Grape contribution to wine.Expectations from new information and technologies.

Wine composition depends on must composition and wine making

Wine is made up of more than one thousand compounds

The majority of them come from the grapes

Grapevine contribution

Mesocarp

Water

Organic acidsMalateTartrate

SugarsGlucoseFructose

Exocarp

Phenolic compoundsTanninsCatechinsAnthocyaninsOther

TerpenesGeraniolLinaloolTerpineolNerolidol

Norisoprenoidsβ-damascenoneβ-ionone

Sulfur compounds

Factors determining the complexity grapevine composition

Environment

Growth &Development

Genotype

Genotype variation

• Cluster size and shape• Berry size and shape• Colour• Taste• Aroma• Etc.

• Rootstock genotype• Cultivar genotype• Somatic variation

Environmental variation

Physical environmentSoilWaterLightTemperature

Cultural conditionsTrellis systemPrunningFertilizationSoil managementIrrigation

Developmental variation resulting from genotype-environment interactions

PollinationFruit set

Cluster size/shape

Berry size

Cluster number Age of the plantFlowering inductionFertility

PollinationIrrigation

Jordan Koutroumanidis, Winetitles

Berry development and ripening

• Large amount of descriptive information on variation between major cultivars as well as empirical information on the effects of environmental factors and growing systems

• Reduced information on the molecular mechanisms responsible for the processes of berry development and ripening

• Almost no information on the genetic control of these processes as well as on the molecular basis of natural variation in composition and in environmental responses

Challenges for Viticulture in the XXI Century

• Quality production under sustainable systems

• Global climate change

Opportunities for Viticulture Research

• Grapevine genome sequence unraveled

• Functional genomics technologies (transcriptomics, proteomics, metabolomics, etc.)

• Prospects to understand nucleotide diversity related to phenotypic diversity

Grapevine genome sequence

• PN40024• Reference gene set (30434)• Reference genetic map (487 Mb)• 41,4% Repetitive DNA• Three ancestral genomes• Large gene families for secondary metabolites production (STS, TPS, etc.)

New tools to understand gene function

• Transcriptomics, Proteomics, Metabolomics provide enhanced tools for phenotypic analyses

• Developmental processes• Environmental responses• Genetic differences among cultivars

• Rapid and improved generation of knowledge on relevant processes

In a first step it should be possible to develop models on how a cultivar system behaves under different variables along its development

Second, we should be able to understand the relationship between genotypic and phenotypic diversity

New tools to understand gene function

• Custom made GrapeGen GeneChip

• 23096 probe sets

• About twice the information in commercial GeneChip

• Represent a consensus of vinifera sequences where overlaps in EST data existed, or individual sequence data from five cultivars: Cabernet Sauvignon, Muscat Hamburg, Pinot Noir, Chardonnay, Shiraz

• Improved annotation and gene representation

BIN annotation facilitates the use of functional analyses software applications

BINCODE NAME IDENTIFIER DESCRIPTION TYPE4.4 Cellular reponse overview.Abiotic stress.Light VVTU33616_x_at Q8W540 Early light-induced protein-like protein related cluster T4.4 Cellular reponse overview.Abiotic stress.Light VVTU40431_at Q8W540 Early light-induced protein-like protein related cluster T4.4 Cellular reponse overview.Abiotic stress.Light VVTU40867_x_at Q8W540 Early light-induced protein-like protein related cluster T4.4 Cellular reponse overview.Abiotic stress.Light VVTU7881_at Q8W540 Early light-induced protein-like protein related cluster T4.4 Cellular reponse overview.Abiotic stress.Light VVTU18150_at Q94F86 Early light inducible protein related cluster T4.4 Cellular reponse overview.Abiotic stress.Light VVTU33020_x_at Q94F86 Early light inducible protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU16733_s_at O82730 Monogalactosyldiacylglycerol synthase related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU35241_at O82730 Monogalactosyldiacylglycerol synthase related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU1390_s_at Q3HVL7 TSJT1-like protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU1295_at Q69F98 Phytochelatin synthetase-like protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU24339_at Q6K1X0 Putative iron-stress related protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU13091_at Q6UK15 Al-induced protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU16936_at Q6UK15 Al-induced protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU19149_at Q6UK15 Al-induced protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU22224_s_at Q6UK15 Al-induced protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU37244_at Q6UK15 Al-induced protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU3222_at Q7Y0S8 Erg-1 related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU3659_at Q7Y0S8 Erg-1 related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU26592_at Q84JR4 Phytochelatin synthase related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU14798_at Q8LGF0 NOI protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU25240_at Q8LGF0 NOI protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU33825_at Q94KH9 Aluminium induced protein related cluster T4.5 Cellular reponse overview.Abiotic stress.Mineral VVTU32192_at Q9S807 Phosphate starvation regulator protein related cluster T4.6 Cellular reponse overview.Abiotic stress.Osmotic VVTU18099_at O04895 Betaine-aldehyde dehydrogenase, chloroplast precursor related cluster T4.6 Cellular reponse overview.Abiotic stress.Osmotic VVTU12252_s_at Q6JSK3 Betaine aldehyde dehydrogenase related cluster T4.6 Cellular reponse overview.Abiotic stress.Osmotic VVTU16349_at Q6S9W9 Betaine-aldehyde dehydrogenase related cluster T4.6 Cellular reponse overview.Abiotic stress.Osmotic VVTU1165_at Q8H5F0 Betaine aldehyde dehydrogenase-like related cluster T

