DOI: 10.1111/tpj.70353 ISSN: 0960-7412

Influence of environmental conditions and seasonality on the metabolome and lipidome of Psychotria viridis leaves

Taynara Simão Matos, Camila Dias Lourenço Dos Santos, Luís Fernando Tófoli, Ílio Montanari Júnior, Márcia Cristina Breitkreitz, Alessandra Sussulini

SUMMARY

Psychotria viridis Ruiz & Pav. has gained significant attention due to its role in the preparation of ayahuasca. This study aimed to improve the understanding of the specialized metabolite profile in P. viridis leaves and to evaluate how growing conditions and seasonality impact this composition. The specimens were grown either in the open field or in the shaded environment of rubber tree (Hevea brasiliensis L.) cultivation, forming a clonal population of the mother plant. Samples were collected in all four seasons of the year. After a three‐phase extraction of the samples, the aqueous and organic phases were analyzed using an ultra‐high‐performance liquid chromatography coupled with electrospray ionization and Orbitrap mass spectrometry (UHPLC‐ESI‐Orbitrap‐MS) system. The acquired data were processed using MS‐DIAL 4.9 and MetaboAnalyst 5.0 for multivariate statistics and pathway activity analysis. Chemical variations were investigated employing principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares discriminant analysis (PLS‐DA). The most important identified compounds for differentiation according to seasonality were flavonoids. The pathways presenting significant variation in response to seasonality were related to energy generation through biosynthesis and consumption of carbohydrates: ascorbate and aldarate metabolism, pentose and glucuronate interconversions, and citrate cycle. Meanwhile, the biosynthesis of flavonoids, flavones, and flavonols was associated with the influence of the cultivation location in full sunlight or shade in an intercrop, indicating a plant response to oxidative stress. In our comprehensive analysis, DMT concentrations did not exhibit any significant statistical variation across the studied conditions.

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