Molecular Networking for Structure-Guided Purification of Diterpene Esters from Euphorbia Species

International audienc

A Molecular Networking Based Workflow for an Efficient Phytochemical Study of Euphorbia pithyusa L. Plant Species

International audienc

Molecular Networking, an Innovative Tool for Natural Products Research: Application to the Phytochemical Studies of Euphorbia Species

International audienc

Accelerating Natural Products Research Workflow by MS/MS Molecular Networking: an Efficient Approach Toward the Study of Euphorbia species

International audienc

Euphorbia dendroides Latex as Source of Jatrophane Esters: Their Isolation, Structural Analysis, Conformational Study and Anti-CHIKV Activity

International audienceAn efficient process was used to isolate six new jatrophane esters, euphodendroidins J (3), K (5), L (6), M, (8), N (10), and O (11), along with seven known diterpenoid esters, namely, euphodendroidins A (4), B (9), E (1), and F (2), jatrophane ester (7), and 3α-hydroxyterracinolides G and B (12 and 13), and terracinolides J and C (14 and 15) from the latex of Euphorbia dendroides. Their 2D structures and relative configurations were established by extensive NMR spectroscopic analysis. The absolute configurations of compounds 1, 11, and 15 were determined by X-ray diffraction analysis. Euphodendroidin F (2) was obtained in 18% yield from the diterpenoid ester-enriched extract after two consecutive flash chromatography steps, making it an interesting starting material for chemical synthesis. Euphodendroidins K and L (5 and 6) showed an unprecedented NMR spectroscopic behavior, which was investigated by variable-temperature NMR experiments and molecular modeling. The structure–conformation relationships study of compounds 1, 5, and 6, using DFT-NMR calculations, indicated the prominent role of the acylation pattern in governing the conformational behavior of these jatrophane esters. The antiviral activity of compounds 1–15 was evaluated against Chikungunya virus (CHIKV) replication

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow. [Figure not available: see fulltext.]</p

Reproducible Molecular Networking Of Untargeted Mass Spectrometry Data Using GNPS.

Herein, we present a protocol for the use of Global Natural Products Social (GNPS) Molecular Networking, an interactive online chemistry-focused mass spectrometry data curation and analysis infrastructure. The goal of GNPS is to provide as much chemical insight for an untargeted tandem mass spectrometry data set as possible and to connect this chemical insight to the underlying biological questions a user wishers to address. This can be performed within one experiment or at the repository scale. GNPS not only serves as a public data repository for untargeted tandem mass spectrometry data with the sample information (metadata), it also captures community knowledge that is disseminated via living data across all public data. One or the main analysis tools used by the GNPS community is molecular networking. Molecular networking creates a structured data table that reflects the chemical space from tandem mass spectrometry experiments via computing the relationships of the tandem mass spectra through spectral similarity. This protocol provides step-by-step instructions for creating reproducible high-quality molecular networks. For training purposes, the reader is led through the protocol from recalling a public data set and its sample information to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions