyme activities and/or 799

anthocyanin turnover cannot be ruled out. It was recently proposed that the stability of the 800

CHS, that catalyzes the first step of flavonoid biosynthesis, is controlled by a proteolytic 801

regulator in response to developmental cues and environmental stimuli (Zhang et al., 2017), 802

unveiling a possible post-translational mechanism controlling flavonoid biosynthesis. 803

804

CONCLUSION 805

We have investigated the core transcriptome of grape berry development and have also 806

identified variety-specific processes that lead to the diverse characteristics of different 807

grapevine genotypes. The core berry development transcriptome, comprising the genes with 808

similar transcriptional trends across all 10 varieties, emphasized that the transition from 809

vegetative growth to maturation relies more on the downregulation of genes associated with 810

vegetative growth than on the induction of ripening-specific traits. This core transcriptional 811

program provides a solid basis for the scientific community, allowing the functional 812

characterization of genes involved in the onset of ripening. The comparison of red and white 813

berries revealed substantial transcriptomic variation among the red cultivars during ripening, 814

correlating with differences in anthocyanin accumulation, suggesting that color development 815

affects the maturation process itself by triggering the transcriptional reprogramming of 816

several biological processes. This hypothesis helps to explain the differences between red and 817

white cultivars but also the differences among red berry varieties. 818

819

MATERIALS AND METHODS 820

Plant material 821



36

Berries were collected during the 2011 season from 10 grapevine (Vitis vinifera L.) cultivars: 822

Sangiovese, Barbera, Negro amaro, Refosco dal peduncolo rosso, Primitivo, Vermentino, 823

Garganega, Glera, Moscato bianco and Passerina. The 10 varieties were cultivated in the same 824

vineyard at the CREA-VE grapevine germplasm collection (Susegana, Veneto region, Italy). 825

All vines were grafted onto SO4 rootstock, had the same age and were cultivated on 826

homogeneous soil with the same training system and cultural practices (soil management, 827

irrigation, fertilization, pruning, and disease control). 828

Samples of berries were harvested at four developmental stages: pea-sized berry stage (Bbch 829

75) at 20 days after flowering (P), ‘berries beginning to touch’ stage (Bbch77) just prior to 830

veraison (PV), berry softening stage (Bbch 85) at the end of veraison (EV) and fully-ripe 831

berry stage (Bbch 89) at harvest (H) (Supplemental Table S3). Each sample consisted of at 832 least 100 berries collected from five vines of each variety block, from both sides of the 833

canopy and from different areas of the clusters. Sampling at stage H considered the 834

commercial Brix degree value of each variety but also the sanitary status of the berries (i.e. 835

clusters were harvested before the first signs of rotting). The berries were collected at the 836

same time of day (9–10 am), immediately frozen in liquid nitrogen and stored at –80 ºC. From 837

each berry sample, three biological replicates were prepared. The Brix degree value of the 838

must was measured using a digital DBR35 refractometer (Giorgio Bormac, Carpi, Italy). 839

840

RNA extraction 841

Total RNA was extracted from approximately 400 mg of berry pericarp tissue (entire berries 842

without seeds) ground in liquid nitrogen, using the Spectrum™ Plant Total RNA kit (Sigma-843

Aldrich, St. Louis, MO, USA) with some modifications (Fasoli et al., 2012). RNA quality and 844

quantity were determined using a Nanodrop 2000 spectrophotometer (Thermo Fisher 845

Scientific, Waltham, MA, USA) and a Bioanalyzer Chip RNA 7500 series II (Agilent 846

Technologies, Santa Clara, CA, USA). 847

848

Library preparation and sequencing 849

The 40 triplicate samples (10 varieties at four stages) yielded 120 non-directional cDNA 850

libraries, which were prepared from 2.5 µg of total RNA using the Illumina TruSeq RNA 851

Sample preparation protocol (Illumina Inc., San Diego, CA, USA). Library quality was 852

determined using a Bioanalyzer Chip DNA 1000 (Agilent). Single-end reads of 100 853



37

nucleotides were obtained using an Illumina Hiseq 1000 sequencer, and sequencing data were 854

generated using the base-calling software Illumina Casava v1.8 (33,438,747 ± 5,610,087 855

reads per sample). 856

857

Sequencing data analysis 858

The reads were aligned onto the Vitis vinifera 12x reference genome PN40024 (Jaillon et al., 859

2007) using TopHat v2.0.6 (Kim et al., 2013) with default parameters. An average of 85.55% 860

of reads was mapped for each sample (Supplemental Table S1). Mapped reads were used to 861 reconstruct the transcripts in Cufflinks v2.0.2 (Roberts et al., 2011) and the reference genome 862

annotation V1 (http://genomes.cribi.unipd.it/DATA). All reconstructed transcripts were 863

merged into a single non-redundant list of 29,287 transcripts using Cuffmerge, which merges 864

transcripts whose reads overlap and share a similar exon structure thus generating a longer 865

chain of connected exons. The gene ID is named as a row of the merged transcripts. Using 866

this novel list of transcripts, the normalized averaged expression of each transcript was 867

calculated as an FPKM value for each triplicate using the geometric normalization method. In 868

total, 26,807 genes were detected as expressed, i.e. the averaged FPKM value was > 0 in at 869

least one sample. Displaying the FPKM distribution of these 26,807 genes across the 40 870

samples (Supplemental Figure S12) showed a bimodal distribution as observed by 871 Hebenstreit et al. (2011) and Nagaraj et al. (2011). Separating FPKM values in two groups: 872

FPKM < 1 and FPKM > 1 enables to fit the main distribution centered at a value of ∼4 log2 873

FPKM, as described by Hebenstreit et al. (2011). Therefore, genes with an expression 874

intensity < 1 FPKM across the 40 samples (roughly corresponding to 1 mRNA per average 875

cell in the sample) were classified as minimally expressed genes and were removed from the 876

gene expression dataset. 877

The remaining dataset of 21,746 transcripts was used in the subsequent experiments. DEGs 878

were identified by comparing each pair of consecutive developmental stages (i.e. P-PV, PV-879

EV, and EV-H) using Cuffdiff v2.0.2 (Trapnell et al., 2012) with a false discovery rate 880

defined by Benjamini-Hochberg multiple tests of 5%. 881

882

Correlation analysis 883

A correlation matrix was prepared using R software and Pearson’s correlation coefficient as 884

the statistical metric in order to compare the 40 transcriptomes. The analysis was performed 885



38

using the log2 (FPKM average + 1) of the 21,746 transcripts considered to be expressed 886

(Vasudevan et al., 2015). Correlation values were converted into distance coefficients to 887

define the height scale of the dendrogram. 888

889

PCA and loading normalization 890

PCAs were carried out using SIMCA P+ v13.0 (Umetrics, Umeå, Sweden). The PCA applied 891

to the ripe berry transcriptomes was carried out using the 10 stage H samples and the 892

corresponding 21,746 gene expression values. Loadings, which represent new values of the 893

variables (genes) in the model plane, were extracted and normalized into linear correlation 894

coefficients between the genes and the principal component, by applying the following 895

formula: 896 × × 897 with N as the total number of genes involved in the PCA, e.g. 21,231 genes in this case. In 898

fact, 515 genes were excluded from the analysis due to the low number of samples (fewer 899

than two) yielding a

1 short title: transcriptomics of grape berry development · 3 50 abstract 51 grapevine berry...

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