Exploring the regulatory role of the grapevine MIXTA homologue in cuticle formation and abiotic stress resilience

The main aim of WiSSaTech project (PRIN P2022LXY3A),supported by the Italian Ministero dell’Università e della Ricerca and European Union-NextGenerationEU, is to investigate eco-friendly and safe alternatives to sulphur dioxide (SO2) in wine production.

Mannoproteins (MPs) are released from the yeast cell wall during alcoholic fermentation and aging on the lees, and influence aspects of wine quality such as haze formation and colour stability. Yet, as this is a slow process with microbiological and sensory risks, the exogenous addition of extracted MPs poses an efficient alternative. While Saccharomyces cerevisiae has long been studied as a prominent source for MPs extraction, their structure and composition greatly differ between yeast species. This may influence their behaviour in the wine matrix and subsequent impact on wine properties. However, although wine yeast species other than S. cerevisiae possibly present an untapped source of MPs, they are still ill-characterised in terms of chemical composition and influence on wine.

The plant surface typically comprises of various epidermal cell types which synthesise and deposit a protective waxy layer known as the cuticle. The cuticle is a significant contributor to important crop traits related to drought tolerance, biotic stress, postharvest fruit quality as well as providing structural support. In this work we have investigated grape berry cuticle formation in the context of the accumulation of anti-fungal specialised metabolites and the ability of the cuticle to structurally cope with the rapid expansion of ripening berries. Metabolic QTL analysis was performed in a grapevine cross population, using chemical profiling data collected via GC-MS analysis for cuticular waxes.

Fungicides play a critical role in managing grapevine downy mildew caused by the oomycete Plasmopara viticola, a biotrophic and polycyclic pathogen with a high risk of fungicide resistance. Zoxamide, categorized as a low to medium resistance risk, disrupts cell division by inhibiting tubulin polymerization. Resistance to zoxamide is uncommon in field isolates. This six-year study (2017-2022) aimed to detect and quantify zoxamide sensitivity in P. viticola populations across varying resistance pressures in Italian grapevine regions. Analysis of 126 samples from 57 vineyards, mainly in North-Eastern Italy, revealed that most samples exhibited EC50, EC95, and MIC values below 0.1 and 10 mg/L of zoxamide, respectively. Nineteen vineyards showed reduced sensitivity (MIC>100 mg/L), but only four samples were characterized by 24-54% resistant oospores at >100 mg/L of zoxamide.

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.