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<p>This study conducted a non-targeted metabolomic analysis of five <i>Coffea arabica</i> L. cultivars grown in the field experimental areas of the&#xa0;Cerrado Mineiro (Minas Gerais State, Brazil) to identify their metabolic fingerprints. The five cultivars selected for this study were chosen based on their specific genetic backgrounds and traits, including disease resistance, productivity, and cup quality. A total of 463 metabolic features were detected in the overall <i>C. arabica</i> metabolome, with the major metabolic classes comprising sugars, amino acids, lipids, phenylpropanoids, and phenolic compounds. Among these, 41 metabolites were identified as key discriminators among the five cultivars. Partial least squares discriminant analysis (PLS-DA) revealed distinct metabolic profiles, highlighting ferulic acid, theobromine, octopamine, rosmarinic acid, and gibberellin as key metabolites. These findings emphasize the importance of phenolic compounds and alkaloids in cultivar discrimination. The most relevant metabolic markers associated with environmental stress tolerance suggest their potential as biochemical indicators for selecting resilient cultivars, thereby contributing to coffee breeding programs. Notably, this study is the first documented characterization of the leaf metabolome of field-grown <i>C. arabica</i> cultivars, with Catiguá MG2 emerging as the most distinct. Our findings demonstrate the efficacy of metabolomic fingerprinting via non-targeted metabolomic as a powerful tool for differentiating coffee cultivars and for precision breeding strategies.</p>

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Non-targeted metabolomic analysis of field-grown Coffea arabica cultivars reveals distinct leaf metabolic signatures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月27日

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  • Jéfyne Campos Carréra,
  • Leonor Guerra-Guimarães,
  • John Charles D’Auria,
  • Luana de Jesus Sartori,
  • Carla Pinheiro,
  • Vânia Aparecida Silva,
  • Margarete Lordelo Volpato,
  • Gladyston Rodrigues Carvalho,
  • Fabio Akira Mori

摘要

This study conducted a non-targeted metabolomic analysis of five Coffea arabica L. cultivars grown in the field experimental areas of the Cerrado Mineiro (Minas Gerais State, Brazil) to identify their metabolic fingerprints. The five cultivars selected for this study were chosen based on their specific genetic backgrounds and traits, including disease resistance, productivity, and cup quality. A total of 463 metabolic features were detected in the overall C. arabica metabolome, with the major metabolic classes comprising sugars, amino acids, lipids, phenylpropanoids, and phenolic compounds. Among these, 41 metabolites were identified as key discriminators among the five cultivars. Partial least squares discriminant analysis (PLS-DA) revealed distinct metabolic profiles, highlighting ferulic acid, theobromine, octopamine, rosmarinic acid, and gibberellin as key metabolites. These findings emphasize the importance of phenolic compounds and alkaloids in cultivar discrimination. The most relevant metabolic markers associated with environmental stress tolerance suggest their potential as biochemical indicators for selecting resilient cultivars, thereby contributing to coffee breeding programs. Notably, this study is the first documented characterization of the leaf metabolome of field-grown C. arabica cultivars, with Catiguá MG2 emerging as the most distinct. Our findings demonstrate the efficacy of metabolomic fingerprinting via non-targeted metabolomic as a powerful tool for differentiating coffee cultivars and for precision breeding strategies.