A comparative analysis of the metabolomes of different berry tissues between Vitis vinifera and wild American Vitis species, supported by a computer-assisted identification strategy

Grape (Vitis vinifera L.) is among the most cultivated plants in the world. Its origin traces back to the Neolithic era, when the first human communities started to domesticate wild Vitis sylvestris L. grapes to produce wines. Domestication modified Vitis vinifera to assume characteristics imparted from the humans, selecting desired traits (e.g. specific aromas), and excluding the undesired ones. This process made this species very different from all the other wild grape species existing around the world, including its progenitor, Vitis sylvestris. Metabolomics is a field of the sciences that comparatively studies the whole metabolite set of two (or more) groups of samples, to point out the chemical diversity and infer on the variability in the metabolic pathways between the groups. Crude metabolomics observation can be often used for hypotheses generation, which need to be confirmed by further experiments. In my case, starting from the grape metabolome project (Mattivi et al. unpublished data), I had the opportunity to put hands on a huge dataset built on the berries of over 100 Vitis vinifera grape varieties, tens of grape interspecific hybrids and few wild grape species analyzed per four years; all included in a single experiment. Starting from this data handling, I designed specific experiments to confirm the hypotheses generated from the observation of the data, to improve compound identification, to give statistical meaning to the differences, to localize the metabolites in the berries and extrapolate further information on the variability existing among the grape genus. The hypotheses formulated were two: 1) several glyco-conjugated volatiles can be detected, identified and quantified in untargeted reverses-phase liquid chromatography-mass spectrometry; 2) The chemical difference between Vitis vinifera and wild grape berries is wider than reported in literature. Furthermore, handling a huge dataset of chemical standards injected under the same conditions of the sample set, I also formulated a third hypothesis: 3) metabolites with similar chemical structures are more likely to generate similar signals in LC-MS, therefore the combined use of the signals can predict the more likely chemical structure of unknown markers. In the first study (chapter 5), the signals putatively corresponding to glycoconjugated volatiles have been first enclosed in a specific portion of the temporal and spectrometric space of the LC-HRMS chromatograms, then they have been subjected to MS/MS analysis and lastly their putative identity have been confirmed through peak intensity correlation between the signals measured in LC-HRMS and GC-MS. In the second study (chapter 6), a multivariate regression model has been built between LC-HRMS signals and the substructures composing the molecular structure of the compounds and its accuracy and efficacy in substructure prediction have been demonstrated. In the third study (chapter 7), I comparatively studied some wild grapes versus some Vitis vinifera varieties separating the basic components of the grape berry (skin, flesh and seeds), with the aim to identify all the detected metabolites that differentiate the two groups, which determine a difference in quality between the wild versus domesticated grapes, especially regarding wine production.