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Veraison marks the transition from berry growth to berry ripening and is a crucial phenological stage in grapevine (Vitis vinifera): the berries become soft and begin to accumulate sugars, aromatic substances, and, in red cultivars, anthocyanins for pigmentation, while the organic acid levels begin to decrease. These changes determine the potential quality of wine. However, rising global temperatures lead to earlier flowering and ripening, which strongly influence wine quality. Here, we combined genotyping-by-sequencing with a bioinformatics pipeline on ∼150 F1 genotypes derived from a cross between the early ripening variety "Calardis Musqué" and the late-ripening variety "Villard Blanc". Starting from 20,410 haplotype-based markers, we generated a high-density genetic map and performed a quantitative trait locus analysis based on phenotypic datasets evaluated over 20 yrs. Through locus-specific marker enrichment and recombinant screening of ∼1,000 additional genotypes, we refined the originally postulated 5-mb veraison locus, Ver1, on chromosome 16 to only 112 kb, allowing us to pinpoint the ethylene response factor VviERF027 (VCost.v3 gene ID: Vitvi16g00942, CRIBIv1 gene ID: VIT_16s0100g00400) as veraison candidate gene. Furthermore, the early veraison allele could be traced back to a clonal "Pinot" variant first mentioned in the seventeenth century. "Pinot Precoce Noir" passed this allele over "Madeleine Royale" to the maternal grandparent "Bacchus Weiss" and, ultimately, to the maternal parent "Calardis Musqué". Our findings are crucial for ripening time control, thereby improving wine quality, and for breeding grapevines adjusted to climate change scenarios that have a major impact on agro-ecosystems in altering crop plant phenology., Competing Interests: Conflict of interest statement. None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

Background: Grapevine cultivars of the Pinot family represent clonally propagated mutants with major phenotypic and physiological differences, such as different colour or shifted ripening time, as well as changes in important viticultural traits. Specifically, the cultivars 'Pinot Noir' (PN) and 'Pinot Noir Precoce' (PNP, early ripening) flower at the same time, but vary in the beginning of berry ripening (veraison) and, consequently, harvest time. In addition to genotype, seasonal climatic conditions (i.e. high temperatures) also affect ripening times. To reveal possible regulatory genes that affect the timing of veraison onset, we investigated differences in gene expression profiles between PN and PNP throughout berry development with a closely meshed time series and over two separate years., Results: The difference in the duration of berry formation between PN and PNP was quantified to be approximately two weeks under the growth conditions applied, using plant material with a proven PN and PNP clonal relationship. Clusters of co-expressed genes and differentially expressed genes (DEGs) were detected which reflect the shift in the timing of veraison onset. Functional annotation of these DEGs fit to observed phenotypic and physiological changes during berry development. In total, we observed 3,342 DEGs in 2014 and 2,745 DEGs in 2017 between PN and PNP, with 1,923 DEGs across both years. Among these, 388 DEGs were identified as veraison-specific and 12 were considered as berry ripening time regulatory candidates. The expression profiles revealed two candidate genes for ripening time control which we designated VviRTIC1 and VviRTIC2 (VIT_210s0071g01145 and VIT_200s0366g00020, respectively). These genes likely contribute the phenotypic differences observed between PN and PNP., Conclusions: Many of the 1,923 DEGs show highly similar expression profiles in both cultivars if the patterns are aligned according to developmental stage. In our work, putative genes differentially expressed between PNP and PN which could control ripening time as well as veraison-specific genes were identified. We point out connections of these genes to molecular events during berry development and discuss potential candidate genes which may control ripening time. Two of these candidates were observed to be differentially expressed in the early berry development phase. Several down-regulated genes during berry ripening are annotated as auxin response factors / ARFs. Conceivably, general changes in auxin signaling may cause the earlier ripening phenotype of PNP.

Grapevine (Vitis vinifera L.) is an economically important crop that needs to comply with high quality standards for fruit, juice and wine production. Intense plant protection is required to avoid fungal damage. Grapevine cultivars with loose cluster architecture enable reducing protective treatments due to their enhanced resilience against fungal infections, such as Botrytis cinerea-induced gray mold. A recent study identified transcription factor gene VvGRF4 as determinant of pedicel length, an important component of cluster architecture, in samples of two loose and two compact quasi-isogenic 'Pinot Noir' clones. Here, we extended the analysis to 12 differently clustered 'Pinot Noir' clones from five diverse clonal selection programs. Differential gene expression of these clones was studied in three different locations over three seasons. Two phenotypically opposite clones were grown at all three locations and served for standardization. Data were correlated with the phenotypic variation of cluster architecture sub-traits. A set of 14 genes with consistent expression differences between loosely and compactly clustered clones-independent from season and location-was newly identified. These genes have annotations related to cellular growth, cell division and auxin metabolism and include two more transcription factor genes, PRE6 and SEP1-like. The differential expression of VvGRF4 in relation to loose clusters was exclusively found in 'Pinot Noir' clones. Gene expression studies were further broadened to phenotypically contrasting F1 individuals of an interspecific cross and OIV reference varieties of loose cluster architecture. This investigation confirmed PRE6 and six growth-related genes to show differential expression related to cluster architecture over genetically divergent backgrounds.

