Paradoxically, in eukaryotic cells, hydrogen peroxide (H2O2) accumulates in response to oxygen deprivation (hypoxia). The source of H2O2 under hypoxia varies according to the species, organs, and tissue. In non-photosynthetic tissues, H2O2 is mainly produced by activation of NAD(P)H-oxidases or by disruption of the mitochondrial electron transport chain (m-ETC). This study showed that hypoxia, and inhibitors of respiration like potassium cyanide (KCN) and sodium nitroprusside (SNP), trigger the production of H2O2 in grapevine buds. However, diphenyleneiodonium, an inhibitor of NAD(P)H-oxidase, did not reduce the H2O2 levels induced by KCN, suggesting that, under respiratory stress, H2O2 is mainly produced by disruption of the m-ETC. On the other hand, γ-aminobutyric acid (GABA), a metabolite that in plants alleviates oxidative stress by activating antioxidant enzymes, reduced significantly the levels of H2O2 induced by KCN and, surprisingly, repressed the expression of genes encoding antioxidant enzymes such as ASCORBATE PEROXIDASE (VvAPX), GLUTATHIONE PEROXIDASE (VvGLPX), SUPEROXIDE DISMUTASE (VvSOD), and one of the CATALASE isoforms (VvCAT1), while VvCAT2 was upregulated. In contrast to GABA, hypoxia, H2O2, and ethylene increased dramatically the expression of genes encoding antioxidant enzymes and enzymes of the alternative respiratory pathway such as ALTERNATIVE NADH-DEHYDROGENASES (VvaNDs) and ALTERNATIVE OXIDASES (VvAOXs). Hence, it is concluded that H2O2 production is stimulated by respiratory stress in grapevine buds, that H2O2 and ethylene act as signalling molecules and activate genes related to the antioxidant defence system, and finally that GABA reduces H2O2 levels by up-regulating the expression of VvCAT2.

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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Based on the results obtained, it is possible to conclude that plicature of the rectus abdominis generates significant physiological changes, such as an increase in IAP and a decrease of P-Comp, which do not have a clinically relevant impact on healthy individuals. Measuring IAP with the modified technique and the assessment of pulmonary function using P-Comp are both reliable and provide a more accurate correlation with such physiologic changes. LEVEL OF EVIDENCE 3: Therapeutic.