Transcriptional analyses of berry development and ripening

2 mm 7 mm 15 mm v 50 v100 120 130-150

Berries

Exocarp

Mesocarp

Seeds

Greeen stages Ripening

Muscat Hamburg3 independent biological replicas2 different years (2005-2006)

Veraison

Total RNA extraction

RNA labeling and GeneChip Hybridization

Cluster analyses (K-means)

Functional analyses (Babelomics)

Functional analyses (Mapman)

Green

Veraison Ripening

Skin

Flesh

BIN Name Elements Corrected P values Green Veraison Skin Veraison Flesh Ripening Skin Ripening Flesh3 Cell wall metabolism 639 0.284 0.996 0.150 5.409 E-4 9.131 E-83.1 Cell wall metabolism.Cell wall biosynthesis 193 3.680 E-4 0.257 0.001 0.595 0.0313.2 Cell wall metabolism.Cell wall modification 296 0.112 0.996 0.818 1.173 E-4 6.438 E-63.4 Cell wall metabolism.Related protein 68 0.301 0.550 0.533 0.123 0.0043.3 Cell wall metabolism.Structural protein 82 0.614 0.001 0.050 0.109 0.799

Cell wall metabolism along berry development in Muscat Hamburg

BIN Name ElementsCorrected p-value

Flesh Skin19 Secondary metabolism 531 0.003 1.56933E-0519.1 Secondary metabolism.Alkaloids 50 0.039 0.93219.4 Secondary metabolism.Phenylpropanoids 271 0.059 3.47419E-0619.4.1 Secondary metabolism.Phenylpropanoids.Flavonoids 193 0.332 0.07019.4.1.1 Secondary metabolism.Phenylpropanoids.Flavonoids.Anthocyanin biosyhthesis 51 0.258 0.48019.4.1.3 Secondary metabolism.Phenylpropanoids.Flavonoids.Flavonoids 128 0.057 0.07119.4.2 Secondary metabolism.Phenylpropanoids.Phytoalexins 50 0.329 4.43752E-0519.4.4 Secondary metabolism.Phenylpropanoids.General pathway 28 0.158 0.008

Flesh Skin

Secondary metabolism differences between CR and RG

RG CR

New tools to understand gene functionGenetic control of relevant traits

• Genetic and molecular identification of genes responsible for relevant traits

• Understanding the relationship among nucleotidic and phenotypic diversity

• Genetic variation• Natural genetic variation (cultivars and clones) • Artificial variants (mutant collections)• Genetic transformation

• Molecular tools• Molecular markers (SSRs and SNPs)

