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

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Presentation on theme: "Grape contribution to wine. Expectations from new information and technologies."— Presentation transcript:

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

2 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

3 Grapevine contribution Mesocarp Water Organic acids Malate Tartrate Sugars Glucose Fructose Exocarp Phenolic compounds Tannins Catechins Anthocyanins Other Terpenes Geraniol Linalool Terpineol Nerolidol Norisoprenoids β-damascenone β-ionone Sulfur compounds

4 Factors determining the complexity grapevine composition Environment Growth & Development Genotype

5 Genotype variation Cluster size and shape Berry size and shape Colour Taste Aroma Etc. Rootstock genotype Cultivar genotype Somatic variation

6 Environmental variation Physical environment Soil Water Light Temperature Cultural conditions Trellis system Prunning Fertilization Soil management Irrigation

7 Developmental variation resulting from genotype-environment interactions Pollination Fruit set Cluster size/shape Berry size Cluster number Age of the plant Flowering induction Fertility Pollination Irrigation

8 Jordan Koutroumanidis, Winetitles Berry development and ripening

9 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

10 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

11 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.)

12 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

13 New tools to understand gene function Custom made GrapeGen GeneChip 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

14 BIN annotation facilitates the use of functional analyses software applications BINCODENAMEIDENTIFIERDESCRIPTIONTYPE 4.4 Cellular reponse overview.Abiotic stress.LightVVTU33616_x_atQ8W540 Early light-induced protein-like protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU40431_atQ8W540 Early light-induced protein-like protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU40867_x_atQ8W540 Early light-induced protein-like protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU7881_atQ8W540 Early light-induced protein-like protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU18150_atQ94F86 Early light inducible protein related clusterT 4.4 Cellular reponse overview.Abiotic stress.LightVVTU33020_x_atQ94F86 Early light inducible protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU16733_s_atO82730 Monogalactosyldiacylglycerol synthase related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU35241_atO82730 Monogalactosyldiacylglycerol synthase related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU1390_s_atQ3HVL7 TSJT1-like protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU1295_atQ69F98 Phytochelatin synthetase-like protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU24339_atQ6K1X0 Putative iron-stress related protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU13091_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU16936_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU19149_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU22224_s_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU37244_atQ6UK15 Al-induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU3222_atQ7Y0S8 Erg-1 related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU3659_atQ7Y0S8 Erg-1 related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU26592_atQ84JR4 Phytochelatin synthase related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU14798_atQ8LGF0 NOI protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU25240_atQ8LGF0 NOI protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU33825_atQ94KH9 Aluminium induced protein related clusterT 4.5 Cellular reponse overview.Abiotic stress.MineralVVTU32192_atQ9S807 Phosphate starvation regulator protein related clusterT 4.6 Cellular reponse overview.Abiotic stress.OsmoticVVTU18099_atO04895 Betaine-aldehyde dehydrogenase, chloroplast precursor related clusterT 4.6 Cellular reponse overview.Abiotic stress.OsmoticVVTU12252_s_atQ6JSK3 Betaine aldehyde dehydrogenase related clusterT 4.6 Cellular reponse overview.Abiotic stress.OsmoticVVTU16349_atQ6S9W9 Betaine-aldehyde dehydrogenase related clusterT 4.6 Cellular reponse overview.Abiotic stress.OsmoticVVTU1165_atQ8H5F0 Betaine aldehyde dehydrogenase-like related clusterT

15 Transcriptional analyses of berry development and ripening 2 mm7 mm15 mmv 50v Berries Exocarp Mesocarp Seeds Greeen stagesRipening Muscat Hamburg 3 independent biological replicas 2 different years ( ) Veraison Total RNA extraction RNA labeling and GeneChip Hybridization Cluster analyses (K-means) Functional analyses (Babelomics) Functional analyses (Mapman)

16 Green VeraisonRipening Skin Flesh BINNameElementsCorrected P values GreenVeraison SkinVeraison FleshRipening SkinRipening Flesh 3Cell wall metabolism E E-8 3.1Cell wall metabolism.Cell wall biosynthesis E Cell wall metabolism.Cell wall modification E E-6 3.4Cell wall metabolism.Related protein Cell wall metabolism.Structural protein Cell wall metabolism along berry development in Muscat Hamburg

17 BINNameElements Corrected p-value FleshSkin 19Secondary metabolism E Secondary metabolism.Alkaloids Secondary metabolism.Phenylpropanoids E Secondary metabolism.Phenylpropanoids.Flavonoids Secondary metabolism.Phenylpropanoids.Flavonoids.Anthocyanin biosyhthesis Secondary metabolism.Phenylpropanoids.Flavonoids.Flavonoids Secondary metabolism.Phenylpropanoids.Phytoalexins E Secondary metabolism.Phenylpropanoids.General pathway FleshSkin Secondary metabolism differences between CR and RG RGCR

