Grapevine (Vitis vinifera) teinturier cultivars are characterized by their typical reddish leaves and red-fleshed berries due to ectopic anthocyanin formation. Wines of these varieties have economic importance as they can be used for blending to enhance the color of red wines. The unique and heritable mutation has been known for a long time but the underlying genetic mechanism still is not yet understood. Here we describe the association of the red-fleshed berry phenotype with a 408 bp repetitive DNA element in the promoter of the VvmybA1 gene (grapevine color enhancer, GCE). Three different clones of ‘Teinturier’ were discovered with two, three and five allelic GCE repeats (MybA1t2, MybA1t3 and MybA1t5). All three clones are periclinal chimeras; these clones share the same L1 layer, but have distinct L2 layers with different quantities of GCE repeats. Quantitative real time PCR and HPLC analysis of leaf and berry samples showed that the GCE repeat number strongly correlates with an increase of the expression of VvmybA1 itself and the VvUFGT gene regulated by it and the anthocyanin content. A model is proposed based on autoregulation of VvmybA1t to explain the red phenotype which is similar to that of red-fleshed apples. This study presents results about the generation and modes of action of three MybA1t alleles responsible for the red-fleshed berry phenotype of teinturier grapevines.

Botrytis bunch rot is one of the economically most important fungal diseases in viticulture (aside from powdery mildew and downy mildew). So far, no active defense mechanisms and resistance loci against the necrotrophic pathogen are known. Since long, breeders are mostly selecting phenotypically for loose grape bunches, which is recently the most evident trait to decrease the infection risk of Botrytis bunch rot. This study focused on plant phenomics of multiple traits by applying fast sensor technologies to measure berry impedance (Z_REL), berry texture, and 3D bunch architecture. As references, microscopic determined cuticle thickness (MS_CT) and infestation of grapes with Botrytis bunch rot were used. Z_REL hereby is correlated to grape bunch density OIV204 (r = −0.6), cuticle thickness of berries (r = 0.61), mean berry diameter (r = −0.63), and Botrytis bunch rot (r = −0.7). However, no correlation between Z_REL and berry maturity or berry texture was observed. In comparison to the category of traditional varieties (mostly susceptible), elite breeding lines show an impressive increased Z_REL value (+317) and a 1-μm thicker berry cuticle. Quantitative trait loci (QTLs) on LGs 2, 6, 11, 15, and 16 were identified for Z_REL and berry texture explaining a phenotypic variance of between 3 and 10.9%. These QTLs providing a starting point for the development of molecular markers. Modeling of Z_REL and berry texture to predict Botrytis bunch rot resilience revealed McFadden R² = 0.99. Taken together, this study shows that in addition to loose grape bunch architecture, berry diameter, Z_REL, and berry texture values are probably additional parameters that could be used to identify and select Botrytis-resilient wine grape varieties. Furthermore, grapevine breeding will benefit from these reliable methodologies permitting high-throughput screening for additional resilience traits of mechanical and physical barriers to Botrytis bunch rot. The findings might also be applicable to table grapes and other fruit crops like tomato or blueberry.

In the era of big and omics data, good organization, management, and description of experimental data are crucial for achieving high-quality datasets. This, in turn, is essential for the export of robust results, to publish reliable papers, make data more easily available, and unlock the huge potential of data reuse. Lately, more and more journals now require authors to share data and metadata according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This work aims to provide a step-by-step guideline for the FAIR data and metadata management specific to grapevine and wine science. In detail, the guidelines include recommendations for the organization of data and metadata regarding (i) meaningful information on experimental design and phenotyping, (ii) sample collection, (iii) sample preparation, (iv) chemotype analysis, (v) data analysis (vi) metabolite annotation, and (vii) basic ontologies. We hope that these guidelines will be helpful for the grapevine and wine metabolomics community and that it will benefit from the true potential of data usage in creating new knowledge being revealed.