In an effort to identify new potent and selective inhibitors of chikungunya virus and HIV-1 and HIV-2 virus replication, the endemic Mascarene species Stillingia lineata was investigated. LC/MS and bioassay-guided purification of the EtOAc leaf extract using a chikungunya virus-cell-based assay led to the isolation of six new (4–9) and three known (1–3) tonantzitlolones possessing the rare C20-flexibilane skeleton, along with tonantzitloic acid (10), a new linear diterpenoid, and three new (11, 13, and 15) and two known (12 and 14) tigliane-type diterpenoids. The planar structures of the new compounds and their relative configurations were determined by spectroscopic analysis, and their absolute configurations were determined through comparison with literature data and from biogenetic considerations. These compounds were investigated for selective antiviral activity against chikungunya virus (CHIKV), Semliki Forest virus, Sindbis virus, and, for compounds 11–15, the HIV-1 and HIV-2 viruses. Compounds 12–15 were found to be the most potent and are selective inhibitors of CHIKV, HIV-1, and HIV-2 replication. In particular, compound 14 inhibited CHIKV replication with an EC50 value of 1.2 μM on CHIKV and a selectivity index of >240, while compound 15 inhibited HIV-1 and HIV-2 with EC50 values of 0.043 and 0.018 μM, respectively. It was demonstrated further that potency and selectivity are sensitive to the substitution pattern on the tigliane skeleton. The cytotoxic activities of compounds 1–10 were evaluated against the HCT-116, MCF-7, and PC3 cancer cell lines.
Recently, a novel online bioinformatics methodology called "Molecular Networking" was developed by P. Dorrestein and his group [1]. This approach consists in organizing and visualizing tandem mass spectrometry (MS2) data through a spectral similarity map, highlighting the presence of homologous MS2 fragmentations and their degree of similarity. Scrutinization of molecular networks enables automated comparison and annotation of complex mixtures, which reflect metabolomes of living organisms [2].
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Molecular networking (MN) is becoming a standard bioinformatics tool in the metabolomic community. Its paradigm is based on the observation that compounds with a high degree of chemical similarity share comparable MS2 fragmentation pathways. To afford a clear separation between MS2 spectral clusters, only the most relevant similarity scores are selected using dedicated filtering steps requiring time-consuming parameter optimization. Depending on the filtering values selected, some scores are arbitrarily deleted and a part of the information is ignored. The problem of creating a reliable representation of MS2 spectra data sets can be solved using algorithms developed for dimensionality reduction and pattern recognition purposes, such as t-distributed stochastic neighbor embedding (t-SNE). This multivariate embedding method pays particular attention to local details by using nonlinear outputs to represent the entire data space. To overcome the limitations inherent to the GNPS workflow and the networking architecture, we developed MetGem. Our software allows the parallel investigation of two complementary representations of the raw data set, one based on a classic GNPS-style MN and another based on the t-SNE algorithm. The t-SNE graph preserves the interactions between related groups of spectra, while the MN output allows an unambiguous separation of clusters. Additionally, almost all parameters can be tuned in real time, and new networks can be generated within a few seconds for small data sets. With the development of this unified interface ( https://metgem.github.io ), we fulfilled the need for a dedicated, user-friendly, local software for MS2 comparison and spectral network generation.
The present work aims to identify new promising plant sources, which could be exploited for their agrochemical properties. A total of 484 natural products from academic libraries were selected for screening against four fungal pathogens, five insects and two plants. On the basis of the hits founded and a literature survey, the flora of source countries (New Caledonia, French Guiana, Madagascar, Panama, South Africa and Greece) was analysed for plants containing the desired scaffolds. Lists of 1800 plant part samples were thus established. The plant parts collected generated 3600 extracts that are being evaluated.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
