Advanced Structural Determination of Diterpene Esters Using Molecular Modeling and NMR Spectroscopy

International audienceThree new jatrophane esters (1−3) were isolated from Euphorbia amygdaloides ssp. semiperfoliata, including an unprecedented macrocyclic jatrophane ester bearing a hemiketal substructure, named jatrohemiketal (3). The chemical structures of compounds 1−3 and their relative configurations were determined by spectroscopic analysis. The absolute configuration of compound 3 was determined unambiguously through an original strategy combining NMR spectroscopy and molecular modeling. Conformational search calculations were performed for the four possible diastereomers 3a−3d differing in their C-6 and C-9 stereocenters, and the lowest energy conformer was used as input structure for geometry optimization. The prediction of NMR parameters (1H and 13C chemical shifts and 1H−1H coupling constants) by density functional theory (DFT) calculations allowed identifying the most plausible diastereomer. Finally, the stereostructure of 3 was solved by comparison of the structural features obtained by molecular modeling for 3a−3d with NMR-derived data (the values of dihedral angles deduced from the vicinal proton−proton coupling constants (3JHH) and interproton distances determined by ROESY). The methodology described herein provides an efficient way to solve or confirm structural elucidation of new macrocyclic diterpene esters, in particular when no crystal structure is available

LC-MS2-Based Dereplication of Euphorbia Extracts with Anti-Chikungunya Virus Activity

International audienceRecently, phorbol esters from Euphorbiaceae have been shown to elicit potent and selective antiviral activity on the replication of Chikungunya virus (CHIKV) in cell culture. With the objective to found new compounds with anti-CHIKV activities, 45 extracts from various plant parts of 11 Mediterranean Euphorbia and one Mercurialis species were evaluated for selective inhibition of CHIKV replication. All EtOAc extracts, especially those prepared from latex, exhibited significant and selective antiviral activity in a Chikungunya virus-cell-based assay. An LC-MS2 dereplication method was then developed to investigate whether known diterpenoids with anti-CHIKV activity, such as the potent anti-CHIKV 12-O-Tetradecanoylphorbol-13-acetate (TPA), phorbol-12,13-didecanoate, and prostratin as well as 24 other commercially available diterpenoids of tigliane-, ingenane-, and daphnane-type for which the anti-CHIKV activity have been established in advance (Nothias-Scaglia et al. 2015), were present in the Euphorbia extracts. Only ingenol-3-mebutate, 13-O-isobutyryl-12-deoxyphorbol-20- acetate, and ingenol-3,20-dibenzoate, all exhibiting weak anti-CHIKV activities, were detected in the EtOAc extracts of E. peplus, E. segetalis ssp. pinea, E. peplus, and E. pithyusa ssp. pithyusa. Given the potent anti-CHIKV activities of these Euphorbia extracts, the present study suggested that their antiviral activities are probably due to untargeted diterpenoids

Phytochemical Investigation of Antiviral Euphorbia Species from Corsica

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High-confidence structural annotation of metabolites absent from spectral libraries.

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries

Surface sensing triggers a broad‐spectrum antimicrobial response in Pseudomonas aeruginosa

Interspecies bacterial competition may occur via cell-associated or secreted determinants and is key to successful niche colonization. We previously evolved Pseudomonas aeruginosa in the presence of Staphylococcus aureus and identified mutations in the Wsp surface-sensing signalling system. Surprisingly, a ΔwspF mutant, characterized by increased c-di-GMP levels and biofilm formation capacity, showed potent killing activity towards S. aureus in its culture supernatant. Here, we used an unbiased metabolomic analysis of culture supernatants to identify rhamnolipids, alkyl quinoline N-oxides and two siderophores as members of four chemical clusters, which were more abundant in the ΔwspF mutant supernatants. Killing activities were quorum-sensing controlled but independent of c-di-GMP levels. Based on the metabolomic analysis, we formulated a synthetic cocktail of four compounds, showing broad-spectrum anti-bacterial killing, including both Gram-positive and Gram-negative bacteria. The combination of quorum-sensing-controlled killing and Wsp-system mediated biofilm formation endows P. aeruginosa with capacities essential for niche establishment and host colonization

Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks.

Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these molecules with exceptional biological properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chemical complexity of the biological matrices in which they are found. The purification of single constituents from such matrices requires such a significant amount of work that it should be ideally performed only on molecules of high potential value (i.e., chemical novelty and biological activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compound identification within complex mixtures. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated analysis strategy for bioactive natural products prioritization. Our approach uses massive molecular networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chemical diversity of crude extracts collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biological targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural extract screening should speed up and rationalize the isolation of bioactive natural products

Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc