Mining microbiome data to identify antagonists of grapevine downy mildew (Plasmopara viticola)

Most of the effects of ultrasound (US) result from the collapse of bubbles due to cavitation. The shockwave produced is associated with shear forces, along with high localised temperatures and pressures. However, the high-speed stream, radical species formation, and heat generated during sonication may also affect the stability of some enzymes and proteins, depending on their chemical structure. Recently, Ce-lotti et al. (2021) reported the effects of US on protein stability in wines. To investigate this further, the effect of temperature (40°C and 70°C; 60s), sonication (20 kHz and 100 % amplitude, for 20s and 60s, leading to the same temperatures as above, respectively), in combination with Aspergillopepsins I (AP-I) supplementation (100 μg/L), was studied on unstable protein concentration (TLPs and chitinases) using HPLC with an UV–Vis detector in a TLPs-supplemented model system and in an unstable white wine.

Aims: Some of the most direct effects of climate variability on grapevines are the changes in the onset and timing of phenological events and in the length of the growing season, which may affect grape quality. The aim of this research was to analyze the projected changes in vine phenology and on grape composition of the Tempranillo variety in Rioja DOCa under different climate change scenarios.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

Grape (Vitis vinifera) is one of the world’s oldest agricultural fruit crops, grown for wine, table grape, raisin, and other products. One of the factors that can cause a reduction in the grape growing area is temperature rise due to climate change. Elevated temperature causes changes in grapevine phenology and fruit chemical composition. Previous studies showed that grape varieties respond differently to a temperature shift of 1.5°C; few varieties had difficulties in the fruit development or could not reach the desired Brix level.

In a context of a sustainable viticulture, a new uprising strategy to improve grape and wine composition (or quality) is the exogenous application of plant activators(Gil-Muñoz et al., 2017)