conference schedule and program with abstracts - Horticulture ...

P-70
Proteomic analysis among different ‘Aglianico’ ecotypes
A.S. Negri*, B. Prinsi, S. Imazio, G. de Lorenzis, O. Failla, A. Scienza, M. Cocucci, L. Espen
Dipartimento di Produzione Vegetale, Università degli Studi di Milano, Milano, Italy
*Corresponding author: alfredo.negri@unimi.it
Molecular markers are extensively used for the identification of different vines. Among them,
one of the techniques of major success is based on the analysis of the Simple Sequence Repeats
(SSRs). Although they are useful in discriminating among different varieties, it is unusual that
they can distinguish clones deriving from the same vine and there is a strong request for devices
that could achieve this purpose. In this view, it could be interesting to evaluate the potential of
proteomics. Using this approach, for instance, it was possible to define the biodiversity of six
ecotypes of Arabidopsis thaliana. Moving from these considerations, three ecotypes of cv.
Aglianico (Taurasi, Taburno and Vulture) were compared. They look identical but give rise to
wines that are characterized by different qualitative features. Through the use of 20 SSRs
markers, it was demonstrated that they belong to the same cultivar because it was not possible to
find any differences. The proteomic analysis was conducted comparing the 2-DE patterns of
berry exocarp in order to set up a method useful in searching for distinctive traits. On the basis of
previous studies that underlined the changes of the proteome of this tissue during maturation or
driven by the environment, the analysis was performed considering two moments of maturation
during the years 2008 and 2009. In order to deal with the high number of data and to sort the
variability among them, gels analysis, after their alignment, was carried out through multivariate
statistical techniques such as Principal Component Analysis and Linear Discriminant Analysis.
The application of such approaches allowed to distinguish the three ecotypes and to isolate the
proteins more responsible of the discrimination models, then characterized by mass
spectrometry. These results confirm the effectiveness of this approach integrating proteomics and
multivariate statistics for the study of intravarietal diversity.
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