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Metabolomics and quantitative RT-qPCR analysis for metabolite profiling of coffee plants: characterization of Coffea anthonyi

par Montis, Andrea
Président du jury Meyer, Franck
Promoteur Van Antwerpen, Pierre
Co-Promoteur Stévigny, Caroline
Publication Non publié, 2023-07-03

Thèse de doctorat

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Résumé :

The present work aimed to highlight the chemical composition of coffee plants by providing a detailed metabolomic study of three different coffee species: Coffea arabica L. (ARA), Coffea canephora Pierre ex A. Froehner (Rubiaceae - CAN) and Coffea anthonyi Stoff. & F. Anthonyi (ANTH). The plants have grown in greenhouses and they originally came from Africa. While ARA and CAN originate from Congo, ANTH originate from Cameroun. Among these three species, only ANTH is not yet marketed all over the world. The chemical composition of its fruits and leaves are not well known as those of ARA and CAN. This means that despite the availability of this wild fruiting coffee species, the coffee industries still focus only on ARA and CAN. This work mainly focuses on the secondary metabolites present in the leaves, in the fruits and in the phloem sap of ARA, CAN and ANTH by using different metabolomic approaches. More specifically, LC-MS and LC-MS/MS techniques were employed. Firstly, an untargeted metabolomic approach performed by LC-MS was used to extract the entire metabolome of the leaves, fruits and phloem sap of coffee plants. This first approach provided preliminary information about the main discriminant metabolites which could be detected in the samples. Moreover, an overview of several tools useful to display metabolomic data has been provided. Both unsupervised and supervised statistical analysis have been performed and hierarchical clustering applied to heatmaps was used. The use of the most recent algorithms for detecting significant molecular signatures from omics data was also shown. Then, a targeted metabolomic approach was used by performing LC-MS/MS analysis. This strategy was useful to get a clear annotation of the metabolites. Indeed, the data obtained through these analyses have been used to build molecular networks (MN). Thanks to this bioinformatic approach the identity of the metabolites which were previously detected has been confirmed. Additionally, more unknown metabolites have been identified based on their similar fragmentation pattern in MS/MS analysis. The results provided information about the chemical composition of coffee leaves by showing that xanthines, chlorogenic acids (CGAs) and mangiferin were found as the main discriminant metabolites responsible of the inter-species differences. The analysis of the phloem sap highlighted significant biochemical differences between species as well. Therefore, additional information regarding the chemical composition of the fruits were provided. Significant differences between the chemical composition of beans and pericarps of some species were detected. Metabolomic analysis have then been coupled to genetic studies in order to relate the content of some metabolites in the leaves and in the fruits of the three species. Since it was observed that the content of purine alkaloids was remarkably different in the leaves and in the fruits of the three coffee species, a part of this work focused on the biosynthetic pathways leading to the synthesis of these compounds. Caffeine, which represents the main alkaloid detected in coffee tissues, was found as one of the main discriminant metabolites between species. Consequently, its biosynthesis in ARA, CAN and ANTH fruits and leaves has been deeply investigated. RT-qPCR experiments were performed and expression of genes related to the enzymes involved in caffeine biosynthesis was explored.