Investigation of Premyrsinane and Myrsinane Esters in Euphorbia cupanii and Euphobia pithyusa with MS2LDA and Combinatorial Molecular Network Annotation Propagation.
Mélissa Nothias-EspositoLouis Felix NothiasRicardo R Da SilvaPascal RetailleauZheng ZhangPieter LeyssenFanny RoussiDavid TouboulJulien PaoliniPieter C DorresteinMarc LitaudonPublished in: Journal of natural products (2019)
The species Euphorbia pithyusa and Euphorbia cupanii are two closely related Mediterranean spurges for which their taxonomic relationships are still being debated. Herein, the diterpene ester content of E. cupanii was investigated using liquid chromatography coupled to tandem mass spectrometry. The use of molecular networking coupled to unsupervised substructure annotation ( MS2LDA) indicated the presence of new premyrsinane/myrsinane diterpene esters in the E. cupanii fractions. A structure-guided isolation procedure yielded 16 myrsinane (11a-h, 12, and 13) and premyrsinane esters (14a-c and 15a-c), along with four 4β-phorbol esters (16a-c and 17) that showed inhibitory activity against chikungunya virus replication. The structures of the 16 new compounds (11a-c, 11h, 12, 13, 14a-c, 15a-c, 16a-c, and 17) were characterized by NMR spectroscopy and X-ray crystallography. To further uncover the diterpene ester content of these two species, the concept of combinatorial network annotation propagation (C-NAP) was developed. By leveraging the fact that the diterpene esters of Euphorbia species are made up of limited building blocks, a combinatorial database of theoretical structures was created and used for C-NAP that made possible the annotation of 123 premyrsinane or myrsinane esters, from which 74% are not found in any compound database.
Keyphrases
- tandem mass spectrometry
- liquid chromatography
- mass spectrometry
- high resolution
- ultra high performance liquid chromatography
- high performance liquid chromatography
- rna seq
- simultaneous determination
- multiple sclerosis
- gas chromatography
- ms ms
- solid phase extraction
- zika virus
- machine learning
- emergency department
- computed tomography
- aedes aegypti
- dengue virus
- high speed
- atomic force microscopy