The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe)

Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to Bryophyllum plants (genus Kalanchoe, Crassulaceae), but their production by these medicinal plants has not been characterized...

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Autores principales:	Pascual García-Pérez, Leilei Zhang, Begoña Miras-Moreno, Eva Lozano-Milo, Mariana Landin, Luigi Lucini, Pedro P. Gallego
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	<i>Kalanchoe</i> plant tissue culture bioactive compounds artificial intelligence plant biotechnology mineral nutrition Botany QK1-989
Acceso en línea:	https://doaj.org/article/cc1090eaf17a4bd1800b926f5055784a
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spelling	oai:doaj.org-article:cc1090eaf17a4bd1800b926f5055784a2021-11-25T18:46:32ZThe Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in <i>Bryophyllum</i> Medicinal Plants (Genus <i>Kalanchoe</i>)10.3390/plants101124302223-7747https://doaj.org/article/cc1090eaf17a4bd1800b926f5055784a2021-11-01T00:00:00Zhttps://www.mdpi.com/2223-7747/10/11/2430https://doaj.org/toc/2223-7747Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to <i>Bryophyllum</i> plants (genus <i>Kalanchoe</i>, Crassulaceae), but their production by these medicinal plants has not been characterized to date. In this work, a combinatorial approach including plant tissue culture, untargeted metabolomics, and machine learning is proposed to unravel the critical factors behind the biosynthesis of phenolic compounds in these species. The untargeted metabolomics revealed 485 annotated compounds that were produced by three <i>Bryophyllum</i> species cultured in vitro in a genotype and organ-dependent manner. Neurofuzzy logic (NFL) predictive models assessed the significant influence of genotypes and organs and identified the key nutrients from culture media formulations involved in phenolic compound biosynthesis. Sulfate played a critical role in tyrosol and lignan biosynthesis, copper in phenolic acid biosynthesis, calcium in stilbene biosynthesis, and magnesium in flavanol biosynthesis. Flavonol and anthocyanin biosynthesis was not significantly affected by mineral components. As a result, a predictive biosynthetic model for all the <i>Bryophyllum</i> genotypes was proposed. The combination of untargeted metabolomics with machine learning provided a robust approach to achieve the phytochemical characterization of the previously unexplored species belonging to the <i>Bryophyllum</i> subgenus, facilitating their biotechnological exploitation as a promising source of bioactive compounds.Pascual García-PérezLeilei ZhangBegoña Miras-MorenoEva Lozano-MiloMariana LandinLuigi LuciniPedro P. GallegoMDPI AGarticle<i>Kalanchoe</i>plant tissue culturebioactive compoundsartificial intelligenceplant biotechnologymineral nutritionBotanyQK1-989ENPlants, Vol 10, Iss 2430, p 2430 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	<i>Kalanchoe</i> plant tissue culture bioactive compounds artificial intelligence plant biotechnology mineral nutrition Botany QK1-989
spellingShingle	<i>Kalanchoe</i> plant tissue culture bioactive compounds artificial intelligence plant biotechnology mineral nutrition Botany QK1-989 Pascual García-Pérez Leilei Zhang Begoña Miras-Moreno Eva Lozano-Milo Mariana Landin Luigi Lucini Pedro P. Gallego The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in <i>Bryophyllum</i> Medicinal Plants (Genus <i>Kalanchoe</i>)
description	Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to <i>Bryophyllum</i> plants (genus <i>Kalanchoe</i>, Crassulaceae), but their production by these medicinal plants has not been characterized to date. In this work, a combinatorial approach including plant tissue culture, untargeted metabolomics, and machine learning is proposed to unravel the critical factors behind the biosynthesis of phenolic compounds in these species. The untargeted metabolomics revealed 485 annotated compounds that were produced by three <i>Bryophyllum</i> species cultured in vitro in a genotype and organ-dependent manner. Neurofuzzy logic (NFL) predictive models assessed the significant influence of genotypes and organs and identified the key nutrients from culture media formulations involved in phenolic compound biosynthesis. Sulfate played a critical role in tyrosol and lignan biosynthesis, copper in phenolic acid biosynthesis, calcium in stilbene biosynthesis, and magnesium in flavanol biosynthesis. Flavonol and anthocyanin biosynthesis was not significantly affected by mineral components. As a result, a predictive biosynthetic model for all the <i>Bryophyllum</i> genotypes was proposed. The combination of untargeted metabolomics with machine learning provided a robust approach to achieve the phytochemical characterization of the previously unexplored species belonging to the <i>Bryophyllum</i> subgenus, facilitating their biotechnological exploitation as a promising source of bioactive compounds.
format	article
author	Pascual García-Pérez Leilei Zhang Begoña Miras-Moreno Eva Lozano-Milo Mariana Landin Luigi Lucini Pedro P. Gallego
author_facet	Pascual García-Pérez Leilei Zhang Begoña Miras-Moreno Eva Lozano-Milo Mariana Landin Luigi Lucini Pedro P. Gallego
author_sort	Pascual García-Pérez
title	The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in <i>Bryophyllum</i> Medicinal Plants (Genus <i>Kalanchoe</i>)
title_short	The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in <i>Bryophyllum</i> Medicinal Plants (Genus <i>Kalanchoe</i>)
title_full	The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in <i>Bryophyllum</i> Medicinal Plants (Genus <i>Kalanchoe</i>)
title_fullStr	The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in <i>Bryophyllum</i> Medicinal Plants (Genus <i>Kalanchoe</i>)
title_full_unstemmed	The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in <i>Bryophyllum</i> Medicinal Plants (Genus <i>Kalanchoe</i>)
title_sort	combination of untargeted metabolomics and machine learning predicts the biosynthesis of phenolic compounds in <i>bryophyllum</i> medicinal plants (genus <i>kalanchoe</i>)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cc1090eaf17a4bd1800b926f5055784a
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The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in <i>Bryophyllum</i> Medicinal Plants (Genus <i>Kalanchoe</i>)

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