Identification of Putative Stage-Specific Grapevine Berry Biomarkers and Omics Data Integration into Networks

Author:

Zamboni Anita1,Di Carli Mariasole1,Guzzo Flavia1,Stocchero Matteo1,Zenoni Sara1,Ferrarini Alberto1,Tononi Paola1,Toffali Ketti1,Desiderio Angiola1,Lilley Kathryn S.1,Pè M. Enrico1,Benvenuto Eugenio1,Delledonne Massimo1,Pezzotti Mario1

Affiliation:

1. Department of Biotechnology, University of Verona, 37134 Verona, Italy (A.Z., F.G., S.Z., A.F., P.T., K.T., M.D., M.P.); Italian National Agency for New Technologies, Energy, and Sustainable Economic Development, Casaccia Research Centre, Dipartimento Biotec, Sezione Genetica e Genomica Vegetale, 00123 Rome, Italy (M.D.C., A.D., E.B.); S-IN Soluzioni Informatiche, 36100 Vicenza, Italy (M.S.); Cam

Abstract

Abstract The analysis of grapevine (Vitis vinifera) berries at the transcriptomic, proteomic, and metabolomic levels can provide great insight into the molecular events underlying berry development and postharvest drying (withering). However, the large and very different data sets produced by such investigations are difficult to integrate. Here, we report the identification of putative stage-specific biomarkers for berry development and withering and, to our knowledge, the first integrated systems-level study of these processes. Transcriptomic, proteomic, and metabolomic data were integrated using two different strategies, one hypothesis free and the other hypothesis driven. A multistep hypothesis-free approach was applied to data from four developmental stages and three withering intervals, with integration achieved using a hierarchical clustering strategy based on the multivariate bidirectional orthogonal projections to latent structures technique. This identified stage-specific functional networks of linked transcripts, proteins, and metabolites, providing important insights into the key molecular processes that determine the quality characteristics of wine. The hypothesis-driven approach was used to integrate data from three withering intervals, starting with subdata sets of transcripts, proteins, and metabolites. We identified transcripts and proteins that were modulated during withering as well as specific classes of metabolites that accumulated at the same time and used these to select subdata sets of variables. The multivariate bidirectional orthogonal projections to latent structures technique was then used to integrate the subdata sets, identifying variables representing selected molecular processes that take place specifically during berry withering. The impact of this holistic approach on our knowledge of grapevine berry development and withering is discussed.

Publisher

Oxford University Press (OUP)

Subject

Plant Science,Genetics,Physiology

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