Omics Approaches for Understanding Grapevine Berry Development: Regulatory Networks Associated with Endogenous Processes and Environmental Responses

Grapevine fruit development is a dynamic process that can be divided into three stages: formation (I), lag (II), and ripening (III), in which physiological and biochemical changes occur, leading to cell differentiation and accumulation of different solutes. These stages can be positively or negatively affected by multiple environmental factors. During the last decade, efforts have been made to understand berry development from a global perspective. Special attention has been paid to transcriptional and metabolic networks associated with the control of grape berry development, and how external factors affect the ripening process. In this review, we focus on the integration of global approaches, including proteomics, metabolomics, and especially transcriptomics, to understand grape berry development. Several aspects will be considered, including seed development and the production of seedless fruits; veraison, at which anthocyanin accumulation begins in the berry skin of colored varieties; and hormonal regulation of berry development and signaling throughout ripening, focusing on the transcriptional regulation of hormone receptors, protein kinases, and genes related to secondary messenger sensing. Finally, berry responses to different environmental factors, including abiotic (temperature, water-related stress and UV-B radiation) and biotic (fungi and viruses) stresses, and how they can significantly modify both, development and composition of vine fruit, will be discussed. Until now, advances have been made due to the application of Omics tools at different molecular levels. However, the potential of these technologies should not be limited to the study of single-level questions; instead, data obtained by these platforms should be integrated to unravel the molecular aspects of grapevine development. Therefore, the current challenge is the generation of new tools that integrate large-scale data to assess new questions in this field, and to support agronomical practices.

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