Display options
Share it on

Metabolomics. 2016;12:39. doi: 10.1007/s11306-015-0927-z. Epub 2016 Jan 23.

GC-MS metabolic profiling of Cabernet Sauvignon and Merlot cultivars during grapevine berry development and network analysis reveals a stage- and cultivar-dependent connectivity of primary metabolites.

Metabolomics : Official journal of the Metabolomic Society

Alvaro Cuadros-Inostroza, Simón Ruíz-Lara, Enrique González, Aenne Eckardt, Lothar Willmitzer, Hugo Peña-Cortés

Affiliations

  1. Max-Planck Institute for Plant Molecular Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany ; MetasysX, Am Mühlenberg 11, 14476 Potsdam-Golm, Germany.
  2. Instituto de Ciencias Biológicas, Universidad de Talca, 2 Norte 685, Talca, Chile.
  3. Max-Planck Institute for Plant Molecular Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.

PMID: 26848290 PMCID: PMC4723623 DOI: 10.1007/s11306-015-0927-z

Abstract

Information about the total chemical composition of primary metabolites during grape berry development is scarce, as are comparative studies trying to understand to what extent metabolite modifications differ between cultivars during ripening. Thus, correlating the metabolic profiles with the changes occurring in berry development and ripening processes is essential to progress in their comprehension as well in the development of new approaches to improve fruit attributes. Here, the developmental metabolic profiling analysis across six stages from flowering to fully mature berries of two cultivars, Cabernet Sauvignon and Merlot, is reported at metabolite level. Based on a gas chromatography-mass spectrometry untargeted approach, 115 metabolites were identified and relative quantified in both cultivars. Sugars and amino acids levels show an opposite behaviour in both cultivars undergoing a highly coordinated shift of metabolite associated to primary metabolism during the stages involved in growth, development and ripening of berries. The changes are characteristic for each stage, the most pronounced ones occuring at fruit setting and pre-Veraison. They are associated to a reduction of the levels of metabolites present in the earlier corresponding stage, revealing a required catabolic activity of primary metabolites for grape berry developmental process. Network analysis revealed that the network connectivity of primary metabolites is stage- and cultivar-dependent, suggesting differences in metabolism regulation between both cultivars as the maturity process progresses. Furthermore, network analysis may represent an appropriate method to display the association between primary metabolites during berry developmental processes among different grapevine cultivars and for identifying potential biologically relevant metabolites.

Keywords: GC–MS; Grapevine berry; Grapevine metabolome; Metabolic profiling; Vitis vinifera

References

  1. Gene. 2007 Nov 1;402(1-2):40-50 - PubMed
  2. BMC Plant Biol. 2011 Nov 18;11:165 - PubMed
  3. PLoS One. 2011 Feb 17;6(2):e14708 - PubMed
  4. BMC Syst Biol. 2007 Jun 04;1:24 - PubMed
  5. BMC Bioinformatics. 2009 Dec 16;10:428 - PubMed
  6. J Exp Bot. 2007;58(7):1851-62 - PubMed
  7. BMC Genomics. 2007 Nov 22;8:428 - PubMed
  8. BMC Genomics. 2007 Nov 22;8:429 - PubMed
  9. Bioinformatics. 2009 Nov 1;25(21):2855-6 - PubMed
  10. Bioinformatics. 2004 Dec 12;20(18):3565-74 - PubMed
  11. Plant Physiol. 2011 Sep;157(1):405-25 - PubMed
  12. Plant Cell. 2002 May;14(5):1093-107 - PubMed
  13. BMC Plant Biol. 2013 Nov 20;13:184 - PubMed
  14. Plant Physiol. 2009 Mar;149(3):1505-28 - PubMed
  15. Science. 2006 Feb 10;311(5762):804-5 - PubMed
  16. Int J Mol Sci. 2014 Mar 10;15(3):4237-54 - PubMed
  17. Plant Cell. 2012 Sep;24(9):3489-505 - PubMed
  18. Plant Physiol Biochem. 2008 Nov;46(11):941-50 - PubMed
  19. PLoS One. 2012;7(6):e39547 - PubMed
  20. Plant Physiol. 2012 Aug;159(4):1713-29 - PubMed
  21. Plant Cell Environ. 2009 Sep;32(9):1211-29 - PubMed
  22. BMC Plant Biol. 2012 Oct 05;12:181 - PubMed
  23. Plant Physiol. 2014 Jan;164(1):55-68 - PubMed
  24. Proteomics. 2009 May;9(9):2503-28 - PubMed
  25. Int J Mol Sci. 2013 Sep 11;14(9):18711-39 - PubMed
  26. J Proteomics. 2011 Aug 12;74(8):1230-43 - PubMed
  27. Plant Physiol. 2014 Mar;164(3):1204-21 - PubMed
  28. J Agric Food Chem. 1999 Dec;47(12):4837-41 - PubMed
  29. Science. 2003 Dec 5;302(5651):1727-36 - PubMed
  30. Plant Physiol. 2010 Apr;152(4):1787-95 - PubMed
  31. Nature. 2001 May 3;411(6833):41-2 - PubMed
  32. New Phytol. 2005 Oct;168(1):9-24 - PubMed
  33. Plant Cell. 2010 Apr;22(4):1190-215 - PubMed
  34. Nucleic Acids Res. 2011 Jan;39(Database issue):D677-84 - PubMed
  35. Trends Biotechnol. 2013 Jan;31(1):29-36 - PubMed
  36. BMC Plant Biol. 2013 Oct 24;13:167 - PubMed
  37. PLoS One. 2013;8(4):e60422 - PubMed
  38. J Exp Bot. 2013 Mar;64(5):1345-55 - PubMed
  39. Bioinformatics. 2007 May 1;23(9):1164-7 - PubMed
  40. BMC Genomics. 2012 Dec 11;13:691 - PubMed
  41. Phys Rev Lett. 2000 Dec 11;85(24):5234-7 - PubMed
  42. Proteomics. 2004 Jan;4(1):78-83 - PubMed
  43. Plant Physiol. 2006 Dec;142(4):1380-96 - PubMed
  44. BMC Plant Biol. 2011 Nov 02;11:149 - PubMed
  45. Plant Physiol. 2010 Nov;154(3):1439-59 - PubMed
  46. Plant Physiol Biochem. 2011 Sep;49(9):1059-63 - PubMed
  47. Glycobiology. 2003 Jul;13(7):41R-53R - PubMed
  48. Plant Physiol Biochem. 2013 Jun;67:105-19 - PubMed
  49. Mol Plant Microbe Interact. 2003 Feb;16(2):115-22 - PubMed
  50. J Exp Bot. 2014 Aug;65(16):4543-59 - PubMed
  51. Bioinformatics. 2005 Apr 15;21(8):1635-8 - PubMed
  52. PLoS One. 2014 Feb 13;9(2):e88844 - PubMed
  53. Mar Drugs. 2011 Dec;9(12):2514-25 - PubMed
  54. Nature. 1998 Jun 4;393(6684):440-2 - PubMed
  55. Nat Protoc. 2006;1(1):387-96 - PubMed
  56. J Biochem. 2005 Jul;138(1):1-4 - PubMed
  57. Plant Physiol Biochem. 2014 Jan;74:141-55 - PubMed
  58. BMC Syst Biol. 2011 Jan 01;5:1 - PubMed
  59. Plant Physiol. 1996 May;111(1):275-83 - PubMed
  60. J Exp Bot. 2012 Oct;63(16):5773-85 - PubMed
  61. Plant Physiol. 2008 Oct;148(2):730-50 - PubMed

Publication Types