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.

Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality. Resistance donors of genetic loci to increase B. cinerea resistance are widely unknown. Promising traits of resistance are represented by physical features like the thickness and permeability of the grape berry cuticle. Sensor-based phenotyping methods or genetic markers are rare for such traits. In the present study, the simple-to-handle I-sensor was developed. The sensor enables the fast and reliable measurement of electrical impedance of the grape berry cuticles and its epicuticular waxes (CW). Statistical experiments revealed highly significant correlations between relative impedance of CW and the resistance of grapevines to B. cinerea. Thus, the relative impedance Zrel of CW was identified as the most important phenotypic factor with regard to the prediction of grapevine resistance to B. cinerea. An ordinal logistic regression analysis revealed a R2McFadden of 0.37 and confirmed the application of Zrel of CW for the prediction of bunch infection and in this way as novel phenotyping trait. Applying the I-sensor, a preliminary QTL region was identified indicating that the novel phenotypic trait is as well a valuable tool for genetic analyses.

Background: The demand for high-throughput and objective phenotyping in plant research has been increasing during the last years due to large experimental sites. Sensor-based, non-invasive and automated processes are needed to overcome the phenotypic bottleneck, which limits data volumes on account of manual evaluations. A major challenge for sensor-based phenotyping in vineyards is the distinction between the grapevine in the foreground and the field in the background - this is especially the case for red-green-blue (RGB) images, where similar color distributions occur both in the foreground plant and in the field and background plants. However, RGB cameras are a suitable tool in the field because they provide high-resolution data at fast acquisition rates with robustness to outdoor illumination., Results: This study presents a method to segment the phenotypic classes 'leaf', 'stem', 'grape' and 'background' in RGB images that were taken with a standard consumer camera in vineyards. Background subtraction is achieved by taking two images of each plant for depth reconstruction. The color information is furthermore used to distinguish the leaves from stem and grapes in the foreground. The presented approach allows for objective computation of phenotypic traits like 3D leaf surface areas and fruit-to-leaf ratios. The method has been successfully applied to objective assessment of growth habits of new breeding lines. To this end, leaf areas of two breeding lines were monitored and compared with traditional cultivars. A statistical analysis of the method shows a significant (p <0.001) determination coefficient R (2)= 0.93 and root-mean-square error of 3.0%., Conclusions: The presented approach allows for non-invasive, fast and objective assessment of plant growth. The main contributions of this study are 1) the robust segmentation of RGB images taken from a standard consumer camera directly in the field, 2) in particular, the robust background subtraction via reconstruction of dense depth maps, and 3) phenotypic applications to monitoring of plant growth and computation of fruit-to-leaf ratios in 3D. This advance provides a promising tool for high-throughput, automated image acquisition, e.g., for field robots.

Due to its perennial nature and size, the acquisition of phenotypic data in grapevine research is almost exclusively restricted to the field and done by visual estimation. This kind of evaluation procedure is limited by time, cost and the subjectivity of records. As a consequence, objectivity, automation and more precision of phenotypic data evaluation are needed to increase the number of samples, manage grapevine repositories, enable genetic research of new phenotypic traits and, therefore, increase the efficiency in plant research. In the present study, an automated field phenotyping pipeline was setup and applied in a plot of genetic resources. The application of the PHENObot allows image acquisition from at least 250 individual grapevines per hour directly in the field without user interaction. Data management is handled by a database (IMAGEdata). The automatic image analysis tool BIVcolor (Berries in Vineyards-color) permitted the collection of precise phenotypic data of two important fruit traits, berry size and color, within a large set of plants. The application of the PHENObot represents an automated tool for high-throughput sampling of image data in the field. The automated analysis of these images facilitates the generation of objective and precise phenotypic data on a larger scale.

Background and Aims Vine balance is defined as a relation between vegetative (mass of dormant pruning wood) and generative (yield) growth. For grapevine breeding, emphasis is usually placed on the evaluation of individual seedlings. In this study, we calculated the mass of dormant pruning wood with the assistance of an automated image-based method for estimating the pixel area of dormant pruning wood. The evaluation of digital images in combination with depth map calculation and image segmentation is a new and non-invasive tool for objective data acquisition. Methods and Results The proposed method was tested on a set of seedlings planted at the Institute for Grapevine Breeding Geilweilerhof, Germany. All images taken in the field were geo-referenced, and the automated method was validated by manual segmentation. Together with additional yield parameters, the vine balance indices can be used to classify seedlings for breeding purposes. Conclusion The computed pruning mass obtained using image-based methods is an accurate, inexpensive and easy method to estimate pruning mass compared with the manual time-consuming measurements. Together with the yield parameters, it is a suitable method for seedling evaluation and can also be used in precision viticulture. Significance of the Study This study demonstrates an image-based evaluation of the pruning mass to be a highly valuable tool for grapevine research and grapevine breeding. Moreover, the tool might be used by industry to monitor vine balance. The key findings reported have the potential to increase grapevine breeding efficiency by using an accurate and objective phenotyping method. [ABSTRACT FROM AUTHOR]