18 New tools to understand gene function Genetic 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)

19 New tools to understand gene function Molecular markers: SNPs SNP289_84 0 Vvi_ SNP593_149 7 Vvi_ SNP699_ SNP929_81i 25 SNP853_ SNP1203_88 42 SNP1323_ SNP1553_ SNP865_80 54 SNP377_251 SNP1481_ SNP1499_ Vvi_ SNP1385_86 75 SNP1055_141 SNP1295_ SNP881_ SNP1057_505 0 SNP663_578 3 SNP311_198 SNP1211_166 5 Vvi_ Vvi_ SNP571_ Vvi_ SNP649_567 8 SNP947_288 SNP1029_57 9 SNP283_32 18 SNP447_ SNP1437_ SNP397_ SNP197_82 0 SNP635_21 5 SNP987_26 24 SNP317_ SNP1423_ Vvi_ SNP1347_100 2 SNP691_139 3 Vvi_ Vvi_ Vvi_ SNP1397_ SNP1583_ SNP1015_67 45 Vvi_ SNP241_ SNP961_139 Vvi_ SNP1495_ SNP1419_ SNP1151_ SNP429_ SNP1445_ Vvi_ Vvi_ SNP1201_99 3 SNP189_131 4 SNP1215_138 5 SNP557_104 7 Vvi_12882 Vvi_ SNP1119_ Vvi_ SNP1187_35 30 SNP653_90 SNP351_85 32 Vvi_ SNP259_199 SNP1363_ Vvi_2319 SNP325_65 10 Vvi_ Vvi_ SNP1411_ SNP421_ Vvi_ SNP897_57 54 SNP1035_ SNP341_196 SNP451_287 0 SNP1507_64 7 SNP1371_ SNP227_ Vvi_ Vvi_ Vvi_ SNP555_ SNP1311_ SNP1335_204 SNP1231_54 7 SNP1079_58 14 VBFT_ SNP1349_ SNP677_509 0 LFY-ET2_351 6 Vvi_ SNP579_ SNP877_ SNP879_ SNP1023_227 5 SNP1045_ SNP1003_336 Vvi_ SNP1001_ SNP355_ SNP453_375 Vvi_ SNP1519_47 28 Vvi_ Vvi_ SNP883_ SNP415_ SNP1391_48 66 Vvi_ SNP817_ SNP459_140 SNP253_ SNP819_ Vvi_7824 Vvi_ SNP1127_ SNP613_315 0 SNP553_98 13 SNP497_ SNP867_170 SNP425_ SNP1493_58 SNP1563_ SNP1219_ SNP1439_90 SNP1453_40 SNP229_112 3 Vvi_ SNP683_120 SNP129_ SNP1427_ SNP1517_271 SNP1527_ SNP269_ SNP851_ SNP357_ SNP517_ SNP1241_ SNP477_239 Vvi_ SNP1025_ SNP1021_ SNP1157_ SNP829_281 0 SNP1293_ SNP437_ SNP1487_41 19 SNP581_ Vvi_ SNP1229_219 Vvi_ SNP1513_153 0 SNP255_265 3 SNP1409_48 9 SNP655_93 15 SNP191_ SNP715_ Vvi_ SNP281_64 51 SNP891_ SNP135_ SNP811_42 59 SNP1559_ Vvi_ SNP1399_81 69 Vvi_ SNP1027_69 0 SNP1071_ SNP1431_ SNP1053_81 14 SNP625_ SNP1471_ SNP855_103 Vvi_ SNP1235_35 28 Vvi_ SNP567_ Vvi_ Vvi_ SNP945_88 SNP1109_253 0 SNP1345_60 1 Vvi_ SNP873_244 SNP709_ SNP1213_99 14 SNP915_88 17 SNP1393_62 19 SNP559_ SNP895_ SNP1043_ SNP1033_

20 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)

21 GeneChips can also help identify genes altered in somatic variants IS1 IS2IS3 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

22 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

23 Diego LijavetzkyCNB-CSIC, Madrid, Spain José Díaz-RiquelmeCNB-CSIC, ETSIA-UPM, Madrid, Spain Lucie FernándezCNB-CSIC Rita FranciscoITQB, Lisboa, Portugal José Antonio CabezasIMIDRA, Madrid, Spain Collaborators: Maria José CarmonaETSIA-UPM Juan CarreñoIMIDA, Murcia, Spain Laurent TorregrosaINRA/SupAgro-UMR, Montpellier, FR Acknowledgements


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