Development and application of CRISPR/Cas in grapevine

In winemaking, several antimicrobial peptides (AMPs) produced by different strains of Saccharomyces cerevisiae were found to be responsible for the inhibition of malolactic fermentation (MLF) carried out by some strains of Oenococcus oeni. However, only two AMPs produced by one of the yeast strains studied were totally identified and their mechanism of action was described. In an attempt to identify new AMPs, a 5-10 kDa peptidic fraction produced by an oenological strain of S. cerevisiae and previously shown to strongly inhibit MLF carried out by a strain of O. oeni was further purified.

In the past, the contribution of non-Saccharomyces yeasts in winemaking has always been considered negative for their limited enological attitude if compared with Saccharomyces cerevisiae. In recent decades there has been a reevaluation of the role of non-Saccharomyces wine yeasts especially when used in combination and in support with S. cerevisiae (mixed fermentation). In this regard, selected non-Saccharomyces yeasts could be profitable used to give distinctive features, to enhance flavor and aroma complexity and to reduce the ethanol content of wines. Further emerging trends in the use of these yeasts are related to their role as bioprotectants and producers of health promoters compounds.

After the onset of grape berry ripening, phloem unloading follows an apoplasmic route into the mesocarp tissue. In the apoplast, most of the unloaded sucrose is cleaved by cell wall invertases

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

The optimal climatic conditions of the region were proved in 1867, when a leaf-print of Vitis tokaiensis was found in a stone from miocen age (13 million years ago).