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Metabolomics Insight into the Variety-Mediated Responses to Aspergillus carbonarius Infection in Grapevine Berries.

Paola GiorniLeilei ZhangLuigi BavarescoChristophe El-NakhelPaola Battilani
Published in: ACS omega (2023)
Limited knowledge regarding the susceptibility of grape varieties to ochratoxin A (OTA)-producing fungi is available to date. This study aimed to investigate the susceptibility of different grape varieties to Aspergillus carbonarius concerning OTA contamination and modulation at the metabolome level. Six grape varieties were selected, sampled at early veraison and ripening, artificially inoculated with A. carbonarius , and incubated at two temperature regimes. Significant differences were observed across cultivars, with Barbera showing the highest incidence of moldy berries (around 30%), while Malvasia and Ortrugo showed the lowest incidence (about 2%). OTA contamination was the lowest in Ortrugo and Malvasia, and the highest in Croatina, although it was not significantly different from Barbera, Merlot, and Sauvignon Blanc. Fungal development and mycotoxin production changed with grape variety; the sugar content in berries could also have played a role. Unsupervised multivariate statistical analysis from metabolomic fingerprints highlighted cultivar-specific responses, although a more generalized response was observed by supervised OPLS-DA modeling. An accumulation of nitrogen-containing compounds (alkaloids and glucosinolates), phenylpropanoids, and terpenoids, in addition to phytoalexins, was observed in all samples. A broader modulation of the metabolome was observed in white grapes, which were less contaminated by OTA. Jasmonates and oxylipins were identified as critical upstream modulators in metabolomic profiles. A direct correlation between the plant defense machinery and OTA was not observed, but the information was acquired and can contribute to optimizing preventive actions.
Keyphrases
  • drinking water
  • machine learning
  • risk assessment
  • risk factors
  • cell wall
  • small molecule
  • mass spectrometry
  • health risk
  • heavy metals
  • human health
  • climate change
  • social media
  • data analysis