LC-HRMS/MS-based metabolomics approaches applied to the detection of antifungal compounds and a metabolic dynamic assessment of orchidaceae

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dc.creatorLima, Gesiane da Silva
dc.creatorLima, Nerilson Marques
dc.creatorRoque, Jussara Valente
dc.creatorAguiar, Deborah Victória Alves de
dc.creatorOliveira, João Victor Ataide
dc.creatorSantos, Gabriel Franco dos
dc.creatorChaves, Andréa Rodrigues
dc.creatorVaz, Boniek Gontijo
dc.date.accessioned2023-05-31T12:55:32Z
dc.date.available2023-05-31T12:55:32Z
dc.date.issued2022-11-16
dc.description.abstractThe liquid chromatography–mass spectrometry (LC-MS)-based metabolomics approach is a powerful technology for discovering novel biologically active molecules. In this study, we investigated the metabolic profiling of Orchidaceae species using LC-HRMS/MS data combined with chemometric methods and dereplication tools to discover antifungal compounds. We analyze twenty ethanolic plant extracts from Vanda and Cattleya (Orchidaceae) genera. Molecular networking and chemometric methods were used to discriminate ions that differentiate healthy and fungal-infected plant samples. Fifty-three metabolites were rapidly annotated through spectral library matching and in silico fragmentation tools. The metabolomic profiling showed a large production of polyphenols, including flavonoids, phenolic acids, chromones, stilbenoids, and tannins, which varied in relative abundance across species. Considering the presence and abundance of metabolites in both groups of samples, we can infer that these constituents are associated with biochemical responses to microbial attacks. In addition, we evaluated the metabolic dynamic through the synthesis of stilbenoids in fungal-infected plants. The tricin derivative flavonoid- and the loliolide terpenoidfound only in healthy plant samples, are promising antifungal metabolites. LC-HRMS/MS, combined with stateof-the-art tools, proved to be a rapid and reliable technique for fingerprinting medicinal plants and discovering new hits and leads.pt_BR
dc.identifier.citationLIMA, Gesiane S. et al. LC-HRMS/MS-based metabolomics approaches applied to the detection of antifungal compounds and a metabolic dynamic assessment of orchidaceae. Molecules, Basel, v. 27, n. 22, e7937, Nov. 2022. DOI: 10.3390/molecules27227937. Disponível em: https://www.mdpi.com/1420-3049/27/22/7937. Acesso em: 17 maio 2023.pt_BR
dc.identifier.doihttps://doi.org/10.3390/molecules27227937
dc.identifier.issne- 1420-3049
dc.identifier.urihttp://repositorio.bc.ufg.br/handle/ri/22648
dc.language.isoengpt_BR
dc.publisher.countrySuicapt_BR
dc.publisher.departmentInstituto de Química - IQ (RMG)pt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLiquid chromatography–mass spectrometrypt_BR
dc.subjectUntargeted metabolomicspt_BR
dc.subjectMetabolic dynamicpt_BR
dc.subjectAntifungal compoundspt_BR
dc.titleLC-HRMS/MS-based metabolomics approaches applied to the detection of antifungal compounds and a metabolic dynamic assessment of orchidaceaept_BR
dc.typeArtigopt_BR

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