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Grapevine ( Vitis vinifera ) is one of the most important perennial crop plants in worldwide. Understanding of developmental processes like flowering, which impact quality and quantity of yield in this species is therefore of high interest. This gets even more important when considering some of the expected consequences of climate change. Earlier bud burst and flowering, for example, may result in yield loss due to spring frost. Berry ripening under higher temperatures will impact wine quality. Knowledge of interactions between a genotype or allele combination and the environment can be used for the breeding of genotypes that are better adapted to new climatic conditions. To this end, we have generated a list of more than 500 candidate genes that may play a role in the timing of flowering. The grapevine genome was exploited for flowering time control gene homologs on the basis of functional data from model organisms like A . thaliana . In a previous study, a mapping population derived from early flowering GF.GA-47-42 and late flowering ‘Villard Blanc’ was analyzed for flowering time QTLs. In a second step we have now established a workflow combining amplicon sequencing and bioinformatics to follow alleles of selected candidate genes in the F 1 individuals and the parental genotypes. Allele combinations of these genes in individuals of the mapping population were correlated with early or late flowering phenotypes. Specific allele combinations of flowering time candidate genes within and outside of the QTL regions for flowering time on chromosome 1, 4, 14, 17, and 18 were found to be associated with an early flowering phenotype. In addition, expression of many of the flowering candidate genes was analyzed over consecutive stages of bud and inflorescence development indicating functional roles of these genes in the flowering control network.

Objective and standardized recording of disease severity in mapping crosses and breeding lines is a crucial step in characterizing resistance traits utilized in breeding programs and to conduct QTL or GWAS studies. Here we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs. As proof of concept, we used leaf discs inoculated with Plasmopara viticola ((Berk. and Curt.) Berl. and de Toni) causing grapevine downy mildew (DM). This oomycete is one of the major grapevine pathogens and has the potential to reduce grape yield dramatically if environmental conditions are favorable. Breeding of DM resistant grapevine cultivars is an approach for a novel and more sustainable viticulture. This involves the evaluation of several thousand inoculated leaf discs from mapping crosses and breeding lines every year. Therefore, we trained a shallow convolutional neural-network (SCNN) for efficient detection of leaf disc segments showing P. viticola sporangiophores. We could illustrate a high and significant correlation with manually scored disease severity used as ground truth data for evaluation of the SCNN performance. Combined with an automated imaging system, this leaf disc-scoring pipeline has the potential to considerably reduce the amount of time during leaf disc phenotyping. The pipeline with all necessary documentation for adaptation to other pathogens is freely available.

Plants display sophisticated mechanisms to tolerate challenging environmental conditions and need to manage their ontogenesis in parallel. Here, we set out to generate an RNA-Seq time series dataset throughout grapevine (Vitis vinifera) early bud development. The expression of the developmental regulator VviAP1 served as an indicator of the progression of development. We investigated the impact of changing temperatures on gene expression levels during the time series and detected a correlation between increased temperatures and a high expression level of genes encoding heat-shock proteins. The dataset also allowed the exemplary investigation of expression patterns of genes from three transcription factor (TF) gene families, namely MADS-box, WRKY, and R2R3-MYB genes. Inspection of the expression profiles from all three TF gene families indicated that a switch in the developmental program takes place in July which coincides with increased expression of the bud dormancy marker gene VviDRM1.