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New metabolites of dietary terpenoids identified using in silico prediction of metabolism and high-resolution mass spectrometry
* 1 , 2 , 1 , 1 , 1 , 2 , 1
1  INRA, Human Nutrition Unit, Université Clermont Auvergne, F63000 Clermont-Ferrand, France
2  University of Alberta, Departments of Computing Science and Biological Sciences, Alberta, Canada


Despite being well absorbed and displaying a range of biological properties, dietary terpenoids have been little studied. Better knowledge about their metabolism will help understanding the health effects of plant foods and provide information on the food metabolome. As part of the FoodBAll project we are investigating the metabolism of terpenes, identifying metabolites and biotransformations involved in their metabolism.

PhytoHub (database that compiles all known metabolites of dietary phytochemicals, including terpenes) and Nexus Meteor (in silico prediction of metabolism), were used to identify biotransformations involved in the metabolism of dietary terpenoids. Selected biotransformations were used to predict the metabolism of camphene, camphor, carvacrol, carvone, caryophyllene, 1,4-cineole, 1,8-cineole, citral, citronellal, cuminaldehyde, p-cymene, fenchone, geraniol, limonene, linalool, menthol, myrcene, nootkatone, perillyl alcohol, pinene, pulegone, terpinen-4-ol and thymol. The metabolism prediction tool “BioTransformer” that is under development at Dr. Wishart’s lab was also used to generate predicted metabolites of the mentioned compounds. The urine of Wistar rats was collected before and after 5 days of the exposure to the dietary monoterpenes (given in the dose of 0.05% of diet). Untargeted metabolomics analysis was performed in urine using high-resolution mass spectrometry (UPLC-QToF).

We identified twenty-two enzymatic reactions involved in the synthesis of terpenoid metabolites described in the literature. In average, 10 metabolites per compound were identified in rat urine, including new and known ones. Identification of metabolites was based on monoisotopic mass and formula match, presence of adducts and specific mass losses indicative of glucuronidation, sulphation and conjugation to amino acids. Validation of identification is being done using MS/MS experiments.

The combination of in silico predictions and in vivo experiment allowed the identification of known and new metabolites of different dietary terpenoids. Predicted metabolites of terpenes will be added in databases such as PhytoHub to complement the database of known metabolites. The validations of the metabolism predictions are helping the development of BioTransformer.


Funding: Grant from the Agence Nationale de la Recherche (#ANR-14-HDHL-0002-02) for FoodBAll project (JPI HDHL). JF is an AgreenSkills fellow.

Keywords: monoterpenoids, in silico metabolism prediction, food metabolome