Coping with extreme climatic events: some lessons from recent work on grapevine under heat peak

The use of the term bioprotection in winemaking refers to the use of non-chemical methods to prevent the development of undesirable microorganisms (yeasts and/or bacteria). The reason for studying this method is mainly as a natural alternative to the addition of sulfites during the pre-fermentation stages. In winemaking, the addition of s02 has multiple functions, the main ones being antiseptic and antioxidant power.

Sulfites (SO2) addition during winemaking is a widespread practice worldwide. This addition is realized at different steps of the winemaking due to the antimicrobial and antioxidant capacity of SO2. In a context of understanding white wines oxidative stability, knowledge about the impact of SO2 on the wine molecular diversity, especially compounds involved in the antioxidant capacity of wine, appears to be very important. In recent years, some studies have shown that SO2 can react with a large number of wine compounds resulting in the formation of numerous adducts. The diversity of compounds involved is important including in particular pyruvic acid, 2-keto-glutaric acid, glyceraldehyde, sugar, phenolics compounds but also amino acids or peptides. Moreover Roullier-Gall et al. have shown using FT-ICR-MS analysis that the molecular composition of wines remains impacted by addition of SO2 to the must (0, 4 and 8 g/hL SO2), several years after winemaking. Indeed, wines made from protected must (8g/hL SO2) contain a larger diversity of CHOS and CHONS compounds than wines made from unprotected must (0 g/hL SO2). The study of the impact of glutathione addition on the sensory oxidative stability has further shown that CHOS and CHONS compounds (amino acids, aromatic compounds and peptides) are markers of the antioxidant metabolome of white wines. This suggests that CHOS and CHONS compounds arise from SO2 adducts formation but also from a protecting effect of SO2 on the antioxidant metabolome of white wines.

For sustainable viticulture, the substitution of chemical inputs with biocontrol products has become one of the most considered strategies. This strategy is based on elicitor-triggered immunity that requires a deep understanding of the molecular mechanisms involved in plant defense activation. Plant immune responses are triggered through the perception of conserved microbe-associated molecular patterns (MAMPs) which are recognized by pattern recognition receptors (PRRs) at the plasma membrane.

Structure-function relationships between the polysaccharide part of S. cerevisiae Mannoprotein Pools (MPs) and their potential to interact with anthocyanins and Protein-Tannins aggregates was previously assessed [1,2].

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.