Project description:Fenbendazole Met ID by an integrative approach using feature-based molecular networking of cross-species in vitro liver microsomal and hepatocellular incubation.
Project description:Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry (HR-MS) in combination with fragment analysis (MS/MS) has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectra pose challenges to downstream analysis, given their complexity and size. Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlopidine. The molecular networking approach also generated networks of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline. The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome. Keywords: Antihypertensive drugs, Drug metabolism, Fragmentation, High-resolution mass spectrometry, Metabolomics, Urine.
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode.
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.
Project description:We analyzed crude extracts from different actinobacteria by LC-MS/MS and analyzed the data by feature-based molecular networking to explore the structural diversity of pyrone compounds.
Project description:This dataset contains the raw data used for MZmine processing and Feature-based molecular networking of semi-purified fractions obtained from A. timonensis used for bioactive molecular networking to reveal contribution of sulfonolipids to observed biological activity.
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode with SCB medium( mzXML file 41-45 blank and 46-50 cultured).
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode with ISP2 medium( mzXML file 21-25 blank and 26-30 cultured).
Project description:Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS and Feature-Based Molecular Networking (FBMN) to analyze metabolites associated with the bacterial genus Yinghuangia under positive ionization mode with ISP4 medium( mzXML file 31-35 blank and 36-40 cultured).