New tools to understand gene functionMolecular markers: SNPs

SNP289_840Vvi_69366SNP593_1497Vvi_18108

SNP699_31120SNP929_81i25

SNP853_31240SNP1203_8842SNP1323_15545SNP1553_39553SNP865_8054SNP377_251SNP1481_15655SNP1499_12663Vvi_228365

SNP1385_8675SNP1055_141SNP1295_22578SNP881_20282

8SNP1057_5050SNP663_5783SNP311_198SNP1211_1665

Vvi_1099213

Vvi_787123

SNP571_22742

Vvi_1032956

9

SNP649_5678SNP947_288SNP1029_579

SNP283_3218

SNP447_24435SNP1437_10037

SNP397_33146

10

SNP197_820SNP635_215

SNP987_2624

SNP317_15544SNP1423_26550Vvi_1035354

11

SNP1347_1002SNP691_1393Vvi_262310Vvi_340011

Vvi_1307620SNP1397_21524SNP1583_15930

SNP1015_6745Vvi_173146SNP241_20148

SNP961_139Vvi_562958

SNP1495_14871SNP1419_18674SNP1151_39777SNP429_10179

SNP1445_21888Vvi_37791Vvi_1280594

7

SNP1201_993SNP189_1314SNP1215_1385SNP557_1047

Vvi_12882Vvi_58922

SNP1119_17650

12

Vvi_41460

SNP1187_3530SNP653_90SNP351_8532Vvi_738737SNP259_199SNP1363_17143

13

Vvi_2319SNP325_6510

Vvi_229223Vvi_122224

SNP1411_56533SNP421_23437Vvi_316340

SNP897_5754SNP1035_22657

14 SNP341_196SNP451_2870

SNP1507_647SNP1371_29010

SNP227_19124Vvi_321226

Vvi_128036Vvi_1127342SNP555_13243

SNP1311_4854

15

SNP1335_204SNP1231_547

SNP1079_5814

VBFT_36127

SNP1349_17448

16

SNP677_5090LFY-ET2_3516Vvi_69879

SNP579_18733

SNP877_26840

SNP879_30862

17 SNP1023_2275SNP1045_29111SNP1003_336Vvi_22115SNP1001_25017SNP355_15426SNP453_375Vvi_161727SNP1519_4728Vvi_19629

Vvi_992044

SNP883_16057SNP415_20958

SNP1391_4866

Vvi_1077778

18

SNP817_20924SNP459_140SNP253_14531SNP819_21033

Vvi_7824Vvi_118742

SNP1127_7049

19

SNP613_3150

SNP553_9813SNP497_28116SNP867_170SNP425_20525SNP1493_58SNP1563_28026

SNP1219_19148

3SNP1439_90SNP1453_40SNP229_112

3

Vvi_119611SNP683_120SNP129_23717SNP1427_12024SNP1517_271SNP1527_14425SNP269_30827SNP851_11029SNP357_37131SNP517_22432SNP1241_20739

SNP477_239Vvi_693453SNP1025_10056SNP1021_16361SNP1157_6463

1SNP829_2810

SNP1293_29411SNP437_12916SNP1487_4119SNP581_11422

Vvi_922733

SNP1229_219Vvi_80554

2SNP1513_1530SNP255_2653SNP1409_489SNP655_9315

SNP191_10032SNP715_26035Vvi_666837

SNP281_6451SNP891_10954SNP135_31657SNP811_4259SNP1559_29164Vvi_1051667SNP1399_8169Vvi_254370

4SNP1027_690

SNP1071_15112SNP1431_58413SNP1053_8114SNP625_27819SNP1471_17924SNP855_103Vvi_531625SNP1235_3528Vvi_1011330

SNP567_34141Vvi_1157244

Vvi_1038360

5SNP945_88SNP1109_2530SNP1345_601Vvi_202111SNP873_244SNP709_25813SNP1213_9914SNP915_8817SNP1393_6219

SNP559_11032

SNP895_38240SNP1043_37841

SNP1033_7655

6

Identification of QTLs and genes

QTL analyses• Flower sex• Berry color• Berry size• Muscat flavor• Seedlessness• Seed number• Leaf shape• Powdery mildeu resistance• Downy mildeu resistance• Pierce’s disease resistance• Nematode resistance (Xiphinema index)• Low magnesium uptake• Flowering time• Veraison time• Veraison period

Spontaneous mutations• Flower sex • Berry color (multiple cultivars)• Berry size (Grenache)• Berry flesh (Ugni blanc)• Muscat flavor (Chaselass)• Acid content• Seedlessness (Sultanina)• Internode length (Pinot Menieur)• Leaf shape (Chaselass)• Cluster size (Carignan RRM)

GeneChips can also help identify genes altered in somatic variants

IS1 IS2 IS3

• Carignan somatic variant RRM• Reiterated Production of reproductive meristems• Delayed flower anthesis• Larger cluster size and complexity

Caused by natural trans-activation from a transposable element insertion in VvTFL1A promoter

Applications in viticulture

• Diagnostic tools•Evaluation of plant physiopathological conditions• Evaluation of the effect of cultural practices

• Breeding tools• Clonal selection, identification and protection• Marker assisted breeding of new cultivars

Tempranillo tinto Tempranillo blanco

Diego Lijavetzky CNB-CSIC, Madrid, SpainJosé Díaz-Riquelme CNB-CSIC, ETSIA-UPM, Madrid, SpainLucie Fernández CNB-CSICRita Francisco ITQB, Lisboa, PortugalJosé Antonio Cabezas IMIDRA, Madrid, Spain

Collaborators:Maria José Carmona ETSIA-UPMJuan Carreño IMIDA, Murcia, SpainLaurent Torregrosa INRA/SupAgro-UMR, Montpellier, FR

Acknowledgements