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Article

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

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Agrobiotech for Health Group, Plant Biology and Soil Science Department, Biology Faculty, University of Vigo, E-36310 Vigo, Spain
2
CITACA—Agri-Food Research and Transfer Cluster, University of Vigo, E-32004 Ourense, Spain
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Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
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I+D Farma Group (GI-1645), Department of Pharmacology, Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Universidade de Santiago de Compostela, E-15782 Santiago de Compostela, Spain
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Health Research Institute of Santiago de Compostela (IDIS), E-15706 Santiago de Compostela, Spain
*
Author to whom correspondence should be addressed.
Current affiliation: Nutrition and Bromatology Group, Department of Analytical Chemistry and Food Science, Faculty of Science, Universidade de Vigo, E-32004 Ourense, Spain.
Academic Editor: Mauro Commisso
Plants 2021, 10(11), 2430; https://doi.org/10.3390/plants10112430
Received: 10 October 2021 / Revised: 8 November 2021 / Accepted: 9 November 2021 / Published: 10 November 2021
(This article belongs to the Special Issue Plant Biotechnology Applications in Secondary Metabolite Production)
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 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 Bryophyllum 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 Bryophyllum 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 Bryophyllum subgenus, facilitating their biotechnological exploitation as a promising source of bioactive compounds. View Full-Text
Keywords: Kalanchoe; plant tissue culture; bioactive compounds; artificial intelligence; plant biotechnology; mineral nutrition; phytochemistry; polyphenols; secondary metabolism Kalanchoe; plant tissue culture; bioactive compounds; artificial intelligence; plant biotechnology; mineral nutrition; phytochemistry; polyphenols; secondary metabolism
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MDPI and ACS Style

García-Pérez, P.; Zhang, L.; Miras-Moreno, B.; Lozano-Milo, E.; Landin, M.; Lucini, L.; Gallego, P.P. The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe). Plants 2021, 10, 2430. https://doi.org/10.3390/plants10112430

AMA Style

García-Pérez P, Zhang L, Miras-Moreno B, Lozano-Milo E, Landin M, Lucini L, Gallego PP. The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe). Plants. 2021; 10(11):2430. https://doi.org/10.3390/plants10112430

Chicago/Turabian Style

García-Pérez, Pascual, Leilei Zhang, Begoña Miras-Moreno, Eva Lozano-Milo, Mariana Landin, Luigi Lucini, and Pedro P. Gallego. 2021. "The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe)" Plants 10, no. 11: 2430. https://doi.org/10.3390/plants10112430

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