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

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. Exocarp Phenolic compounds

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

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

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

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

  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 Age of the plant Flowering induction Fertility Cluster number Cluster size/shape Pollination Fruit set Berry size Pollination Irrigation

  8. Berry development and ripening Jordan Koutroumanidis, Winetitles

  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 • 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

  14. BIN annotation facilitates the use of functional analyses software applications

  15. Transcriptional analyses of berry development and ripening Greeen stages Veraison Ripening 2 mm 7 mm 15 mm v 50 v100 120 130-150 Berries Muscat Hamburg 3 independent biological replicas 2 different years (2005-2006) Exocarp Mesocarp Seeds • Total RNA extraction • RNA labeling and GeneChip Hybridization • Cluster analyses (K-means) • Functional analyses (Babelomics) • Functional analyses (Mapman)

  16. Cell wall metabolism along berry development in Muscat Hamburg Veraison Ripening Green Skin Flesh

  17. Secondary metabolism differences between CR and RG Flesh Skin RG CR

  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. 7 1 2 3 4 5 6 SNP1347_100 2 SNP945_88 SNP1439_90 0 SNP691_139 3 SNP1109_253 SNP1513_153 SNP1453_40 SNP829_281 SNP613_315 SNP1027_69 0 3 0 0 0 SNP1345_60 1 SNP255_265 SNP229_112 3 Vvi_2623 10 Vvi_3400 11 Vvi_1196 11 Vvi_2021 11 SNP1071_151 12 SNP1409_48 9 SNP683_120 SNP873_244 SNP1293_294 11 17 SNP1431_584 13 13 SNP129_237 SNP553_98 13 SNP709_258 Vvi_13076 20 SNP1053_81 SNP655_93 14 15 SNP1427_120 SNP437_129 24 SNP497_281 16 16 SNP1213_99 14 SNP625_278 19 SNP1397_215 24 SNP1517_271 SNP1487_41 19 SNP915_88 17 25 SNP867_170 SNP1471_179 24 SNP1527_144 SNP581_114 22 25 SNP1393_62 19 SNP425_205 SNP855_103 SNP1583_159 30 SNP269_308 27 25 SNP1493_58 Vvi_5316 SNP851_110 29 26 SNP1563_280 SNP1235_35 28 SNP357_371 31 SNP191_100 32 SNP559_110 32 Vvi_9227 33 Vvi_10113 30 SNP517_224 32 SNP715_260 35 SNP1241_207 39 Vvi_6668 SNP1015_67 37 45 SNP895_382 40 Vvi_1731 46 SNP567_341 41 SNP1043_378 41 SNP241_201 48 Vvi_11572 44 SNP477_239 SNP1219_191 48 53 SNP281_64 51 SNP961_139 Vvi_6934 58 SNP1229_219 SNP891_109 54 Vvi_5629 SNP1025_100 56 54 SNP1033_76 55 Vvi_805 SNP135_316 57 SNP1021_163 61 SNP811_42 59 Vvi_10383 60 SNP1157_64 63 SNP1559_291 64 SNP1495_148 71 12 Vvi_10516 67 9 SNP1419_186 74 SNP1399_81 69 8 10 SNP1151_397 77 Vvi_2543 70 SNP429_101 79 SNP1201_99 3 SNP1057_505 0 SNP189_131 4 11 SNP663_578 3 SNP649_567 SNP1215_138 8 5 SNP289_84 0 SNP1445_218 88 SNP311_198 SNP947_288 SNP557_104 7 5 Vvi_377 91 Vvi_6936 6 9 SNP1211_166 SNP1029_57 Vvi_12805 94 SNP593_149 7 Vvi_10992 13 SNP197_82 Vvi_1810 0 8 SNP283_32 18 Vvi_12882 SNP635_21 5 13 22 Vvi_589 Vvi_7871 23 SNP699_311 20 SNP929_81i 25 SNP447_244 35 Vvi_4146 0 SNP1437_100 37 SNP987_26 24 SNP571_227 42 SNP853_312 SNP397_331 40 46 18 SNP1203_88 42 SNP1119_176 50 SNP1323_155 45 SNP1553_395 53 17 SNP1023_227 5 Vvi_10329 56 SNP865_80 54 SNP317_155 44 SNP1045_291 11 SNP377_251 SNP1187_35 30 SNP1003_336 55 15 SNP1481_156 15 SNP1423_265 50 SNP653_90 Vvi_221 32 SNP1499_126 63 SNP351_85 SNP1001_250 Vvi_10353 17 54 SNP677_509 0 Vvi_2283 65 Vvi_7387 14 37 SNP355_154 26 SNP259_199 SNP453_375 LFY-ET2_351 6 SNP341_196 43 27 16 SNP1363_171 0 Vvi_1617 Vvi_6987 9 SNP1385_86 75 SNP451_287 SNP1519_47 28 SNP1055_141 Vvi_2319 78 Vvi_196 29 10 SNP1507_64 7 SNP1295_225 SNP325_65 SNP1371_290 10 SNP881_202 82 SNP1335_204 19 7 SNP1231_54 Vvi_2292 23 SNP1079_58 Vvi_9920 14 44 Vvi_1222 24 SNP227_191 24 SNP579_187 33 SNP817_209 24 Vvi_3212 26 SNP459_140 SNP1411_565 33 31 SNP883_160 SNP877_268 57 40 SNP253_145 VBFT_361 27 SNP421_234 37 SNP415_209 58 SNP819_210 33 Vvi_1280 36 Vvi_3163 40 Vvi_11273 42 Vvi_7824 SNP1391_48 66 42 SNP555_132 43 Vvi_1187 SNP1127_70 49 SNP897_57 54 SNP1035_226 SNP1349_174 SNP879_308 Vvi_10777 57 48 62 78 SNP1311_48 54 New tools to understand gene function Molecular markers: SNPs

  20. Identification of QTLs and genes • 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) • 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

  21. 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

  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 blanco Tempranillo tinto

  23. Acknowledgements Diego Lijavetzky CNB-CSIC, Madrid, Spain José Díaz-Riquelme CNB-CSIC, ETSIA-UPM, Madrid, Spain Lucie Fernández CNB-CSIC Rita Francisco ITQB, Lisboa, Portugal José Antonio Cabezas IMIDRA, Madrid, Spain Collaborators: Maria José Carmona ETSIA-UPM Juan Carreño IMIDA, Murcia, Spain Laurent Torregrosa INRA/SupAgro-UMR, Montpellier, FR

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